The Comparison Combination of Naïve Bayes Classification Algorithm with Fuzzy C-Means and K-Means for Determining Beef Cattle Quality in Semarang Regency
(1) Universitas Negeri Semarang
(2) Universitas Negeri Semarang
(3) Universitas Negeri Semarang
Abstract
performs data processing to do the classification of beef cattle quality. The data used are
196 data record taken from data in 2016 and 2017. The data have 3 variables for
determining the quality of beef cattle in Semarang regency namely age (month), Weight
(Kg), and Body Condition Score (BCS) . In this research, used the combination of Naïve
Bayes Classification and Fuzzy C-Means algorithm also Naïve Bayes Classification and
K-Means. After doing the combinations, then conducted analysis of the results of which
type of combination that has a high accuracy. The results of this research indicate that the
accuracy of combination Naïve Bayes Classification and K-Means has a higher accuracy
than the combination of Naïve Bayes Classification and Fuzzy C-Means. This can be seen
from the combination accuracy of Fuzzy C-Means algorithm and Naïve Bayes Classifier
of 96,67 while combination of K Means Clustering and Naïve Bayes Classifier algorithm
is 98,33%, so it can be concluded that combination of K Means Clustering algorithm and
Naïve Bayes Classifier is more recommended for determining the quality of beef cattle in
Semarang regency.
Keywords
Full Text:
PDFReferences
Selviana, N. I., & Mustakim. 2016. Analisis Perbandingan K-Means dan Fuzzy C-Means untuk Pemetaan Motivasi Balajar Mahasiswa. Seminar Nasional Teknologi Informasi, Komunikasi dan Industri (SNTIKI), 8, 95–105.
Nurzahputra, A., Muslim, M. A., & Khusniati, M. 2017. Penerapan Algoritma KMeans Untuk Clustering Penilaian Dosen Berdasarkan Indeks Kepuasan Mahasiswa. Techno.COM, 16(1), 17–24.
Oliveira, J.V.D. & Pedrycz, W. 2007. Advances in fuzzy clustering and its applications. London: Wiley.
Megawati, N., Mukid, M. A., & Rahmawati, R. 2013. Segmentasi Pasar Pada Pusat Perbelanjaan Menggunakan Fuzzy C-Means (Studi Kasus: Rita Pasaraya Cilacap). Jurnal Gaussian, 2(4), 343–350.
Agusta, Y. 2007. "K-Means Penerapan Permasalahan dan Metode Terkait,"
Jurnal Sistem dan Informatika, 3, 47-60.
Bai, L., Liang, J., & Dang, C. 2011. An initialization method to simultaneously find initial cluster centers and the number of clusters for clustering categorical data. Knowledge-Based Systems, 24(6), 785–795.
Zhang, H., & Su, J. 2007. Naive bayesian classifiers for ranking.
www.cs.unb.ca/profs/hzhang/ publications/NBRanking. Diakses pada tanggal 17 Januari 2018 jam 15.10.
Alam, A., Dwijatmiko, S. & Sumekar, W. 2014. Motivasi Peternak Terhadap Aktivitas Budidaya Ternak Sapi Potong Di Kabupaten Buru Provinsi Maluku Farmers. Agromedia, 32(2), 75-89.
Abidin, Z. 2002. Penggemukan Sapi Potong. Jakarta : Agro Media Pustaka.
Josaputri, C. A., Sugiharti, E., Arifudin, R. 2016. Decision Support Systems for The Determination of Cattle with Superior Seeds using AHP and SAW Method. Scientific Journal of Informatics, 3(2), 21-30.
Dunham, M. H. 2003. Data Mining Introductory and Advance Topics, New Jersey: Prentice Hall.
Sutoyo, M. N., & Sumpala, A. T. 2015. Penerapan Fuzzy C- Means untuk Deteksi Dini Kemampuan Penalaran Matematis. Scientific Journal of Informatics, 2(2), 129–136.
Somantri, O., Wiyono, S., & Dairoh. 2016. Metode K-Means untuk Optimasi Klasifikasi Tema Tugas Akhir Mahasiswa Menggunakan Support Vector Machine ( SVM ). Scientific Journal of Informatics, 3(1), 34–45.
Sugiharti, E., Firmansyah, S., & Devi, F. R. 2017. Predictive Evaluation Of Performance Of Computer Science Students Of Unnes Using Data Mining Based On Naïve Bayes Classifier (NBC). Journal of Theoretical and Applied Information Technology, 95(4), 902–911.
Safri, Y., Arifudin, R., Muslim, M. A. 2018. K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The Poor. Scientific Journal of Informatic. 5(1), 9-18.
B.Venkatalakshmi, M.V Shivsankar. 2014. Heart Disease Diagnosis Using Predictive Data mining. International Journal of Innovative Research in Science, Engineering and Technology. Vol 3(3), 1873-1877.
Novakovic, et al. 2017. Evaluation of Classification Models in Machine Learning. Theory and Applications of Mathematics & Computer Science, 7(1), 39–46.
Refbacks
- There are currently no refbacks.
Scientific Journal of Informatics (SJI)
p-ISSN 2407-7658 | e-ISSN 2460-0040
Published By Department of Computer Science Universitas Negeri Semarang
Website: https://journal.unnes.ac.id/nju/index.php/sji
Email: [email protected]
This work is licensed under a Creative Commons Attribution 4.0 International License.