Analysis of Corn Agriculture Data to Predict Harvest Results with Data Mining Algorithm C4.5

##plugins.themes.academic_pro.article.main##

Nike Suyeni Pangaribuan
Freddy Marpaung

Abstract

The agricultural sector is one of the most dependent sectors for living in Indonesia, corn is also a source of raw material for the industrial sector including the food industry in Indonesia and is used for animal feed, especially poultry. Toko Gilang Advanta 77 is one of the places that produces corn crops. In addition to producing corn crops, Toko Gilang Advanta 77 sells corn crops to factories that manage the corn harvest. Because of the large demand for corn for consumption and sale, both as food and animal feed ingredients. Toko Gilang Advanta 77 has one of the obstacles, namely it does not have a supporting technology in suggesting yield predictions which results in a lack of information needed to increase crop yields. . So the authors designed a system with a yield prediction model using data mining techniques with the C4.5 algorithm, the program to be used is web programming, with a database. With this system, it is hoped that Toko Gilang Advanta 77 can easily predict the yield of corn crops.

##plugins.themes.academic_pro.article.details##

How to Cite
Pangaribuan, N. S., & Freddy Marpaung. (2020). Analysis of Corn Agriculture Data to Predict Harvest Results with Data Mining Algorithm C4.5. Login : Jurnal Teknologi Komputer, 14(2), 235-243. Retrieved from http://login.seaninstitute.org/index.php/Login/article/view/53

References

[1] S. Purwanto, “Perkembangan Produksi dan Kebijakan Peningkatan Produksi Jagung,” Jagung Tek. Produksi dan Pengemb., 2007.
[2] J. P. Gultom and A. Rikki, “Implementasi Data Mining menggunakan Algoritma C-45 pada Data Masyarakat Kecamatan Garoga untuk Menentukan Pola Penerima Beras Raskin,” Kumpul. Artik. Karya Ilm. Fak. Ilmu Komput., vol. 02, no. 01, pp. 11–19, 2020.
[3] D. W. Sitohang and A. Rikki, “Implementasi Algoritma K- Means Clustering untuk Mengelompokkan Data Gizi Balita pada Kecamatan Garoga Tapanuli Utara,” vol. 02, pp. 80–92, 2019.
[4] P. M. Hasugian, “Pengujian Algoritma C4.5 dengan Aplikasi Weka Dalam Pembentukan Pohon Keputusan,” J. Mantik Penusa, 2018.
[5] R. Sowmya and K. R. Suneetha, “Data Mining with Big Data,” in Proceedings of 2017 11th International Conference on Intelligent Systems and Control, ISCO 2017, 2017, doi: 10.1109/ISCO.2017.7855990.
[6] I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques. 2016.
[7] S. Wahyuningsih and D. R. Utari, “Perbandingan Metode K-Nearest Neighbor , Naive Bayes dan Decision Tree untuk Prediksi Kelayakan Pemberian Kredit,” Konf. Nas. Sist. Inf. 2018, 2018.
[8] A. Rohman and M. Rochcham, “KOMPARASI METODE KLASIFIKASI DATA MINING UNTUK PREDIKSI KELULUSAN MAHASISWA,” Neo Tek., 2019, doi: 10.37760/neoteknika.v5i1.1379.
[9] N. T. Romadloni, I. Santoso, and S. Budilaksono, “Perbandingan Metode Naive Bayes , Knn Dan Decision Tree Terhadap Analisis Sentimen Transportasi Krl,” J. IKRA-ITH Inform., 2019.
[10] E. P. Cynthia and E. Ismanto, “Metode Decision Tree Algoritma C.45 Dalam Mengklasifikasi Data Penjualan Bisnis Gerai Makanan Cepat Saji,” Jurasik (Jurnal Ris. Sist. Inf. dan Tek. Inform., 2018, doi: 10.30645/jurasik.v3i0.60.
[11] I. Sutoyo, “IMPLEMENTASI ALGORITMA DECISION TREE UNTUK KLASIFIKASI DATA PESERTA DIDIK,” J. Pilar Nusa Mandiri, 2018, doi: 10.33480/pilar.v14i2.926.
[12] M. Firmansyah and R. Aufany, “Implementasi Metode Decision Tree Dan Algoritma C4.5 Untuk Klasifikasi Data Nasabah Bank,” Infokam, 2016.