INTEGRASI MLOPS UNTUK MONITORING, DETEKSI DRIFT, DAN RETRAINING ADAPTIF PADA SISTEM PRODUKSI MACHINE LEARNING BERBASIS SENSOR GAS

• NOVIA HERIYANI
• 14230034

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ABSTRAK

ABSTRAK

 

Nama : Novia Heriyani

NIM : 14230034

Program Studi : Ilmu Komputer

Fakultas : Teknologi Informasi

Jenjang : Strata Dua (S2)

Peminatan : Software Engineering & Data Science

Judul Tesis : Integrasi Mlops Untuk Monitoring, Deteksi Drift, Dan Retraining Adaptif Pada Sistem Produksi Machine Learning Berbasis Sensor Gas

 

Model machine learning yang diterapkan pada sistem produksi berbasis data sensor berpotensi mengalami degradasi kinerja akibat perubahan distribusi data yang bersifat dinamis (data drift). Tanpa mekanisme pemantauan dan pembaruan model yang terstruktur, performa model cenderung menurun seiring waktu. Penelitian ini mengusulkan integrasi pendekatan Machine Learning Operations (MLOps) untuk membangun sistem monitoring, deteksi data drift, dan retraining model secara adaptif dalam lingkungan produksi machine learning. Evaluasi dilakukan menggunakan Gas Sensor Array Drift Dataset dari UCI Machine Learning Repository dengan skema pemrosesan data berbasis batch untuk mensimulasikan aliran data operasional. Deteksi drift dilakukan menggunakan Population Stability Index (PSI) dan Kullback–Leibler Divergence (KL) sebagai dasar pemicu retraining adaptif. Hasil eksperimen menunjukkan bahwa strategi retraining adaptif mampu menjaga stabilitas kinerja model secara lebih konsisten dibandingkan pendekatan tanpa retraining dan retraining periodik, serta mendukung pengelolaan siklus hidup model yang terukur dan dapat direproduksi.

Kata kunci: Data Sensor Gas, Deteksi Data Drift, Machine Learning Operations (MLOps), Retraining Adaptif, Sistem Machine Learning Produksi

KATA KUNCI

Sistem informasi monitoring produksi,Integrasi,Sistem Produksi Machine Learning

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DAFTAR PUSTAKA

DAFTAR PUSTAKA

 

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