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All Journal Jurnal Informatika Progres
M. Fikri Haikal Ayatullah
Informatika, Universitas Muhammadiyah Makassar
Computer Science & IT

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OPTIMASI KLASIFIKASI JENIS INDUSTRI KECIL MENENGAH (IKM) MENGGUNAKAN DEEP NEURAL NETWORK BERBASI SHAP ANALYSIS M. Fikri Haikal Ayatullah; Desi Anggreani; Rizki Yusliana Bakti; Muhammad Faisal; Muhammad Syafaat; M. Agusalim
PROGRESS Vol 18 No 2 (2026): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i2.463

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.
Co-Authors Bakti, Rizki Yusliana Desi Anggreani M. Agusalim Muhammad Faisal Muhammad Syafaat S. Kuba