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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Information System for Educators and Professionals : Journal of Information System
Bambang Kriswantara
Universitas Bina Insani
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Other

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Penerapan Framework ITIL V3 dalam Evaluasi Teknologi Informasi CBT Online pada SMK Bina Karya Mandiri Neisita Ayuningtias; Harjunadi Wicaksono; Bambang Kriswantara; Wiwit Priyadi; Indry Ayu Lestary
INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Vol 10 No 1 (2025): INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS (Juni 2025)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/isbi.v10i1.3417

Abstract

Teknologi informasi (TI) telah menjadi pilar penting dalam kemajuan sektor pendidikan, termasuk dalam sistem evaluasi pembelajaran. Salah satu inovasi yang diterapkan di lembaga pendidikan adalah sistem Computer-Based Test (CBT) yang memungkinkan ujian dilakukan secara digital. SMK Bina Karya Mandiri Bekasi mengimplementasikan sistem CBT Online untuk mendukung proses evaluasi akademik dengan lebih dari 5000 peserta didik pada tahun ajaran 2023/2024. Meskipun demikian, masih terdapat kendala teknis, terutama terkait gangguan koneksi yang disebabkan oleh keterbatasan kapasitas bandwidth yang tidak memadai pada infrastruktur jaringan. Penelitian ini bertujuan untuk mengevaluasi dan menganalisis infrastruktur TI dalam sistem CBT Online menggunakan framework ITIL V3, khususnya pada domain Service Design. Evaluasi dilakukan melalui observasi, wawancara, dan kuesioner yang mengukur persepsi pengguna terhadap berbagai aspek infrastruktur TI. Hasil audit menunjukkan bahwa terdapat beberapa area yang perlu diperbaiki, antara lain manajemen kapasitas, pemantauan kinerja, dan kebijakan keamanan. Berdasarkan temuan tersebut, penelitian ini memberikan rekomendasi untuk meningkatkan kualitas dan efisiensi sistem CBT Online, seperti memperkuat manajemen kapasitas dan pemulihan bencana, serta meningkatkan pemantauan keamanan. Implementasi rekomendasi ini diharapkan dapat meningkatkan kinerja sistem CBT Online di SMK Bina Karya Mandiri, sehingga proses ujian dapat berjalan lebih lancar dan efektif.
Comparative Study of Machine Learning Algorithms Using Bagging and XGBoost Techniques for Breast Cancer Classification Rully Pramudita; Dwi Ismiyana Putri; Bambang Kriswantara; Vina Zahrotun Nazah; Rahmat Budiarto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7216

Abstract

Machine learning (ML) has become an important data-driven approach for classification and prediction, including applications in medical diagnosis. Ensemble methods can improve classifier performance by combining complementary learning mechanisms. However, systematic evidence on the sequential use of Bagging and XGBoost across different classifier architectures remains limited. This study develops a staged ensemble framework in which five classifiers—Support Vector Machine (SVM), Neural Network (NN), Logistic Regression (LR), Decision Tree (DT), and K-Nearest Neighbours (KNN)—are first optimized through Bagging and subsequently enhanced using XGBoost. The experiments were conducted on the Breast Cancer Wisconsin (Diagnostic) dataset under a consistent 70:30 train–test protocol. Performance was assessed using accuracy, confusion matrices, ROC curves, and Area Under the Curve (AUC), while repeated experiments were used to examine statistical significance. The results show that the staged Bagging–XGBoost approach improves both predictive accuracy and class discrimination across the evaluated classifier types. Neural Network achieved the largest improvement, with mean accuracy increasing from 93.1% to 97.0% across repeated experiments. The findings demonstrate that the sequential framework can improve non-tree-based as well as tree-based classifiers, providing empirical evidence for broader use of staged ensemble integration in breast cancer classification.
Co-Authors Dwi Ismiyana Putri Harjunadi Wicaksono Indry Ayu Lestary Neisita Ayuningtias Rahmat Budiarto Rully Pramudita Vina Zahrotun Nazah Wiwit Priyadi