Prediksi Hasil Panen Pertanian Salak di Daerah Tapanuli Selatan Menggunakan Algoritma SVM (Support Vector Machine)
DOI:
https://doi.org/10.30865/klik.v4i2.1246Keywords:
South Tapanuli; Salak Agriculture; Prediction; Support Vector Machine; AccuracyAbstract
South Tapanuli is an area known for its extensive salak farming. Salak farming in the South Tapanuli area is one of the economic sources of the people in the South Tapanuli area. The South Tapanuli Regional Agriculture Office is an agency engaged in various fields of agriculture, in its annual activity it records the results of salak agricultural production. Salak agricultural production results that are obtained often experience changes, so we need a system to make predictions, the goal is to find out the yield of salak farming. With the application of the SVM (Support Vector Machine) algorithm, it has been successfully carried out with a total of 28 data on salak agricultural yields in the South Tapanuli Region year 2019 - 2020. The error rate obtained from the RMSE (Root Mean Square Error) in making the prediction model is 1,49 while the accuracy level is 44%
Downloads
References
M. AR, “Sains, Teknologi, Dan Nilai-Nilai Moral,” Elkawnie, vol. 2, no. 2, p. 109, 2016, doi: 10.22373/ekw.v2i2.2657.
H. Priyatno, Potensi Buah Salak Sebagai Suplemen Obat dan Pangan, 1st ed. Surakarta: Muhammadiyah University Press, 2018.
E. P. K. Orpa, E. F. Ripanti, and T. Tursina, “Model Prediksi Awal Masa Studi Mahasiswa Menggunakan Algoritma Decision Tree C4.5,” J. Sist. dan Teknol. Inf., vol. 7, no. 4, p. 272, 2019, doi: 10.26418/justin.v7i4.33163.
Suhardjono, W. Ganda, and H. Abdul, “Prediksi Waktu Kelulusan Mahasiswa Menggunakan Svm Berbasis Pso,” Bianglala Inform., vol. 7, no. 2, pp. 97–101, 2019.
A. Pratama, R. C. Wihandika, and D. E. Ratnawati, “Implementasi algoritme support vector machine (SVM) untuk prediksi ketepatan waktu kelulusan mahasiswa,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 2, no. April, pp. 1704–1708, 2018.
I. Colanus, R. Drajana, and A. Bode, “Support Vector Machine Untuk Prediksi Produksi Tanaman Pangan di Provinsi Gorontalo,” J. NOE, vol. 4, no. 2, pp. 3–10, 2021.
R. H. Kusumodestoni and S. Sarwido, “Komparasi Model Support Vector Machines (Svm) Dan Neural Network Untuk Mengetahui Tingkat Akurasi Prediksi Tertinggi Harga Saham,” J. Inform. Upgris, vol. 3, no. 1, 2017, doi: 10.26877/jiu.v3i1.1536.
P. Rifkie, Algoritma Machine Learning. Bandung: Informatika Bandung, 2021.
M. Fitri, M. Anastasia, and A. T. Mohammad, Data Mining Konsep dan Penerapannya. Yogyakarta: Deepublish, 2021.
F. S. Jumeilah, “Penerapan Support Vector Machine (SVM) untuk Pengkategorian Penelitian,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 1, no. 1, pp. 19–25, 2017, doi: 10.29207/resti.v1i1.11.
I. C. R. Drajana, “Metode Support Vector Machine Dan Forward Selection Prediksi Pembayaran Pembelian Bahan Baku Kopra,” Ilk. J. Ilm., vol. 9, no. 2, pp. 116–123, 2017, doi: 10.33096/ilkom.v9i2.134.116-123.
H. W. Herwanto, T. Widiyaningtyas, and P. Indriana, “Penerapan Algoritme Linear Regression untuk Prediksi Hasil Panen Tanaman Padi,” J. Nas. Tek. Elektro dan Teknol. Inf., vol. 8, no. 4, p. 364, 2019, doi: 10.22146/jnteti.v8i4.537.
N. Suryani, A. S. Fitrani, D. Normalization, and M. M. Normalization, “Prediction Of Election Participant With Malang City Demographic Data Using The K - Nn Algorithm,” vol. 6, no. 36, pp. 2369–2376, 2022.
I. Suprayogi, Trimaijon, and Mahyudin, “Model Prediksi Liku Kalibrasi Menggunakan Pendekatan Jaringan Saraf Tiruan (ZST) (Studi Kasus?: Sub DAS Siak Hulu),” J. Online Mhs. Fak. Tek. Univ. Riau, vol. 1, no. 1, pp. 1–18, 2014.
R. I. Akhsanu, Panduan Pembuatan Flowchart. Surabaya: Fakultas Kesehatan masyarakat Departemen Administrasi Dan Kebijakan Kesehatan, 2017.
E. Jubile, Python Untuk Programmer Pemula. Jakarta: PT Elex Media Komputindo, 2019.
Nurhayati, Teknik Ensemble Learning Untuk Peningkatan Performa Akurasi Model Prediksi (Seleksi Mahasiswa Penerima Beasiswa). Tangerang: Pascal Books, 2022.
N. F. Aldi, H. N. Hanum, and H. Roni, Analisis Sentimen Terhadap Pembatasan Sosial Menggunakan Deep Learning. Bandung: Kreatif Industri Nusantara, 2020.
A. M. F. Alwan, A. Roni, and A. R. Maulana, Tutorial Optimasi Single Exponential Smoothing Menggunakan Algoritma Genetika. Bandung: Kreatif Industri Nusantara, 2020.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Prediksi Hasil Panen Pertanian Salak di Daerah Tapanuli Selatan Menggunakan Algoritma SVM (Support Vector Machine)
ARTICLE HISTORY
Issue
Section
Copyright (c) 2023 Rakhmat Kurniawan, Aidil Halim, Henni Melisa
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).
