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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) J@TI (TEKNIK INDUSTRI) OPSI SITEKIN: Jurnal Sains, Teknologi dan Industri Sinergi agriTECH Jurnal Ilmiah Teknik Industri OPERATION EXCELLENCE: Journal of Applied Industrial Engineering PENELITIAN DAN KARYA ILMIAH Panggung Jurnal Sistem dan Manajemen Industri Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal SOLMA International Journal of Supply Chain Management INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Jurnal Ilmiah Teknik Industri Abdimas Universal Yumary: Jurnal Pengabdian kepada Masyarakat JURNAL PENELITIAN DAN KARYA ILMIAH LEMBAGA PENELITIAN UNIVERSITAS TRISAKTI Jurnal Riset Teknik Industri Jurnal Pengabdian Mandiri JURNAL AGROINDUSTRI HALAL Jurnal Penelitian dan Karya Ilmiah Lembaga Penelitian Universitas Trisakti JUARA: JURNAL WAHANA ABDIMAS SEJAHTERA JURNAL TEKNIK INDUSTRI JURNAL ABDI MASYARAKAT INDONESIA (JAMIN) Panggung Spektrum Industri
Winnie Septiani
Universitas Trisakti
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Journal : Jurnal Ilmiah Teknik Industri

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

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. 
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 A.A. Ketut Agung Cahyawan W Achmad Noerkhaerin Putra Adi Sucipto, Adi Adiatmaja, Novianto Hary Agung Hananto Alkatiri, Mush’ab Syakieb Andakara Edo Wardhana Annisa Dewi Akbari, Annisa Dewi Anwar Martua Pasaribu Ardiansyah, Dino Astri Rinanti Astrin Ega Dahana Audinia Angraeni Bambang Cholis Benediktus Prayoga Wijayanto Benza Hadi Perdana Brata, Ewaldo Dadan Umar Daihani Dedy Sugiarto Dewayana, Triwulandari Satitidjati Dian Mardi Safitri Didien Suhardini Dino Ardiansyah Djuana, Tjhwa Endang Dorina Hetharia Dorina Hetharia Dwiyana, Triwulandari Satitidjati Elfira Febriani Harahap Emelia Sari Ewaldo Brata Fachrul, Melati Feuranita Faradilla, Arnes Farhan, Mhd. Andi Fitria Helmanila Garamba, Niscaya Filijomasi Gustina Amran, Tiena Gustina Amran Harahap, Elfira Febriani Hartini Harumi Yuniarti Hendrawan, Diana Irvindiaty Hindayanti , Heni I Nyoman Larry Julianto Idriwal Mayusda Ika Wahyu Utami Indah Permata Sari Ischak, Mohammad Ivanna Ivanna Iveline Anne Marie Johnson Saragih Kansa, Syafa Khairunnisa, Minda KHOMSIYAH Liesbetini Haditjaroko Lukmanul Hakim Lukmanul Hakim M. Syamsul Maarif Marimin , Martino Luis Maulidya , Rahmi Melati Ferianita Fachrul Melati Feuranita Fachrul Minarti, Astari Minda Khairunnisa Mohammad Ischak Montreano, Donny Nataya Charoonsri Rizani Nazaruddin Nazaruddin Nora Azmi Nora Azmi Novia Rahmawati Pudji Astuti PUDJI ASTUTI Ratna Mira Yojana Ratnaningsih Ruhiyat Richy Wijaya Rico Bramandita Sabhira, Chintya Salwa Sally Cahyati Samy Natsir Alwinny Satriyo Maulana Sucipto Adi Suwiryo Suryana, Yunita Friscillia Suryani Suryani Taufik Djatna Theodora, Tania Tjhwa Endang Djuana Triwulandari S Dewayana, Triwulandari S Triwulandari Satitidjati Dewayana Triwulandari Satitidjati Dewayana Triwulandari Satitidjati Dwiyana Triwulandari SD, Triwulandari Umar Daihani, Dadan Utami, Nur Aulia Wahyuni, Annisa Tri Wegig Murwonugroho Yandra Arkeman Yogi Setyawan Yojana, Ratna Mira