OPTIMASI KLASIFIKASI JENIS INDUSTRI KECIL MENENGAH (IKM) MENGGUNAKAN DEEP NEURAL NETWORK BERBASI SHAP ANALYSIS
##plugins.themes.academic_pro.article.main##
Abstract
Small and Medium Industries (SMEs) in Makassar City face a significant gap between high labor absorption (66.25%) and low GDP contribution (20%), often due to conventional and experience-based determination of industry types. This study implements a Deep Neural Network (DNN) model to classify four categories of SMEs, Bread and Cake, Processed Food, Vehicle Repair, and Textiles and Clothing to facilitate data-driven decisions. Using a supervised learning approach on 31,824 data samples for the 2022-2024 period, this model was developed through feedforward and backpropagation mechanisms. The results showed superior performance with overall accuracy of (93.99%) and balanced accuracy (96.70%), which signified an increase of (15.88%) compared to the Naïve Bayes baseline model. All F1-scores above (90%) indicate strong performance stability in each class. Furthermore, SHAP's analysis revealed that textual features (87.1%) were the dominant factors, followed by the type of business entity and investment value. This study confirms that DNN is effective in modeling complex non-linear interactions, providing objective tools for classification and strategic economic planning in Makassar City.
##plugins.themes.academic_pro.article.details##
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
[2] T. Darmawati and Y. Arafat, "Model Produktivitas Tenaga Kerja dan Daya Saing Industri Mikro Kecil Serta Implementasinya Dalam Mendukung Pertumbuhan Ekonomi di Indonesia," Jurnal Ekonomi, pp. 384–395, 2022.
[3] K. N. Reihanah, D. A. I. Maruddani, and T. Widiharih, "CLUSTERING KARAKTERISTIK INDUSTRI KECIL DAN MENENGAH DI KOTA KENDARI MENGGUNAKAN ALGORITMA k-PROTOTYPES," J. Gaussian, vol. 12, no. 3, pp. 340–351, 2024, doi: 10.14710/j.gauss.12.3.340-351.
[4] N. Alfarisi and F. D. Sitania, “Sinkronisasi Data Terhadap Industri Kecil Menengah (IKM) Pada Dinas Perindutsrian, Perdagangan, Koperasi, Dan IKM Provinsi Kalimantan Timur,” J. Tek. Ind., vol. 2, no. 1, pp. 63–70, 2024, doi: 10.30872/jatri.v2i1.1234.
[5] M. Kiron, M. Islam, and E. Chowdhury, “Green Technologies and Sustainability Enhancing small and medium-sized enterprises ’ sustainability in global value chains : The impact of multinational corporations on Bangladesh ’ s garment industry,” Green Technol. Sustain., vol. 4, no. 1, p. 100265, 2026, doi: 10.1016/j.grets.2025.100265.
[6] D. Baez et al., “ScienceDirect ScienceDirect Enterprises : Case of the Bakery Industry in Colombia Analysis of Strategic Marketing in Small and Medium-sized of the Bakery Industry in Colombia Harold Enterprises : Analysis of Strategic Marketing in Small and Medium-sized,” Procedia Comput. Sci., vol. 231, pp. 601–606, 2024, doi: 10.1016/j.procs.2023.12.178.
[7] Yusniati, W. Aya Rumbia, and L. Pipi, “Pengaruh Karakteristik Industri Kecil Menengah Terhadap Keuntungan Usaha di Kabupaten Konawe,” J. Prog. Ekon. Pembang., vol. 9, no. 2, pp. 128–138, 2024, doi: 10.33772/jpep.v9i2.790.
[8] M. Naved, M. Asim, and T. Ahmad, “Prediction of concrete compressive strength using deep neural networks based on hyperparameter optimization,” Cogent Eng., vol. 11, no. 1, p., 2024, doi: 10.1080/23311916.2023.2297491.
[9] M. Chintya Adelina and Z. Situmorang, “Machine Learning for Classification of Ikm Programs At the Department of Industry and Trade of Langkat Regency,” vol. 02, no. 01, pp. 1649–1656, 2024.
[10] G. Gunawan, A. Aimar Akbar, and W. Andriani, “Application of deep neural network with stacked denoising autoencoder for ECG signal classification,” J. Intell. Decis. Support Syst., vol. 7, no. 2, pp. 173–187, 2024, doi: 10.35335/idss.v7i2.247.
[11] L. Zancato and A. Chiuso, “A novel deep neural network architecture for non-linear system identification,” IFAC-PapersOnLine, vol. 54, no. 7, pp. 186–191, 2021, doi: 10.1016/j.ifacol.2021.08.356.
[12] D. Arya Pamungkas, I. Lucia Kharisma, D. Sartika Simatupang, and kamdan, “Implementasi Deep Neural Network pada Perancangan Aplikasi Deteksi Token Scam Blockchain Ethereum,” Sist. J. Sist. Inf., vol. 12, no. 3, pp. 927–937, 2023, [Online]. Available: http://sistemasi.ftik.unisi.ac.id
[13] Z. C. Dwinnie, L. Khairani, M. A. M. Putri, J. Adhiva, and M. I. F. Tsamarah, “Application of the Supervised Learning Algorithm for Classification of Pregnancy Risk Levels,” Public Res. J. Eng. Data Technol. Comput. Sci., vol. 1, no. 1, pp. 26–33, 2023, doi: 10.57152/predatecs.v1i1.806.