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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknologi Rekayasa Mesin Media Mesin: Majalah Teknik Mesin Indonesian Journal of Science and Technology Jurnal Otomasi Kontrol dan Instrumentasi Jurnal Teknik Mesin dan Mekatronika (Journal of Mechanical Engineering and Mechatronics) Jurnal Teknik Mesin Indonesia EPI International Journal of Engineering Indonesian Journal of Computing, Engineering, and Design International Journal of Sustainable Transportation Technology Asean Journal of Science and Engineering (AJSE) Indonesian Journal of Multidiciplinary Research (IJoMR) Indonesian Journal of Community and Special Needs Education (IJCSNE) Mechanical Engineering for Society and Industry Automotive Experiences Journal of Mechanical Design and Testing (JMDT) Journal of Community Services: Sustainability and Empowerment Jurnal Polimesin ASEAN Journal for Science and Engineering in Materials
Triawan, Farid
Department Of Mechanical Engineering, Faculty Of Engineering And Technology, Sampoerna University, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Social Sciences Transportation Other

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Journal : Jurnal Polimesin

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Machine fault detection through sound analysis using MFCC and machine learning Chang, Steven Henderson; Purnomo, Ariana Tulus; Bhakti, Muhammad Agni Catur; Mulia, Vania Katherine; Rizky, Agyl Fajar; Fernandez, Nikolas Krisma Hadi; Triawan, Farid
Jurnal Polimesin Vol 23, No 3 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v23i3.6653

Abstract

This study addresses the need for automated damage and failure detection in industrial machinery through sound analysis and machine learning. Traditional methods rely on human experts to identify faults using microphones, which can be time-consuming, stressful, and prone to errors such as limited perception, subjectivity, and inconsistency. This study leverages machine learning to create a more objective and efficient alternative. Mel-Frequency Cepstral Coefficients (MFCCs) were employed for feature extraction, capturing intricate sound patterns associated with machinery faults. Through rigorous experimentation, 11 MFCC coefficients were identified as optimal. The Support Vector Machine (SVM) emerged as the best-performing classifier compared to LightGBM and XGBoost, achieving a training accuracy of 83.12% and testing accuracy of 82.50%. The dataset was split between 80% for training and 20% for testing. The small gap between training and testing accuracy indicates an ideal model with no signs of over fitting, under fitting, or data leakage. Real-world simulations validated the model’s efficacy under various operational scenarios, demonstrating its readiness for industrial deployment. This study highlights the effectiveness of sound analysis and SVM classification in proactive maintenance, offering a reliable tool to reduce downtime and maintenance costs while enhancing operational efficiency and reliability.
A quad-cliff mechanism for eco-printing by pounding technique: design, manufacturing, and testing Triawan, Farid; Dyota, Arya Smara; Kamila, Fatima Tasya; Saptaji, Kushendarsyah; Fernandez, Nikolas Krisma Hadi; Silitonga, Arridina Susan; Sebayang, Abdi Hanra
Jurnal Polimesin Vol 22, No 5 (2024): October
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jpl.v22i5.5738

Abstract

Indonesia produces many types of textile products, such as clothing and custom fabrics often with unique patterns. To generate the patterns, there are many methods, including eco-printing by pounding process. However, the process, which was later referred to as eco-pounding, requires much time and energy, which can have a negative impact, such as musculoskeletal disorders, on the human body. To address this issue, the present work proposes a machine that can help the process of eco-pounding. Shigley’s method is applied to guide the design process of the machine. The design and manufacturing processes of the eco-pounding machine are presented, in which three machine design models are first introduced and then analyzed for finalization by benchmarking method. Subsequently, a machine model that uses a so-called quad-cliff mechanism is selected for manufacturing and testing. As a result, the proposed machine can achieve the design requirements that were set. Three pounding movements per second can be obtained by the machine, with possible increases by an engine upgrade. This machine can be considered a prototype for a semi-automatic eco-printing process by pounding technique.
Co-Authors AA Sudharmawan, AA Abdi Hanra Sebayang Abdulkareem Sh. Mahdi Al-Obaidi Adi Sutanto Aditiya Harjon Al-Obaidi, Abdulkareem Sh. Mahdi Alfatih, Shahreza Agung Ali, Muchammad Fachrizal Alvin Widjaya Amelia Nuraisyah Quinsi Jemy Anak Agung Ngurah Perwira Redi Anak Agung Ngurah Perwira Redi Andy Saputro Angelica, Putri Antonio, Ryan Anwar Ilmar Ramadhan Ariana Tulus Purnomo Ariq Naufal Rabbani Arnaz Sholeh Arridina Susan Silitonga - Asep Bayu Dani Nandiyanto Asmelash Gebremariam Asriyanti, A. Azhari, Azmir Bahagia, Katherine Luckman Belfaqih, Idrus Husin Bentang Arief Budiman Bilqist Imeilia Az Zahra Bryan, Jonathan Budiarta, Wahyu Nur Budiman , Bentang Arief Budiman, Bentang Arief Chang, Steven Henderson Charline Tiara Rehuellah Pingak Denatra, Geraldy Dewa, Gilang Raka Rayuda Dindamilenia Choirunnisa Hardiyasanti Djamari, Djati Djati Wibowo Djamari Djati Wibowo Djamari Dyota, Arya Smara Elin Rachmawati Eryanto, Ery Fadhila, Fahriza Fernandez, Nikolas Krisma Hadi Fini Bestiara Fitri Endrasari Frandito, Raffy Gabriela Chelvina Santiuly Girsang Girsang, Gabriela Chelvina Santiuly Githa, Arum Given Chris Yuenan Glenn Hanaya Gloria Ellysian Aprilia Hadisujoto, Budi Handani, Nadya Geraldine Hofifah, Siti Nur I Ketut Adhiarta Laksemana Ida Hamidah Ifat, Ifat Ignatius Pulung Nurprasetio Ignatius Pulung Nurprasetio Ihza Lisiano Al Qushai Ilham Prasetyo Ilman Arpi islam, sri susilawati Iwan Setiawan Iza, Nurlaely Rohmatul Kalam, Ikramin Kamila, Fatima Tasya Ken Risa Ayu Ndaru Ningrum Khoiriyah, Nisa Kushendarsyah Saptaji Kusuma, Felicia Laela Nur Fitria Laely Muryanti Lestari, Putri Yuni Lestari, Tika Endah Lubis, Steven Abed Daniel Maria Anggi Octaviani Mei Syaroh Misbakhul Munir Mochammad Rafli Ramadhani Muhamad Rausyan Fikri Muhammad Agni Catur Bhakti Muhammad Akhsin Muflikhun Muhammad Aziz Muhammad Dzulkifly Muhammad Johan Alibasa Muhammad Kunta Biddinika Muhammad, Azka Aulia Mulia, Vania Katherine Musafri Yetti Nanda Indriana Natayu, Amartya Nikolas Krisma Hadi Fernandez Nikolas Krisma Hadi Fernandez Noorfi Azizah Rahim Nurfais, Ahmad Octary, Astrid Vanya Ong, Janice Putra, Indra Pertama Putri, Silmi Ridwan Qurriyatus Zahro Raffy Frandito Rafie Djajasoepena Rahmanda, Wahyu Rakshidatu Lestaluhu Ramadhan, Ammar Ramadhona Saville Reflin, Rhainna Rheizkhira Rizky, Agyl Fajar Robby Dwianto Widyantara Romadhon , Yusuf Safety Husna Pangestika Septiani, Tasya Diana Siagian, Michael Soritua Putrapratama Silmi Ridwan Putri Sineri, Gabriel Ayrton Andares Sinta Widianingrum Siswojo, Nathanael Ansel Siti Nur Hofifah Siti Zulaikah Sri Nararia Anggita Damayanti Surya Danusaputro Liman Sutrisno, Dwiartha Pambudi Alif Syamsuri, Ady Tong Keong Sai Trisnadewi, Titin Tumada, Azhari Unang Solihin Utomo, Muhammad Satrio Vania Katherine Mulia Wahyu Nur Budiarta Wandy Wandy Wibowo, Djati Wildan Rahmawan Yetti, Musafri Zulaikah, Siti