1. Home
  2. Vol 2, No 1 (2024)
  3. Adelina

MACHINE LEARNING FOR CLASSIFICATION OF IKM PROGRAMS AT THE DEPARTMENT OF INDUSTRY AND TRADE OF LANGKAT REGENCY

Mimi Chintya Adelina(1*), Wanayumini Wanayumini(2), Zakarias Situmorang(3)
(1) Universitas Potensi Utama, North Sumatra, Indonesia
(2) Universitas Potensi Utama, North Sumatra, Indonesia
(3) Universitas Potensi Utama, North Sumatra, Indonesia
(*) Corresponding Author

Abstract

This research attempts to address these challenges by constructing a classification model using the Naive Bayes algorithm. Naive Bayes is a statistical algorithm that can be employed to predict the probability of membership in a class based on the available data. This method can assist the Department of Industry and Trade of Langkat Regency in selecting targeted programs and identifying SMEs (Small and Medium Enterprises) with potential success. The research will involve the collection and analysis of data regarding SMEs in Langkat Regency, including information about the industry type, geographic location, and business formality status. This data will be utilized to train the Naive Bayes classification model to predict the potential success of programs offered by the Department of Trade and Industry. Consequently, it is anticipated that this model can aid in more effective and efficient decision-making in the management of SME programs

References


Agarwal, S., Jha, B., Kumar, T., Kumar, M., & Ranjan, P. (2019). Hybrid of Naive Bayes and Gaussian Naive Bayes for Classification: A Map Reduce Approach. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(6), 266–268.

Barus, S. P. (2021). Implementation of Naïve Bayes Classifier-based Machine Learning to Predict and Classify New Students at Matana University. Journal of Physics: Conference Series, 1842(1). https://doi.org/10.1088/1742-6596/1842/1/012008

Fitrianah, D., Dwiasnati, S., H, H. H., Baihaqi, K. A., Komputer, I., Informatika, T., & Buana, U. M. (2021). Penerapan Metode Machine Learning untuk Prediksi Nasabah Potensial menggunakan Algoritma Klasifikasi Naïve Bayes. 14(2), 92–99.

Hayami, R., Soni, & Gunawan, I. (2022). Klasifikasi Jamur Menggunakan Algoritma Naïve Bayes. Jurnal CoSciTech (Computer Science and Information Technology), 3(1), 28–33. https://doi.org/10.37859/coscitech.v3i1.3685

Huriah, D. A., Nuris, N. D., Usaha, B., Mining, D., Naive, A., & Dalam, B. (2023). Klasifikasi penerima bantuan sosial umkm menggunakan algoritma naïve bayes. Jurnal Mahasiswa Teknik Informatika, 7(1), 360–365.

Ismail, M., Hassan, N., & Saleh Bafjaish, S. (2020). Journal of Soft Computing and Data Mining Comparative Analysis of Naive Bayesian Techniques in Health-Related for Classification Task. Journal of Soft Computing and Data Mining, 1(2), 1–10.http://penerbit.uthm.edu.my/ojs/index.php/jscdm

Osisanwo, F. Y., Akinsola, J. E. T., Awodele, O., Hinmikaiye, J. O., Olakanmi, O., & Akinjobi, J. (2017). Supervised Machine Learning Algorithms : Classification and Comparison. 48(3), 128–138.

Pratama, R., & Izman Herdiansyah, M. (2023). Prediksi Customer Retention Perusahaan Asuransi Menggunakan Machine Learning. Sistem Informasi Dan Komputer), 12, 96–104.

Ratnasari, A., & Kirwani, D. H. (2015). Peranan Industri Kecil Menengah (Ikm) Dalam Penyerapan Tenaga Kerja Di Kabupaten Ponorogo. Jurnal Pendidikan Ekonomi, 1(3), 11–17.https://ejournal.unesa.ac.id/index.php/jupe/article/view/3625

Roihan, A., Sunarya, P. A., & Rafika, A. S. (2020). Pemanfaatan Machine Learning dalam Berbagai Bidang: Review paper. IJCIT (Indonesian Journal on Computer and Information Technology), 5(1), 75–82. https://doi.org/10.31294/ijcit.v5i1.7951

Roihan, A., Sunarya, P. A., & Rafika, A. S. (2020). Pemanfaatan Machine Learning dalam Berbagai Bidang: Review paper. IJCIT (Indonesian Journal on Computer and Information Technology), 5(1), 75–82. https://doi.org/10.31294/ijcit.v5i1.7951

Sumpena, J., & Kurnia H., N. (2019). Analisis Prediksi Kelulusan Siswa PKBM Paket C Dengan Metoda Algoritma Naive Bayes. Tedc, 13(2), 127–133. http://ejournal.poltektedc.ac.id/index.php/tedc/article/view/13


Article Statistic

Abstract view : 0 times
PDF views : 0 times

Dimensions Metrics

The PDF file you selected should load here if your Web browser has a PDF reader plug-in installed (for example, a recent version of Adobe Acrobat Reader).

If you would like more information about how to print, save, and work with PDFs, Highwire Press provides a helpful Frequently Asked Questions about PDFs.

Alternatively, you can download the PDF file directly to your computer, from where it can be opened using a PDF reader. To download the PDF, click the Download link above.

Fullscreen Fullscreen Off

Full Text: PDF

How To Cite This :

Refbacks

  • There are currently no refbacks.