PERFORMANCE OF K-MEANS CLUSTERING AND KNN CLASSIFIER IN FISH FEED SELLER DETERMINATION MODELS

Esmi Nur Fitri; M. Hafidz Ariansyah; Sri Winarno; Fikri Budiman; Asih Rohmani; Junta  Zeniarja; Edi Sugiarto

doi:10.52436/1.jutif.2023.4.3.725

Esmi Nur Fitri Information Systems Department, Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia
M. Hafidz Ariansyah Information Systems Department, Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia
Sri Winarno Information Systems Department, Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia
Fikri Budiman Information Systems Department, Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia
Asih Rohmani Informatics Engineering Department, Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia
Junta Zeniarja Informatics Engineering Department, Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia
Edi Sugiarto Informatics Engineering Department, Faculty of Computer Science, Universitas Dian Nuswantoro, Indonesia

DOI: https://doi.org/10.52436/1.jutif.2023.4.3.725

Keywords: Fish Feed, K-Means Clustering, KNN Classifier

Abstract

Feed is a crucial variable because it can determine the success of fish farming. Farmers can use two types of artificial feed, namely alternative feed and pellets. Many cultivators need pellets as the main food 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 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 according to several criteria like the number of types of feed, price, order, delivery, payment, availability of discounts, and the number of assessments. This study conducted a predictive analysis to determine the criteria for selecting fish feed sellers in Kendal Regency by utilizing the K-Means Clustering and KNN Classifier methods in the classification method. This research aims to compare the fish feed seller classification method where the pattern of fish feed seller is identified by K-Means Clustering and KNN Classifier, and then the researcher conducts performance appraisal and evaluation. The results of this study are decision-making patterns to help formulate strategies for cultivators and other interested parties. For verifying the method used, measurements were made to obtain an accuracy value where K-Means was 98.6% and KNN was 86.7%.The results of this study indicate that the K-Means Clustering and KNN Classifier methods can classify the selection of freshwater fish feed sellers in Kendal Regency.

Downloads

Download data is not yet available.

References

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. Prasetyoi, Salahuddin, and 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.

Khairunnisa, J. Eska, M. F. L. Sibuea, "Application Of K-Means Clustering Method To Cluster Students’english Skill Jason English Course," Jurnal Teknik Informatika (Jutif), vol. 3, no. 3, pp. 479-485, 2022, doi : https://doi.org/10.20884/1.jutif.2022.3.3.167

G. T. Enggiel, H. D. Purnomo, "Application Of K-Means Method In The Spread Of Positive Cases Of Covid-19 In Salatiga City," Jurnal Teknik Informatika (Jutif), vol. 3, no. 5, pp. 1323-1328, 2022, doi : https://doi.org/10.20884/1.jutif.2022.3.5.356

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.

J. Muschelli, "ROC and AUC with a binary predictor: a potentially misleading metric," Journal of classification, vol. 37, no. 3, pp. 696-708, 2020, doi : https://doi.org/10.1007/s00357-019-09345-1.

N. A. Obuchowski and J. A. Bullen, "Receiver operating characteristic (ROC) curves: review of methods with applications in diagnostic medicine," Physics in Medicine & Biology, vol. 63, no. 7, 2018, doi : 10.1088/1361-6560/aab4b1.

Ardiyansyah, P. A. Rahayuningsih, and 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.

A. Bastian, "Penerapan algoritma k-means clustering analysis pada penyakit menular manusia (studi kasus kabupaten Majalengka)," Jurnal Sistem Informasi, vol. 14, no. 1, pp. 28-34, 2018, doi : https://doi.org/10.21609/jsi.v14i1.566

A. Aziz, A. Siregar, C. Zonyfar, "Penerapan Algoritma K-Means dan Fuzzy C-Means untuk Pengelompokan Kabupaten Kota Berdasarkan Produksi Padi di Provinsi Jawa Barat," Scientific Student Journal for Information, Technology and Science, vol. 3, no.1, pp. 1-8, 2022.

R. A. Indraputra, R. Fitriana,"K-Means Clustering Data COVID-19," Jurnal Teknik Industri, vol. 10, no. 3, pp. 275-282, 2020, doi: https://doi.org/10.25105/jti.v10i3.8428.

Z. Nabila, A. R. Isnain, Permata, Z. Abidin, "Analisis Data Mining Untuk Clustering Kasus Covid-19 Di Provinsi Lampung Dengan Algoritma K-Means," Jurnal Teknologi Dan Sistem Informasi, vol. 2, no. 2, pp. 100-108, 2021, doi : https://doi.org/10.33365/jtsi.v2i2.868

Nikmatun, I. Alvi, I. Waspada, "Implementasi Data Mining untuk Klasifikasi Masa Studi Mahasiswa Menggunakan Algoritma K-Nearest Neighbor," Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, vol. 10, no. 2, pp. 421-432, 2019, doi : https://doi.org/10.24176/simet.v10i2.2882.

K. Mittal, G. Aggarwal, P. Mahajan, “Performance study of K-nearest neighbor classifier and K-means clustering for predicting the diagnostic accuracy,” International Journal of Information Technology, vol. 11, no. 3, pp. 535-540, 2019, doi : 10.1007/s41870-018-0233-x.

M. Manjusha, R. Harikumar, “Performance analysis of KNN classifier and K-means clustering for robust classification of epilepsy from EEG signals,” In 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), March 2016, pp. 2412-2416, doi : 10.1109/WiSPNET.2016.7566575.

PERFORMANCE OF K-MEANS CLUSTERING AND KNN CLASSIFIER IN FISH FEED SELLER DETERMINATION MODELS

Abstract

Downloads

References

Most read articles by the same author(s)