Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) The Journal of Information Technology, Computer Science, and Electrical Engineering
Andisyah Putra
Universitas Pembangunan Panca Budi
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Penerapan Algoritma Decision Tree (C4.5) dalam Menentukan Kelayakan Penerima Bantuan Sosial Bayu Eka Susanto; Andisyah Putra; Ridwan; Sujatmiko Ginting; Muhammad Amin
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 3 (2026): Februari 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i3.793

Abstract

The accurate targeting of social assistance recipients is a critical challenge in public policy implementation, particularly in efforts to reduce poverty and social inequality. This study aims to apply the Decision Tree C4.5 algorithm to determine the eligibility of social assistance beneficiaries based on socio-economic data. The research employs a quantitative approach using data mining techniques, where data preprocessing, model construction, and performance evaluation are conducted systematically. The C4.5 algorithm is selected due to its ability to handle numerical and categorical data and to produce interpretable decision rules. The results indicate that the proposed model achieves a high classification performance, with income level emerging as the most influential attribute, followed by household dependents and housing conditions. The generated decision tree provides clear and transparent rules that facilitate understanding of eligibility determination. These findings demonstrate that the C4.5 algorithm is effective not only in terms of accuracy but also in supporting explainable decision-making processes. The study concludes that integrating Decision Tree C4.5 into social assistance management can enhance objectivity, transparency, and policy effectiveness. This research contributes to the development of data-driven decision support systems in the public sector and offers practical insights for improving the accuracy of social assistance distribution.
Analysis of the application of Gemini AI for informatics learning using the k-means clustering algorithm Bayu Eka Susanto; Sujatmiko Ginting; Andisyah Putra; Jheki Pranta Singarimbun; Ridwan; Sigit Prabowo
Journal of Information Technology, computer science and Electrical Engineering Vol. 2 No. 3 (2025): October 2025 - January 2026
Publisher : Yayasan Sinergi Multidimensi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61306/jitcse.v2i3.235

Abstract

This research aims to analyze the learning performance of vocational high school students in Informatics subjects thru the integration of Generative AI (Gemini) with the K-Means Clustering algorithm, an approach that is still rarely applied in the Indonesian educational context. The research data includes 28 students, consisting of 8 males and 20 females, whose performance was analyzed based on assignment, quiz, and exam scores. The clustering results yielded three main performance groups: high, medium, and low clusters. Gemini integration helps accelerate the process of interpreting the clustering results and provides a deeper understanding of students' learning patterns. The research findings indicate that the combination of generative AI and cluster analysis can generate more accurate insights to support the implementation of adaptive learning and data-driven decision-making by teachers. Additionally, this study contributes to the development of learning analytics at the vocational education level, while also opening opportunities to implement more personalized learning strategies. Further research could expand the data scope, test other clustering algorithms, and develop an analytics dashboard to facilitate data utilization by educational institutions.
Co-Authors Amin, Muhammad Bayu Eka Susanto Jheki Pranta Singarimbun Ridwan Sigit Prabowo Sujatmiko Ginting