eprintid: 50609 rev_number: 30 eprint_status: archive userid: 22775 dir: disk0/00/05/06/09 datestamp: 2025-07-07 08:43:19 lastmod: 2025-07-07 08:43:19 status_changed: 2025-07-07 08:43:19 type: thesis metadata_visibility: show contact_email: 7787230023@untirta.ac.id creators_name: Hermawan, Galih Prihasetya creators_id: 7787230023 contributors_type: http://www.loc.gov/loc.terms/relators/THS contributors_type: http://www.loc.gov/loc.terms/relators/THS contributors_name: KURNIAWAN, BOBBY contributors_name: BAHAUDDIN, ACHMAD contributors_id: 197612132008121001 contributors_id: 197812212005011002 corp_creators: FAKULTAS TEKNIK UNIVERSITAS SULTAN AGENG TIRTAYASA title: MODEL PREDIKSI DAN SISTEM PERINGATAN DINI PENURUNAN KINERJA KOMPRESOR BERBASIS MACHINELEARNING ispublished: pub subjects: HD subjects: HD28 subjects: QA subjects: T1 subjects: TJ divisions: Industri full_text_status: restricted keywords: machine learning, predictive maintenance, compressor failure prediction, early warning system abstract: Di era industri 4.0, efisiensi operasional dan keandalan peralatan menjadi kunci untuk menjaga kontinuitas produksi dan keselamatan kerja, terutama pada kompresor yang beroperasi di lingkungan korosif. Penelitian ini bertujuan untuk mengevaluasi performa algoritma machine learning XGBoost, Random Forest, dan Support Vector Regression (SVR) dalam membangun model prediksi kegagalan kompresor berbasis data sensor serta mengembangkan sistem early detection warning. Metode yang digunakan mencakup preprocessing data sensor, analisis korelasi, normalisasi, serta penerapan dan validasi model dengan teknik cross validation dan uji statistik residual. Hasil penelitian menunjukkan bahwa XGBoost adalah model terbaik dengan nilai R² 0.984. Sistem peringatan dini berbasis analisis residual dan 3-sigma rule berhasil membedakan tiga tingkat keparahan potensi kegagalan kompresor, dengan rekomendasi tindakan yang sesuai. Pendekatan ini dapat meningkatkan keandalan operasional dan mendukung implementasi predictive maintenance yang lebih efisien, khususnya pada kompresor di lingkungan korosif. date: 2025-06-17 date_type: published pages: 176 institution: Fakultas Teknik Universitas Sultan Ageng Tirtayasa department: Pascasarjana Teknik Industri dan Manajemen thesis_type: masters thesis_name: mphil referencetext: Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021). Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. In Journal of Cleaner Production (Vol. 289). Elsevier Ltd. https://doi.org/10.1016/j.jclepro.2021.125834 Akhtar, M., Tanveer, M., & Arshad, Mohd. (2024). HawkEye: Advancing Robust Regression with Bounded, Smooth, and Insensitive Loss Function. http://arxiv.org/abs/2401.16785 Aminzadeh, A., Sattarpanah Karganroudi, S., Majidi, S., Dabompre, C., Azaiez, K., Mitride, C., & Sénéchal, E. (2025). 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Master thesis, Fakultas Teknik Universitas Sultan Ageng Tirtayasa. document_url: https://eprints.untirta.ac.id/50609/9/Galih%20Prihasetya%20Hermawan_7787230023_Fulltext.pdf document_url: https://eprints.untirta.ac.id/50609/1/Galih%20Prihasetya%20Hermawan_7787230023_01.pdf document_url: https://eprints.untirta.ac.id/50609/2/Galih%20Prihasetya%20Hermawan_7787230023_02.pdf document_url: https://eprints.untirta.ac.id/50609/3/Galih%20Prihasetya%20Hermawan_7787230023_03.pdf document_url: https://eprints.untirta.ac.id/50609/4/Galih%20Prihasetya%20Hermawan_7787230023_04.pdf document_url: https://eprints.untirta.ac.id/50609/5/Galih%20Prihasetya%20Hermawan_7787230023_05.pdf document_url: https://eprints.untirta.ac.id/50609/6/Galih%20Prihasetya%20Hermawan_7787230023_Ref.pdf document_url: https://eprints.untirta.ac.id/50609/7/Galih%20Prihasetya%20Hermawan_7787230023_Lamp.pdf document_url: https://eprints.untirta.ac.id/50609/10/Galih%20Prihasetya%20Hermawan_7787230023_CP.pdf