Jurnal Teknologi Informasi dan Multimedia
Jurnal Teknologi Informasi dan Multimedia (JTIM) is a scientific journal in computers that contains research results and literature studies. It managed by Research and Development Center (PUSLITBANG) Sekawan Institute Nusa Tenggara, Mataram, NTB. This journal is managed by lecturers and practitioners from various University backgrounds in Indonesia, especially Mataram, NTB. JTIM is published 4 (four) times a year, namely every May, August, November and February.
Implementasi Naïve Bayes pada Sistem Informasi Posyandu Digital untuk Klasifikasi Stunting Balita Berbasis Website
Posyandu plays a central role in child growth monitoring and stunting prevention at the commu-nity level; yet Posyandu Lelede 2, Desa Lelede, continues to rely on handwritten registers as its primary recording medium. This dependence generates three recurring operational problems: low data accuracy, difficulty retrieving children's growth histories, and chronic delays in submitting reports to the puskesmas, all of which compromise the effectiveness of early nutritional interven-tion. To address these limitations, the present study designed and implemented a web-based po-syandu information system incorporating the Gaussian Naive Bayes algorithm for automated stunting classification. Development followed the Waterfall methodology through five sequential phases- requirements analysis, design, implementation, testing, and maintenance- with the system constructed in PHP using the Laravel framework and MySQL as the database management system. The anthropometric variables processed included age, body weight, height, and sex of the children. A total of 1,164 records were utilized, comprising 1,000 training records constructed based on WHO anthropometric standards and 164 primary testing records obtained directly from Posyan-du Lelede 2 from December 2025 to February 2026. Evaluation via confusion matrix demonstrated a classification accuracy of 82.31% against WHO anthropometric standards, while Black Box Testing confirmed that all functional modules operated as specified. The system is therefore con-sidered a viable digital model for strengthening posyandu services and child health data manage-ment at the grassroots level.
Copyright (c) 2026 Mursalin Mursalin, Asmaul Husna RS, Sirajunnasihin Sirajunnasihin, Hendra Setiawan, Ari Kurniawati https://creativecommons.org/licenses/by-sa/4.0
Implementasi Metode First Expired First Out (FEFO) pada Sistem Manajemen Inventaris Distributor Daging Berbasis Web
Frozen meat inventory management by distributors requires the implementation of a system that can maintain product quality while reducing the risk of losses due to expired meat. CV. Heimon Anugrah Pangan still experiences several obstacles in manual stock recording which causes data synchronization and accumulation of old products in the warehouse. This study aims to imple-ment FEFO based on expiration dates in a web-based inventory management system. The system development technique used in this study is the waterfall method with steps starting from needs analysis, system design, implementation, testing, and maintenance. The system was created using PHP and MYSQL with in-bound and outbound management features, notification of stock ap-proaching expiration, and automatic reports in PDF format. As a result, the FEFO method can be implemented by the system with product expenditure priorities based on batches and the nearest expiration date. In system trials with black box testing, all features were proven to work accord-ing to user expectations. With this system, inventory management can be carried out effectively, efficiently, and with minimal human error.
Copyright (c) 2026 Laely Syafitri, Susanto Susanto, Titis Handayani https://creativecommons.org/licenses/by-sa/4.0
Analisis Komparatif Firefly Algorithm dan Particle Swarm Optimization dalam Optimasi K-Means untuk Pengelompokan Ketimpangan Pendapatan Antarprovinsi di Indonesia
Income inequality among provinces in Indonesia reflects differences in welfare levels influenced by the social and economic characteristics of each region. This study aims to compare the performance of Par-ticle Swarm Optimization (PSO) and Firefly Algorithm (FA) in optimizing the number of clusters in K-Means clustering to group Indonesian provinces based on seven factors related to income inequality, namely Human Development Index (HDI), Number of Poor Population, Open Unemployment Rate, In-flation, Provincial Minimum Wage, GDP per Capita, and Labor Force Participation Rate. The data used are secondary data from 2024 covering 38 provinces. The analytical methods include data standardi-zation using Z-Score, K-Means clustering, and cluster number optimization using PSO and FA evalu-ated by Silhouette Coefficient (SC). The results show that both optimization methods improved clus-tering quality compared to K-Means without optimization, which yielded an SC of 0.23. PSO produced an SC of 0.28 with an optimal cluster number of 5, while FA produced an SC of 0.39 with an optimal cluster number of 3. FA proved superior in generating a more optimal and representative clustering structure. The clustering results reveal distinct characteristics among clusters that can serve as a ba-sis for formulating more targeted income inequality reduction policies in accordance with the charac-teristics of each regional group.
Copyright (c) 2026 Adinda Adinda, Fahrezal Zubedi, Nisky Imansyah Yahya https://creativecommons.org/licenses/by-sa/4.0
Assessment Manajemen Risiko Keamanan Sistem Informasi Menggunakan Framework ITIL V3 Domain Service Operation
Information system security is a critical aspect in supporting the continuity of information tech-nology services, particularly in educational institutions that heavily rely on digital systems. This study aims to evaluate the maturity level of information system security risk management using the Information Technology Infrastructure Library (ITIL) V3 framework within the Service Oper-ation domain. This research employs a quantitative approach with total sampling, involving 13 respondents who are directly engaged in information system management at the research site. Data were collected through questionnaires and interviews. The research instrument was devel-oped based on four subdomains of ITIL V3 Service Operation: Event Management, Incident Man-agement, Problem Management, and Access Management. Instrument validity was tested using the Pearson productmoment correlation, and reliability was measured using Cronbach's Alpha. The results indicate that the overall maturity level is 3.44, which falls into the Defined Process category (Level 3), approaching Managed and Measurable (Level 4). Event Management obtained the highest value at 3.88 (Level 4), followed by Access Management at 3.37 (Level 3), Incident Management at 3.32 (Level 3), and Problem Management at 3.21 (Level 3). Gap analysis reveals a discrepancy of 1.56 from the expected optimized condition at Level 5. These findings suggest that although processes have been implemented and documented, improvements are still required par-ticularly in root cause analysis (Problem Management), access control (Access Management), and continuous performance evaluation. This study is expected to serve as a reference for enhancing IT service governance and information system security management based on ITIL practices.
Copyright (c) 2026 Andi Sofyan Anas, Rifqi Hammad, I Nyoman Switrayana, Muhammad Haris Nasri https://creativecommons.org/licenses/by-sa/4.0
Perbandingan Metode Biclustering untuk Pengelompokan Wilayah Berdasarkan Faktor Penyebab Kusta di Sulawesi
Leprosy is one of the health issues that spread due to various social and environmental factors. It is a public health issue whose spread is influenced by various social and environmental factors. The aim of this study is to group districts/cities on the island of Sulawesi based on the factors that influence the spread of leprosy using a biclustering approach. The methods used were Cheng & Church (CC) and Iterative Signature Algorithm (ISA), which can cluster data simultaneously on the dimensions of area and variables. The data used are secondary data from 2024 covering eight variables and a number of districts/cities as the units of observation. The analysis stages included pre-processing, missing value handling using mean imputation, data standardisation, and application of the two biclustering methods. The performance was evaluated using Mean Squared Residue (MSR), the Liu & Wang index, and variance. The results of the study show that the CC method produces an average MSR value of 0.006015, which is lower than the ISA method's value of 0.015006. The average Liu & Wang index value for the CC method was 0.2905, lower than the ISA method's value of 0.7356. Furthermore, the average variance in the CC method was 0.07737, which was lower than the ISA method at 2.7859. Based on these three evaluation criteria, the Cheng & Church method is more effective in grouping regions based on factors influencing the spread of leprosy in Sulawesi.
Copyright (c) 2026 Revo Asiki, Fahrezal Zubedi, Armayani Arsal https://creativecommons.org/licenses/by-sa/4.0
Analisis Pola Pembelian Konsumen Menggunakan Algoritma FP-Growth pada Data Transaksi Restaurant Burger
Fast-food restaurants generate large volumes of transaction data that can be utilized to understand customer purchasing behavior and support business decision-making. However, transaction data are often used only for operational reporting, limiting their potential for identifying product association patterns. This study aims to apply the Frequent Pattern Growth (FP-Growth) algorithm to discover frequent itemsets and association rules from burger restaurant transaction data and implement the results in a web-based application. The dataset used consists of 2,001 burger restaurant transactions collected from Kaggle, covering the period 2021–2023. The research process included data preprocessing, transaction transformation, FP-Tree construction, frequent itemset extraction, and association rule generation using a minimum support threshold of 2 transactions and a minimum confidence threshold of 60%. The results revealed that the most frequent items were Save Point Sundae (191 transactions), Health Potion Smoothie (181 transactions), and Cheat Code Cookies (164 transactions). Several association rules achieved a confidence value of 100%, indicating a strong co-occurrence relationship between products. Furthermore, the rules Avatar Avocado -> Cosmic Rings and Cosmic Rings -> Avatar Avocado obtained a lift ratio of 1.50, demonstrating a positive association between the two items. These findings indicate that FP-Growth is effective in identifying customer purchasing patterns and can support promotional strategies, product bundling, and inventory management through data-driven decision-making.
Copyright (c) 2026 Nindya Alifia Khumaira, Dadang Priyanto, Hairani Hairani, Galih Hendro Martono, Moch. Syahrir, Husain Husain https://creativecommons.org/licenses/by-sa/4.0
Perancangan dan Implementasi Sistem Ticketing Support untuk Pengelolaan Pengaduan Pelanggan
CV. Metamorphz Technology Solutions is a company operating in the information technology sector that receives various complaints and service requests from customers in its daily opera-tions. Previously, the customer complaint management process was carried out manually, causing inefficiencies in recording, tracking, and resolving complaint tickets. This condition resulted in slow customer response times and difficulty in monitoring complaint status in realtime. This study aims to implement support ticketing system to manage customer complaints at CV. Meta-morphz Technology Solutions. The research method used is the Agile Development method, which consists of five stages: requirements analysis, system design, implementation, testing, and maintenance. The system was developed using a web-based platform with a SQL Server data-base, equipped with a chatbot feature based on a Natural Language Processing (NLP) algorithm using a Rule-Based and TF-IDF (Term Frequency-Inverse Document Frequency) approach for au-tomatic classification and matching of complaint categories. The chatbot acts as the first line of customer interaction in receiving, classifying, and providing initial responses to complaints before forwarding them to the relevant technical team. The results show that the ticketing sys-tem integrated with the chatbot successfully streamlines the complaint handling process, enabling structured complaint recording, automated ticket assignment to the relevant technical team, and realtime monitoring of complaint status. System testing using the Black-Box Testing method showed that all system features function according to requirements, with a test success rate of 100% from nine testing scenarios conducted. The implementation of this ticketing system im-proves response time and service quality to customers, as well as facilitating performance moni-toring of the technical support team.
Copyright (c) 2026 Affi Himmawan, Nur Wakhidah https://creativecommons.org/licenses/by-sa/4.0
Implementasi Ensemble Voting Classifier untuk Analisis Sentimen Publik terhadap Kontroversi Pertandingan Indonesia vs Bahrain pada Platform Youtube
The qualifying match between the Indonesian National Team and Bahrain sparked diverse public responses that escalated into controversy, particularly in digital spaces such as YouTube comment sections. This phenomenon prompted research to analyze public sentiment using a machine learn-ing approach to understand trends and polarization of public opinion. The research data was ini-tially collected as many as 1,000 raw comments through the YouTube scraping process, which then went through a cleaning stage resulting in 984 valid comments. The research data was ob-tained through scraping YouTube comments, The valid data is then labeled using a combination of lexicon (au-to-suggest) and manual validation approaches into three categories, namely positive, negative and neutral. The preprocessing stage focused on normalizing non-standard language (slang) and handling negations to maintain contextual meaning. Next, feature extraction was per-formed using Feature Union, which combines word- and character-based TF-IDF, as well as nu-meric features such as text length and punctuation proportion. To address data imbalance, the SMOTE method was applied to improve minority class representation. The model used was an Ensemble Voting Classifier with a soft voting approach, which combines a calibrated Support Vector Machine, Logistic Regression, and Random Forest. Model optimization was performed us-ing GridSearchCV to obtain the best parameters. The evaluation results showed that the model performed well with an accuracy of 89.34%, a precision of 89.17%, a recall of 89.34%, and an F1-score of 89.18%. Furthermore, the application of SMOTE and negation handling has been shown to help reduce bias toward the majority class. The application of the SMOTE method has been shown to significantly improve model performance compared to the baseline model without oversampling, which only achieved an accuracy of 88.83%. Overall, this ensemble approach with multidimensional feature engineering is effective in producing an accurate sentiment analysis model for evaluating public response to sporting events.
Copyright (c) 2026 Dlovan Ferdiansyah, Susanto Susanto, Ardi Pramono https://creativecommons.org/licenses/by-sa/4.0
Rancang Bangun Sistem Informasi Monitoring Satuan Pelayanan Pemenuhan Gizi Berbasis Digital Checklist
This study aims to develop a digital checklist-based operational activity monitoring information system at the Nutrition Fulfillment Service Unit (SPPG). The problem faced is that the monitoring process is still conducted manually using paper-based forms, resulting in unstructured records, difficulty in retrieving historical data, and time-consuming manual reporting. The research method used is Research and Development (R&D), with the system development method using Rapid Application Development (RAD). The research stages include problem identification, data collection through observation, interviews, and literature studies, system design, development, and system implementation. The results of this study are a web-based monitoring information system comprising 43 checklist items across three inspection categories (personal protective equipment, kitchen hygiene, and food ingredient preparation), six main modules, and eight user roles managed through Role-Based Access Control (RBAC). Compared to the previous condition, this system transforms the recording process from manual paper-based to digital, changes data storage from previously difficult-to-retrieve records to automatically documented data, and replaces the manu-al reporting process with automated PDF report generation. Functional testing using the black-box testing method across 8 test scenarios yielded a 100% success rate, confirming that all modules operate in accordance with the designed specifications.
Copyright (c) 2026 Fahar Abdul Aziz, Suendri Suendri https://creativecommons.org/licenses/by-sa/4.0
Aplikasi Media Pembelajaran Pendidikan Pancasila Kelas 1 SD Berbasis Android
The learning process of Pancasila Education in elementary schools requires learning media that can improve students’ engagement and learning interest. This study aims to develop an An-droid-based learning media application for first-grade elementary school Pancasila Education as an interactive learning medium. The development method used in this study is Multimedia Devel-opment Life Cycle (MDLC), which consists of concept, design, material collecting, assembly, test-ing, and distribution stages. The developed application provides learning materials, interactive drag-and-drop quizzes, and multiple-choice tests. System testing was conducted using the black box method to ensure that all application features function properly. In addition, usability testing involved 18 students using the System Usability Scale (SUS) instrument and obtained an average score of 79,17, which is included in the "Acceptable" category with a "Good" adjective rating. The results indicate that the application is easy to use, attractive, and helpful in supporting students’ learning process. Therefore, the developed application can be used as an interactive learning me-dium for Pancasila Education in elementary schools.
Copyright (c) 2026 Fikri Sahira Al-Mahdi, Sigit Sugiyanto, Tito Pinandita, Feri Wibowo https://creativecommons.org/licenses/by-sa/4.0
Optimization of Support Vector Machine Using SMOTE and Grid Search for Kidney Health Data Classification
Kidney disease is a highly prevalent health problem that can seriously impact the quality of life of those affected. To improve diagnostic accuracy, machine learning methods are widely used to classify patient data. Class imbalance (imbalanced data) is one of the problems that often occurs in the classification process and can affect the performance of machine learning models, especially in detecting minority classes. This study aims to improve the performance of the Support Vector Machine (SVM) algorithm by applying the SMOTE (Synthetic Minority Over-sampling Tech-nique) and Grid Search methods in the data classification process. SMOTE is used to balance the class distribution by adding synthetic data to the minority class, while Grid Search is used to ob-tain optimal model parameters. The results show that the SVM model without handling data im-balance produces relatively low performance with an accuracy value of 51%, precision 17%, re-call 33%, and F1-score 23%. After applying the SMOTE method, the model performance increases significantly to 81% accuracy, 81% precision, 80% recall, and 81% F1-score. Furthermore, the ap-plication of Grid Search to the SVM + SMOTE model provides the best results with an accuracy of 84%, precision 82%, recall 81%, and F1-score 81% with an AUC value of 0,92. The findings of this study indicate that the combination of SMOTE and Grid Search is effective in improving the per-formance of the SVM algorithm in data classification. The novelty of this study demonstrates that data imbalance management and hyperparameter optimization play a crucial role in producing more accurate and optimal classification models.
Copyright (c) 2026 Muhammad Maulana, Zulkipli Zulkipli, Tanwir Tanwir, Dading Oktaviadi Resmiranta, Naufal Hanif, Raisul Azhar https://creativecommons.org/licenses/by-sa/4.0
Hyperparameter Tuning Metode Long Short-Term Memory (LSTM) untuk Prediksi Harga Bawang Merah di Pasar Tradisional Jawa Timur
Shallots are one of the essential food commodities in East Java, the price of which frequently fluctuates due to seasonal influences and distribution factors. This price uncertainty often weakens the community's purchasing power and makes it difficult for farmers and sellers to make decisions. Therefore, this study proposes a daily shallot price prediction model for nine regions in East Java using the Long Short-Term Memory (LSTM) method with hyperparameter tuning to obtain the best-performing model. This study utilizes daily price data from PIHPS (Pusat Informasi Harga Pangan Strategis / Strategic Food Price Information Center) over four years, from January 1, 2021, to December 31, 2024. The research stages begin with data preprocessing, which includes imputing missing values to ensure continuity of the time sequence, data normalization, and arrangement of data based on temporal order (time series). To obtain the optimal model, the data was split into 80% for training and 20% for testing. Based on the experimental results, the optimal model configuration was found using a Window Size of 7 (the last seven days of data), 114 Epochs, and a Batch Size of 64. Furthermore, the reliability of the model was measured using MAPE, where the average across all regions fell below 10%, which is classified as "Very Good" based on MAPE interpretation standards. Additional testing using 2025 data as an independent dataset further confirmed that the model remains consistent and stable, as evidenced by an average MAPE of 3.10% across the nine regions.
Copyright (c) 2026 M. Latifur Rahman, Mamluatul Hani'ah, Muhammad Afif Hendrawan https://creativecommons.org/licenses/by-sa/4.0
Game Edukasi Ilmu Pengetahuan Sosial (IPS) Berbasis Android menggunakan Metode R&D 4D di SMP Negeri 2 Rawalo
Technological advancements have had a significant impact on the world of education, particularly in accelerating and simplifying the learning process for Social Studies (IPS) courses. This study aims to design an Android-based educational game for 9th-grade students at SMP Negeri 2 Rawalo using the Research and Development (R&D) method and the 4D model (define, design, develop, disseminate). This game was designed using the Construct 3 application and named “Society in A Game: Game of Social Changes and Digital Economy Growth.” The study involved 20 students, and the research utilized pre-tests and post-tests to measure improvements in learning outcomes as well as an application evaluation test using the System Usability Scale (SUS) based on a 1–4 Likert scale. The average pre-test score of 57.8 increased to 77.6 on the post-test, representing a 34.2% increase. Additionally, the average SUS score of 74.5 falls into the “Acceptable” category. The research results indicate that Android-based educational games can serve as an interactive learning medium while introducing the use of technology in the field of education.
Copyright (c) 2026 Annas Ridwan Pangestu, Sigit Sugiyanto, Tito Pinandita, Dimara Kusuma Hakim https://creativecommons.org/licenses/by-sa/4.0
Penerapan Teknik Steganografi dalam Gambar pada Aplikasi Telegram Menggunakan Metode Least Significant Bit (LSB)
In the digital era, the threat of data leakage demands secure information exchange mechanisms on instant messaging applications. This study aims to implement and test the robustness of the Least Significant Bit (LSB) steganography method on image media transmitted through the Telegram platform to ensure information confidentiality and create secure cyber communication against eavesdropping. The novelty of this research lies in the comparative analysis of the pure LSB method's robustness against Telegram's compression protocols, which frequently corrupt lower bits in digital media. This study employs an experimental quantitative approach by testing 15 digital image samples in PNG, BMP, and JPG formats sourced from the Google public repository. The experimental process involves embedding text messages using StegOnline tools, transmission via Telegram, extraction, and image quality analysis based on Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) metrics. The test results indicate that the LSB method achieves a 100% extraction success rate and a Bit Error Rate (BER) of 0 when utilizing the "send document" feature due to its lossless transmission nature, with optimal visual quality indicated by PSNR values above 58 dB. Conversely, transmission through the "send image" feature causes extraction failure due to the platform's compression that corrupts the secret bits. This study concludes that the accuracy stability of LSB steganography highly depends on selecting a lossless transmission for-mat to fully guarantee data integrity.
Copyright (c) 2026 Lu’lu’ Atik Fitriyani, Fahmi Fachri https://creativecommons.org/licenses/by-sa/4.0
Analisis Performansi Media Penyimpanan Data IP Camera Menggunakan Metode PPDIOO
Closed-Circuit Television (CCTV) systems operating continuously for 24 hours generate large volumes of video data, requiring storage media that provide stable performance and high reliabil-ity under continuous workloads. Although dedicated CCTV hard disk drives (HDDs) are widely deployed in surveillance systems, studies that directly compare their performance with standard Personal Computer (PC) HDDs in Internet Protocol (IP) Camera-based surveillance environments remain limited. This study aims to analyze and compare the performance of these two storage media using the Prepare, Plan, Design, Implement, Operate, and Optimize (PPDIOO) methodology. The experimental setup consisted of 10 Hikvision IP cameras with 1080p resolution connected to a Network Video Recorder (NVR). Performance evaluation was conducted based on sequential write speed, recording stability, frame drops, operating temperature, and storage capacity utilization. Testing was performed using CrystalDiskMark, iVMS-4200, Hik-Connect, and Hard Disk Senti-nel. The results demonstrate that the dedicated CCTV HDD provides more stable performance than the standard PC HDD. The CCTV HDD achieved a sequential write speed of 206.67–207.00 MB/s, while the PC HDD reached 205.10–205.85 MB/s. During continuous recording, the CCTV HDD ex-perienced only one dropped frame, whereas the PC HDD recorded approximately four to six dropped frames. In addition, the CCTV HDD maintained a more stable operating temperature throughout the 24-hour recording period. Storage capacity analysis indicated that a surveillance system with 10 cameras operating at a bitrate of 4 Mbps requires approximately 422 GB of stor-age per day or 11.6 TB for one month. The main contribution of this study is a comprehensive per-formance evaluation of dedicated CCTV storage compared with conventional PC HDDs, providing practical recommendations for selecting reliable storage media in IP Camera-based surveillance systems.
Copyright (c) 2026 Ayu Vidiya Wati, I Putu Hariyadi, Lalu Zazuli Azhar Mardedi https://creativecommons.org/licenses/by-sa/4.0
Game Edukatif pada Materi Teks Wawancara untuk Mendukung Literasi Membaca Siswa Sekolah Dasar
This study aims to develop an educational game-based learning media using Smart Apps Creator 3, named “SIMAKA,” to support elementary school students’ reading literacy in interview text materials. The method used is Research and Development (R&D) with the ADDIE model, which includes the stages of analysis, design, development, implementation, and evaluation. Data collection techniques involve validation questionnaires and user responses, which are analyzed quantitatively in descriptive form using percentages. The results show that the media achieved a feasibility level of 92.8% from experts, 95% from teachers, and 93% from students, all categorized as very good. These findings indicate that the developed media is feasible to be used as an alternative learning tool to support students’ reading literacy.
Copyright (c) 2026 Ragil Dian Purnama Putri, Wiwik Kusuma, Tri Lestari https://creativecommons.org/licenses/by-sa/4.0
Optimizing Green-Synthesized Chitosan Nanoparticles for Anticancer Drug Delivery Using Artificial Intelligence: A Systematic Literature Review
Chitosan nanoparticles (CNPs) are promising anticancer drug-delivery carriers because of their biodegradability, mucoadhesive behavior, drug-loading capacity, and surface modifiability. However, previous studies have often examined green synthesis, CNP formulation, artificial intel-ligence (AI), and anticancer delivery as separate research domains, leaving limited synthesis of how AI-assisted optimization can improve environmentally sustainable CNP systems for cancer therapy. This systematic literature review evaluated studies published from 2016 to 2025 on green-synthesized CNPs optimized through AI, machine learning, or statistical optimization approaches for anticancer applications. A Scopus search using the Boolean string "chitosan AND anticancer AND optimization" identified 95 records. After eligibility screening, 42 records were excluded because they were older than 2016, non-article publications, non-English records, or out-side the oncology/green-CNP scope, leaving 53 studies for review. The evidence was synthesized into four domains: optimization and predictive modeling, green synthesis and material innova-tion, targeted and multifunctional nanocarriers, and preclinical efficacy and translational readiness. Across the included studies, optimized CNPs showed particle sizes ranging from approxi-mately 5 to 473 nm, encapsulation efficiency up to 98%, and zeta potentials from -31 to +98 mV. Reported therapeutic improvements included enhanced cytotoxicity, reduced IC50 values, sus-tained release up to 72 h, and inhibition rates up to 82% in selected cancer models. Nevertheless, cross-study comparison was limited by inconsistent model-validation metrics, incomplete toxicity reporting, limited in vivo validation, and insufficient scalability assessment. Integrating AI-guided optimization with green CNP synthesis can accelerate sustainable nanomedicine design, but future studies should prioritize standardized reporting, risk-of-bias control, life-cycle assess-ment, and regulatory translation.
Copyright (c) 2026 Halil Akhyar, Ahmad Taufik S, Susi Rahayu https://creativecommons.org/licenses/by-sa/4.0
Implementasi Multi-Authentication pada Smart Door Lock Berbasis ESP8266
Home security has become a primary concern, where conventional lock systems have significant weaknesses such as risk of loss, ease of duplication, and inability for remote monitoring. This re-search develops a Smart Door Lock system based on ESP8266 and Blynk with a multi-sensor and multi-authentication approach to overcome these problems. The system integrates RFID RC522 and 4x4 keypad as two-factor authentication (2FA) that can be toggled as needed, equipped with IR MH-B sensor for forced entry detection, ESP32-CAM for visual documentation, and Blynk platform for remote management. The research uses the ADDIE method which includes analysis, design, development, implementation, and evaluation stages with Black Box Testing approach. Imple-mentation results show the system successfully functions with a success rate of 98.29% from 1,405 tests. Average RFID reading time 447.5ms, total access time 1.27 seconds, and Blynk notification delay 2.38 seconds, meeting the response target of < 3 seconds. The 2FA mechanism proved effec-tive with 100% validation accuracy, toggle mode functions perfectly with EEPROM persistence. Remote management features via Blynk achieved 100% reliability for adding and removing RFID cards, changing passwords, and remote control. The IR sensor successfully distinguished legal and forced openings with 97.5% accuracy. ESP32-CAM integrated perfectly with automatic trigger via REST API for visual documentation to Telegram. The system is considered feasible as a modern, secure, and easy-to-operate home security solution.
Copyright (c) 2026 Lilik Widyawati, Husain Husain, Kurniadin Abdul Latif, L. Irvan Sastrawan https://creativecommons.org/licenses/by-sa/4.0
Evaluasi Sistem Retrieval-Augmented Generation Berbasis Low-Code dalam Meningkatkan Akurasi Konseptual Pembelajaran Sosiologi
The utilization of Large Language Models (LLMs) in higher education offers significant efficiency, yet it introduces critical risks of information hallucination and conceptual bias, particularly in the discipline of Sociology. This study aims to evaluate the performance of a Retrieval-Augmented Generation (RAG) system based on the low-code platform n8n as a robust solution for hallucina-tion mitigation. The system integrates semantic search using Supabase as a vector database and the Gemini 2.5 Flash model to restrict response generation exclusively to verified academic litera-ture. The research employed an Experimental Single-System Evaluation method with a du-al-evaluation approach (quantitative and qualitative) across 50 test instruments. Quantitative testing using the ROUGE-L metric recorded a mean score of 0.354, indicating adequate structural similarity despite variations inherent to the paraphrasing nature of LLMs in analytical tasks. Thematic qualitative analysis of evaluator comments revealed 98.2% positive sentiment, with the dominant theme being “Conceptually Accurate”. Ultimately, 100% of the expert panel declared the system suitable for implementation as a reliable supplementary learning medium.
Copyright (c) 2026 Rusmanto, Jasrino Maulana Putra https://creativecommons.org/licenses/by-sa/4.0
Sistem Rekomendasi Pekerjaan Berbasis Kompetensi Mahasiswa Menggunakan Pendekatan Content-Based Filtering
Students often experience difficulties in obtaining job recommendations that match their academic competencies and personal interests. This problem is caused by the suboptimal utilization of stu-dents' academic data in the job matching process, resulting in recommendations that are often less relevant to students' profiles and abilities. Therefore, a job recommendation system is needed that can process academic data and user preferences to produce more appropriate job recommenda-tions that meet students' needs. This study aims to develop a web-based job recommendation sys-tem that utilizes Grade Point Average (GPA) with a Content-Based Filtering approach. The devel-oped system matches student profiles—including GPA, major, skills, and interests—with job characteristics in the information technology field. The level of match between student profiles and job profiles is calculated using the Cosine Similarity algorithm. Evaluation of system performance is carried out by implementing a 5-fold cross-validation scheme and using Top-3 Accuracy and Exact Accuracy metrics. The test results show that the system obtained an average Top-3 Accuracy score of 85.0% and Exact Accuracy of 54.7%. These results demonstrate that the developed system is capable of generating relevant job recommendations with a high level of consistency. Therefore, this system is expected to contribute to supporting students' decision-making process in planning career paths that align with their academic achievements and individual preferences.
Copyright (c) 2026 Intan Sulistyaningrum Sakkinah, Muhammad Ainul Fikri, Rosyida Arian Rahma https://creativecommons.org/licenses/by-sa/4.0
Pemanfaatan IoT dalam Sistem Kontrol Lingkungan Tanaman Drosera sessilifolia Menggunakan Fuzzy Type-2
Drosera sessilifolia is a carnivorous plant that requires optimal environmental conditions, par-ticularly in terms of light intensity, temperature, and water level. This study aims to utilize the In-ternet of Things (IoT) to implement an environmental control system using Type-2 Fuzzy Logic for monitoring and controlling the environmental parameters of Drosera sessilifolia. The system uses an ESP32 microcontroller supported by several sensors, including a DS18B20 temperature sensor, an LDR light intensity sensor, and a water level sensor, to monitor environmental parameters ac-curately. The acquired data are processed using the Type-2 Fuzzy method to generate control deci-sions for actuators such as a heater, peltier, lamp, peristaltic pump, and fan. The Blynk platform is used to display environmental conditions, including temperature, light intensity, water level, and actuator decision status in real time. Based on the results of a 7-day indoor test conducted in the morning and at night, the average morning temperature was 30.04°C and the average nighttime temperature was 27.91°C, while the average light intensity was 1278 ADC in the morning and 2525.43 ADC at night, and the average water level was 1.16 cm in the morning and 0.99 cm at night. Based on 14 observations over 7 days, the system showed appropriate control responses in all observed scenarios, resulting in a 100% response success rate for the detected conditions. The novelty of this study lies in the implementation of an IoT-based Type-2 Fuzzy system specifically designed for the indoor cultivation of Drosera sessilifolia by simultaneously controlling temper-ature, light intensity, and water level. In addition, this study highlights the novelty of integrating Type-2 Fuzzy Logic and IoT for carnivorous plants, which remains very limited, especially for the cultivation of Drosera sessilifolia in a controlled indoor environment. The test results show that the system is capable of controlling temperature, light intensity, and water level effectively and responsively to environmental changes. The Type-2 Fuzzy method also produces stable and adap-tive control decisions. This system offers a solution for real-time indoor monitoring and environ-mental control of Drosera sessilifolia.
Copyright (c) 2026 Rhimba Aulia, Chrystia Aji Putra, Henni Endah Wahanani https://creativecommons.org/licenses/by-sa/4.0
Pemetaan Spasial Jemaat GPPS Betlehem Lombok Menggunakan Algortima K-Means Clustering dan Leaflet.js
The church as a place for people to gather requires accurate information to store congregational data. With the Geographic Information System (GIS) it will be easier for church officials to map congregations that number more than hundreds of people and share locations of scheduled activities. The purpose of this study is to implement the K-Means algorithm to map the distribution of the GPPS Bethlehem congregation so that it can provide informative and easy-to-understand spatial visualization. The clustering algorithm method groups multiple data sets by explaining how data within a group has similar characteristics and how they differ from other groups. The data in the form of geographic coordinates (latitude and longitude) of the congregation's location was then processed using the K-Means algorithm with a predetermined number of clusters. The processing results showed that the data was successfully grouped into several clusters based on location proximity, each cluster having a centroid as the center point of the group, the centroid value changing at each iteration until it reached a convergent condition. Based on the research results, it can be concluded that: The clustering process produces several groups (clusters) that represent the distribution pattern of congregations in a particular area with a clear cluster center (centroid) and visualization using Leaflet.js is able to display the clustering results in the form of an interactive map that is informative and easy for users to understand.
Copyright (c) 2026 Ahmat Adil, Bambang Krimono Triwijoyo, Heroe Santoso, Muhammad Azwar, Raymond Putra Suntana https://creativecommons.org/licenses/by-sa/4.0
Efektivitas IndoBERT pada Klasifikasi Sentimen Evaluasi Dosen: Studi Komparatif Support Vector Machine dan Naive Bayes
Sentiment analysis of student feedback plays an important role in evaluating the quality of teach-ing and learning processes in higher education. Qualitative comments in Student Evaluation of Teaching (SET) provide deeper insights than numerical ratings. However, they are expressed in unstructured textual form, making large-scale analysis difficult to conduct consistently and sys-tematically. Therefore, Natural Language Processing (NLP) approaches are required to automati-cally identify sentiment tendencies within student comments. This study aims to compare the per-formance of Gaussian Naive Bayes and Support Vector Machine (SVM) algorithms for classifying sentiment in SET comments using IndoBERT-based text embeddings. The research follows the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, including stages of data understanding, data preparation, modeling, evaluation, and deployment. Text comments were preprocessed and transformed into numerical vectors using IndoBERT Sentence-BERT em-beddings to capture contextual semantic relationships between words. These embeddings were then used as input features for both classification algorithms. Evaluation results show that the In-doBERT + SVM model achieved an accuracy of 93.88%, outperforming IndoBERT + Naive Bayes which obtained 92.40%. The SVM model also demonstrated more balanced precision, recall, and F1-score values across sentiment classes. These findings indicate that SVM is more effective in uti-lizing high-dimensional contextual embeddings for sentiment classification of student feedback.
Copyright (c) 2026 Tifanny Nabarian, Maryam Hasnaa' Syamila, Salman El Farisi, Ananto Dwi Saputro https://creativecommons.org/licenses/by-sa/4.0
Rancang Bangun Manajemen Aset Laboratorium Sistem Informasi FMIPA Untan Berbasis Website
This study aims to design and build a website-based laboratory asset management information system at the Information Systems Department, Faculty of Mathematics and Natural Sciences, Tanjungpura University. The main problem with the current system is that the asset management process is still carried out manually, resulting in delays in recording, potential data errors, and limited access to information. In addition, the lack of an integrated system capable of supporting real-time asset management indicates a gap between the need for effective asset management and current system capabilities. The system development method used is the Fountain Model, which is iterative and flexible in supporting the software design and development process. System evalua-tion was conducted using the Black-Box Testing method to assess the suitability of system func-tions, and the System Usability Scale (SUS) to measure system usability from the user perspective. The test results showed that all system features run according to the functional requirements, with no critical errors found during Black Box testing. In addition, the evaluation results using the SUS method yielded a score of 82.5, which falls within the excellent category and indicates that the sys-tem is easy to use and acceptable to users. The implementation of this system also improved the ef-ficiency of the asset management process, sped up data retrieval, and reduced the risk of recording errors compared to the previous manual system. Thus, the developed information system not only addresses asset management problems but also improves service quality and data-driven deci-sion-making in the laboratory environment.
Copyright (c) 2026 Ferdy Febriyanto, Renny Puspita Sari https://creativecommons.org/licenses/by-sa/4.0
Perancangan Aplikasi FinTech Locavest sebagai Sistem Pendukung Keputusan Analisis Kelayakan Lokasi Usaha Menggunakan Metode Design Thinking
The development of financial technology (fintech) and artificial intelligence (AI) opens up oppor-tunities to increase efficiency and objectivity in business decision-making, including business loca-tion selection. However, the integration of fintech, property, and AI-based location analysis in a single mobile platform has rarely been comprehensively studied. This study designed a prototype of the Locavest application as an AI-based fintech solution for business location feasibility analy-sis using a Design Thinking approach. The development method included the stages of empathize, define, ideate, prototype, and test to ensure the suitability of the features to the needs of MSMEs and novice investors. Prototype validation was conducted using the System Usability Scale (SUS) with 10 user respondents, resulting in an average score of 85, indicating a very good level of usability (category "excellent"). This application is expected to become an integrated decision support sys-tem that can help users analyze business locations and property investments more quickly, meas-urably, and easily. The contribution of this research lies in the integration of fintech, property, and AI features in a single mobile application and the use of Design Thinking methods to produce in-novative and user-friendly digital solutions.
Copyright (c) 2026 Muhamad Ikhsan Tazudin, Randhu Fulki Ramadhan, Hyuga Ramdhan Noer, Genta Nazwar Tarempa https://creativecommons.org/licenses/by-sa/4.0
Penerapan E-CRM Berbasis Web Menggunakan Metode RAD pada Showroom Wali Sanga Motor
Customer service at Wali Sanga Motor Showroom is still conducted manually, causing delays in information delivery, unstructured customer data management, and less optimal handling of or-ders and customer complaints. This study aims to develop a web-based Electronic Customer Rela-tionship Management (E-CRM) system using the Rapid Application Development (RAD) method to improve service effectiveness and customer relationship management. The RAD method was applied through stages of requirements planning, prototyping design, system construction, and implementation. The system was developed using the Laravel framework and MySQL database with main features including product information management, online motorcycle ordering, cus-tomer complaint services, and administrative data management. System testing was carried out using User Acceptance Test (UAT) and White Box Testing on all functional requirements that had been designed. The test results show that the system runs 100% in accordance with the UAT sce-narios and all main modules function according to user needs without any significant logical er-rors. The implementation of this E-CRM system is expected to improve service efficiency, acceler-ate responses to customers, and support integrated and sustainable management of customer ser-vice data at Wali Sanga Motor Showroom.
Copyright (c) 2026 Jihan Salsabila Arifin, Bayu Priyatna, Fitria Nurapriani, April Lia Hananto https://creativecommons.org/licenses/by-sa/4.0
Utilizing Natural Language Processing for the Analysis of BMKG Decadal Atmospheric Dynamics Reports in 2025
The Meteorology, Climatology, and Geophysics Agency (BMKG) publish decennial reports that provide valuable insights into Indonesia's meteorological conditions and their temporal fluctuations. However, due to their narrative structure, conducting direct quantitative analysis is problematic. This study seeks to address this issue by using a transparent, repeatable natural language processing (NLP) method to identify temporal trends in climatic conditions favourable to the formation of acid rain. The collection contains 36 BMKG decadal atmospheric dynamics studies for 2025. The proposed approach entails gathering textual input, performing basic preprocessing (case normalization, character sanitization, space-based tokenization, and stop-word removal), and subsequently employing predefined keyword dictionaries for analysis. These dictionaries delineate weather conditions that either facilitate or inhibit the formation of acid rain. The scores for acid rain conditions are determined by the frequency of specific keywords, adjusted for the document's length. Subsequently, they are categorized into groups utilizing statistical thresholds derived from the mean and standard deviation of the adjusted scores. Non-parametric statistical tests are employed to examine temporal patterns with greater specificity. The findings indicate that normalized acid rain scores are elevated in the initial years of the decade, specifically 2025, before gradually declining until year-end. The Spearman rank correlation test reveals a statistically significant negative correlation between normalized scores and time (\rho = -0.494, p = 0.0022). The Mann–Kendall test indicates a significant downward trend (Z = -2.902). These results demonstrate that the climatic conditions responsible for acid rain occurred only temporarily, rather than year-round. The core element of this work is a straightforward, lexicon-based NLP approach that is easily understood, replicable, and applicable, and can transform narrative atmospheric reports into structured quantitative metrics. This is beneficial for research on atmospheric dynamics and environmental analysis with official written data.
Copyright (c) 2026 Ardiansyah Ramadhan, Aaz Muhmmad Hafidz Azis https://creativecommons.org/licenses/by-sa/4.0
Game Edukasi dengan Narasi Bercabang tentang Sejarah Puputan Margarana
History education in Indonesia still tends to be one-sided and lacks interactivity, which can di-minish interest in studying history. In this game, players will take on the role of a fictional young Balinese character who fought to defend Indonesia’s independence in Bali. This study aims to cre-ate an educational game with a branching narrative about the history of the Puputan Margarana. The contribution of this research lies in the application of branching narratives to explore the his-torical story of the Puputan Margarana interactively. This game was developed using a branching narrative with a Branch and Bottleneck narrative pattern for the Windows and WebGL platforms. An evaluation was conducted on 21 junior high school student respondents using the GUESS-18 instrument. The game evaluation results showed an average total score of 4.10 on a 5-point scale, with the Stealth dimension receiving the highest score of 4.24 and the Enjoyment dimension re-ceiving the lowest score of 3.90. Validity test results for the additional dimensions showed that all items were valid (r > 0.443; p < 0.05), and reliability tests confirmed that the additional dimen-sions were reliable (alpha > 0.7). The research results indicate that the developed educational game received a positive response from players.
Copyright (c) 2026 Raditya Lungguk Satya Putra, Chrystia Aji Putra, Andreas Nugroho Sihananto https://creativecommons.org/licenses/by-sa/4.0
Game Virtual Tour Pengenalan Sistem Tata Surya Berbasis VR Google Cardboard dan Voice Command
The solar system is an essential topic in elementary school science education, playing a role in developing students' critical thinking skills and scientific understanding. However, conventional teaching methods often make students passive recipients of information, discouraging interaction and independent exploration. This can decrease interest and engagement in learning. Therefore, a more interactive and engaging approach is necessary to teach students about the solar system ef-fectively. This game uses VR technology and voice commands to provide an immersive experience exploring the solar system. Players can interact with objects in the solar system. It also features voice control, which allows students to interact with objects in the solar system and access addi-tional information. The average score on the questionnaire survey of the application was 86.30%. Integrating VR and voice commands has proven effective in increasing students' interest in learn-ing and facilitating navigation in immersive learning environments.
Copyright (c) 2026 Muh Ulil Alkhaq, Siti Asmiatun https://creativecommons.org/licenses/by-sa/4.0
Rancang Bangun Aplikasi Laporan Keuangan Berbasis Web Dengan Metode Prototype (Studi Kasus: Praktik Mandiri Bidan Nining)
Technological advances in the digital era have had a significant impact on various aspects of life, including the financial reporting system. One form of this transformation is the use of web-based applications to facilitate the process of managing and presenting financial data. PMB Nining, an Independent Midwife Practice in Sukatenang Village, Bekasi Regency, still carries out manual financial reporting using books, which risks causing recording errors, physical data damage, and delays in reporting. This study aims to design and build a web-based financial reporting system using the prototype method with the Laravel framework. This approach involves users in every stage of the design so that the system developed is in accordance with their needs. In addition, system testing is carried out using the black box testing method to ensure that each function runs according to specifications. The test results show that all main features, such as user authentication, transaction recording, data validation, printing reports in PDF and Excel formats, and staff data management, function properly and achieve a 100% success rate. The system is also able to handle various types of input, both valid and invalid. The presence of dashboard and report filter features also supports a more efficient financial monitoring process. Thus, this system can be a solution to improve the accuracy and effectiveness of financial reporting at PMB Nining, while also contributing to the development of information technology in the health services sector.
Copyright (c) 2026 Dewi Sonia Putrie, Aditya Dwi Putro, Gunawan Wibisono https://creativecommons.org/licenses/by-sa/4.0