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

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Dynamic Programming-Based Distribution Route Optimization with WinQSB and Field Validation in a Footwear SME Nadiya Maharani; Fadil Abdullah; Wan Habibi Rahman Barus; Muhammad Alif Ihsan
INKOFAR Vol. 10 No. 1 (2026)
Publisher : Politeknik META Industri Cikarang

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Abstract

Background Product distribution is a logistics activity that affects the operational efficiency of MSMEs, particularly in the footwear industry in the Cibaduyut area of Bandung. The main problem identified is the lack of standardized distribution routes, resulting in deliveries still being based on drivers’ intuition. This situation leads to inconsistent routes, fuel waste, and inefficient delivery times Purpose This study aims to optimize distribution routes using the Dynamic Programming method, validated with WinQSB, and compared against the Traveling Salesman Problem (TSP) and Dijkstra’s Algorithm. Methodology The methodology employed is an applied quantitative approach using data on distances between distribution points, travel time, fuel consumption, and GPS tracking. Findings The optimization results show a reduction in distribution distance from 35.2 km to 26.8 km and in delivery time from 82 minutes to 61 minutes. Efficiency improvements were 23.86% for distance, 25.61% for time, and 24.26% for fuel costs. Validation using WinQSB and GPS data confirmed that the optimization results align with field conditions. Implications The proposed route optimization provides a practical decision-support tool for MSMEs to improve distribution efficiency, reduce transportation costs, and enhance delivery performance. The findings can assist logistics managers in developing standardized distribution routes and serve as a reference for implementing data-driven distribution planning in small and medium-sized manufacturing enterprises. Originality This study contributes by integrating Dynamic Programming with WinQSB validation and GPS-based field verification to optimize distribution routes for footwear MSMEs. Unlike previous studies that primarily relied on theoretical optimization or simulation, this research demonstrates the practical applicability of the proposed approach in a real distribution network while benchmarking its performance against the Traveling Salesman Problem (TSP) and Dijkstra’s Algorithm.
QUALITY CONTROL ANALYSIS FOR REDUCING DEFECTS IN RAYON NE 30 YARN USING SIX SIGMA DMAIC Afriani Kusumadewi; Susi Maulinawati; Fadil Abdullah
INKOFAR Vol. 10 No. 1 (2026)
Publisher : Politeknik META Industri Cikarang

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Background The textile industry is required to consistently produce high-quality products to maintain competitiveness in an increasingly competitive global market. One of the major quality challenges in spinning industries is the occurrence of product defects that reduce production efficiency and increase manufacturing costs Purpose This study aimed to analyze the quality control performance of Rayon Ne 30 yarn production at PT. X using the Six Sigma DMAIC (Define–Measure–Analyze–Improve–Control) methodology. Methodology A quantitative descriptive approach was employed using historical production and quality control data collected from January to April 2026. Data were analyzed through Critical to Quality (CTQ) identification, Pareto analysis, Defects Per Million Opportunities (DPMO), Sigma capability measurement, Statistical Process Control (SPC) using P-chart, Fishbone analysis, and Five Whys analysis to identify dominant root causes. Findings The results indicated that the production process experienced fluctuating defect levels throughout the observation period, with the highest defect rate occurring in April. Crossing was identified as the dominant defect, followed by Ring and Loose Winding, collectively contributing approximately 85% of total production defects. Implications Process capability analysis showed that the production process had not yet achieved the Six Sigma performance target, while SPC analysis indicated the presence of process variation requiring further investigation. Root cause analysis revealed that machine setting instability, inadequate preventive maintenance, inconsistent operating methods, and operator-related factors were the primary contributors to defect occurrence. Originality Based on these findings, several improvement strategies were proposed, including preventive maintenance scheduling, machine calibration standardization, operator competency improvement, and standardized operating procedures. Since the proposed improvements were not implemented during the study period, their effectiveness could not be statistically validated. Future studies are recommended to conduct industrial implementation and long-term monitoring to evaluate the impact of the proposed improvements on process capability and defect reduction.
Optimizing to Predict Purchase Intention in Fashion Thrifting Using Artificial Neural Networks Approach Fadil Abdullah; Manase Sahat H Simarangkir; Adie Kusna Wibowo; Abdullah Rizky Alfatih; Nadiya Maharani
INKOFAR Vol. 10 No. 1 (2026)
Publisher : Politeknik META Industri Cikarang

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Background Thrifting has emerged as a prominent trend within the fashion industry, driven by increasing consumer awareness of sustainability and the demand for affordable fashion alternatives Purpose This study develops an Artificial Neural Network (ANN) model to optimize purchase intention for thrifting fashion products based on trends, online promotions, and brand image Methodology The model uses three node variations (10, 20, 30), two hidden layers, a sigmoid activation function, 10,000 iterations, and a feed-forward propagation algorithm. The 30-node configuration performed best, achieving a determination coefficient of 0.97 during training and 0.98 during testing, indicating high predictive accuracy. Findings The findings confirm that trends, online promotions, and brand image significantly influence purchase intention, demonstrating the model’s potential to optimize marketing strategies. By leveraging ANN, businesses can enhance marketing efficiency, adapt to market dynamics, and improve decision-making Implications This research highlights the effectiveness of AI-driven methodologies in analyzing consumer behavior and supporting targeted marketing efforts. The model’s success also suggests broader AI integration possibilities in strategic planning for the fashion industry Originality this study contributes to the literature by providing deeper insights into purchase intention formation and offers practical implications for improving marketing efficiency and strategic decision-making in sustainable fashion businesses
Analytical Data for Sewing Production Efficiency: A Model Based on Artificial Neural Networks (ANNs) Fadil Abdullah; Afriani Kusumadewi; Tina Martina; - Kuswinarti; Fandi Achmad
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.
Pressure Loss Analysis In Clean Water Supply Distribution At The Regional Drinking Water Company (Pdam) Muhammad Noval; Fadil Abdullah; Zulfadillah
INKOFAR Vol. 10 No. 1 (2026)
Publisher : Politeknik META Industri Cikarang

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Background Water distribution systems require accurate analysis of flow characteristics and head losses to ensure efficient hydraulic performance. Manual calculations using fluid mechanics equations can be used to determine flow losses in pipes and fittings, while hydraulic simulation software such as EPANET 2.0 provides an alternative approach for analyzing water distribution systems. However, differences between manual calculations, Microsoft Excel calculations, and EPANET 2.0 simulations require further evaluation to assess the consistency and accuracy of the results. Purpose This study aims to calculate water flow and head losses in a water distribution system using manual calculations and Microsoft Excel, and to compare the results with those obtained using EPANET 2.0 software Methodology The study applies hydraulic calculations based on the Reynolds number (Re), Darcy–Weisbach equation for major head loss, and minor loss equation for fittings and other hydraulic components. The equations used include (Re = \frac{\rho VD}{\mu} = \frac{VD}{\nu}), (h_f = f\frac{LV^2}{2gD}), and (h_m = K\frac{V^2}{2g}). Microsoft Excel is used to facilitate the calculation of friction factors and pipe head losses. The results are subsequently compared with simulations conducted using EPANET 2.0, which models physical components including junctions, reservoirs, tanks, emitters, pipes, pumps, valves, and minor losses. The Hazen–Williams equation is applied in EPANET 2.0 for head-loss calculations. Findings The analysis provides comparative results of flow characteristics and head losses obtained through manual calculations, Microsoft Excel, and EPANET 2.0 simulations. The comparison indicates the extent of agreement between the analytical and software-based approaches and identifies differences resulting from the calculation methods and hydraulic assumptions applied in each approach. Implications The comparison provides practical information regarding the applicability of manual calculations, Microsoft Excel, and EPANET 2.0 for hydraulic analysis. The results can support students, researchers, and practitioners in selecting an appropriate calculation and simulation approach for evaluating water distribution systems and pipe-flow losses. Originality This study contributes a comparative framework for evaluating hydraulic head-loss calculations using conventional fluid mechanics equations, Microsoft Excel, and EPANET 2.0. The comparison provides an integrated perspective on the consistency of manual and software-based hydraulic analysis.
Screen-printed anti-radiation woven fabric with conductive carbon layer and Corona plasma treatment optimized by regression method Mutiara Tanjung; Valentinus Galih Vidia Putra; Fadil Abdullah
INKOFAR Vol. 10 No. 1 (2026)
Publisher : Politeknik META Industri Cikarang

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Abstract

Background Electromagnetic radiation from electronic devices, including mobile phones, has encouraged the development of textile materials that can provide electromagnetic shielding. Conductive materials such as conductive carbon can be applied to textile surfaces to improve their ability to absorb electromagnetic radiation. Purpose This study aims to investigate the effect of Corona plasma discharge pretreatment on the electromagnetic radiation shielding performance of woven fabrics coated with conductive carbon. The study also aims to optimize the plasma treatment conditions based on plasma exposure time and electrode distance using regression analysis. Methodology Woven fabrics were treated using Corona discharge plasma with a tip-plane electrode configuration under atmospheric pressure, room temperature, and ambient gas conditions. Two plasma treatment parameters were varied: plasma exposure time and electrode distance. Following plasma pretreatment, conductive carbon was applied to the woven fabrics using screen printing and pretreatment coating techniques. The resulting fabrics were then evaluated based on their ability to reduce electromagnetic radiation generated by a mobile phone. Regression analysis was applied to model the relationship between plasma treatment parameters and radiation levels. Findings The results indicate that Corona plasma pretreatment combined with conductive carbon coating can improve the electromagnetic radiation shielding performance of woven fabrics. The lowest electromagnetic radiation level was obtained at an electrode distance of 4 cm and a plasma exposure time of 3 minutes. The regression model showed an R-squared value of 0.9231, indicating that the model provided a strong explanation of the relationship between plasma exposure time and radiation level. Implications The findings demonstrate that plasma pretreatment can be used as part of the fabrication process for conductive textile materials designed for electromagnetic radiation shielding. Originality The originality of this study lies in the application of regression analysis to optimize the design of woven fabrics treated with Corona discharge plasma and coated with conductive carbon for electromagnetic radiation shielding. The combination of plasma pretreatment, conductive carbon screen printing, and regression-based optimization provides an approach for determining suitable plasma treatment conditions for the development of anti-radiation textile materials.
Quantum Neural Network Approach to Economic Corruption Decision-Making and Optimal Anti-Corruption Policy Risita Dwi Astuti; Wiwiek Eka Mulyani; Valentinus Galih Vidia Putra; Surya Mega Wijaya; Juliany Ningsih Mohamad; Arief Dewanto; Fadil Abdullah
INKOFAR Vol. 10 No. 1 (2026)
Publisher : Politeknik META Industri Cikarang

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Abstract

Background Economic corruption remains a major challenge because it generates substantial fiscal losses, weakens institutional performance, and undermines public trust. Classical economic approaches, particularly Becker’s rational choice framework, explain corruption decisions primarily through the expected utility derived from illegal gains, the probability of detection, and the severity of punishment. Purpose This study aims to develop a Quantum Becker Model (QBM) based on Quantum Neural Networks (QNNs) to provide a quantum probabilistic representation of individual and organizational corruption decisions. Methodology The proposed model represents individual cognitive states as qubits, with their evolution controlled by parameterized quantum rotation gates. Organizational corruption contagion is represented through quantum entanglement between individual decision states. Anti-corruption policy is formulated as an optimization problem by minimizing a Hamiltonian-based social loss function using a hybrid quantum gradient descent approach. Findings Numerical simulations demonstrate that the QBM can represent nonlinear relationships between corruption decisions, detection probability, and policy interventions. The model also captures organizational corruption contagion through interactions between interconnected cognitive states, providing behavioral dynamics that are not readily represented by conventional expected-utility models. The simulation results indicate that increasing the probability of corruption detection through effective auditing and institutional transparency produces a stronger deterrence effect than relying solely on increasing the severity of sanctions. Implications The findings suggest that anti-corruption strategies should place greater emphasis on increasing the perceived and actual probability of detection through effective auditing, transparency, and institutional monitoring. . Originality The originality of this study lies in integrating Becker’s economic theory of crime with quantum machine learning through a Quantum Neural Network framework. By representing cognitive uncertainty using qubits and organizational contagion using quantum entanglement, the proposed approach offers a novel computational perspective for analyzing nonlinear corruption behavior and designing economically efficient anti-corruption policies.
Co-Authors - Kuswinarti Abdullah Rizky Alfatih Adi Kusrianto Adie Kusna Wibowo Afriani Kusumadewi Agung Haryanto Agus Widodo Anggara, Ikbal Aprilia, Hafiza Arief Dewanto Asshiddiq, Fajar Rasyid Barus, Wan Habibi Rahman Candrawati, Nia Dayani, Camillana Calistafo Dewi, Siska Astari Endah Purnomosari Fandi Achmad Fathani, Moh. Ayip Hafiza Aprilia Haryanto, Agung Ihsan, Muhammad Alif Indra Mahyudi S Iphov Kumala Sriwana Joko Prasetyo Kusrianto, Adi Maharani, Nadiya Mohamad, Juliany Ningsih Muhamad Atho Rifaitulloh Muhammad Alif Ihsan Muhammad Noval mulyani, wiwiek eka Mutiara Tanjung Nadiya Maharani Noval, Muhamad Novian, Mikhael Dio Nurmalinda Rahmawati Purnomosari, Endah Putra, Valentinus Galih Vidia Putri, Safira Hutama Rahmawati, Nurmalinda Risita Dwi Astuti Riswandi, Andri Simarangkir, Manase Sahat H Siska Astari Dewi Siti Nur Hamidah Siti Nurhamidah Surya Mega Wijaya Susi Maulinawati Tina Martina Valentinus Galih Vidia Putra Valentinus Galih Vidia Putra Wan Habibi Rahman Barus Widarini, Wari Wiwiek Eka Mulyani Zulfadillah zulfadillah, zulfadillah