Klasifikasi Varietas Unggul Padi Menggunakan Metode Bagging, Boosting, dan Extremely Randomized Trees
DOI:
https://doi.org/10.29313/statistika.v22i2.1455Keywords:
Klasifikasi, Bagging, Boosting, Extremely Randomized Trees.Abstract
Rice is one of the agricultural products which is the main commodity in Indonesia. Supporting factors that play a very important role in efforts to increase rice production are superior varieties. Superior rice varieties have characteristics that are similar to one another. Thus, farmers must choose the varieties used through a classification process to determine the appropriate type of rice. At this stage, three methods are used: bagging, boosting, and extremely randomized trees. From the analysis results, the overall method of extremely randomized trees has more optimal capabilities compared to the bagging and boosting methods. This is indicated by the three parameters, sensitivity, specificity, and accuracy, which have the highest values.
References
Alfaro, E., Gamez, M., dan Garcia, N. (2013). adabag: An R Package for Classification with Boosting and Bagging. Journal of Statistical Software, 11-35.
Alpaydin, E. (2010). Introduction to Machine Learning. London: The MIT Press.
Arhami, M.Kom, M dan Nasir,M. S. T. (2020). Data Mining-Algoritma dan Implementasi. Penerbit Andi.
Breiman, L. 1996. Bagging Predictors. Machine Learning, 123-140.
Fernanda, J. W. (2012). Boosting Neural Network dan Boosting Cart Pada Klasifikasi Diabetes Militus Tipe II. Jurnal Matematika. Volume 2. Nomor 2. ISSN : 1693-1394
Galar, M dan Fransico. (2012). A review on Ensembles for the class Imbalance Problem: Bagging,Boosting and Hybrid-Based Approaches” IEEE Transactions On Systems, Man, And Cybernetics—Part C: Application And Reviews. Volume 42. Nomor 4.
Geurts, P. Ernst, D dan Wehenkel, L. (2006). Extremely randomized trees. Mach Learn. DOI 10.1007/s10994-006-6226-1
Gunadi, G. Sensuse, D.I. (2016). Penerapan Metode Data Mining Market Basket Analysis Terhadap Data Penjualan Produk Buku Dengan Menggunakan Algoritma Apriori dan Frequent Pattern Growth (FP-Growth): Studi Kasus Percetakan PT. Gramedia. Jurnal Telematika Mkom. 4(1).
Gurning, I. P., Yuprin, A. D., dan Taufik, E. N. (2019). Trend dan Estimasi Produksi Padi dan Konsumsi Beras di Provinsi Kalimantan Tengah. Journal Socio Economics Agricultural, 14(1), 48-61.
Hakim, L. Sartono, B dan Saefuddin, A. (2017). Bagging Based Ensemble Classification Method on Imbalance Datasets. JCSN - International Journal of Computer Science and Network, Volume 6, Issue 6.
Han, J. Kamber, M. (2006). Data mining concepts and techniques second edition. San Francisco: Diane Cerra.
Han, J., Kamber, M., dan Pei, J. (2012). Data Mining Concepts and Techniques. San Fransisco: Morgan Kauffman.
Laksana, E. A., dan Sulianta, F. (2017). Analisis Dan Studi Komparatif Algoritma Klasifikasi Genre Musik. Semnasteknomedia Online, 5(1), 2-1.
Okun, O.(2011). Feature Selection and Ensemble in Machine Learning Bioinformatics: Algoritmic Classification and Implementations. United States of America: IGI Global.
Rachman, A. Furkon, M.T, & Ramdani, F. (2019). Klasifikasi Varietas Unggul Padi menggunakan Algoritme C4.5. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer. 3(9).
Rahman, R & Afroz, F. (2013). Comparison of Various Classification Techniques Using Different Data Mining Tools for Diabetes Diagnosis. Journal of Software Engineering and Applications.
Saefudin, A. (2015). Penerapan Teknik Ensemble untuk Menangani Ketidakseimbangan Kelas pada Prediksi Cacat Software. Journal of Software Engineering. Volume 1. Nomor 1.
Sartono, B & Syafitri. (2010). “Metode pohon gabungan: solusi pilihan untuk mengatasi kelemahan pohon regresi dan klasifikasi tunggal”. Forum Statistika dan Komputasi, 15 (1), 1-7.
Shinta, A. (2001). Ilmu Usaha Tani. Universitas Brawijaya Press.
Simatupang, P, & Rusastra, I. W. (2004). Kebijakan Pembangunan Sistem Agribisnis Padi. Ekonomi Padi dan Beras Indonesia, 31-52.
Tanha, J. Abdi, Y. Samadi, N. Razzaghi, N, & Asadpour, M. (2020). Boosting methods for multi-class imbalanced data classification: an experimental review. Journal of Big Data. 7:70.
Triyanto, A. C, & Hardinto, P. (2013). Analisis Produktivitas Sektor Pertanian Komoditi Tanaman Padi Berbasis Agribisinis Dalam Peningkatan Ekonomi. JESP, 5(1), 53-62.
Alpaydin, E. (2010). Introduction to Machine Learning. London: The MIT Press.
Arhami, M.Kom, M dan Nasir,M. S. T. (2020). Data Mining-Algoritma dan Implementasi. Penerbit Andi.
Breiman, L. 1996. Bagging Predictors. Machine Learning, 123-140.
Fernanda, J. W. (2012). Boosting Neural Network dan Boosting Cart Pada Klasifikasi Diabetes Militus Tipe II. Jurnal Matematika. Volume 2. Nomor 2. ISSN : 1693-1394
Galar, M dan Fransico. (2012). A review on Ensembles for the class Imbalance Problem: Bagging,Boosting and Hybrid-Based Approaches” IEEE Transactions On Systems, Man, And Cybernetics—Part C: Application And Reviews. Volume 42. Nomor 4.
Geurts, P. Ernst, D dan Wehenkel, L. (2006). Extremely randomized trees. Mach Learn. DOI 10.1007/s10994-006-6226-1
Gunadi, G. Sensuse, D.I. (2016). Penerapan Metode Data Mining Market Basket Analysis Terhadap Data Penjualan Produk Buku Dengan Menggunakan Algoritma Apriori dan Frequent Pattern Growth (FP-Growth): Studi Kasus Percetakan PT. Gramedia. Jurnal Telematika Mkom. 4(1).
Gurning, I. P., Yuprin, A. D., dan Taufik, E. N. (2019). Trend dan Estimasi Produksi Padi dan Konsumsi Beras di Provinsi Kalimantan Tengah. Journal Socio Economics Agricultural, 14(1), 48-61.
Hakim, L. Sartono, B dan Saefuddin, A. (2017). Bagging Based Ensemble Classification Method on Imbalance Datasets. JCSN - International Journal of Computer Science and Network, Volume 6, Issue 6.
Han, J. Kamber, M. (2006). Data mining concepts and techniques second edition. San Francisco: Diane Cerra.
Han, J., Kamber, M., dan Pei, J. (2012). Data Mining Concepts and Techniques. San Fransisco: Morgan Kauffman.
Laksana, E. A., dan Sulianta, F. (2017). Analisis Dan Studi Komparatif Algoritma Klasifikasi Genre Musik. Semnasteknomedia Online, 5(1), 2-1.
Okun, O.(2011). Feature Selection and Ensemble in Machine Learning Bioinformatics: Algoritmic Classification and Implementations. United States of America: IGI Global.
Rachman, A. Furkon, M.T, & Ramdani, F. (2019). Klasifikasi Varietas Unggul Padi menggunakan Algoritme C4.5. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer. 3(9).
Rahman, R & Afroz, F. (2013). Comparison of Various Classification Techniques Using Different Data Mining Tools for Diabetes Diagnosis. Journal of Software Engineering and Applications.
Saefudin, A. (2015). Penerapan Teknik Ensemble untuk Menangani Ketidakseimbangan Kelas pada Prediksi Cacat Software. Journal of Software Engineering. Volume 1. Nomor 1.
Sartono, B & Syafitri. (2010). “Metode pohon gabungan: solusi pilihan untuk mengatasi kelemahan pohon regresi dan klasifikasi tunggal”. Forum Statistika dan Komputasi, 15 (1), 1-7.
Shinta, A. (2001). Ilmu Usaha Tani. Universitas Brawijaya Press.
Simatupang, P, & Rusastra, I. W. (2004). Kebijakan Pembangunan Sistem Agribisnis Padi. Ekonomi Padi dan Beras Indonesia, 31-52.
Tanha, J. Abdi, Y. Samadi, N. Razzaghi, N, & Asadpour, M. (2020). Boosting methods for multi-class imbalanced data classification: an experimental review. Journal of Big Data. 7:70.
Triyanto, A. C, & Hardinto, P. (2013). Analisis Produktivitas Sektor Pertanian Komoditi Tanaman Padi Berbasis Agribisinis Dalam Peningkatan Ekonomi. JESP, 5(1), 53-62.
