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All Journal Tekinfo | Scientific Journal of Industrial and Information Engineering
Dwi Adi Purnama
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Computer Science & IT Industrial & Manufacturing Engineering

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Integration of Data Driven Approach Using AHP, Clustering, AR-MBA, and Classification to Improve Performance of MSME Restaurant Aji Adinata Firjatullah; Reyhandi Rachmad Fadila Wicaksono; Dwi Adi Purnama
Tekinfo: Jurnal Ilmiah Teknik Industri dan Informasi Vol 14 No 2 (2026)
Publisher : Program Studi Teknik Industri Universitas Setia Budi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31001/tekinfo.v14i2.2628

Abstract

The restaurant industry in Indonesia has experienced rapid growth alongside technological advancements and digitalization. Innovations in service, marketing, and operations have become key factors in improving business performance amid intense competition. SMEs such as Warung Makan Mantap Jaya in Yogyakarta need to implement the right strategies to adapt, enhance customer satisfaction, and strengthen their competitiveness. This study aims to formulate data-driven performance improvement strategies by integrating the Analytical Hierarchy Process (AHP), Clustering, Association Rule–Market Basket Analysis (AR-MBA), and Classification methods using data from 150 respondents. The results indicate that, based on the AHP method, renovating the premises is the top priority with the highest weight of 0.389, followed by discount innovations (0.365) and menu promotions (0.235). Clustering analysis identified three customer segments with distinct characteristics, where the majority of customers fall into the dissatisfied group, characterized by low visit frequency, low spending, and dissatisfaction with service. Meanwhile, some customers exhibit high satisfaction levels with higher visit frequency and spending. AR-MBA analysis identified purchasing patterns that generated menu bundling recommendations, such as combinations of Iced Tea, Chicken with Water Spinach, and Bakwan; Fried Egg and Mineral Water; and Soy Sauce Chicken and Tea Juice. Additionally, a classification method was used to predict customer satisfaction levels based on the resulting data patterns
Pemodelan Peramalan Permintaan Produk Baru Sebelum Peluncuran menggunakan Difusi Bass di Industri Kreatif Dwi Adi Purnama
Tekinfo: Jurnal Ilmiah Teknik Industri dan Informasi Vol 13 No 2 (2025)
Publisher : Program Studi Teknik Industri Universitas Setia Budi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31001/tekinfo.v13i2.2604

Abstract

Micro, Small, and Medium Industries (IMKM) necessitate significant focus due to their economic contributions. In Indonesia, the creative industry, exhibits diverse and complex product innovations and variations, alongside a heightened risk of market failure. A market forecasting prediction model is essential prior to the product launch. However, this task becomes challenging when sales data is insufficient. A forecasting model applicable to the pre-launch stage of a new product is the Bass diffusion model, which relies on the optimization of model parameters. This study seeks to model the forecasting of new product demand prior to launch using the Bass diffusion model, specifically for creative industry products, and to assess the adoption pattern of these products within IMKM. The study identified a bass diffusion model applicable to the creative industry through an analysis of fifteen batik products, determining the optimal p, q, and m parameters based on the minimal error value. This study evaluates the adoption characteristics of IMKM batik products, noting similarities to the sales patterns of computer products and a product life cycle ranging from twelve to thirty-six months. This study identifies the parameter values for creative industry products, with an average p parameter of 0.0336, a q parameter of 0.3770, and a m parameter of 497.27. The long dress, as a category of women's fashion, exhibits the longest product life cycle, significant market potential, and a rapid diffusion rate, characterized by an average p parameter of 0.0198, a q parameter of 0.3739, and a m parameter of 721.
Co-Authors Aji Adinata Firjatullah Reyhandi Rachmad Fadila Wicaksono