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The Journal of Information Technology, Computer Science, and Electrical Engineering
Published by Yayasan Sinergi Multidimensi Kreatif
ISSN : -     EISSN : 30464900     DOI : https://doi.org/10.30596/jitcse
Core Subject :
The Journal of Information Technology, Computer Science, and Electrical Engineering (JITCSE) is a premier publication dedicated to advancing research and innovation at the intersection of these dynamic fields. With a focus on cutting-edge developments and emerging trends, JITCSE serves as a vital platform for scholars, researchers, and practitioners to share their latest findings and insights. Covering a broad spectrum of topics including software engineering, artificial intelligence, network security, digital systems, and renewable energy, JITCSE showcases rigorous and impactful research that drives technological progress forward.
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Articles 205 Documents
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Application of the K-Means Algorithm for Grouping Student Characteristics Based on Attendance and Learning Outcomes to Support Academic Evaluation Muslim; Nova Mayasari
Journal of Information Technology, computer science and Electrical Engineering Vol. 3 No. 1 (2026): February-May 2026
Publisher : Yayasan Sinergi Multidimensi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jitcse.v3i1.259

Abstract

Identifying student learning characteristics is essential for supporting evidence-based academic evaluation and improving educational quality in higher education. This study proposes the application of the K-Means clustering algorithm to classify students according to attendance and learning outcomes. The research utilized academic records comprising attendance percentage, assignment scores, midterm examination scores, final examination scores, and final course scores. Data preprocessing included cleaning and Min–Max normalization prior to clustering. The optimal number of clusters was identified using the Elbow Method, while clustering quality was assessed through the Silhouette Score. Experimental results revealed that the dataset was optimally partitioned into three clusters, corresponding to high-performing, moderate-performing, and academically at-risk student groups. The obtained Silhouette Score of 0.72 demonstrates good cluster compactness and separation, indicating that the selected variables effectively represent student academic characteristics. The proposed clustering model provides meaningful insights into student learning profiles and offers practical support for lecturers and study program administrators in implementing data-driven academic evaluation, targeted learning interventions, and the development of an Early Warning System (EWS). These findings demonstrate the potential of Educational Data Mining techniques to enhance academic decision-making and improve student success in higher education.
Analysis of Egg Productivity in Buras Chickens Fed Fermented Feed with Various Bioactivators Dini Julia Sari Siregar; Warisman
Journal of Information Technology, computer science and Electrical Engineering Vol. 3 No. 1 (2026): February-May 2026
Publisher : Yayasan Sinergi Multidimensi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jitcse.v3i1.260

Abstract

This study aimed to analyze the egg productivity of native chickens fed fermented feed using different bioactivators. The primary issue underlying this study was the relatively low egg productivity of indigenous chickens in Indonesia, which ranges from 60 to 120 eggs per hen per year, as well as the high cost of conventional feed, accounting for approximately 60–70% of total production costs. As an alternative solution, a bioconversion technology in the form of feed fermentation was applied to break down the crude fiber bonds in rice bran and corn, improve feed quality and crude protein content, reduce antinutritional compounds, and enhance nutrient digestibility in the digestive tract of chickens. The experiment was conducted using a field laboratory approach based on a non-factorial Completely Randomized Design (CRD) consisting of four dietary treatments with five replications. Each experimental unit contained three 7-month-old native laying hens, resulting in a total of 60 experimental birds. The basal diet consisted of corn (51%), rice bran (30%), soybean meal (10%), fish meal (3%), cooking oil (2%), limestone (1%), and mineral mix (3%). The diet was mixed with selected bioactivator solutions and palm sugar, followed by anaerobic incubation for an optimum fermentation period of 4–7 days. The observed parameters included daily feed intake, daily egg production expressed as Hen Day Production (HDP), average egg weight, and feed efficiency measured by Feed Conversion Ratio (FCR). The collected data were analyzed using Analysis of Variance (ANOVA) to determine the significance of treatment effects. The results demonstrated that fermented feed supplemented with the Starbio bioactivator (P3) produced the most favorable performance, resulting in the highest HDP (54.80%), the greatest average egg weight (46.50 g), and the lowest FCR (3.13), indicating the highest feed efficiency. These findings are expected to provide a scientific basis for smallholder poultry farmers in selecting appropriate commercial bioactivators for producing high-quality fermented feed, thereby reducing feed production costs and supporting the sustainable development of native chicken farming.
DEVELOPMENT OF PERSONAL DATA PROTECTION IN ARTIFICIAL INTELLIGENCE SYSTEMS FOR PROCESSING LEGAL NOTARY DOCUMENTS Deri Sembiring; Gloria Gita Putri Ginting; Ayu Kurnia Sari
Journal of Information Technology, computer science and Electrical Engineering Vol. 3 No. 1 (2026): February-May 2026
Publisher : Yayasan Sinergi Multidimensi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The development of Artificial Intelligence (AI) technology, particularly Natural Language Processing (NLP) and Optical Character Recognition (OCR), has brought significant efficiencies to legal document processing. In notarial practice, AI can be utilized for data extraction, automating deed drafting, verifying identity validity, and precisely and quickly identifying potential risky clauses. Conclusion Legal Alignment: By embedding Privacy by Design principles directly into the technical pipeline, the system bridges the gap between AI automation and legal compliance, satisfying both the strict confidentiality mandates of the Notary Position Law (UUJN) and the technical protection standards of the Personal Data Protection Law (UU PDP).
ANALYSIS OF LABOR PRODUCTIVITY IN IRONING AND BEAUTIFICATION WORK USING THE WORK SAMPLING METHOD Sopar Parulian
Journal of Information Technology, computer science and Electrical Engineering Vol. 3 No. 1 (2026): February-May 2026
Publisher : Yayasan Sinergi Multidimensi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jitcse.v3i1.262

Abstract

Labor is one of the most crucial and dynamic resources in construction projects, contributing significantly to total project costs, typically ranging from 30% to 50%. The efficiency of labor utilization directly impacts project schedule and budget targets. This study aims to analyze labor productivity in high-rise building structural works, specifically rebar binding (ironing) and formwork (begisting) installation, using the Work Sampling method. Field observations recorded proportions of effective work, essential contributory work, and ineffective work. Labor Utilization Rate (LUR) was calculated to quantify productivity. Based on a 5-day observation period totaling 1,920 sample observations, the LUR for rebar work was determined to be 65.94%, while formwork achieved 61.98%. Both values exceed the construction industry standard baseline of 50%, yet substantial room for optimization exists by mitigating ineffective work (19.79% for rebar and 22.71% for formwork). Primary causes of ineffective time include material supply delays, congested work zones, equipment shortages, and worker fatigue. Recommended improvement strategies include Just-in-Time (JIT) material delivery, optimized site layout for fabrication zones, and restructured rest break schedules.
Sustainable Supplier Selection in the Chemical Industry Based on the Multi-Attributive Border Approximation Area Comparison (MABAC) Method Fazrul Hamonangan Tanjung; Ari Pradana
Journal of Information Technology, computer science and Electrical Engineering Vol. 3 No. 1 (2026): February-May 2026
Publisher : Yayasan Sinergi Multidimensi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In supply chain management, supplier selection is recognized as one of the most critical and prominent purchasing functions, as it significantly contributes to enhancing a company's competitive strategy. Sustainability refers to an organization's ability to make real-time decisions without adversely affecting future environmental conditions, societal well-being, or business stability. Since the production of most chemical products is highly hazardous and has the potential to cause irreversible environmental damage as well as negative impacts on public health, sustainable supplier selection has become increasingly important. Therefore, this paper addresses this issue by proposing a multi-criteria decision-making (MCDM) framework for sustainable supplier selection in the chemical industry. Based on the specific characteristics of the chemical industry, this study employs the Analytic Hierarchy Process (AHP) to evaluate criteria across the economic, social, and environmental dimensions. Subsequently, the Multi-Attributive Border Approximation Area Comparison (MABAC) method is applied to rank the supplier alternatives. The proposed approach and decision-making model can assist sustainable supply chain managers in the chemical industry in selecting more sustainable suppliers, responding rapidly to market demands, and maintaining a high level of competitiveness in the marketplace.

Page 21 of 21 | Total Record : 205

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