ANALISIS PREDIKSI HASIL PRODUKSI TANAMAN CABAI MENGGUNAKAN METODE MULTI LINIER REGRESI
Abstract
This study aims to predict the yield of chili plants in Indonesia using multiple linear regression method. In this study, the variables analyzed include irrigation volume, temperature, soil moisture, soil pH, and plant growth parameters such as stems, branches, and leaves. Data were collected from 100 chili plant samples planted in Jatikusuma Village, Deli Serdang Regency, for 63 days. The method used for the analysis is multiple linear regression, which is applied to produce a prediction model of harvest yield. Multi linear regression method is used to perform forecasting with the development of the dependent variable (Y), namely the amount of production with independent variables consisting of x1 = plant growth rate, x2 = moisture, x3 = temperature, x4 = volume, x5 = soil pH, x6 = stem, x7 = branch, x8 = leaf. The results of the prediction analysis in this study obtained the intercept coefficient value is 153.94 from the total data of 100 samples, resulting in the level of fit of the multi linear regression model with an R2 score of 1.00 which shows the level of accuracy in a prediction of these results is very good.
References
[1] M. R. Rizqullah and S. Taufik, “Analisis Pendapatan Usahatani Cabai Merah di Desa Talang Kemang Kecamatan Rantau Bayur Kabupaten Banyuasi Provinsi Sumatera Slematan,” J. Ilmu Pertan. Agronitas, vol. 2, no. 1, pp. 54–62, 2020.
[2] B. C. Subambhi, S. Mardiana, & Faoeza, and H. Saragih, “Jurnal Ilmiah Pertanian (JIPERTA) Analisis Location Quotient (LQ) Tanaman Cabai Besar (Capsicum annuum L.) di Provinsi Sumatera Utara Location Quotient (LQ) Analysis of Large Chili Plant (Capsicum annuum L.) in North Sumatera,” J. Ilm. Pertan., vol. 2, no. 2, pp. 169–179, 2019.
[3] S. S. R. Zamili Nista, Harahap Gustami, “Faktor-Faktor yang Mempengaruhi Permintaan dan Penawaran Cabe Merah di Pasar Raya MMTC Medan,” J. Ilm. Pertan., vol. 2, no. 1, pp. 77–86, 2020.
[4] Z. Sirajuddin and P. Liskawati Kamba, “Farmer’s Perception on Information and Communication Technology Implementation in Agricultural Extension,” J. Penyul., vol. 17, no. 2, pp. 136–144, 2021.
[5] V. M. M. Siregar and N. F. Siagian, “Implementation of Fingerprint Sensors for Fingerprint Reader Prototypes Using a Microcontroller,” IOTA, vol. 02, no. 1, pp. 47–59, 2022, doi: 10.31763/iota.v2i1.559.
[6] V. M. M. Siregar, K. Sinaga, and M. A. Hanafiah, “Prototype of Water Turbidity Measurement With Fuzzy Method using Microcontroller,” IOTA, vol. 2, no. 2, pp. 76–97, 2022, doi: 10.31763/iota.v2i2.593.
[7] I. M. Siregar, M. Yunus, and V. M. M. Siregar, “Prototype of Garbage Picker Ship Robot Using Arduino Nano Microcontroller,” IOTA, vol. 2, no. 3, pp. 150–168, 2022, doi: 10.31763/iota.v2i3.540.
[8] I. M. Siregar, N. F. Siagian, and V. M. M. Siregar, “Design of an Electric Light Control Device Using Arduino Uno Microcontroller-Based Short Message Service,” IOTA, vol. 02, no. 2, pp. 98–110, 2022, doi: 10.31763/iota.v2i2.560.
[9] H. Sugara, V. M. M. Siregar, K. Sinaga, M. A. Hanafiah, and H. D. Pardede, “SAW and Electre Methods Implementation for Scholarship Awardee Decision,” Iota, vol. 01, no. 4, pp. 208–219, 2021, doi: 10.31763/iota.v1i4.496.
[10] E. Damanik and I. M. Siregar, “PENGEMBANGAN SISTEM CUSTOMER RELATIONSHIP MANAGEMENT BERBASIS WEB PADA PT. TERUS MEGA TARA JAKARTA,” J. Tek. Inf. dan Komput., vol. 4, no. 1, pp. 60–69, 2021, doi: 10.37600/tekinkom.v4i1.278.
[11] V. M. M. Siregar et al., “Decision support system for selection of food aid recipients using SAW method,” 2022, p. 030019. doi: 10.1063/5.0094385.
[12] A. S. Manalu, I. M. Siregar, N. J. Panjaitan, and H. Sugara, “RANCANG BANGUN INFRASTRUKTUR CLOUD COMPUTING DENGAN OPENSTACK PADA JARINGAN LOKAL MENGGUNAKAN VIRTUALBOX,” J. Tek. Inf. dan Komput., vol. 4, no. 2, p. 303, Dec. 2021, doi: 10.37600/tekinkom.v4i2.335.
[13] P. Dani, P. Adi, N. E. Mustamu, V. Marudut, M. Siregar, and V. Sihombing, “Drone simulation for agriculture and LoRa based approach,” IOTA, vol. 01, no. 4, pp. 221–235, 2021, doi: 10.31763/iota.v1i4.501.
[14] 2020 Savira, et al, “eduFarm : Aplikasi Petani Milenial untuk Meningkatkan Produktivitas di Bidang Pertanian,” Automata, vol. 1, no. 2, pp. 28–38, 2020.
[15] N. Riskya and S. Yuliana, “Penerapan Data Mining Untuk Prediksi Perilaku Pelanggan Menggunakan Multiple Linear Regression,” J. Inform. dan Tek. Elektro Terap., vol. 11, no. 3, 2023, doi: 10.23960/jitet.v11i3.3194.
[16] R. I. Wati, S. Subejo, and Y. F. Maulida, “Problematika, Pola, Dan Strategi Petani Dalam Mempersiapkan Regenerasi Di Daerah Istimewa Yogyakarta,” J. Ketahanan Nas., vol. 27, no. 2, p. 187, 2021, doi: 10.22146/jkn.65568.
[17] Y. Aryani, “Sistem Informasi Penjualan Barang Dengan Metode Regresi Linear Berganda Dalam Prediksi Pendapatan Perusahaan,” J. Ris. Sist. Inf. dan Teknol. Inf., vol. 2, no. 2, pp. 39–51, 2020, doi: 10.52005/jursistekni.v2i2.47.
[18] “Implementasi Machine Learning untuk Prediksi Harga Mobil Bekas dengan Algoritma Regresi Linear berbasis Web,” J. Ilm. Komputasi, vol. 21, no. 4, Dec. 2022, doi: 10.32409/jikstik.21.4.3327.
[19] I. B. M. Swarbawa, I. G. Arta Wibawa, and I. K. Gede Suhartana, “Prediksi Hasil Panen Padi Di Kabupaten Jembrana Dengan Metode Linear Regression,” JELIKU (Jurnal Elektron. Ilmu Komput. Udayana), vol. 11, no. 3, p. 671, 2023, doi: 10.24843/jlk.2023.v11.i03.p24.
[20] G.- MARDIATMOKO, “Pentingnya Uji Asumsi Klasik Pada Analisis Regresi Linier Berganda,” BAREKENG J. Ilmu Mat. dan Terap., vol. 14, no. 3, pp. 333–342, 2020, doi: 10.30598/barekengvol14iss3pp333-342.
[21] M. Abdul muthalib, I. Irfan, K. Kartika, and S. M. Selamat Meliala, “Pengiraan Pose Model Manusia Pada Repetisi Kebugaran Ai Pemograman Python Berbasis Komputerisasi,” INFOTECH J., vol. 9, no. 1, pp. 11–19, 2023, doi: 10.31949/infotech.v9i1.4233.
[22] I. Muhammad, Y. A. Lesnussa, H. W. M. Patty, M. S. Noya Van Delsen, and M. Y. Matdoan, “Peramalan Jumlah Mahasiswa Baru Menggunakan Metode Double Exponential Smoothing (Studi Kasus : Mahasiswa Baru Universitas Pattimura Ambon Tahun 2017),” Var. J. Stat. Its Appl., vol. 2, no. 1, pp. 27–33, 2020, doi: 10.30598/variancevol2iss1page27-33.
[23] H. T. Irawan, I. Pamungkas, Fitriadi, K. Hadi, and T. M. A. Pandria, “Penggunaan Moving Average dan Exponential Smoothing untuk Memprediksi Kebutuhan Beras Raskin di Kota Banda Aceh,” J. Optim., vol. 6, no. 2, pp. 2–9, 2020.
[2] B. C. Subambhi, S. Mardiana, & Faoeza, and H. Saragih, “Jurnal Ilmiah Pertanian (JIPERTA) Analisis Location Quotient (LQ) Tanaman Cabai Besar (Capsicum annuum L.) di Provinsi Sumatera Utara Location Quotient (LQ) Analysis of Large Chili Plant (Capsicum annuum L.) in North Sumatera,” J. Ilm. Pertan., vol. 2, no. 2, pp. 169–179, 2019.
[3] S. S. R. Zamili Nista, Harahap Gustami, “Faktor-Faktor yang Mempengaruhi Permintaan dan Penawaran Cabe Merah di Pasar Raya MMTC Medan,” J. Ilm. Pertan., vol. 2, no. 1, pp. 77–86, 2020.
[4] Z. Sirajuddin and P. Liskawati Kamba, “Farmer’s Perception on Information and Communication Technology Implementation in Agricultural Extension,” J. Penyul., vol. 17, no. 2, pp. 136–144, 2021.
[5] V. M. M. Siregar and N. F. Siagian, “Implementation of Fingerprint Sensors for Fingerprint Reader Prototypes Using a Microcontroller,” IOTA, vol. 02, no. 1, pp. 47–59, 2022, doi: 10.31763/iota.v2i1.559.
[6] V. M. M. Siregar, K. Sinaga, and M. A. Hanafiah, “Prototype of Water Turbidity Measurement With Fuzzy Method using Microcontroller,” IOTA, vol. 2, no. 2, pp. 76–97, 2022, doi: 10.31763/iota.v2i2.593.
[7] I. M. Siregar, M. Yunus, and V. M. M. Siregar, “Prototype of Garbage Picker Ship Robot Using Arduino Nano Microcontroller,” IOTA, vol. 2, no. 3, pp. 150–168, 2022, doi: 10.31763/iota.v2i3.540.
[8] I. M. Siregar, N. F. Siagian, and V. M. M. Siregar, “Design of an Electric Light Control Device Using Arduino Uno Microcontroller-Based Short Message Service,” IOTA, vol. 02, no. 2, pp. 98–110, 2022, doi: 10.31763/iota.v2i2.560.
[9] H. Sugara, V. M. M. Siregar, K. Sinaga, M. A. Hanafiah, and H. D. Pardede, “SAW and Electre Methods Implementation for Scholarship Awardee Decision,” Iota, vol. 01, no. 4, pp. 208–219, 2021, doi: 10.31763/iota.v1i4.496.
[10] E. Damanik and I. M. Siregar, “PENGEMBANGAN SISTEM CUSTOMER RELATIONSHIP MANAGEMENT BERBASIS WEB PADA PT. TERUS MEGA TARA JAKARTA,” J. Tek. Inf. dan Komput., vol. 4, no. 1, pp. 60–69, 2021, doi: 10.37600/tekinkom.v4i1.278.
[11] V. M. M. Siregar et al., “Decision support system for selection of food aid recipients using SAW method,” 2022, p. 030019. doi: 10.1063/5.0094385.
[12] A. S. Manalu, I. M. Siregar, N. J. Panjaitan, and H. Sugara, “RANCANG BANGUN INFRASTRUKTUR CLOUD COMPUTING DENGAN OPENSTACK PADA JARINGAN LOKAL MENGGUNAKAN VIRTUALBOX,” J. Tek. Inf. dan Komput., vol. 4, no. 2, p. 303, Dec. 2021, doi: 10.37600/tekinkom.v4i2.335.
[13] P. Dani, P. Adi, N. E. Mustamu, V. Marudut, M. Siregar, and V. Sihombing, “Drone simulation for agriculture and LoRa based approach,” IOTA, vol. 01, no. 4, pp. 221–235, 2021, doi: 10.31763/iota.v1i4.501.
[14] 2020 Savira, et al, “eduFarm : Aplikasi Petani Milenial untuk Meningkatkan Produktivitas di Bidang Pertanian,” Automata, vol. 1, no. 2, pp. 28–38, 2020.
[15] N. Riskya and S. Yuliana, “Penerapan Data Mining Untuk Prediksi Perilaku Pelanggan Menggunakan Multiple Linear Regression,” J. Inform. dan Tek. Elektro Terap., vol. 11, no. 3, 2023, doi: 10.23960/jitet.v11i3.3194.
[16] R. I. Wati, S. Subejo, and Y. F. Maulida, “Problematika, Pola, Dan Strategi Petani Dalam Mempersiapkan Regenerasi Di Daerah Istimewa Yogyakarta,” J. Ketahanan Nas., vol. 27, no. 2, p. 187, 2021, doi: 10.22146/jkn.65568.
[17] Y. Aryani, “Sistem Informasi Penjualan Barang Dengan Metode Regresi Linear Berganda Dalam Prediksi Pendapatan Perusahaan,” J. Ris. Sist. Inf. dan Teknol. Inf., vol. 2, no. 2, pp. 39–51, 2020, doi: 10.52005/jursistekni.v2i2.47.
[18] “Implementasi Machine Learning untuk Prediksi Harga Mobil Bekas dengan Algoritma Regresi Linear berbasis Web,” J. Ilm. Komputasi, vol. 21, no. 4, Dec. 2022, doi: 10.32409/jikstik.21.4.3327.
[19] I. B. M. Swarbawa, I. G. Arta Wibawa, and I. K. Gede Suhartana, “Prediksi Hasil Panen Padi Di Kabupaten Jembrana Dengan Metode Linear Regression,” JELIKU (Jurnal Elektron. Ilmu Komput. Udayana), vol. 11, no. 3, p. 671, 2023, doi: 10.24843/jlk.2023.v11.i03.p24.
[20] G.- MARDIATMOKO, “Pentingnya Uji Asumsi Klasik Pada Analisis Regresi Linier Berganda,” BAREKENG J. Ilmu Mat. dan Terap., vol. 14, no. 3, pp. 333–342, 2020, doi: 10.30598/barekengvol14iss3pp333-342.
[21] M. Abdul muthalib, I. Irfan, K. Kartika, and S. M. Selamat Meliala, “Pengiraan Pose Model Manusia Pada Repetisi Kebugaran Ai Pemograman Python Berbasis Komputerisasi,” INFOTECH J., vol. 9, no. 1, pp. 11–19, 2023, doi: 10.31949/infotech.v9i1.4233.
[22] I. Muhammad, Y. A. Lesnussa, H. W. M. Patty, M. S. Noya Van Delsen, and M. Y. Matdoan, “Peramalan Jumlah Mahasiswa Baru Menggunakan Metode Double Exponential Smoothing (Studi Kasus : Mahasiswa Baru Universitas Pattimura Ambon Tahun 2017),” Var. J. Stat. Its Appl., vol. 2, no. 1, pp. 27–33, 2020, doi: 10.30598/variancevol2iss1page27-33.
[23] H. T. Irawan, I. Pamungkas, Fitriadi, K. Hadi, and T. M. A. Pandria, “Penggunaan Moving Average dan Exponential Smoothing untuk Memprediksi Kebutuhan Beras Raskin di Kota Banda Aceh,” J. Optim., vol. 6, no. 2, pp. 2–9, 2020.
Published
2024-12-31
How to Cite
SAHPUTRA, Sahputra et al.
ANALISIS PREDIKSI HASIL PRODUKSI TANAMAN CABAI MENGGUNAKAN METODE MULTI LINIER REGRESI.
Jurnal Teknik Informasi dan Komputer (Tekinkom), [S.l.], v. 7, n. 2, p. 619-627, dec. 2024.
ISSN 2621-3079.
Available at: <https://jurnal.murnisadar.ac.id/index.php/Tekinkom/article/view/1512>. Date accessed: 15 may 2025.
doi: https://doi.org/10.37600/tekinkom.v7i2.1512.
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