Penerapan Algoritma K-Nearest Neighbor untuk Menentukan Potensi Ekspor Komoditas Pertanian di Provinsi Sulawesi Tengah

Hajra Rasmita Ngemba; I Made Randhy Raivandy; Syaiful Hendra; Rizka Ardiansyah; Kadek Agus Dwi Wijaya; Deny Wiria Nugraha; Mohamad Irfan

doi:10.26905/jtmi.v9i2.10235

Penerapan Algoritma K-Nearest Neighbor untuk Menentukan Potensi Ekspor Komoditas Pertanian di Provinsi Sulawesi Tengah

Hajra Rasmita Ngemba, I Made Randhy Raivandy, Syaiful Hendra, Rizka Ardiansyah, Kadek Agus Dwi Wijaya, Deny Wiria Nugraha, Mohamad Irfan

Abstract

Agriculture is a highly robust sector in Indonesia. This is evidenced in Central Sulawesi Province, where the gross domestic product (GDP) from the agricultural sector, based on constant prices from 2018 to 2021, continues to experience growth. Such conditions suggest that commodities in the agricultural sector have the potential to become export products, enabling a greater economic boost for the region. Before engaging in exports, it is necessary to identify which commodities have potential. One way to determine this is by applying Klassentypology. To simplify the process, it can be implemented in machine learning using the K-Nearest Neighbor algorithm. K-Nearest Neighbor is chosen because this algorithm can handle data containing noise and has good adaptability when given new data. In this research, two machine learning models were developed. The first model is used to classify whether a commodity is advancing or lagging, while the second model is used to classify commodities that grow rapidly and slowly. The highest accuracy obtained from the first model is 96.23%. Meanwhile, the highest accuracy from the second model is 93.49%.

Keywords

K-Nearest Neighbor; Agriculture; Klassen Typology; Confusion Matrix;

Full Text:

PDF

References

S. Tumangkeng, “Analisis Potensi Ekonomi Di Sektor Dan Sub Sektor Pertanian, Kehutanan Dan Perikanan Kota Tomohon,” J. Berk. Ilm. Efisiensi, vol. 18, no. 1, p. 12, 2018, [Online]. Available: https://ejournal.unsrat.ac.id/index.php/jbie/article/view/20678.

BPS, “Provinsi Sulawesi Tengah Dalam Angka 2022,” Badan Pus. Stat. Sulawesi Teng., 2022, [Online]. Available: https://sulteng.bps.go.id/publication/2022/02/25/d8cccf7c0b42c3d9ff80b8c6/provinsi- sulawesi-tengah-dalam-angka-2022.html.

A. Rajab and Rusli, “Penentuan Sektor-Sektor Unggulan yang ada pada Kabupaten Takalar melalui Analisis Tipologi Klassen,” GROWTH J. Ilm. Ekon. Pembang., vol. 1, no. 1, pp. 16–38, 2019, [Online]. Available: https://stiemmamuju.e-journal.id/GJIEP/article/view/13.

S. I. Dai, “DEVELOPMENT OF SUPERIOR COMMODITIES IN THE

AGRICULTURAL SECTOR IN AN EFFORT TO IMPROVE THE ECONOMY

(Pengembangan Komoditas Unggulan Sektor Pertanian Dalam Upaya Peningkatan Perekonomian),” Gorontalo Dev. Rev., vol. 2, no. 1, p. 44, 2019, doi:10.32662/golder.v2i1.466.

S. Rahayu, “Penentuan Agribisnis Unggulan Komoditi Pertanian Berdasarkan Nilai Produksi di Kabupaten Kerinci,” J-MAS (Jurnal Manaj. dan Sains), vol. 6, no. 1, p.154, 2021, doi: 10.33087/jmas.v6i1.242.

R. K. Dinata, H. Akbar, and N. Hasdyna, “Algoritma K-Nearest Neighbor dengan Euclidean Distance dan Manhattan Distance untuk Klasifikasi Transportasi Bus,” Ilk. J. Ilm., vol. 12, no. 2, pp. 104–111, 2020, doi: 10.33096/ilkom.v12i2.539.104-111.

D. S. H. Putri, F. R. Hernovianty, and E. Yuniarti, “Analisis Komoditas Unggulan Berbasis Pertanian di Kecamatan Sekadau Hilir, Kabupaten Sekadau,” JeLAST J. Elektron. Laut, Sipil, Tambang, vol. 7, no. 2, pp. 1–6, 2020.

N. Ferdian, Z. Hasid, and E. U. A. Ghaffar, “ANALISIS PERENCANAAN

PEMBANGUNAN EKONOMI KABUPATEN KUTAI KARTANEGARA,” J. Ilm.

Akunt. DAN Keuang., vol. 4, no. 2, pp. 873–894, 2021.

D. Kurniawan and A. Saputra, “Penerapan K-Nearest Neighbour dalam Penerimaan Peserta Didik dengan Sistem Zonasi,” J. Sist. Inf. Bisnis, vol. 9, no. 2, p. 212, 2019, Jurnal Teknologi dan Manajemen Informatika (2023) doi: 10.21456/vol9iss2pp212-219.

R. Wahyudi, M. Orisa, and N. Vendyansyah, “Penerapan Algoritma K-Nearest Neighbors Pada Klasifikasi Penentuan Gizi Balita (Studi Kasus Di Posyandu Desa Bluto),” JATI (Jurnal Mhs. Tek. Inform., vol. 5, no. 2, pp. 750–757, 2021, doi: 10.36040/jati.v5i2.3738.

Y. Yuliska and K. U. Syaliman, “Peningkatan Akurasi K-Nearest Neighbor Pada Data Index Standar Pencemaran Udara Kota Pekanbaru,” IT J. Res. Dev., vol. 5, no. 1, pp. 11–18, 2020, doi: 10.25299/itjrd.2020.vol5(1).4680.

A. D. W. M. Sidik, I. Himawan Kusumah, A. Suryana, Edwinanto, M. Artiyasa, and A. Pradiftha Junfithrana, “Gambaran Umum Metode Klasifikasi Data Mining,” Fidel. J. Tek. Elektro, vol. 2, no. 2, pp. 34–38, 2020, doi: 10.52005/fidelity.v2i2.111.

E. Tangkelobo, W. Mayaut, H. Listanto, I. Binanto, and N. F. Sianipar, “Perbandingan Algoritma Klasifikasi Random Forest , Gaussian Naive Bayes , dan K-Nearest untuk Data Tidak Seimbang dan Data yang diseimbangkan dengan metode Random Undersampling pada dataset LCMS Tanaman Keladi Tikus,” 2023.

D. M. Meliala and P. Hasugian, “Perbandingan Algoritma K-Nearest Neighbor Dengan Decision Tree Dalam Memprediksi Penjualan Makanan Hewan Peliharaan Di Petshop Dore Vet Clinic,” Respati, vol. 15, no. 3, p. 35, 2020, doi: 10.35842/jtir.v15i3.369.

S. Sahar, “Analisis Perbandingan Metode K-Nearest Neighbor dan Naïve Bayes Clasiffier Pada Dataset Penyakit Jantung,” Indones. J. Data Sci., vol. 1, no. 3, pp. 79–86, 2020, doi: 10.33096/ijodas.v1i3.20.

J. A. Saputra and S. A. Aklani, “Analisis Komparasi Algoritma K-Nearest Neighbor Dan Support Vector Machine Dengan Pendekatan Multi Dataset,” vol. 13, no. 03, pp. 415–421, 2022.

S. T. Rizaldi and M. Mustakim, “Perbandingan Teknik Pembagian Data untuk Klasifikasi Sarana Akses Air pada Algoritma K- Nearest Neighbor dan Naïve Bayes Classifier,” Semin. Nas. Teknol. Informasi, Komun. dan Ind. 12, pp. 130–137, 2020.

H. Rasmita Ngemba, S. Hendra, K. Agus Dwijaya, H. Ladania, and M. Aristo Indrajaya, “Comparative Analysis of C4.5 And Naïve Bayes Algorithms for Classification of Food Vulnerable Areas,” Tadulako Sci. Technol. J., vol. 3, no. 1, pp. 2776–4893, 2022.

D. Normawati and S. A. Prayogi, “Implementasi Naïve Bayes Classifier Dan Confusion Matrix Pada Analisis Sentimen Berbasis Teks Pada Twitter,” J. Sains Komput. Inform. (J-SAKTI, vol. 5, no. 2, pp. 697–711, 2021, [Online]. Available: http://ejurnal.tunasbangsa.ac.id/index.php/jsakti/article/view/369.

H. K. Karthikeya, K. Sudarshan, and D. S. Shetty, “Prediction of Agricultural Crops using KNN Algorithm,” Int. J. Innov. Sci. Res. Technol., vol. 5, no. 5, pp. 1422–1424,2020, [Online]. Available: http://agricoop.nic.in/sites/default/files/Annual_rpt_2016.

A. Bode, “K-Nearest Neighbor Dengan Feature Selection Menggunakan Backward Elimination Untuk Prediksi Harga Komoditi Kopi Arabika,” Ilk. J. Ilm., vol. 9, no. 2, pp. 188–195, 2017, doi: 10.33096/ilkom.v9i2.139.188-195.

W. T. Panjaitan, E. Utami, and H. Al Fatta, “PREDIKSI PANEN PADI

MENGGUNAKAN ALGORITMA K-NEAREST NEIGBOUR,” Pros. SNATIF, pp.

–628, 2018.Jurnal Teknologi dan Manajemen Informatika (2023)

L. P. N. Budiarti, N. Hidayat, and T. Afirianto, “Implementasi Algoritme Modified K-Nearest Neighbor (MKNN) untuk Diagnosis Penyakit Tanaman Cengkeh,” J.Pengemb. Teknol. Inf. dan Ilmu Komput. Univ. Brawijaya, vol. 2, no. 12, pp.7149–7156, 2018.

DOI: https://doi.org/10.26905/jtmi.v9i2.10235