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Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE)
Published by Universitas Muhammadiyah Sumatera Utara
ISSN : -     EISSN : 27213838     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering
ournal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) is being published in the months of March and September. It is academic, online, open access (abstract), peer reviewed international journal. The aim of the journal is to: Disseminate original, scientific, theoretical or applied research in the field of Engineering and allied fields. Dispense a platform for publishing results and research with a strong empirical component. Aqueduct the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. Seek original and unpublished research papers based on theoretical or experimental works for the publication globally. Publish original, theoretical and practical advances in Computer Science & Engineering, Information Technology, Electrical and Electronics Engineering, Electronics, Communication and Telecommunication, Education Science and all interdisciplinary streams of Social Sciences. Impart a platform for publishing results and research with a strong empirical component. Create a bridge for significant gap between research and practice by promoting the publication of original, novel, industry-relevant research. Solicit original and unpublished research papers, based on theoretical or experimental works. Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) covers all topics of all engineering branches. Some of them are Computer Science & Engineering, Information Technology, Electronics & Communication, Electrical and Electronics, Electronics and Telecommunication, Education Science and all interdisciplinary streams of Social Sciences.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 12 Documents
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Search results for , issue "vol 7, no 2 (2026)" : 12 Documents clear
Development of a Complaint System to Improve Services for Senior High School Students Juandana Kawuladini Putra; Muhammad Rifqi Fadhlan Rahmatullah
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 2 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i2.31953

Abstract

Improving the efficiency and transparency of school services in the digital era requires complaint handling that is documented accurately and coordinated across units. This study developed a website-based student complaint system for SMAN 1 Babadan, Ponorogo, as a strategic step to ensure that every student is heard and receives an appropriate response. The research applied a research and development approach within the System Development Life Cycle using the Waterfall model, comprising analysis, design, implementation, testing, and maintenance. The system design was expressed through Unified Modeling Language diagrams, namely use case, activity, sequence, and table relationship diagrams, while the implementation used the PHP CodeIgniter framework with a MySQL database. The development itself followed three stages: requirement planning, system design, and implementation. Black-box testing involving students and administrators showed that all system functions, including login, student data management, complaint submission with file attachments, follow-up status recording, and report printing, operated as required. User acceptance testing conducted by the appointed school assessor produced a total score of 41 out of a maximum of 50, or 82%, which falls into the Very Good category. The developed system was therefore feasible for supporting student complaint services.
GIS-Based Sustainable Regional Planning Modeling for Predicting the Conversion of Agricultural Land into Residential Areas in Klambir Lima Village, Deli Serdang Regency Ruth Riah Ate Tarigan; Abdi Sugiarto; Ramayani Marpaung
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 2 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i2.32347

Abstract

The rapid conversion of agricultural land into residential areas presents a significant challenge to sustainable regional planning, particularly in areas experiencing population growth and urban expansion. This study aims to develop a GIS-based sustainable regional planning model for predicting agricultural land conversion into residential areas in Klambir Lima Village, Deli Serdang Regency, Indonesia. The study integrates Geographic Information System (GIS) technology with spatial analysis to identify land-use changes and determine areas with high potential for future conversion. Spatial variables include land-use history, distance to roads, distance to residential areas, population density, accessibility, and proximity to public facilities. Historical spatial data are processed through GIS to identify patterns of agricultural land conversion, while predictive analysis is applied to estimate potential future changes. The resulting spatial model generates a land-conversion risk map that can be used to identify agricultural areas requiring protection and areas potentially suitable for residential development. The proposed model provides a data-driven approach for supporting sustainable regional planning by integrating spatial information, land-use prediction, and development considerations. The study is expected to contribute to more effective agricultural land protection, controlled residential expansion, and evidence-based regional planning in Deli Serdang Regency.
Modeling Factors of Product Classification Proximity in Data Mining Applications S N M P Simamora
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 2 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i2.29628

Abstract

In today's nano-technology era, as device sizes shrink, the volume of data generated has grown exponentially. This increase manifests in both the number and size of files. Real-world data corroborates this trend. Given the massive scale of data, efficient methods are required to extract and process information to meet specific needs and preferences. One promising approach is data mining, particularly in analyzing the relationships between frequently purchased products. For instance, in a convenience store, if a customer selects product Z1, what is the likelihood that they will also purchase product Z2? How does this pattern extend to subsequent purchases? By understanding these associations, retailers can optimize product placement, enhancing customer convenience and increasing sales. Furthermore, retailers can streamline procurement processes by focusing on products with strong association patterns, thereby reducing costs. This research employs data mining techniques, specifically the Apriori algorithm, to investigate product associations in a convenience store using data collected between May 1 and September 30, 2023. The CRISP-DM methodology guided the research. The findings reveal significant associations between products purchased by customers, as evidenced by the highest frequent itemsets and association rules. Additionally, the data underscores the need to classify consumer goods into food and beverage categories. The analysis indicates that the purchase of food items does not necessarily lead to the purchase of beverages, and vice versa. Consequently, it can be concluded that a customer's purchasing decisions are primarily driven by their immediate needs or preferences rather than established product associations or habitual behavior.
Anomaly Detection in MariaDB/MySQL Database Activity Using Isolation Forest Based on Binary Log Extraction and Temporal Aggregation Bagas Triaji
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 2 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i2.31998

Abstract

This study proposes a behavior-based framework for detecting anomalous activity in MariaDB/MySQL databases using unsupervised machine learning. Database activity was extracted from ROW-format binary logs using the pymysqlreplication library, then transformed through One-Hot Encoding and one-minute temporal aggregation into a 23-feature representation covering 82 active time slots. Isolation Forest was implemented with PyCaret using a contamination value of 0.08. The model achieved a ROC AUC of 0.9679 and 100% recall, detecting all four anomalous intervals with three false positives among 78 normal observations. Feature analysis identified transaction payload size as the strongest anomaly indicator.A controlled resampling experiment reduced anomaly prevalence from 4.9% to 0.07% while preserving the original empirical characteristics. Under this condition, precision fell to 0.87%, whereas recall remained at 100% and ROC AUC reached 0.9759. Recurring benign structural events, particularly schema initialization, could receive higher anomaly scores than genuine transaction spikes under extreme class imbalance. These findings show that the framework is effective for short-window detection but requires adaptive contamination tuning, dynamic thresholding, or explicit handling of recurring benign events for long-term deployment. An interactive Streamlit dashboard was also developed for operational monitoring.
Development of an IoT-Based Prepaid KWh Meter Monitoring System with Web Monitoring and Electricity Token Balance Notifications (Case Study: PUPR Sumatera VIII Apartment Complex) Bika Abdul Sandi; Jenny Putri Hapsari
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 2 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i2.32331

Abstract

Rusun PUPR Sumatera VIII faces three problems with its prepaid electricity system: the kWh meter alarm is inaudible from inside the units because the meters are centrally located and enclosed, there is no record of electricity consumption available to residents, and there is a risk of damage to electronic equipment due to sudden power outages. The research method was carried out by measuring the output of the prepaid kWh meter using a PZEM-004T sensor, which is then read by an ESP32 microcontroller and transmitted to a website server. The remaining token is calculated as the difference between the input token value and the energy consumed, and the system automatically sends a notification to residents via WhatsApp when the remaining token falls below 5 kWh. The test results of the PZEM-004T sensor show an average precision of 98.88 %, an accuracy of 98.12 %, and an error of 1.88 %. Based on these results, the system is feasible to be implemented in the apartment building.
Development E-Module Augmented Reality Assisted To Improve Writing Skills Elementary School Students Risky Erillia
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 2 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i2.30264

Abstract

This study aims to determine the feasibility and effectiveness of an augmented reality assisted e-module. This type of research is research and development. This study uses a 4-D development model consisting of four stages: define, design, develop, and disseminate. The study was conducted at SD IT Hamas. The subjects in the study were 20 fourth grade students, consisting of 9 female students and 11 male students. The object of the study was an augmented reality assisted e-module.. The data collection techniques used were observation, tests, and questionnaires. The data analysis techniques were (1) feasibility analysis.augmented reality assisted e-module; (b) effectiveness analysisaugmented reality assisted e-module. Based on the research results, it is known that the results of the media expert validation are:96.85% is in the very feasible category, and the results of the material expert validation are 80.00% in the feasible category. Meanwhile, in the pretest trial the pretest score percentage was 56.95%, and the posttest score was 80%. Based on this description, augmented reality assisted e-modules are feasible and effective for improving students' writing skills at SD IT Hamas.
A Spatial Analysis-Based PSI Model for Post-Crisis Social Vulnerability Mitigation Priorities Muit Sunjaya; Akbar Idaman; Hamidah Azzahra S. Lubis
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 2 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i2.32032

Abstract

Post-crisis social vulnerability is a crucial factor influencing a region's ability to face, respond to, and recover from a disaster. Differences in socioeconomic conditions, access to basic services, demographic characteristics, and adaptive capacity cause variations in the level of social vulnerability between regions. This study aims to develop a social vulnerability mitigation priority model in Medan City by integrating the Preference Selection Index (PSI) method with a sub-district-based spatial analysis approach. The study used 21 sub-districts in Medan City as decision alternatives evaluated based on seven criteria: economic vulnerability, disruption of access to basic services, post-crisis social impacts, demographic vulnerability, community social capital, community recovery capacity, and institutional and mitigation support. Research data were obtained through questionnaires and then aggregated into a decision matrix for the PSI calculation process. The PSI method was used to objectively generate criteria weights and obtain preference index values for each region. Next, the preference index values were transformed into a mitigation priority index using the inverse approach PMi=1−θi, where a higher PM value indicates a greater need for mitigation interventions. The results of the study indicate that Medan Belawan District has the highest mitigation priority with a PSI value of 0.5556 and a PM value of 0.4444, followed by Medan Labuhan District with a PSI value of 0.5986 and a PM value of 0.4014, and Medan Marelan District with a PSI value of 0.6253 and a PM value of 0.3747. These results indicate that the integration of the PSI method and spatial analysis is able to produce a data-based decision support model to identify priority areas for mitigating social vulnerability more objectively and systematically.
Web-Based Student Savings Management Information System Using Laravel Framework at SD Negeri 1 Jagapura Kulon Ahlan Ramadhani
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 2 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i2.32367

Abstract

The administration of student savings is an essential school activity that demands meticulous recording and high accountability. At SD Negeri 1 Jagapura Kulon, this process previously relied on manual physical logbooks and simple spreadsheets, leading to calculation vulnerabilities, risks of document damage or loss, an absence of audit trails for transaction corrections, and significant delays in periodic financial reporting. This study aimed to design and develop a web-based Student Savings Management Information System utilizing the modern Laravel 12 framework and MySQL database. The software development life cycle followed the Waterfall model, encompassing requirements analysis, system design through UML and ERD modeling, software implementation, and testing. The developed application integrates dynamic Role-Based Access Control (RBAC), class-based data scoping via the ScopedByKelas trait, real-time recording of deposits and withdrawals, an internal maker-checker workflow for transaction correction approval, and automated compilation of six comprehensive financial report types in PDF format. Functional verification was conducted using Black-Box Testing across 14 operational scenarios, all of which achieved a 100% pass rate. The results demonstrate that the system successfully satisfies all functional specifications, providing an accurate, structured, and transparent digital solution for managing student savings.
Design, Architectural Evaluation, and Feature Engineering Analysis of a Machine Learning-Based Network Intrusion Detection System Using Random Forest Ensemble Walat Ali Ahmed
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 2 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i2.30793

Abstract

In the modern network environment characterized by highly heterogeneous network architectures, the security landscape is dominated by a persistent and burgeoning threat scenario of extremely high complexity. Security systems based on signature detection are not efficient at protecting against new threats, and they are vulnerable to evasion strategies.In this research, we propose a novel machine-learning network intrusion detection system (NIDS) designed for multiclass network attack classification based on a weighted Random Forest algorithm. The proposed model was tested experimentally on the well-known benchmark dataset KDDCUP99 that contained 494,021 network connections. We designed the preprocessing pipeline based on macro-class level categorical label mapping, ordinal attribute mapping, and Min-Max numerical normalization.The evaluation was performed using the validation subset that was kept separate (i.e., an out-of-sample test) from the training process and consisted of 148,207 records. The proposed model achieved an overall accuracy of 99.97% in multiclass classification. We focus on discussing the severe class imbalance, and the experimental results indicate that our method is capable of achieving satisfactory predictive performance for minority classes, as the proposed classifier maintains a precision score as high as 90.91% for a rare attack class User-to-Root (U2R). Finally, we provide a complete comparison of the proposed approach with a set of state-of-the-art intrusion detection baselines.
Performance Comparison of Logistic Regression and Neural Networks in Predicting Stunting Prevalence in Aceh Province Alfiya Zahara; Dahlan Abdullah; Muhammad Daud; Muhammad Sayuti; Nurdin Nurdin
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 2 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i2.32229

Abstract

Stunting remains a significant public health problem in Indonesia, particularly in Aceh Province, where differences in prevalence across districts/cities require data-driven approaches to support targeted intervention planning. This study aims to compare the predictive performance of Logistic Regression and Neural Networks in classifying stunting prevalence across 23 districts/cities in Aceh Province. A quantitative approach was employed using secondary data from the Aceh Provincial Health Profile for 2022–2025, comprising 92 observations. The predictor variables included exclusive breastfeeding coverage, improved sanitation coverage, and vitamin A supplementation coverage, while stunting prevalence was transformed into a binary classification, with 0 representing prevalence of ≤20% and 1 representing prevalence of 20%. Logistic Regression and Multilayer Perceptron Neural Networks were implemented and evaluated using Accuracy, Precision, Recall, F1-score, and Area Under the Curve (AUC). The results showed that both models were capable of classifying stunting prevalence, while Neural Networks demonstrated better overall predictive performance. Logistic Regression achieved an Accuracy of 0.78 and an AUC of 0.82, whereas Neural Networks achieved an Accuracy of 0.89, Precision of 0.87, Recall of 0.90, and F1-score of 0.88. The findings indicate that Neural Networks provide stronger predictive performance in identifying high-stunting categories, while Logistic Regression offers greater interpretability for understanding relationships between health indicators and stunting prevalence. Therefore, Neural Networks have potential as a predictive approach for supporting the identification of high-risk areas and data-driven stunting intervention planning in Aceh Province.

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