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All Journal Building of Informatics, Technology and Science Bulletin of Informatics and Data Science Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Fathudin, Dedin
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Other

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Optimization of ID3 Structure for Academic Performance Analysis using Ant Colony Algorithm Fathudin, Dedin; Ambarsari, Erlin Windia; Paramita, Aulia
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5353

Abstract

This study investigates the optimization of the ID3 algorithm for academic performance analysis using the Ant Colony Optimization (ACO) method. The primary research problem addressed is the inefficiency and overfitting of traditional ID3 in complex and noisy datasets. Therefore, the ACO method is integrated to enhance the ID3 structure, improving classification accuracy and computational efficiency. The research objectives include developing a decision tree model based on assignment, mid-term, and final exam scores for student performance evaluation. The method combines ID3's decision-making capabilities with ACO's optimization process, which uses pheromone trails to find optimal paths in constructing the decision tree. Temporary results show that the ACO-ID3 model achieves an accuracy of 85% with improved consistency and lower variability compared to the traditional ID3 model, which has an accuracy of 89% but higher variability; this indicates that while traditional ID3 may slightly outperform in accuracy, the ACO-ID3 model provides more stable and reliable performance across different data subsets. The study concludes that ACO-ID3 is a practical and effective tool for academic performance analysis, particularly in cases requiring consistent and reliable classification
PENGGUNAAN EVENT VIEWER PADA WINDOWS DALAM MENEMUKAN MASALAH Kustian, Nunu; Fathudin, Dedin; Ambarsari, Erlin Windia
Semnas Ristek (Seminar Nasional Riset dan Inovasi Teknologi) Vol 6, No 1 (2022): SEMNAS RISTEK 2022
Publisher : Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/semnasristek.v6i1.5826

Abstract

Perangkat komputer menjadi kebutuhan masyarakat, untuk menyelesaikan pekerjaannya secara sistem yang terintegrasi. Masalah yang sering terjadi adalah pengguna komputer tidak mengetahui kerentanan sistem, diantaranya adalah aktivitas komputer yang tidak wajar; dalam hal ini, program yang tidak seharusnya dijalankan atau ada di komputer. Beberapa tahapan dapat digunakan untuk menganalisis aktivitas tersebut. Oleh karena itu, pada penelitian ini menggunakan Windows Event Viewer untuk Pengguna Sistem Operasi Windows sebagai pemecahan masalahnya. Event Viewer adalah modul snap-in dari Windows; utilitas yang digunakan untuk memeriksa kesalahan di kedua sistem dan aplikasi Windows. Event Viewer di Windows adalah salah satu alat yang digunakan untuk meninjau sistem individual dan administrator untuk memecahkan masalah melalui diagnostik log aktivitas abnormal yang sudah masuk dalam Event Viewer. Metode yang digunakan pada penelitian ini adalah forensik, yang dimana tujuannya adalah untuk menemukan kesalahan sistem berdasarkan skenario yang dibuat pada penelitian ini sebagai ilustrasi implementasi Event Viewer. Hasil yang didapatkan dari penelitian ini adalah Event Viewer dapat mendeteksi siapa saja yang berhasil masuk berdasarkan tanggal dan waktu sehingga perlu membatasi hak akses pada komputer yang digunakan.Kata Kunci: Diagnosis, Event Viewer, Windows
Hybrid Chaos-Isolation Forest Framework for Anomaly Detection in Indonesia’s Public Procurement Ambarsari, Erlin Windia; Desyanti, Desyanti; Fathudin, Dedin
Bulletin of Informatics and Data Science Vol 4, No 2 (2025): November 2025
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v4i2.137

Abstract

This study proposes and empirically evaluates a Hybrid Chaos-Isolation Forest (HC-iForest) framework for detecting anomalies in Indonesia’s public procurement datasets. The purpose of this research is to address the difficulty of identifying irregular procurement patterns, as existing assessment mechanisms remain largely descriptive and retrospective. The framework integrates chaos-based temporal descriptors—permutation entropy, turning points, and volatility—with statistical indicators to enhance sensitivity to nonlinear and irregular time series. Using monthly procurement data from the Open Contracting Data Standard (OCDS) covering the period from 2019 to 2024, the model identified anomalous fiscal patterns associated with year-end budget adjustments and procurement surges. Empirical evaluation using correlation, ablation, and statistical validation shows that the hybrid model introduces non-redundant anomaly information, achieving a Spearman rank correlation of approximately 0.75 compared to the baseline Isolation Forest, with reduced overlap at intermediate thresholds (Jaccard similarity of 0.20 at the Top 5%). These results confirm that chaos-driven features improve model stability and interpretability. The findings reveal that anomalies are systemic manifestations of institutional and fiscal behavior rather than random deviations. The HC-iForest framework offers a data-driven early-warning mechanism for oversight agencies such as LKPP and ICW, strengthening transparency and accountability in public spending. Future studies may extend this framework through neural or spatiotemporal hybrid architectures to support intelligent and adaptive fiscal monitoring systems
Co-Authors Desyanti Desyanti Erlin Windia Ambarsari KUSTIAN, NUNU Paramita, Aulia