Perbandingan Model Klasifikasi Supervised Machine Learning dalam Knowledge Discovery Layanan TI Pertamina Prabumulih: Comparison of Supervised Machine Learning Classification Models in Knowledge Discovery of Pertamina Prabumulih IT Services

Shafa Aurelliza Arian; Putri Rahel Alifia; Bagus Prihantoro; Muhammad Iqbal Disriansyah; Ken Ditha Tania; Alsella Meiriza; Ahmad Rifai

doi:10.57152/malcom.v6i2.2617

Authors

Shafa Aurelliza Arian Universitas Sriwijaya
Putri Rahel Alifia Universitas Sriwijaya
Bagus Prihantoro Universitas Sriwijaya
Muhammad Iqbal Disriansyah Universitas Sriwijaya
Ken Ditha Tania Universitas Sriwijaya
Alsella Meiriza Universitas Sriwijaya
Ahmad Rifai Universitas Sriwijaya

DOI:

https://doi.org/10.57152/malcom.v6i2.2617

Keywords:

Klasifikasi, Knowledge Discovery, Layanan TI, Supervised Machine Learning

Abstract

Pengelolaan data layanan Teknologi Informasi (TI) di Pertamina Prabumulih memerlukan pendekatan analitik untuk meningkatkan efektivitas penanganan dan mendukung pengambilan keputusan berbasis data. Penelitian ini bertujuan membandingkan performa beberapa model klasifikasi supervised machine learning pada layanan TI periode 2020–2025 menggunakan pendekatan knowledge discovery melalui teknik data mining terhadap 8.627 data awal. Tahapan penelitian meliputi preprocessing, pelabelan kelas, penanganan ketidakseimbangan data dengan Synthetic Minority Over-sampling Technique (SMOTE), serta pembagian data dengan skenario 70:30, 80:20, dan 90:10. Proses klasifikasi dilakukan menggunakan algoritma Naïve Bayes, Support Vector Machine (SVM), dan Random Forest. Evaluasi model menggunakan metrik accuracy, precision, recall, dan F1-score. Hasil menunjukkan bahwa skenario 90:10 memberikan performa terbaik, dengan Support Vector Machine (SVM) mencapai accuracy 0,8806, precision 0,8757, recall 0,8941, dan F1-score 0,8788, melampaui algoritma lainnya. Kategori desktop hardware teridentifikasi sebagai kasus terbanyak selama periode penelitian. Temuan ini dapat dimanfaatkan sebagai dasar strategis untuk prioritas penanganan layanan, alokasi sumber daya, serta penguatan Knowledge Management guna peningkatan layanan TI secara terarah, efektif, dan berkelanjutan.

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