Implementasi Model Neural Network untuk Prediksi Maintenance Mesin Berdasarkan Parameter Operasional pada Era Industri 4.0
DOI:
https://doi.org/10.22441/fifo.2026.v18i1.007Keywords:
Neural Network, Pemeliharaan prediktif, Machine LearningAbstract
Penelitian ini bertujuan untuk mengembangkan model Neural Network untuk memprediksi kegagalan mesin berdasarkan parameter operasional. Pemeliharaan prediktif telah menjadi penting dalam meningkatkan kinerja dan keandalan mesin dengan memprediksi potensi kegagalan dan memungkinkan tindakan pemeliharaan preventif. Studi ini menggunakan berbagai teknik preprocessing, termasuk pengkodean data kategorikal, normalisasi, dan oversampling acak, untuk meningkatkan kualitas data dan kinerja model. Model Neural Network, yang terdiri dari beberapa lapisan tersembunyi dengan fungsi aktivasi ReLU dan lapisan output sigmoid, dilatih menggunakan algoritma optimisasi Adam dan fungsi loss binary cross-entropy. Hyperparameter seperti jumlah epoch, learning rate, batch size, dan dropout rate dioptimalkan untuk meningkatkan kinerja model. Model ini mencapai akurasi tinggi sebesar 97,8%, precision sebesar 96,9%, recall sebesar 97,8%, dan F1-score sebesar 97,3%, menunjukkan kemampuannya untuk mengenali pola kompleks dalam data operasional mesin. Analisis komparatif dengan model lain seperti k-Nearest Neighbor (k-NN), Logistic Regression, dan Support Vector Machine (SVM) menunjukkan bahwa Neural Network memiliki kinerja yang unggul. Selain itu, teknik Ensemble Learning yang menggabungkan SVM dan Logistic Regression sebagai base learners dan Neural Network sebagai meta-learner, menunjukkan peningkatan akurasi prediksi. Metrik evaluasi seperti presisi, recall, dan F1-score memberikan penilaian komprehensif tentang kinerja model. Analisis confusion matrix mengungkapkan area yang memerlukan perbaikan dalam menangani kelas minoritas. Secara keseluruhan, studi ini menyimpulkan bahwa Neural Network sangat efektif untuk aplikasi pemeliharaan prediktif, memberikan prediksi yang akurat dan andal yang meningkatkan efisiensi operasional dan mengurangi biaya pemeliharaan. Penelitian di masa depan akan fokus pada peningkatan kinerja model untuk kelas minoritas dan validasi model pada kondisi operasional yang lebih beragam.
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