DECISION TREE SIMPLIFICATION THROUGH FEATURE SELECTION APPROACH IN SELECTING FISH FEED SELLERS
Abstract
Feed is a crucial variable because it can determine the success of fish farming. Breeders can use two types of artificial feed, namely alternative feed and pellets. Many cultivators need pellets as the main consumption for the fish they are cultivating because the pellets contain a composition that has been adjusted to their needs based on the type and age of the fish. However, currently, cultivators are facing a problem, namely the high price of fish pellets on the market. Therefore, an analysis of the classification of the selection of fish feed sellers is needed that is adjusted to several criteria like the number of types of feed, price, order, delivery, and availability of discounts. This study conducted a classification analysis of simplification of characteristics in selecting fish feed sellers in Kendal Regency that would then be compared with a model without feature selection by utilizing the Decision Tree C4.5 method. The results of this study are the decision tree with the best performance where C4.5 with the application of the selected feature has an accuracy value of 92%, while C4.5 without the selection feature has an accuracy of 86.8%. The results of this study indicate that the C4.5 method with the application of selection features is better than C4.5 without selection features so that it can be applied to the selection of freshwater fish feed sellers in Kendal Regency.
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Badan Riset Dan Sumber Daya Manusia Kelautan Dan Perikanan, “Kkp Gencarkan Pelatihan Guna Wujudkan Ketersediaan Pakan Ikan Mandiri,” 2022. https://kkp.go.id/brsdm/artikel/44444-kkp-gencarkan-pelatihan-guna-wujudkan-ketersediaan-pakan-ikan-mandiri (accessed Oct. 2, 2022).
Badan Pusat Statistik, "Jumlah Kecamatan Menurut Kabupaten/Kota di Provinsi Jawa Tengah 2019-2021," 2022. https://jateng.bps.go.id/indicator/101/457/1/jumlah-kecamatan-menurut-kabupaten-kota-di-provinsi-jawa-tengah.html (accessed Oct. 2, 2022).
Statistik KKP, "Produksi Perikanan Per Kabupaten Kota," 2022. https://statistik.kkp.go.id/home.php?m=prod_kabkota&i=2#panel-footer, (accessed Oct. 2, 2022).
Yunaidi, P. A. Rahmanta, A. Wibowo. "Aplikasi pakan pelet buatan untuk peningkatan produktivitas budidaya ikan air tawar di desa Jerukagung Srumbung Magelang," Jurnal Pemberdayaan: Publikasi Hasil Pengabdian kepada Masyarakat, vol. 3, no. 1, pp. 45-54, 2019, doi : https://doi.org/10.12928/jp.v3i1.621
A. Prasetyo, Salahuddin, Amirullah, "Prediksi Produksi Kelapa Sawit Menggunakan Metode Regresi Linier Berganda," Jurnal Infomedia: Teknik Informatika, Multimedia & Jaringan, vol. 6, no. 2, pp. 76-80, 2021, doi : http://dx.doi.org/10.30811/jim.v6i2.2343.
Wijaya, H. Derajad, S. Dwiasnati, "Implementasi Data Mining dengan Algoritma Naïve Bayes pada Penjualan Obat," Jurnal Informatika, vol. 7, no. 1, pp. 1-7, 2020, doi : https://doi.org/10.31294/ji.v7i1.6203.
T. H. Zebua, F. Riandari, “Implementation Of Data Mining With C4. 5 Algorithm For Determining The Home Industry Product Marketing Strategy,” Journal of Intelligent Decision Support System (IDSS), vol. 4, no. 4, pp. 113-121, 2021, doi : https://doi.org/10.35335/idss.v4i4.37.
G. W. Dickson, "An analysis of vendor selection systems and decisions," Journal of purchasing, vol. 2, no.1, pp. 5-17, 1966, doi : https://doi.org/10.1111/j.1745-493X.1966.tb00818.x.
I. M. A. Agastya, “Pengaruh stemmer bahasa Indonesia terhadap peforma analisis sentimen terjemahan ulasan film,” Jurnal Tekno Kompak, vol. 12, no. 1, pp. 18-23, 2018, doi : https://doi.org/10.33365/jtk.v12i1.70.
P. S. Raja, K. Sasirekha, and K. Thangavel, “A novel fuzzy rough clustering parameter-based missing value imputation,” Neural Computing and Applications, vol. 32, pp. 10033-10050, 2020, doi : https://doi.org/10.1007/s00521-019-04535-9.
C. Thapa, P. C. M. Arachchige, S. Camtepe, "Splitfed: When federated learning meets split learning," In Proceedings of the AAAI Conference on Artificial Intelligence, 2022, vol. 36, no. 8, pp. 8485-8493, doi : https://doi.org/10.1609/aaai.v36i8.20825.
R. Y. Choi, A. S. Coyner, J. Kalpathy- Cramer, M. F. Chiang, and J. P. Campbell, "Introduction to machine learning, neural networks, and deep learning," Translational Vision Science & Technology, vol. 9, no. 2, pp. 14-14, 2020, doi : https://doi.org/10.1167/tvst.9.2.14.
D. Ariyoga, “Perbandingan Metode Seleksi Fitur Filter, Wrapper, Dan Embedded Pada Klasifikasi Data Nirs Mangga Menggunakan Random Forest Dan Support Vector Machine (Svm),” Universitas Islam Indonesia, 2022.
S. Lu, S. Shen, J. Huang, M. Dong, J. Lu, W. Li, "Feature selection of laserinduced breakdown spectroscopy data for steel aging estimation," Spectrochimica Acta - Part B Atomic Spectroscopy, vol. 150, pp. 49–58, 2018, doi : https://doi.org/10.1016/j.sab.2018.10.006.
T. Desyani, A. Saifudin, Y. Yulianti. "Feature Selection Based on Naive Bayes for Caesarean Section Prediction," IOP Conference Series: Materials Science and Engineering, 2020, vol. 879. no. 1, doi : 10.1088/1757-899X/879/1/012091.
Pritalia, G. L. "Analisis Komparatif Algoritme Machine Learning pada Klasifikasi Kualitas Air Layak Minum," KONSTELASI: Konvergensi Teknologi dan Sistem Informasi, vol. 2, no. 1, pp. 43-55, 2022, doi : https://doi.org/10.24002/konstelasi.v2i1.5630.
P. Ardiyansyah, Rahayuningsih, R. Maulana, “Analisis Perbandingan Algoritma Klasifikasi Data Mining Untuk Dataset Blogger Dengan Rapid Miner,” Jurnal Khatulistiwa Informatika, vol. 6, no. 1, 2018, doi : https://doi.org/10.31294/jki.v6i1.3799.
S. Faisal, "Klasifikasi data minning menggunakan algoritma c4. 5 terhadap kepuasan pelanggan sewa kamera cikarang," Techno Xplore: Jurnal Ilmu Komputer dan Teknologi Informasi, vol. 4, no.1, pp. 38-45, 2019, doi : https://doi.org/10.36805/technoxplore.v4i1.541.
Y. I. Kurniawan, A. Fatikasari, M. L. Hidayat, M. Waluyo, "Prediction For Cooperative Credit Eligibility Using Data Mining Classification With C4. 5 Algorithm," Jurnal Teknik Informatika (JUTIF), vol. 2, no. 2, pp. 67-74, 2021, doi: https://doi.org/10.20884/1.jutif.2021.2.2.49
M. Ula, A. F. Ulva, M. Mauliza, M. A. Ali, Y. R. Said, "Application Of Machine Learning In Determining The Classification Of Children's Nutrition With Decision Tree," Jurnal Teknik Informatika (Jutif), vol. 3, no. 5, pp. 1457-1465, 2022, doi : https://doi.org/10.20884/1.jutif.2022.3.5.599
B. Kaminski, M. Jakubczyk, P. Szufel. “A framework for sensitivity analysis of decision trees,” Central European Journal of Operations Research, vol. 26, pp. 135–159, 2018, doi : https://doi.org/10.1007/s10100-017-0479-6.
J. Eska, “Penerapan Data Mining Untuk Prediksi Penjualan Wallpaper Menggunakan Algoritma C4.5,” JURTEKSI (Jurnal Teknologi dan Sistem Informasi), vol. 2, no. 2, pp. 9-13, 2018, doi : 10.31227/osf.io/x6svc.
O. Ahmed, A. Brifcani, "Gene Expression Classification Based on Deep Learning," 4th Scientific International Conference Najaf (SICN), 2019, vol. 2019, pp. 145-149, doi : 10.1109/SICN47020.2019.9019357 .
A. Bommert, X. Sun, B. Bischl, J. Rahnenführer, M. Lang,"Benchmark for filter methods for feature selection in high-dimensional classification data," Computational Statistics & Data Analysis, vol. 143, pp. 106839, 2020, doi : https://doi.org/10.1016/j.csda.2019.106839.
B. Remeseiro,V. B. Canedo, "A review of feature selection methods in medical applications," Computers in biology and medicine, vol. 112, pp. 103375, 2019, doi : https://doi.org/10.1016/j.compbiomed.2019.103375
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