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All Journal JRSI (Jurnal Rekayasa Sistem dan Industri) Jurnal INTECH Teknik Industri Universitas Serang Raya Airlangga Journal of Innovation Management Texere Jurnal Riset dan Inovasi Pembelajaran IMTechno: Journal of Industrial Management and Technology Jurnal Inkofar Jurnal Sains dan Informatika : Research of Science and Informatic Multidisciplinary Indonesian Center Journal Jurnal Sains dan Teknologi
Fadil Abdullah
Politeknik META Industri Cikarang
Religion Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Analytical Data for Sewing Production Efficiency: A Model Based on Artificial Neural Networks (ANNs) Abdullah, Fadil; Kusumadewi, Afriani; Martina, Tina; Kuswinarti, -; Achmad, Fandi
Texere Vol 23, No 2 (2025): Texere Volume 23 Nomor 2 Tahun 2025
Publisher : Politeknik STTT Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53298/texere.v23i2.07

Abstract

The labor-intensive apparel manufacturing sector is continually focused on meeting output goals, necessitating continuous improvements in production efficiency. Achieving targets at the lowest feasible cost is crucial for production management efficiency, especially in clothing production. The sewing component plays a vital role in enhancing the usefulness of clothing through a series of steps to produce ready-made garments. To optimize this process, we developed a model using an artificial intelligence (AI)-based method, specifically Artificial Neural Networks (ANNs), to enhance sewing production efficiency. The model focused on optimizing parameters that significantly influenced efficiency. Our results demonstrate that the ANNs model, with 1000 iterations, successfully replicates empirical data with an R-squared value of 0.98. The research introduces the novel use of an ANNs model with a five-node configuration and 1000 iterations, proving effective in optimizing sewing process parameters. This AI-based approach is a powerful tool for improving production efficiency in the textile industry, making significant theoretical and practical contributions. The findings offer substantial practical implications for practitioners in the textile industry and provide a robust framework for optimizing sewing production process parameters to achieve higher efficiency.
Optimasi Perencanaan Produksi Menggunakan Linear Programming dan Analisis Sensitivitas Pada UMKM Coffee Suganda Majalengka Hamidah, Siti Nur; Aprilia, Hafiza; Abdullah, Fadil
IMTechno: Journal of Industrial Management and Technology Vol. 7 No. 1 (2026): Vol. 7 No. 1 (2026): Januari 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/imtechno.v7i1.11608

Abstract

Micro, Small, and Medium Enterprises (MSMEs) in the angkringan-based coffee shop sector have significant economic potential; however, many of them still face challenges in production decision-making due to limited resources and the lack of data-driven planning. This study aims to optimize the beverage production mix at Coffee Suganda, an MSME coffee shop located in Majalengka Regency, which produces 25 product variants, with three main products: palm sugar milk coffee, creamy milk coffee, and chocolate. The research applies Linear Programming (LP) to maximize profit under constraints related to raw materials, production time and is further strengthened by sensitivity analysis to examine the robustness of the optimal solution under parameter changes. Research data were collected through field observations, interviews with the business owner, and records of production and sales activities. The results indicate that the Linear Programming approach provides a more optimal production combination compared to the existing conditions and improves resource utilization efficiency. The sensitivity analysis reveals that changes in raw material availability and selling prices significantly affect the optimal solution, highlighting the importance of adaptive production planning. This study contributes practical insights for angkringan-based coffee shop MSMEs in developing data-driven and adaptive production strategies, as well as academic contributions to the literature on MSME production optimization
Co-Authors - Kuswinarti, - Afriani Kusumadewi Agus Widodo Anggara, Ikbal Aprilia, Hafiza Barus, Wan Habibi Rahman Candrawati, Nia Dayani, Camillana Calistafo FANDI ACHMAD Hamidah, Siti Nur Haryanto, Agung Ihsan, Muhammad Alif Iphov Kumala Sriwana Joko Prasetyo Kusrianto, Adi Maharani, Nadiya Muhamad Atho Rifaitulloh Muhammad Noval mulyani, wiwiek eka Noval, Muhamad Nurmalinda Rahmawati Purnomosari, Endah Putra, Valentinus Galih Vidia Putri, Safira Hutama Rahmawati, Nurmalinda Tina Martina, Tina Valentinus Galih Vidia Putra Widarini, Wari