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All Journal Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Journal of Computer Science Artificial Intelligence and Communications
Gilang Redzav Bagaswara
Universitas Al-Ghifari
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Engineering

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Explainable Artificial Intelligence-Based Early Warning System for Student Dropout Prediction Aan Ansen Andryadi; Samsan; Gilang Redzav Bagaswara
Journal of Computer Science, Artificial Intelligence and Communications Vol 3 No 1 (2026): May 2026
Publisher : Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/jocsaic.v3i1.177

Abstract

Student dropout remains one of the major challenges faced by higher education institutions because it negatively affects academic performance, institutional reputation, and educational sustainability. Early identification of students at risk of dropping out enables universities to implement timely interventions that improve student retention. Although machine learning models have demonstrated promising predictive performance, many of these approaches operate as black-box systems, limiting their transparency and practical adoption in educational decision-making. Therefore, this study proposes an Explainable Artificial Intelligence (XAI)-Based Early Warning System for student dropout prediction by integrating the XGBoost classification algorithm with SHapley Additive exPlanations (SHAP). The proposed framework consists of six stages: data collection, data preprocessing, feature engineering, machine learning model development, explainability analysis, and performance evaluation. To demonstrate the proposed methodology, a synthetic dataset representing higher education student records was utilized. The simulated experimental results showed that the proposed model achieved an Accuracy of 89.5%, Precision of 84.0%, Recall of 82.0%, F1-Score of 83.0%, and an ROC-AUC of 0.93. Furthermore, SHAP analysis identified Grade Point Average (GPA), Attendance Rate, Previous Semester GPA, Financial Status, and Learning Management System (LMS) activity as the most influential factors affecting student dropout prediction. By combining high predictive performance with interpretable explanations, the proposed Early Warning System provides transparent decision support for lecturers and academic advisors, facilitating data-driven interventions aimed at improving student retention in higher education institutions.
Model Peningkatan Berkelanjutan ITIL 4 sebagai Strategi Adaptasi Organisasi terhadap Faktor Eksternal PESTLE Aan Ansen Andryadi; Samsan; Gilang Redzav Bagaswara; Mochamad Rizky Fauzan; Rega Agustiana; Muhammad Naufal
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 5 No. 2 (2026): September 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v5i2.1444

Abstract

Organisasi masa kini dihadapkan pada tuntutan untuk mampu beradaptasi dalam mengelola layanan teknologi informasi (TI) ditengah dinamika lingkungan makro yang bergerak cepat. Analisis PESTLE lazimnya dimanfaatkan untuk mengidentifikasi ancaman-ancaman dari luar organisasi, sementara ITIL 4 lewat pendekatan Continual Improvement lebih menitikberatkan pada pembenahan layanan secara internal. Namun demikian, penetapan kedua konsep tersebut kerap dilakukan secara terpisah, yang pada akhirnya memunculkan kesenjangan antara perencanaan strategis dan pelaksanaan teknis di lapangan. Riset ini ditujukan untuk menyusun sebuah model integrasi konseptual yang mempertautan PESTLE dengan Continual Improvement ITIL 4, dengan memanfaatkan pendekatan Systematic Literature Review (SLR). Sejumlah 27 artikel ilmiah yang terbit pada rentang 2019-2024 ditelaah guna membangun model dimaksud. Hasil sintesis memperlihatkan bahwa pemanfaatan indikator PESTLE sebagai pemicu (trigger) pada masing-masing fase pembenahan ITIL 4 dapat memperkokoh ketahanan operasional sekaligus mempercepat kesigapan dalam merespons gangguan (disrupsi). Simulasi yang diterapkan pada studi kasus modernisasi sistem katalog disebuah instansi pemerintah mengonfirmasi bahwa model ini berhasil mengubah tekanan regulasi maupun tren sosial menjadi langkah-langkah perbaikan teknis TI yang dapat diukur.
Co-Authors Andryadi, Aan Ansen Mochamad Rizky Fauzan Muhammad Naufal Rega Agustiana Samsan