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All Journal Jurnal Ilmiah Teknik Industri PROSIDING SEMINAR NASIONAL CENDEKIAWAN Abdimas Universal Jurnal Bakti Masyarakat Indonesia JURNAL TEKNIK INDUSTRI
Iveline Anne Marie
Trisakti University
Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Journal : Jurnal Ilmiah Teknik Industri

 1 
Perbandingan Performansi Teknik Klasifikasi Breakdown Mesin pada Proses Produksi Pembuatan Battery Mobil Iveline Anne Marie; Lukmanul Hakim; Dedy Sugiarto; Winnie Septiani
Jurnal Ilmiah Teknik Industri Vol. 18, No. 1, Juni 2019
Publisher : Department of Industrial Engineering Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/jiti.v18i1.7232

Abstract

Data mining is useful in finding interesting patterns of hidden information in a database with specified algorithms. Management of uncertainty in the automotive industry supply chain, with case data at PT QQQ that produce car batteries, classification techniques are used to manage uncertainty in the case of engine breakdown. Based on the utilization of classification techniques, performance comparison analysis was carried out from several methods, namely Decision Tree, Bagging, Boosting and Random Forest. The research data is divided into testing data (75%) and training data (25%). This study uses Software R for analysis needs. The need for testing the goodness of the model uses package (caret) help to see the value of accuracy, sensitivity and specificity. The analysis shows that the Random Forest and Bagging method is superior compared to the Decision Tree and Boosting methods based on accuracy criteria, while the sensitivity criteria, Bagging and Boosting methods are superior to Random Forest and DecisionTree. The lowest sensitivity value is owned by the Decision Tree Method, which indicates that the ability of the method is weak in predicting very few classes. 
Co-Authors Adi Sucipto, Adi Adianto Adianto Ahmad Farhan Amal Witonohadi Andi Arni Soraya Annisa Dewi Akbari Annisa Widya Putri Annisa Widya Putri Auliya Dewi Oktriana Cindy Claudia Dadan Umar Daihani Dara Mustika Dedy Sugiarto Didien Suhardini Dorina Hetharia Emelia Sari Emelia Sari Eriyatno . Erna Regina Supriatna Ferian Rizkyka Triswara Habyba, Anik Nur Henry Candra Idriwal Mayusda Indah Permata Sari Intan Emeralda Isnaeni Nabilah Lukmanul Hakim Mohammad Ali Wafa Monika Tangel Muhammad Aditya Putra Muhammad Aditya Putra Muhammad Daffa Alrifqi Nilla Nilla Nora Azm Nora Azmi Nue Jihan Widayanti Nur Azizah Putri Utami Parwadi Moengin PUDJI ASTUTI Rahmi Khairunisa Rina Fitriana Rosi Theresia Silvi Ariyanti Suhartati Agoes Susan Sulaiman Susan Sulaiman Teofilus Nathanael Chaiyadi Teofilus Nathanael Chaiyadi Tiena Gustina Amran Triwulandari Satitidjati Dewayana Winnie Septiani Yandra Arkeman Yayang Ade Suprana