Analisis Algoritma C45 dan Regresi Linear untuk Memprediksi Hasil Panen Kelapa Sawit
- Nurahman Nurahman Universitas Darwan Ali, Sampit, Indonesia
-
Nindi Ernawati
*
Universitas Darwan Ali, Sampit,
Indonesia
(*) Corresponding Author
Abstract
Indonesia, as one of the main producers of palm oil in the world, has an agricultural sector that is very influential on the national economy, especially through palm oil exports. Prediction of oil palm yields is crucial to improve efficiency in planning and resource management. This study was conducted to compare the performance of two prediction methods, namely the C45 Algorithm and Linear Regression, in predicting oil palm yields. The formulation of the problems raised in this study includes: (1) How does the performance of the C45 Algorithm and Linear Regression compare in predicting oil palm yields? (2) How accurate are the predictions generated by the two algorithms based on historical data on crop yields? (3) What are the factors that influence the choice between C45 Algorithm and Linear Regression for oil palm yield prediction? The data used in this study is historical data from PT. Surya Inti Sawit Kahuripan, which includes 106 data blocks. The variables analyzed included land area, number of trees, number of trees per hectare, planting year, soil type, fertilizer use plan, and yield in tons. Data analysis was carried out using the C45 Algorithm, which forms a decision tree based on historical data, and the Linear Regression method, which analyzes the linear relationship between independent variables and dependent variables. Prediction accuracy is measured using Root Mean Squared Error (RMSE). The results show that the C45 Algorithm has a lower RMSE value than Linear Regression, indicating that the C45 Algorithm provides more accurate predictions.
Downloads
References
A. Cynthia, S. Siregar, P. S. Veni, and R. Ingesti, “ANALISIS KUALIFIKASI TENAGA KERJA PANEN TERHADAP CAPAIAN NORMA PANEN KELAPA SAWIT (Elaies guineensis Jacq),” 2024.
D. Suryani, A. Yulianti, E. L. Maghfiroh, and J. Alber, “Klasifikasi Kualitas Produk Kelapa Sawit Menggunakan Metode Naïve Bayes Quality Classification of Palm Oil Products Using Naïve Bayes Method,” 2022. [Online]. Available: http://sistemasi.ftik.unisi.ac.id
Abubakar Ahmed and Ishak Yusoff Mohd, “Gambaran Umum Peran Petani Kecil dalam Sistem Produksi Kelapa Sawit dalam Perubahan Iklim,” NsifatElingkungan danPolusiTteknologi Jurnal Ilmiah Triwulanan Internasional, pp. 2055–2071, 2022, [Online]. Available: www.neptjournal.com
Darmono, “PEMBERDAYAAN PEKEBUN KELAPA SAWIT SWADAYA MENUJU PEMBANGUNAN PERKEBUNAN BERKELANJUTAN DI KABUPATEN BERAU,” vol. 5, no. 1, 2021.
Situmorang Sebastian Redi, “PENERAPAN DATA MINING UNTUK MEMPREDIKSI JUMLAH PRODUKSI KELAPA SAWIT MENGGUNAKAN METODE REGRESI LINEAR BERGANDA (Studi Kasus : PT. SGSR Mujur Sinar Gunung Sawit Raya),” 2023.
Afonso Jose et al., “Model regresi tidak secara inheren bersifat prediktif. Tinjauan edukatif tentang mengapa penelitian olahraga dan kedokteran olahraga harus lebih berhati-hati dengan istilah "prediktor" (Versi 1),” 2024.
Lestari Indah Hidayah Nur, “PENGARUH KOMPENSASI DAN BUDAYA ORGANISASI TERHADAP PRODUKTIVITAS DENGAN MEDIASI MOTIVASI KERJA (Studi Kasus Pada PT. Hutan Rindang Banua),” JBS (Jurnal Berbasis Sosial) Pendidikan IPS STKIP Al Maksum, vol. 1, no. 2, 2020.
N. Rusmilawati and P. T. Prasetyaningrum, “Penerapan Data Mining Dalam Prediksi Hasil Produksi Kelapa Sawit PT Borneo Ketapang Indah Menggunaka Metode Linier Regression,” 2021.
Noviana Githa and Ardiani Fani, “ANALISIS PENDAPATAN PETANI KELAPA SAWIT SEBELUM DAN SELAMA COVID-19 (STUDI KASUS: KABUPATEN PADANG LAWAS UTARA) ANALYSIS OF OIL PALM FARMERS INCOME BEFORE AND DURING COVID-19 (CASE STUDY: NORTH PADANG LAWAS DISTRICT),” 2020.
Aswan Novita, Fadhillah Yusra, Siregar Hasan Noor Muhammad, Puspita Rina Nurkhasanah, Mahaji Toga, and Harahap Utari Angelia, “Analisis Regresi Linier dalam Estimasi Produktivitas Kelapa Sawit(Elaeis guineensis) Di Kabupaten Tapanuli Selatan,” INNOVATIVE: Journal Of Social Science Research, vol. 4, no. 1, pp. 10264–10276, 2024.
N. Ambar Suharyanti, K. Mizuno, and A. Sodri, “Pengaruh defisit air pada periode berbunga pada produktivitas kelapa sawit di lahan gambut,” in E3S Web of Conferences, EDP Sciences, Nov. 2020. doi: 10.1051/e3sconf/202021105005.
D. Sri Rahayu, J. Afifah, and S. Intan, “SENTIMAS: Seminar Nasional Penelitian dan Pengabdian Masyarakat Classification of Diabetes Mellitus Using C4.5 Algorithm, Support Vector Machine (SVM) and Linear Regression Klasifikasi Penyakit Diabetes Melitus Menggunakan Algoritma C4.5, Support Vector Machine (SVM) dan Regresi Linear,” 2023. [Online]. Available: https://journal.irpi.or.id/index.php/sentimas
A. Agung and Putri, “Penerapan Data Mining Untuk Mengestimasi Laju Data Mining Usage To Estimate Civil Growth In Denpasar,” vol. 6, no. 1, pp. 37–44, 2023.
V. Arinal and M. Azhari, “Penerapan Regresi Linear Untuk Prediksi Harga Beras Di Indonesia,” Jurnal Sains dan Teknologi, vol. 5, no. 1, p. |pp, 2023, doi: 10.55338/saintek.v5i1.1417.
C. E. Simbolon, “Penerapan Algoritma Regresi Linier Sederhana Dalam Memprediksi Keuntungan dan Kerugian Kelapa Sawit Pt. Sri Ulina Ersada Karina,” 2021.
A. Prasetyo, Salahuddin, and Amirullah, “Prediksi Produksi Kelapa Sawit Menggunakan Metode Regresi Linier Berganda,” Multimedia & Jaringan, vol. 6, no. 2, 2021.
W. Ananda, M. Safii, and M. Fauzan, “Prediksi Jumlah Hasil Panen Sawit Menggunakan Algoritma Naive Bayes,” 2021.
D. Nurhasanah, D. Ayu Lestari, S. Simatupang, S. Informasi, and S. Royal Kisaran, “PEMILIHAN KUALITAS PRODUK KELAPA SAWIT MENGGUNAKAN METODE NAÏVE BAYES DI LABUHANBATU SELATAN,” Jurnal Teknologi Komputer dan Sistem Informasi) Februari, no. 1, pp. 24–31, 2023, [Online]. Available: http://jurnal.goretanpena.com/index.php/teknisi
M. Marsella and W. Susanti, “Penerapan Algoritma Naïve Bayes untuk Klasifikasi Spare Part Industri Kelapa Sawit,” Jurnal Mahasiswa Aplikasi Teknologi Komputer dan Informasi, vol. 4, no. 3, pp. 88–99, 2022.
Yusuf Yusril, “ANALISIS PERBANDINGAN SISTEM PENDUKUNG KEPUTUSAN MENGGUNAKAN METODE SAW DAN WP DALAM PENILAIAN KINERJA TENAGA KONTRAK,” 2022.
A. Bravo, T. Tursina, and H. Sastypratiwi, “Penerapan Metode Naive Bayes Untuk Penentuan Bibit Kelapa Sawit Berdasarkan Kondisi Daerah Tanam dan Perawatan Tanaman,” Jurnal Sistem dan Teknologi Informasi (JustIN), vol. 11, no. 1, p. 101, Jan. 2023, doi: 10.26418/justin.v11i1.52277.
I. Ubaedi and Y. M. Djaksana, “OPTIMASI ALGORITMA C4.5 MENGGUNAKAN METODE FORWARD SELECTION DAN STRATIFIED SAMPLING UNTUK PREDIKSI KELAYAKAN KREDIT,” Sistem Informasi |, vol. 9, no. 1, 2022.
Juwita Ratna Ayu, Pratama Rizky Adi, Mudzakir Al Tohirin, and Sukmawati Emilia Cici, “PERBANDINGAN ALGORITMA C4.5 DAN REGRESI LINEAR DALAM MEMPREDIKSI KETERLAMBATAN PEMBAYARAN UANG KULIAH,” Konferensi Nasional Penelitian dan Pengabdian (KNPP) Ke-3, 2023.
I. N. G. Neyun, W. Sulandari, and I. Slamet, “Hibrida Autoregresiive Integrated Moving Average dan Fuzzy Time Series Cheng untuk Prediksi Harga Saham Autoregresiive Integrated Moving Average and Fuzzy Time Series Cheng Hybrid for Predicting Stock Price,” Jurnal Bumigora Information Technology (BITe), vol. 5, no. 2, pp. 139–150, 2023, doi: 10.30812/bite/v5i1.2972.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Analisis Algoritma C45 dan Regresi Linear untuk Memprediksi Hasil Panen Kelapa Sawit
Pages: 1155-1163
Copyright (c) 2024 Nurahman Nurahman, Nindi Ernawati
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).
