Feasibility Strategy on Giving Capital for Salt Farmers in Increasing Economic Productivity Using KNN Classification Model
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The strategy of increasing the salt business in the development of the marine (coastal) economy is very significant in boosting the productivity of the salt. The business of salt that located on Coastal has very good prospects in the future because it is supported by a lot of potential for vacant land, climate, human resources, market opportunities and abundant raw materials for the manufacture of people's salt. Socio-economic potential is the basic capital in the development of salt production in North Aceh Regency, especially Aceh Province. The current issue for most salt farmers is that the quality of salt is still low which is caused by the existing access to capital by farmers, other problems, lack of capital is one of the causes of the decline in salt productivity so that the salt produced is not in accordance with market demand.Salt farmers experienced the impact of a decrease in the productivity of business capital, which became an obstacle for the investor of capital. This can be resolved by the financier. Then the bank can provide funds to salt farmers according to their needs. Therefore, the importance of research in implementing a banking strategy in increasing the productivity of salt farmers in accordance with the business of the farmer group. It is very important to analyze the productivity of salt farmers after being given capital to assess the success of the capital provided to salt farmers. This research method begins with a survey of strategic information in providing business capital for salt farmers which is useful in increasing the productivity of salt farmers. The results of the analysis are entered into the K-Nearest Neighbor classification model in the feasibility of providing capital using a classification in order to see groups of productive salt farmers after being given capital. The results of the test with the k-means clustering model are in the form of group 2 classification values with a distance value of 0.210 based on the criteria included in the strategy for providing business capital.
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[2] N. Komaryatin, “Pengembangan faktor Produksi untuk meningkatkan pendapatan petani garam,” Pengemb. Fakt. Produksi untuk Meningkat. pendapatan petani garam, vol. Vol. 26, 2012.
[3] A. Antonites, “The organization of salt production in early first millennium CE South Africa,” ournal Anthropol. Archaeol., pp. 31–42, 2016.
[4] S. Sukesi, “Analisis Perilaku Masyarakat Petambak Garam Terhadap Hasil Usaha di Kota Pasuruan,” J. Mitra Ekon. dan Manaj. Bisnis, vol. 2, no. 2, pp. 225–244, 2011.
[5] N. ikah, R., & Kusumaningsih, “Faktor-Faktor Yang Mempengaruhi Tingkat Pendapatan Petani Garam Kawasan Pesisir Kabupaten Rembang,” J. Ilm. Ekon., vol. 13(2), pp. 316–330, 2018.
[6] A. M. Prabawanti, “Analisis Sistem dan Prosedur Akuntansi Pemberian Kredit Modal Kerja Dalam Upaya Peningkatan Pengendalian Intern (Studi Pada BPR PermataArtha Surya Surabaya),” J. Adm. Bisnis, vol. 17–2, 2014.
[7] & H. Iski, N., Kusnadi, N., “Pengaruh Kredit terhadap Pendapatan Petani Kopi Arabika di Kabupaten Aceh Tengah Provinsi Aceh,” J. Manaj. Agribisnis, vol. 13(2), pp. 132–132, 2016.
[8] D. G. Sari, Y. R., Setyono, B., & Manar, “mplementasi Program Pemberdayaan USAha Garam Rakyat (Pugar) Di Desa Kedungmutih Kecamatanwedung Kabupaten Demak Tahun 2011-2013,” J. Polit. Gov. Stud., vol. 4(2), pp. 191–205, 2015.
[9] G. Tayebi, Z., & Onel, “Microcredit programs, poverty and vulnerability in rural Iran,” 2016, pp. 1376-2016–109834.
[10] and A. C. N. Nwaru, J. C., C. E. Onyenweaku, “Relative technical efficiency of credit and non-credit user crop farmers,” African Crop Sci. J., vol. 14, no. 3, pp. 241–251, 2006.
[11] E. Fecke, W., & Al., “Determinants of Loan Demand in Agriculture?: Empirical Evidence from Germany,” Agric. Financ. Rev., 2016, doi: https://doi.org/10.1108/AFR-05-2016-0042.
[12] A. Eswanto, E., Andini, R., & Oemar, “Pengaruh Tingkat Suku Bunga Pinjaman, Non Performing Loan, Dana Pihak Ketiga, Inflasi Dan Produk Domestik Regional Bruto Terhadap Permintaan Kredit Bank Umum di Jawa Tengah Periode 2009-2013,” J. Account., vol. 2(2), 2016.
[13] R. Rizieq, “Analisis Dampak Kredit Pertanian pada Pertumbuhan Sektor Pertanian dan Ekonomi,” J. Repos. FP UPB, vol. 9(2), 2010.
[14] M. P. Todaro, Pembangunan Ekonomi di Dunia Ketiga. Jilid 1, 1st ed. jakarta: Erlangga, 2003.
[15] I. A. L. H. dan Bauer, “Access to Micro credit and its Impact on Farm Profit Among Rural Farmers in Dryland of Sudan,” Glob. Adv. Res. J. Agric. Sci., vol. 2 (3), pp. 88–102, 2013.
[16] J. Islam, K. Z., Sipiläinen, T., & Sumelius, “Access to microfinance: does it matter for profit efficiency among small scale rice farmers in Bangladesh?,” Middle East J. Sci. Res., vol. 9(3), pp. 311–323, 2011.
[17] R. G. Adebayo, O. O., & Adeola, “ources and uses of agricultural credit by small scale farmers in Surulere Local Government Area of Oyo State,” Anthropol., vol. 10(4), pp. 313–314, 2008.
[18] M. Saboor, A., Hussain, M., & Munir, “Impact of micro credit in alleviating poverty: An Insight from rural Rawalpindi, Pakistan.,” j. life soc. sci, vol. 7(1), pp. 90–97, 2009.
[19] M. Elfiana, E., & Mukhlis, “Klasifikasi Kelayakan Pemberian Modal Usaha Kelompok Petani Garam Menggunakan K Nearest Neighbors dalam Rangka Pembedayaan Ekonomi Masyarakat di Kab Bireun,” J. Sains Pertan., no. 1(1), p. 210901.
[20] N. Santoso, P. H., Fauziah, F., & Nurhayati, “Application of Data Mining Classification for Covid-19 Infected Status Using Algortima Naïve Method: Application of Data Mining Classification for Covid-19 Infected Status Using Algortima Naïve Method,” J. Mantik, vol. 4(1), pp. 267–275, 2020.
[21] R. Ula, M., Pratama, A., Asbar, Y., Fuadi, W., Fajri, R., & Hardi, “A New Model of The Student Attendance Monitoring System Using RFID Technology,” J. Phys. Conf. Ser. (Vol. 1807, No. 1, p. 012026). IOP Publ., vol. 1807, 2021.
[22] I. D. Yulianto, L. D., Triayudi, A., & Sholihati, “Implementation Educational Data Mining For Analysis of Student Performance Prediction with Comparison of K-Nearest Neighbor Data Mining Method and Decision Tree C4,” J. Mantik, vol. 4(1), pp. 441–451, 2020.
[23] D. T. Larose, “Data mining methods & models,” 2006.
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