Analisis prediktif maintenance berbasis IoT untuk mengurangi downtime pada mesin produksi
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predictive maintenance,
downtime,
efisiensi mesin,
manufaktur
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Abstract
Downtime mesin merupakan salah satu penyebab utama penurunan produktivitas di industri manufaktur. Sistem pemeliharaan konvensional seperti preventive maintenance sering kali belum mampu mendeteksi potensi kerusakan secara dini. Teknologi Internet of Things (IoT) menawarkan pendekatan predictive maintenance yang mampu memantau kondisi mesin secara real-time dan memprediksi kegagalan sebelum terjadi. Penelitian ini bertujuan untuk menganalisis efektivitas sistem predictive maintenance berbasis IoT dalam mengurangi downtime mesin produksi. Penelitian dilakukan pada lini produksi PT SDE di Sulawesi Selatan dengan menggunakan sensor getaran, suhu, dan arus listrik yang diintegrasikan ke dalam platform IoT. Desain penelitian menggunakan metode eksperimen semu (quasi-experimental) dengan perbandingan antara kondisi before dan after penerapan sistem selama 3 bulan. Data dianalisis menggunakan paired t-test untuk melihat perbedaan signifikan waktu downtime. Hasil menunjukkan bahwa rata-rata downtime mesin menurun dari 18,4 jam/bulan menjadi 9,7 jam/bulan (penurunan 47,3%). Nilai t = 6,58 dengan p < 0,001 menunjukkan perbedaan signifikan. Sistem IoT juga memungkinkan perawatan dilakukan secara prediktif berdasarkan data kondisi aktual mesin. Dengan demikian, implementasi predictive maintenance berbasis IoT terbukti efektif dalam meningkatkan keandalan mesin dan efisiensi operasional industri manufaktur.
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