Optimasi Ambang Batas Sensitif Biaya untuk Pemeliharaan Prediktif Menggunakan CSTOLR, CSTORF, dan CSTOXGB: Cost-Sensitive Threshold Optimization for Predictive Maintenance Using CSTOLR, CSTORF, and CSTOXGB

Abi Rafdi Hasbur Rahman; Riska Dhenabayu; Achmad Kautsar; Anita Safitri

doi:10.57152/malcom.v6i4.2846

Authors

Abi Rafdi Hasbur Rahman Universitas Negeri Surabaya
Riska Dhenabayu Universitas Negeri Surabaya
Achmad Kautsar Universitas Negeri Surabaya
Anita Safitri Universitas Negeri Surabaya

DOI:

https://doi.org/10.57152/malcom.v6i4.2846

Keywords:

Ketidakseimbangan Kelas, Pembelajaran Sensitif terhadap Biaya, Pemeliharaan Prediktif, Optimasi Ambang Batas

Abstract

Ketidakseimbangan kelas dalam Predictive Maintenance (PdM) dapat menyebabkan model Machine Learning bias terhadap kelas mayoritas sehingga kegagalan mesin tidak terdeteksi. Risiko meningkat karena False Negative (FN) menimbulkan kerugian lebih besar dibandingkan False Positive (FP). Penelitian ini mengevaluasi integrasi Cost-Sensitive Learning (CS) dan Threshold Optimization (TO) untuk meminimalkan Total Misclassification Cost (TMC) pada berbagai rasio biaya FN terhadap FP. CS menerapkan pembobotan biaya selama pelatihan, sedangkan TO menentukan ambang keputusan optimal berdasarkan data validasi. Logistic Regression (LR), Random Forest (RF), dan Extreme Gradient Boosting (XGBoost) dikembangkan menjadi CSTOLR, CSTORF, dan CSTOXGB melalui kombinasi kedua pendekatan tersebut. Eksperimen menggunakan dataset sintetis AI4I 2020 Predictive Maintenance dengan rasio biaya 5:1, 10:1, 20:1, dan 50:1. Kinerja dibandingkan pada model tanpa pembobotan biaya, dengan pembobotan biaya, serta kombinasi pembobotan dan optimasi ambang batas. Hasil menunjukkan integrasi CS dan TO konsisten menurunkan TMC pada ketiga model. CSTOXGB menghasilkan TMC terendah sebesar 41 pada rasio 10:1, dengan ambang optimal 0,38 dan satu FN, atau turun 51,2% dibandingkan XGBoost dasar. Model ini juga mencatat TMC terendah atau setara pada skenario. CSTOLR mengurangi FN secara signifikan, tetapi menghasilkan FP tinggi. Temuan ini menunjukkan efektivitas integrasi CS dan TO dalam menangani klasifikasi PdM dengan biaya kesalahan asimetris.

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