Analisis Perbandingan Algoritma Klasifikasi Data Mining Untuk Penentuan Bibit Unggul
Abstract
Cocoa is one of the leading commodity crops from the growing plantation sub-sector, mostly cultivated by farmers in the form of community plantations. Cultivation is carried out so that it can continue to produce seeds and trees independently for the sustainability of the commodity. The process currently carried out in the selection of superior seeds is carried out by staff who are specifically assigned to that section. The process of selecting superior cocoa plant seeds should have been recorded and stored. Data mining is a process that uses statistical techniques, mathematics, artificial intelligence, and machine learning to extract and identify useful information and related knowledge from various large databases. Based on this problem, the C5.0 and K-Nearest Neighbor algorithms are applied to carry out the comparison process in predicting the need for superior seeds to be used. It will be able to provide accurate information and can be used as a consideration in the stock of superior cocoa seed needs. The process using the C5.0 and K-Nearest Neighbor algorithms has been successfully carried out, from the testing process carried out that the C5.0 algorithm has a better performance result of 87.50% compared to the K-Nearest Neighbor algorithm of 62.50%.
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U. Surapati and M. Jannah, “Penerapan Data Mining Menggunakan Metode K-Means Untuk Mengetahui Minat Customer Dalam Pembelian Merchandise Kpop,” J. Sains dan Teknol., vol. 5, no. 3, pp. 875–884, 2024, doi: 10.55338/saintek.v5i3.2739.
R. E. Marpaung, “Penerapan Algoritma K-Means Dalam Mengclustering Kualitas Bibit Kelapa Sawit Di PPKS Marihat,” Progr. Stud. Sist. Inf., vol. 1, no. 1, pp. 7–15, 2022.
Rizki Ananda Putra Fajar, Rakhmat Kurniawan, and Sriani, “Penerapan Metode Vikor dalam Pemilihan Bibit Unggul Pohon Karet,” Da’watuna J. Commun. Islam. Broadcast., vol. 4, no. 4, pp. 1550–1560, 2024, doi: 10.47467/dawatuna.v4i4.1842.
S. Vratiwi and N. Padang, “Penerapan Metode Naïve Bayes Pada Sistem Penunjang Keputusan Bibit Unggul Kelapa Sawit,” J. Pustaka, vol. 4, no. 2, pp. 31–37, 2024.
M. S. Pangestu and M. A. Fitriani, “Perbandingan Perhitungan Jarak Euclidean Distance, Manhattan Distance, dan Cosine Similarity dalam Pengelompokan Data Bibit Padi Menggunakan Algoritma K-Means,” Sainteks, vol. 19, no. 2, p. 141, 2022, doi: 10.30595/sainteks.v19i2.14495.
A. D. Adhi Putra, “Analisis Sentimen pada Ulasan pengguna Aplikasi Bibit Dan Bareksa dengan Algoritma KNN,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 8, no. 2, pp. 636–646, 2021, doi: 10.35957/jatisi.v8i2.962.
A. Fikri Sallaby, “Analysis of Missing Value Imputation Application with K-Nearest Neighbor (K-NN) Algorithm in Dataset,” Int. J. Informatics Comput. Sci., vol. 5, no. 2, pp. 141–144, 2021, doi: 10.30865/ijics.v5i2.3185.
H. Azis, P. Purnawansyah, F. Fattah, and I. P. Putri, “Performa Klasifikasi K-NN dan Cross Validation Pada Data Pasien Pengidap Penyakit Jantung,” Ilk. J. Ilm., vol. 12, no. 2, pp. 81–86, 2020, doi: 10.33096/ilkom.v12i2.507.81-86.
R. Ella Sari, Solikhun, and F. Rizky, “Penerapan Algoritma C5.0 dalam Memprediksi Persediaan Buah pada UD. Bunda Syafira Buah,” JUKI J. Komput. dan Inform., vol. 3, no. 2, pp. 59–63, 2021, doi: 10.53842/juki.v3i2.62.
F. N. Giustin, B. N. Sari, and T. N. Padilah, “Application of C5.0 Algorithm in Prediction of Learning Outcomes in Calculus Subject,” J. Appl. Eng. Technol. Sci., vol. 3, no. 2, pp. 90–97, 2022, doi: 10.37385/jaets.v3i2.673.
A. T. Yuliandari, Z. Sari, and V. R. S. Nastiti, “Pemetaan Mata Kuliah Yang Berpengaruh Pada Kelulusan Tidak Tepat Waktu Mahasiswa Informatika UMM Menggunakan SOM,” J. Repos., vol. 3, no. 1, pp. 111–120, 2024, doi: 10.22219/repositor.v3i1.31016.
T. Permana, A. M. Siregar, A. F. N. Masruriyah, and A. R. Juwita, “Perbandingan Hasil Prediksi Kredit Macet pada Koperasi Menggunakan Algoritma KNN dan C5.0,” Conf. Innov. Appl. Sci. Technol., vol. 3, no. 1, pp. 737–746, 2020, [Online]. Available: http://publishing-widyagama.ac.id/ejournal-v2/index.php/ciastech/article/view/1970.
F. N. Umma, B. Warsito, and D. A. I. Maruddani, “Klasifikasi Status Kemiskinan Rumah Tangga Dengan Algoritma C5.0 Di Kabupaten Pemalang,” J. Gaussian, vol. 10, no. 2, pp. 221–229, 2021, doi: 10.14710/j.gauss.v10i2.29934.
E. Novianto, A. Hermawan, and D. Avianto, “Klasifikasi Algoritma K-Nearest Neighbor, Naive Bayes, Decision Tree Untuk Prediksi Status Kelulusan Mahasiswa S1,” Rabit J. Teknol. dan Sist. Inf. Univrab, vol. 8, no. 2, pp. 146–154, 2023, doi: 10.36341/rabit.v8i2.3434.
D. P. Indini, Mesran, and Dito Putro Utomo, “Penerapan Data Mining Dalam Pengelompokan Data Reseller di Telkomsel Authorized Partner (TAP) Deli Tua Dengan Algoritma K-Means,” J. Ilm. Media Sisfo, vol. 17, no. 2, pp. 189–202, 2023, doi: 10.33998/mediasisfo.2023.17.2.1391.
M. Mesran, M. Syahrizal, S. Sarwandi, S. Aripin, D. P. Utomo, and A. Karim, “A comparison of the performance of data mining classification algorithms on medical datasets with the application of data normalization,” AIP Conf. Proc., vol. 3048, no. 1, 2024, doi: 10.1063/5.0207994.
U. R. Amanda and D. P. Utomo, “Penerapan Data Mining Algoritma Hash Based Pada Data Pemesanan Buah Impor Cv. Green Uni Fruit,” KOMIK (Konferensi Nas. Teknol. Inf. dan Komputer), vol. 5, no. 1, 2021.
B. S. Pranata and D. P. Utomo, “Penerapan Data Mining Algoritma FP-Growth Untuk Persediaan Sparepart Pada Bengkel Motor (Study Kasus Bengkel Sinar Service),” Bull. Inf. Technol., vol. 1, no. 2, pp. 83–91, 2020.
I. Arfyanti, M. Fahmi, and P. Adytia, “Penerapan Algoritma Decision Tree Untuk Penentuan Pola Penerima Beasiswa KIP Kuliah,” Build. Informatics, Technol. Sci., vol. 4, no. 3, pp. 1196–1201, 2022, doi: 10.47065/bits.v4i3.2275.
N. H. Harani and F. S. Damayanti, “Implementasi Algoritma C5.0 Untuk Menentukan Pelanggan Potensial Di Kantor Pos Cimahi,” J. SITECH Sist. Inf. dan Teknol., vol. 4, no. 1, pp. 69–76, 2021, doi: 10.24176/sitech.v4i1.6281.
D. Fitrianah, W. Gunawan, and A. Puspita Sari, “Studi Komparasi Algoritma Klasifikasi C5.0, SVM dan Naive Bayes dengan Studi Kasus Prediksi Banjir Comparative Study of Classification Algorithm between C5.0, SVM and Naive Bayes with Case Study of Flood Prediction,” Februari, vol. 21, no. 1, pp. 1–11, 2022.
R. N. Amalda, N. Millah, and I. Fitria, “Implementasi Algoritma C5.0 Dalam Menganalisa Kelayakan Penerima Keringanan Ukt Mahasiswa Itk,” Teorema Teor. dan Ris. Mat., vol. 7, no. 1, p. 101, 2022, doi: 10.25157/teorema.v7i1.6692.
A. Putri et al., “Komparasi Algoritma K-NN, Naive Bayes dan SVM untuk Prediksi Kelulusan Mahasiswa Tingkat Akhir,” MALCOM Indones. J. Mach. Learn. Comput. Sci., vol. 3, no. 1, pp. 20–26, 2023, doi: 10.57152/malcom.v3i1.610.
J. Supriyanto, D. Alita, and A. R. Isnain, “Penerapan Algoritma K-Nearest Neighbor (K-NN) Untuk Analisis Sentimen Publik Terhadap Pembelajaran Daring,” J. Inform. dan Rekayasa Perangkat Lunak, vol. 4, no. 1, pp. 74–80, 2023, doi: 10.33365/jatika.v4i1.2468.
R. Sari, “Analisis Sentimen Pada Review Objek Wisata Dunia Fantasi Menggunakan Algoritma K-Nearest Neighbor (K-Nn),” EVOLUSI J. Sains dan Manaj., vol. 8, no. 1, pp. 10–17, 2020, doi: 10.31294/evolusi.v8i1.7371.
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