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All Journal JITU : Journal Informatic Technology And Communication JIKTEKS : Jurnal Ilmu Komputer dan Teknologi Informasi
Wicaksono Yuli Sulistyo
Universitas Siber Muhammadiyah
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Library & Information Science

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Integrasi Moodle API dan LLM dalam Otomasi Monitoring Capaian Pembelajaran Lulusan Berbasis OBE Wicaksono Yuli Sulistyo; Joko Supriyanto
JITU Vol 10 No 1 (2026)
Publisher : Universitas Boyolali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jitu.v10i1.2307

Abstract

The implementation of Outcome-Based Education (OBE) necessitates transparency and accountability in mapping Program Learning Outcomes (PLO). Nevertheless, the manual mapping process from an extensive array of courses (60 Course Learning Outcomes/CLO) to 10 PLOs frequently encounters administrative constraints and a high risk of human error. This study aims to design and develop an integrated OBE monitoring system utilizing the Moodle API for grade synchronization and the OpenAI API for academic documentation assistance. Adopting the Design Science Research (DSR) methodology, the system was implemented using native PHP. The findings demonstrate that Moodle API integration facilitates the real-time automation of grade retrieval per sub-CLO. Simultaneously, the application of Mermaid.js effectively transforms these data into dynamic traceability visualizations. Moreover, the implementation of the OpenAI API (GPT) provides significant cognitive assistance for faculty members in drafting Semester Learning Plans (RPS) aligned with competency standards. This system establishes a robust data infrastructure for institutions to perform accurate and transparent curriculum evaluations.
Analisis Performa Algoritma Decision Tree dengan Correlation-based Feature Selection (CFS) untuk Deteksi Serangan Jaringan Pandu Satria; Wicaksono Yuli Sulistyo
JIKTEKS : Jurnal Ilmu Komputer dan Teknologi Informasi Vol. 4 No. 03 (2026): Agustus
Publisher : Faatuatua Media Karya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70404/jikteks.v4i03.726

Abstract

Keamanan jaringan membutuhkan Intrusion Detection System (IDS) yang mampu mendeteksi serangan secara akurat dan efisien. Tingginya jumlah fitur pada data jaringan dapat meningkatkan kompleksitas model dan beban komputasi. Penelitian ini mengevaluasi pengaruh Correlation-based Feature Selection (CFS) terhadap kinerja algoritma Decision Tree pada dataset NSL-KDD dan CICIDS2017. Model tanpa seleksi fitur digunakan sebagai baseline dan dibandingkan dengan model berbasis CFS menggunakan metrik Accuracy, Precision, Recall, F1-Score, ROC-AUC, serta waktu pelatihan. Hasil menunjukkan bahwa CFS secara signifikan mengurangi jumlah fitur dan waktu pelatihan pada kedua dataset, tetapi berdampak pada penurunan performa klasifikasi, terutama Recall pada NSL-KDD. Uji Wilcoxon Signed-Rank pada 10-Fold Cross-Validation menunjukkan adanya perbedaan yang signifikan (p = 0,001953). Temuan ini menunjukkan adanya trade-off antara efisiensi komputasi dan kemampuan deteksi, sehingga penerapan CFS perlu disesuaikan dengan prioritas sistem IDS.
Co-Authors Joko Supriyanto Pandu Satria