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Muhammad Wali
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International Journal Software Engineering and Computer Science (IJSECS)
Published by Lembaga Komunitas Informasi Teknologi Aceh
ISSN : 27764869     EISSN : 27763242     DOI : https://doi.org/10.35870/ijsecs
Core Subject : Science,
Computer Science & IT
IJSECS is committed to bridge the theory and practice of information technology and computer science. From innovative ideas to specific algorithms and full system implementations, IJSECS publishes original, peer-reviewed, and high quality articles in the areas of information technology and computer science. IJSECS is a well-indexed scholarly journal and is indispensable reading and references for people working at the cutting edge of information technology and computer science applications..
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 423 Documents
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Integration of Edge Computing and Wireless Sensors for Energy Efficiency Monitoring in Solar Panels Cut Susan Octiva; T. Irfan Fajri; Handry Eldo; Ayuliana Ayuliana; Nur Amalia Hasma
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.6797

Abstract

Increased demand for renewable energy has driven the development of efficient monitoring systems to optimize solar panel performance. This study aims to implement and evaluate the integration of edge computing technology with wireless sensor networks (WSN) in real-time solar panel energy efficiency monitoring systems. This approach is designed to overcome the limitations of conventional monitoring systems that still rely on centralized computing and exhibit high latency in data collection. The research method includes designing an edge computing-based system architecture, installing wireless sensors to measure key parameters (voltage, current, light intensity, and temperature), and applying energy efficiency algorithms at the edge to process data locally. The data is then sent to the cloud for in-depth analysis and visualization of system performance. Testing was conducted by comparing data transmission efficiency, response time, and measurement accuracy between edge-based and conventional systems. The results of the study show that the integration of edge computing and wireless sensors can increase monitoring efficiency by up to 28.4%, reduce system latency by 35.7%, and increase data accuracy by 12.6% compared to conventional systems that are entirely cloud-based. In addition, bandwidth consumption is significantly reduced because the computing process is done on the edge.
Design and Development of a Vulnerability Simulation-Based Cybersecurity Training Platform for Secure Programming Habib Nurfaizal; Afrizal Zein
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.6809

Abstract

The increasing number of attacks on web applications necessitates strengthening secure programming competencies among computer science students. However, cybersecurity learning is often constrained by ethical and legal limitations, as direct testing on real-world systems is not permissible. This study designed and implemented a web-based cybersecurity training platform that provides a simulated vulnerability environment for secure programming practice. The methodology covers learning needs analysis, system design, vulnerability module implementation, and integration of defensive coding features. The platform operates as an online virtual laboratory accessible via www.kampuscyber.unaux.com, with modules addressing SQL Injection, Cross-Site Scripting (XSS), Cross-Site Request Forgery (CSRF), File Upload Vulnerability, Insecure Direct Object Reference (IDOR), Command Injection, Directory Traversal, Weak Authentication, and Insecure Cookie handling. Each module maps programming errors directly to their security consequences, paired with defensive coding solutions. The evaluation involved 15 students enrolled in a cybersecurity training program. Across 10 modules, students achieved a 79.33% success rate in completing exploitation tasks and 65.33% in providing secure programming solutions — a gap that points to the greater difficulty of defensive over offensive competency. These findings indicate that the platform offers a safe and controlled environment for web vulnerability learning and mitigation practice, and may serve as an ethical alternative for practice-based secure programming education without exposing real-world systems to risk.
Automated Regression Testing Procedure Based on ISO/IEC 29119 to Improve Software Testing Efficiency in a Software Development Environment Galuh Oka Safitri; Leni Susanti
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.6810

Abstract

The growing complexity of modern software systems has increased the importance of effective software testing practices to ensure system reliability and quality throughout the development lifecycle. Among various testing activities, regression testing plays a crucial role in verifying that newly introduced changes do not negatively affect previously functioning system components (Yoo & Harman, 2012). However, in many organizations regression testing is still conducted manually, which can be time-consuming and may delay software release cycles. This study aims to design an automated regression testing procedure based on the ISO/IEC 29119 software testing standard in order to improve testing efficiency within a software development environment. A case study was conducted at PT. ABCD to examine the existing regression testing practices. Data were collected through semi-structured interviews with members of the Quality Assurance (QA) team, direct observation of testing activities, and analysis of testing documentation. The research process consisted of analyzing the current regression testing workflow, performing gap analysis with the ISO/IEC 29119 framework, and designing an automated regression testing procedure aligned with the standard. The study focused on five system modules comprising 420 regression test cases. The results indicate that manual regression testing required approximately eight working days to complete, with an average execution time of about nine minutes per test case. After implementing the proposed automated testing procedure, the execution time was reduced to approximately one day, or about 1.14 minutes per test case, resulting in an efficiency improvement of approximately 87.5%. These findings suggest that integrating automated regression testing with a structured testing framework such as ISO/IEC 29119 can significantly improve testing efficiency while also supporting better documentation, traceability, and process consistency. From a practical perspective, the proposed approach may help development teams accelerate testing cycles and support faster delivery of software updates in dynamic development environments.
Development of a Mixed Reality Based Learning Application for Microcontroller Education Using the Luther Sutopo Multimedia Development Method Antoni Pribadi; Ratu Natalia Marjani Ufayrah; Andri Nofiar. Am; Nurkholis Nurkholis; M. Alkadri Perdana
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.6817

Abstract

The rapid advancement of immersive technologies has created new opportunities to improve learning effectiveness, particularly in technical education such as microcontroller systems. Conventional teaching methods still rely heavily on static visualizations and theoretical explanations, which limit students' ability to understand complex hardware interactions. A mixed reality (MR)-based learning application was developed to improve students' conceptual understanding of microcontroller components and their functions in an interactive and immersive environment. The development process followed the Luther–Sutopo multimedia development method, consisting of six stages: concept, design, material collecting, assembly, testing, and distribution. The application was built using Unity and Blender and deployed on the Oculus Meta Quest 2 platform. Usability testing involved 10 vocational students with prior knowledge of microcontrollers. The application achieved an average usability score of 4.54 out of 5 (90.8%), placing it within the "very feasible" category. Beyond feasibility metrics, the system appeared to strengthen students' spatial understanding, engagement, and interaction with microcontroller components. These findings suggest that pairing mixed reality with a structured multimedia development model can meaningfully improve the effectiveness of technical education.
Evaluating Vibe Coding as an AI-Orchestrated Development Methodology: A Case Study on Accelerating Complex Web-Based Educational Management Systems Iqbal Muhammad Adiatma
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.6823

Abstract

The emergence of generative AI has disrupted conventional software development practices, prompting considerable skepticism among IT professionals about whether such tools displace rather than augment human expertise. This study introduces "Vibe Coding" as a collaborative methodology — one in which AI operates as a capable partner, not a substitute — requiring human guidance for review, analysis, and iterative refinement of generated outputs; the primary objective is to assess whether Vibe Coding, when structured through Model Context Protocol (MCP) and schema engineering, can materially reduce development time for complex web systems — including CRUD operations, API integration, and custom business logic — relative to conventional approaches such as Waterfall. Two research questions drive the inquiry: (1) Can Vibe Coding compress development timelines for complex systems from months to days? and (2) How effective is AI as a collaborative partner in sustaining output quality through human-in-the-loop validation? A single case study approach was employed, applying the methodology to develop an ISO 9001:2015-compliant Management Information System (MIS) for Pondok Pesantren Abu Hurairah Mataram as a solo developer project, with metrics tracked across seven days including total development time, time per phase (planning, development, debugging, and deployment), proportion of AI-generated code (70–85%), prompt and iteration counts, bug frequency, debugging duration, total lines of code (LOC), and feature implementation success rate. Results show a completed system in seven days, with 70–85% of the codebase AI-generated and 15–30% manually refined for business logic, debugging, and performance tuning; human intervention effectively countered AI hallucinations throughout, repositioning the developer's role from syntax-level coding toward architectural orchestration and quality control. These findings suggest Vibe Coding raises productivity for solo developers in AI-saturated environments, though rigorous human oversight remains non-negotiable for production-grade systems.
The Use of Local Wisdom-Based E-LKPD in Science and Social Studies Learning for Grade IV Students of Cluster V, Seririt District Made Sutawan; I Gede Suwindia; Ni Nyoman Kurnia Wat
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.6828

Abstract

This study was conducted in response to a persistent gap in contextual and meaningful IPAS learning at the elementary level — one that, despite growing attention in the literature, has not been adequately addressed through systematic synthesis. The specific concern is whether electronic student worksheets grounded in local wisdom, particularly those carrying the values of Tri Kaya Parisudha, can be said to produce consistent, documentable effects on students' critical thinking and character development. Previous studies have addressed pieces of this question, but none have drawn them together with sufficient rigor. A Systematic Literature Review was therefore conducted, following the PRISMA protocol across four stages: identification, screening, eligibility, and inclusion. Source materials were drawn from accredited national journals and relevant scientific publications. Of 45 articles initially identified, 10 met the inclusion criteria and were subjected to descriptive qualitative analysis. The findings indicate that local wisdom-based electronic worksheets produced measurable gains in learning outcomes, motivation, active participation, and critical thinking. The incorporation of Tri Kaya Parisudha values — manacika, wacika, and kayika — also appeared to reinforce students' attitudinal development in ways that standard digital worksheets did not. Taken together, the evidence positions local wisdom-based electronic worksheets as a contextually grounded and educationally sound resource for IPAS instruction in elementary schools, one that serves cognitive and character goals simultaneously.
ETL Pipeline with DTO Normalization for IPOS Data Integration in Spring Boot Adhi Septian Nugroho; Yeremia Alfa Susetyo
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.6850

Abstract

IPOS point-of-sale software, widely used by Indonesian small and medium retail enterprises (UMKM), exports transaction data as Excel files with no enforced schema—producing format-variable, multi-row receipt blocks with heterogeneous date representations, locale-dependent numeric formats, and embedded unit strings that resist conventional relational import. Transforming these unstructured exports into a relational database requires a structured architectural approach capable of handling format variability, type inconsistency, and record duplication. This study designs and implements a Spring Boot-based ETL (Extract, Transform, Load) service that applies the Data Transfer Object (DTO) pattern through ten purpose-specific DTO classes covering each pipeline phase, structured within a four-layer Model-View-Controller (MVC) architecture (Controller-Service-Repository-Entity). The Extractor employs a streaming Excel reader with dynamic column-layout detection based on header keywords, producing raw String-typed ExtractedReceipt and ExtractedItem DTOs. The Transformer applies six normalization steps via four utility classes—StringNormalizer, DateParser (seven date-format patterns), NumberParser (Indonesian and Western currency formats), and a HashSet-based duplicate detector—converting raw strings into typed ValidatedReceipt and ValidatedItem DTOs with explicit error logging. The Loader performs batch inserts per 1,000 records using pre-loaded duplicate sets for O(1) lookup. The pipeline operates asynchronously, returning a jobId immediately while processing continues on a background thread. Functional evaluation across ten scenarios yielded a 100% pass rate, covering valid files, invalid file types, date-format heterogeneity, embedded-unit quantity strings, Indonesian numeric formats, cross-file and intra-file duplicate detection, grand-total reconciliation tolerance, and product-variation tracking. Performance observation shows that files of 200–500 receipts complete within 5–15 seconds. These results indicate that a DTO-centric, explicitly mapped ETL pipeline over Spring Boot MVC provides a maintainable, auditable, and production-ready solution for UMKM retail data integration.
A Multi-Criteria Decision Support System for Burial Plot Selection Using the TOPSIS Method: A Web-Based Approach Khoirunnisya Khoirunnisya; Muhamad Arief Yulianto
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.6860

Abstract

Rapid and accurate decision-making is essential in burial plot selection, particularly in urgent situations where families must simultaneously evaluate multiple criteria — price, available facilities, and land type. At TPU Al-Qobri, the absence of structured decision tools has allowed the manual selection process to persist, producing inconsistent outcomes and placing unnecessary burden on families during emotionally difficult circumstances. This study develops a web-based Decision Support System (DSS) using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to support objective burial plot selection based on multiple weighted criteria. System development follows the Software Development Life Cycle (SDLC), covering planning, analysis, design, implementation, testing, and maintenance phases. TOPSIS is applied to rank available burial plot alternatives and generate recommendations that are traceable and consistent across evaluation sessions. Testing results confirm that the system produces accurate rankings, reduces selection time, and improves administrative service management at the cemetery level. The proposed system demonstrates that structured, criterion-based decision support can replace subjective manual processes in public cemetery administration.
A Comparative Performance Analysis of Naïve Bayes, LSTM, and BiLSTM with Data Balancing Techniques for Sentiment Analysis of EasyCash Application Reviews Fitri Abelia; Fitriyani Fitriyani
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.6862

Abstract

This study compares the performance of Naïve Bayes, Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) models in sentiment analysis of EasyCash application reviews, with data balancing techniques applied throughout the process. The dataset was collected from the Google Play Store and processed through cleaning, tokenization, stemming, and normalization. Sentiment labeling classified reviews into positive, neutral, and negative categories. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied prior to model training. Feature extraction was conducted using TF-IDF, and models were evaluated on accuracy, precision, recall, and F1-score. Naïve Bayes outperformed both LSTM and BiLSTM, producing higher accuracy and more stable results across evaluation metrics. The findings suggest that simpler machine learning models can be more effective than deep learning approaches when working with limited and imbalanced datasets. Careful data preprocessing, appropriate balancing techniques, and deliberate model selection remain central to achieving reliable sentiment classification performance in fintech applications.
Revisiting Feature Scaling in Linear Regression: An Empirical Study on Microsoft Stock Price Prediction Farhan Mahfudz; Khoirunnisya Khoirunnisya
International Journal Software Engineering and Computer Science (IJSECS) Vol. 6 No. 1 (2026): APRIL 2026
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET) - Lembaga KITA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/ijsecs.v6i1.6873

Abstract

Stock price prediction occupies a central position in quantitative finance, bearing directly on risk management, portfolio construction, and investment decision-making. This study evaluated the effect of feature scaling on linear regression performance in predicting Microsoft (MSFT) stock prices. A quantitative experimental design was employed, drawing on historical MSFT stock data spanning 2014 to 2024. Preprocessing involved data cleaning, outlier treatment via the Interquartile Range (IQR) method, and feature standardization through Z-score normalization. Two experimental conditions were tested: linear regression without feature scaling and linear regression with feature scaling. Model performance was assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). Both conditions produced nearly identical results — R² approaching 0.99, with negligible divergence across all error metrics. The evidence suggests that feature scaling does not meaningfully alter the predictive behavior of linear regression. For simple linear models operating without regularization, scaling appears to be an unnecessary preprocessing step, a finding that warrants more deliberate evaluation of preprocessing decisions in machine learning pipelines.

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