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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 Seek original and unpublished research papers based on theoretical or experimental works for the publication globally.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 Solicit original and unpublished research papers, based on theoretical or experimental works.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 1 (2026)" : 12 Documents clear
Optimized Hybrid CLDNN Architecture with Enhanced Temporal-Spatial Feature Extraction for Robust Automatic Modulation Classification in Cognitive Radio Networks Daryan Pratama Alifi; Hane Yorda Dinata; Galura Muhammad Suranegara; Ichwan Nul Ichsan
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 1 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i1.28935

Abstract

Automatic Modulation Classification (AMC) is a pivotal technology for efficient spectrum management in future cognitive radio networks. While Deep Learning has advanced the field, standard Convolutional Neural Networks (CNN) often struggle to capture long-term temporal dependencies in signals affected by fading. This study proposes an Optimized Hybrid CLDNN architecture that integrates a "Wide-Kernel" CNN (k=7) for enhanced spatial feature extraction and a "High-Capacity" LSTM (100 units) for robust temporal modeling. Experimental validation using the RadioML 2016.10a dataset demonstrates that the proposed optimizations yield significant performance gains. Specifically, the model achieves a classification accuracy of 84.5% at 0 dB SNR, outperforming standard baselines in the critical transition regime. Furthermore, it reaches a peak accuracy of 92.4% at high SNR (+18 dB). A notable finding is the reduction of inter-class confusion between 16-QAM and 64-QAM, where the misclassification rate is suppressed to approximately 15%, confirming the architecture's effectiveness in resolving hierarchical modulation ambiguities in dynamic wireless environments.
Explainable Data-Driven Machine Learning for Identifying MBG Program Beneficiaries in Medan City Solly Solly Aryza; Al Khowarizmi; Muhammad Furqon; Zulkarnain Lubis; Abdul Razak Nasution
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 1 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i1.29560

Abstract

The effectiveness of social assistance programs depends heavily on the accuracy and transparency of beneficiary identification. In many urban areas, including Medan, challenges such as incomplete data, administrative bias, and inefficient targeting often lead to inclusion and exclusion errors in determining beneficiaries of the MBG (Makan Bergizi Gratis) program. This study aims to develop an explainable data-driven machine learning model to improve the accuracy and transparency of identifying eligible MBG program beneficiaries. The research employs a quantitative approach using socio-economic and demographic datasets collected from local government records, including variables such as household income, employment status, education level, household size, housing conditions, and access to public services. Several machine learning algorithms, including Random Forest, Gradient Boosting, and Logistic Regression, are implemented to classify potential beneficiaries. To enhance transparency and interpretability, the model integrates Explainable Artificial Intelligence (XAI) techniques, such as SHAP (Shapley Additive Explanations), to identify the most influential factors affecting eligibility predictions. The results demonstrate that the proposed data-driven model significantly improves the accuracy of beneficiary classification while providing interpretable insights into key socio-economic indicators influencing eligibility. The findings indicate that income level, employment status, household dependency ratio, and housing conditions are among the most critical determinants in identifying eligible recipients. The implementation of explainable machine learning models supports more transparent and accountable decision-making in social assistance programs. This research contributes to the development of data-driven governance by providing a robust analytical framework for improving the targeting efficiency of social welfare programs in urban areas. Practically, the proposed framework can assist policymakers and local government agencies in designing fairer and more efficient beneficiary identification systems for the MBG program in Medan City, ultimately supporting better resource allocation and improved social welfare outcomes.
Development of a Blockchain-Based Digital Data Identity Management System Ardi Birawinata; Muhammad Nasir
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 1 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i1.29044

Abstract

The management of student identity data requires a system that ensures data security, integrity, and transparency. Conventional data management methods are vulnerable to data redundancy and manipulation, particularly in educational institutions. This study aims to develop a blockchain-based digital identity data management system for SMK Bina Sriwijaya Palembang using the Prototyping development method. The Prototyping approach was selected to allow iterative system evaluation and validation, especially for complex Smart Contract logic. The system was implemented as a web-based application using React JS for the front-end and Node.js for the back-end, with Ethereum Smart Contracts developed in Solidity. Ganache was used as a local blockchain network for testing, while IPFS was integrated for decentralized storage of digital assets. The results show that the proposed system successfully secures student identity data, prevents unauthorized data manipulation, and improves the efficiency of data verification processes. This study demonstrates that blockchain technology combined with a prototyping approach can provide a reliable solution for digital identity management in educational environments.
Optimization of Three-Phase Load Balancing to Improve the Efficiency and Reliability of Power Transformers at the Tanjung Morawa Substation Arnold Fernando Sinurat; Moranain Mungkin
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 1 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i1.29561

Abstract

Three-phase load imbalance in power transformers is one of the main problems that can reduce efficiency, increase power losses, accelerate the increase in winding temperature, and reduce the life of transformer insulation. This condition often occurs in distribution systems due to uneven load growth between phases. This study aims to optimize three-phase load balancing to improve the efficiency and reliability of power transformers at the Tanjung Morawa Substation. The research method uses a quantitative approach based on transformer operational data, including measuring the current of each phase, calculating the percentage of load imbalance, analyzing copper losses, and evaluating efficiency and reliability indices. The optimization model is carried out by simulating load redistribution between phases using a mathematical approach to minimize current deviations from the average current. Evaluation parameters include reducing power losses, increasing transformer efficiency, and improving the value of the imbalance factor according to IEC and IEEE standards. The results show that before optimization, the level of load imbalance was in the moderate to high category, which resulted in increased power losses and increased operating temperatures. After the load balancing optimization process, there was a significant decrease in the percentage of imbalance, a decrease in power losses, and an increase in transformer efficiency. Furthermore, the estimated insulation life shows an improvement due to reduced thermal stress. This study demonstrates that a systematic, data-driven three-phase load balancing optimization strategy can sustainably improve the performance, efficiency, and reliability of power transformers. The proposed model can be implemented as part of an asset management and preventive maintenance strategy in electric power systems.
Optimizing Lecturer Performance Assessment through Web-Based Application Design and Deployment Solly Aryza; Zulkarnain Lubis; Alkho Warizmi
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 1 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i1.26501

Abstract

The evaluation of lecturer performance is a critical process in higher education, directly influencing academic quality, institutional reputation, and continuous improvement in teaching outcomes. Conventional evaluation methods, often paper-based or fragmented digital systems, face challenges such as inefficiency, lack of transparency, and limited accessibility. To address these issues, this research presents the design and deployment of a web-based application for lecturer performance assessment, aimed at optimizing the evaluation process through automation, standardization, and real-time data access. The system was developed using a structured software development life cycle (SDLC) approach, combining requirements analysis, database design, user interface prototyping, and iterative testing. Key features of the application include secure data management, multi-role access (administrators, lecturers, students), automated scoring mechanisms, and interactive dashboards for performance visualization. The evaluation of system usability was conducted using the System Usability Scale (SUS), achieving an average score of 82.5, which indicates high user acceptance and effectiveness. Results show that the proposed web-based system significantly improves efficiency by reducing evaluation processing time by up to 65% compared to manual methods. Furthermore, the integration of real-time analytics enhances decision-making for academic leaders in designing lecturer development programs. This research demonstrates that a well-structured web-based application can play a vital role in ensuring transparency, accountability, and continuous quality improvement in higher education institutions.Keywords: Lecturer performance evaluation, web-based application, higher education, system usability, academic quality assurance. 
Decision Support System for Evaluating Textile Supplier Performance Based on Weights by Envelope and Slope and Mixed Aggregation by Comprehensive Normalization Technique for Multi-Criteria Setiawansyah Setiawansyah; Junhai Wang; Pritasari Palupiningsih; Sufiatul Maryana
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 1 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i1.29131

Abstract

The textile industry is highly dependent on supplier performance in ensuring the quality of raw materials, timely delivery, price stability, and supply continuity. The complexity of supplier evaluation involving many criteria often leads to subjectivity and inconsistencies in decision-making when using conventional approaches. This study proposes a decision support system to evaluate textile supplier performance based on a combination of Weights by Envelope and Slope (WENSLO) and Mixed Aggregation by Comprehensive Normalization Technique for Multi-Criteria (MACONT). The WENSLO method is used to determine the weight of criteria objectively based on data distribution characteristics, while MACONT is applied to assess and rank supplier alternatives through a comprehensive normalization and aggregation process. The case study was conducted involving nine suppliers and five evaluation criteria, namely material quality, timeliness, price, supply capacity, and responsiveness. The results of the study indicate that the proposed model is capable of producing clear and stable supplier rankings, with Supplier A9, Supplier A7, and Supplier A2 occupying the top three positions. These findings demonstrate that the integration of WENSLO and MACONT can enhance the objectivity and consistency of decision-making, as well as provide a more reliable and relevant framework for evaluating textile suppliers to support data-driven supply chain management.
Academic Performance Prediction of PTIK Students through Machine Learning Models at Universitas Negeri Medan Tansa Trisna Astono Putri; Reni Rahmadani; Rosma Siregar; Hanapi Hasan
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 1 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i1.29570

Abstract

This study addressed the need for an effective approach to predicting student academic performance in higher education using data-driven methods. The study aimed to implement machine learning models to predict the academic performance of students in the Information and Communication Technology Education Study Program at Universitas Negeri Medan. A quantitative predictive design was employed using a dataset of 40 student records. Five classification models were tested, namely Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Naïve Bayes. The results showed that all models produced strong predictive performance. Decision Tree achieved the highest accuracy at 93.1%, Logistic Regression produced the highest precision at 95.9% and the highest F1-score at 93.2%, while Support Vector Machine obtained the highest recall at 93.2%. These findings indicated that machine learning was feasible for predicting student academic performance in the study program. The study concluded that Logistic Regression provided the most balanced overall performance and had strong potential to support early academic intervention and data-based academic decision making in higher education.
Secure Data Management: An Implementation of Advanced Encryption Standard in a Flutter-Based Notes App Adetokunbo Abayomi Adenowo; Mary A. Adedoyin; Oluwasegun J. Adebiyi
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 1 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i1.26969

Abstract

Given increasingly critical concerns about data security in the digital age, safeguard of sensitive information in mobile applications due to its global interconnectivity becomes an imperative. This research focuses on the protection of information on mobile applications, demonstrated through the design, development and evaluation of a Flutter-based Notes App that employs an encryption algorithm to ensure confidentiality and integrity of user data. Specifically, this research adopts the 256-bit Advanced Encryption Standard (AES) as the core mechanism of the Notes App, on assumption of its robust security features that it ensures data protection during both storage and transmission. To ascertain the choice, it compares the performance of the AES- 256 against that of 128-bit and 192-bit key sizes using key metrics such as encryption and decryption time, memory usage, power consumption, and error rate. Programmatically generated dataset, alongside graphical analyses, where used to illustrate the performance differences across the three key sizes. Findings from the aforementioned, reveal that while AES-256 incurs marginally higher resource usage compared to its smaller key-size counterparts, it delivers significantly enhanced security. The increase in processing time and memory usage shows a negligible impact on performance, affirming its practicality for real-world applications. The seamless operation of the Notes App during encryption and decryption processes further validated the suitability of AES-256 for mobile platforms. It is therefore safe to conclude that AES-256 provides the optimal balance between security and performance, thus makes it the preferred choice for protecting sensitive information in mobile applications.
Sentiment Analysis of the Skyscanner Application on Google Play Store with a Comparison of Naive Bayes and Support Vector Machines Laely Triana; Wahju Tjahjo Saputro; Dewi Chirzah
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 1 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i1.29212

Abstract

The digital world is growing rapidly and has a significant impact on the tourism sector. Therefore, technology must adapt to developments to meet human needs. Travel booking services such as Skyscanner allow users to book flights, accommodation, and transportation online through the app. With the large number of Skyscanner user reviews on the Google Play Store. The majority of data reviews use Indonesian languanges; sentiment analysis is needed to determine user sentiment towards the app. This study aims to analyze user sentiment towards the Skyscanner app using collected user comment review data. The data is then classified into two sentiment classes: positive and negative. The classification results using a comparison of two algorithms, Naive Bayes and Support Vector Machine, SVM produced a higher accuracy of 89.74%. Naive Bayes achieves lower accuracy 82.08% than SVM. This concludes that the SVM algorithm is more effective in producing optimal classification accuracy than its comparison algorithm, Naive Bayes.
Product-Based Design Approach for Business Process Improvement: A Case Study of the SIMARSIP System at the Malang Civil Registry Office Karina Nine Amalia; Muhammad Rafi Thufail; Oktalia Juwita
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 1 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i1.29573

Abstract

This study investigates inefficiencies in the SIMARSIP digital archiving system used by the Malang City Civil Registry Office and proposes business process improvements by applying Root Cause Analysis (Fishbone and 5 Whys) and Product-Based Design, using qualitative data from interviews, observations, and document analysis as well as BPMN modeling and Bizagi simulation, which demonstrate that the redesigned processes reduce document submission cycle time by 50% (from 38 to 19 minutes), improve document search efficiency by 44%, enhance user flexibility through self-registration login features, and increase productivity despite reducing staff from five to four, thereby indicating that the integration of Root Cause Analysis and Product-Based Design is effective for modernizing digital public service systems.

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