Penerapan Metode Support Vector Machine (SVM) Dalam Klasifikasi Produktivitas Padi
-
Hamim Tohari
*
UIN Maulana Malik Ibrahim, Malang,
Indonesia
- Sri Harini UIN Maulana Malik Ibrahim, Malang, Indonesia
- Muhammad Ainul Yaqin UIN Maulana Malik Ibrahim, Malang, Indonesia
- Irwan Budi Santoso UIN Maulana Malik Ibrahim, Malang, Indonesia
- Cahyo Crysdian UIN Maulana Malik Ibrahim, Malang, Indonesia (*) Corresponding Author
Abstract
Indonesia is one of the largest rice producing countries in the world. Almost 95% of the Indonesian population consumes rice as a mandatory staple food, so that every year the demand for rice increases along with the increase in population. East Java is known as the largest rice producing province in Indonesia. To optimize rice production, Central Java province can group rice producing cities or districts. This aims to see and find out cities or districts that have the potential to produce rice as well as find out areas that have less than optimal rice production. To see whether the need for rice in East Java province is met, it is necessary to predict rice productivity in the East Java region so that it can be used as a basis for efforts to increase rice yields for the next period. In this research, the Support Vector Machine (SVM) method was used to classify data for predicting crop yields in East Java province. The advantage of the SVM algorithm is that it can be used for classification and regression problems with linear kernels or non-linear kernels. The data used is agricultural statistical data obtained from the website jatim.bps.go.id. The data is then analyzed using a data mining process. The results of this research are in the form of a prediction pattern with a decision tree which can be used as a basis for predictions in estimating harvest results in the next period. From dividing 80% training data and 20% testing data, results were obtained with 80% accuracy when predicting category '0' (not on target), and 100% accuracy when predicting category '1' (on target). And overall, the classification model has an accuracy of 88%. The contribution to be achieved in this research is to provide ideas for data processing in the agricultural sector.
Downloads
References
I. Febriani, M. Safii and O. Alfina, “Implementasi Data Mining Peningkatan Produksi Beras Menggunakan Metode K-Means Clustering,” Majalah Ilmiah Methoda, vol. 12, no. 3, 2022.
A. Setia Budi, P. Hadi Susilo, and N. Nafi’iyah, “SVM ALGORITHM FOR PREDICTING RICE YIELDS,” Jurnal Teknologi Informasi dan Pendidikan, vol. 13, no. 2, 2020.
S. Rokhmah, A. Susilowati, and M. Intan, “Klasifikasi Data untuk Prediksi Hasil Panen Tanaman Padi di Wilayah Kabupaten Sukoharjo Menggunakan Algoritma C4.5,” JURTI, vol. 6, no. 2, 2022.
S. Wijayanto and M. Y. Fathoni, “Pengelompokkan Produktivitas Tanaman Padi di Jawa Tengah Menggunakan Metode Clustering K-Means,” Jurnal Penelitian Ilmu dan Teknologi Komputer, vol. 13, no. 2, 2021
B. Satria, E. M. Harahap and Jamilah, “Peningkatan Produktivitas Padi Sawah (Oryza sativa L.) Melalui Penerapan Beberapa Jarak Tanam dan Sistem Tanam,” Jurnal Agroekoteknologi FP USU, vol. 5, no. 3, 2017.
S. M. S. Sianturi and N. A. Hasibuan, “Analisa Data Pertanian Tanaman Pangan untuk Memprediksi Hasil Panen dengan Data Mining Algoritma C.45 (Studi Kasus : Dinas Tanaman Pangan dan Holtikutura Provinsi Sumut),” Jurnal Pelita Informatika, vol. 7, no. 4, 2019.
A. Desiani, Irmeilyana, H. Hanum, Y. Andriani, S. I. Maiyanti, C. M. Uteh and I. Rayani, “Penerapan Metode Support Vector Machine Dalam Klasifikasi Bunga Iris,” Indonesian Journal of Applied Informatics, vol. 7, no. 1, 2022.
A. Zalvadila, L. Syafie, and H. Darwis, “Klasifikasi Penyakit Tanaman Bawang Merah Menggunakan Metode SVM dan CNN,” Jurnal Pengembangan IT, vol. 8, no. 3, 2023.
E. Rasmikayati and D. A. Faisal, “DINAMIKA PRODUKTIVITAS PADI DITINJAU DARI FLUKTUASI SUSUT HASIL SERTA FAKTOR SOSIAL, EKONOMI DAN BUDAYA YANG MEMPENGARUHINYA,” Agribisnis dan Sosial Ekonomi Pertanian, vol. 1, no. 2, 2016.
E. Haryatmi and S. P. Hervianti, “Penerapan Algoritma Support Vector Machine untuk Model Prediksi Kelulusan Mahasiswa Tepat Waktu,” JURNAL RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 2, 2021.
R. R. Fiska, “Penerapan Teknik Data Mining dengan Metode Support Vector Machine (SVM) untuk Memprediksi Siswa yang Berpeluang Drop Out (Studi Kasus di SMKN 1 Sutera),” SATIN-Sains dan Teknologi Informasi, vol. 3, no. 1, 2017.
M. F. Akbarollah, W. Wiyanto, D. Ardiatma, and A. T. Zy, “Penerapan Algoritma K-Nearest Neighbor Dalam Klasifikasi Penyakit Jantung,” Journal of Computer System and Informatics (JoSYC), vol. 4, no. 4, 2023, doi: 10.47065/josyc.v4i4.4071.
D. E. Safitri and A. S. Fitrani, “IMPLEMENTASI METODE KLASIFIKASI DENGAN ALGORITMA SUPPORT VECTOR MACHINE KERNEL GAUSSIAN RBF UNTUK PREDIKSI PARTISIPASI PEMILU TERHADAP DEMOGRAFI KOTA SURABAYA,” Indonesian Journal of Business Intelligence (IJUBI), vol. 5, no. 1, 2022, doi: 10.21927/ijubi.v5i1.2259.
D. Vernanda, N. N. Purnawan, T. H. Apandi, and Haryati, “ANALISIS DATA UNTUK KLASIFIKASI TINGKAT KEMATANGAN BUAH NANAS MENGGUNAKAN SVM,” Jurnal Ilmiah Ilmu dan Teknologi Rekayasa, vol. 4, 2022, doi: 10.31962/jiitr.vvii.67.
Y. Amrozi, D. Yuliati, A. Susilo, N. Novianto, and R. Ramadhan, “Klasifikasi Jenis Buah Pisang Berdasarkan Citra Warna dengan Metode SVM,” Jurnal Sisfokom (Sistem Informasi dan Komputer), vol. 11, no. 3, 2022, doi: 10.32736/sisfokom.v11i3.1502.
F. R. Lumbanraja, F. Lufiana, Y. Heningtyas and K. Muludi, “Implementasi Support Vector Machine (SVM) untuk Klasifikasi Pederita Diabetes Mellitus,” Jurnal Komputasi, vol. 10, no. 1, 2022.
I. M. Parapat, M. T. Furqon and Sutrisno, “Penerapan Metode Support Vector Machine (SVM) Pada Klasifikasi Penyimpangan Tumbuh Kembang Anak,” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 10, 2018.
A. Handayanto, K. Latifa, N. D. Saputro, and R. R. Waliyansyah, “Analisis dan Penerapan Algoritma Support Vector Machine (SVM) dalam Data Mining untuk Menunjang Strategi Promosi,” JUITA : Jurnal Informatika, vol. 7, no. 2, 2019.
E. Haryatmi and S. Pramita Hervianti, “Penerapan Algoritma Support Vector Machine Untuk Model Prediksi Kelulusan Mahasiswa Tepat Waktu,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 2, 2021, doi: 10.29207/resti.v5i2.3007.
A. Darmawan, N. Kustian dan W. Rahayu, “Implementasi Data Mining Menggunakan Model SVM untuk Prediksi Kepuasan Pengunjung Taman Tabebuya,” Jurnal String, vol. 2, no. 3, 2018.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Penerapan Metode Support Vector Machine (SVM) Dalam Klasifikasi Produktivitas Padi
Pages: 175-183
Copyright (c) 2023 Hamim Tohari, Sri Harini, Muhammad Ainul Yaqin, Irwan Budi Santoso, Cahyo Crysdian
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
