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All Journal At-Tadbir : jurnal ilmiah manajemen MABIS: Manajemen dan Bisnis Jurnal Ilmiah Teknik Industri Journal of Industrial Engineering G-Tech : Jurnal Teknologi Terapan Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Jurnal PKM Manajemen Bisnis Jurnal Kalibrasi Sewagati Jurnal Industri Furnitur dan Pengolahan Kayu (JIFKA) Alamtana Benefit: Jurnal Manajemen dan Bisnis
Ma’rifah, Julia Dewi
Politeknik Industri Furnitur Dan Pengolahan Kayu
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Development of a Decision Support System Based on the Weighted Product to Optimize Production Scheduling in the Furniture Industry Yessi Nasia Ulfia; Dian Eko Hari Purnomo; Fesa Putra Kristianto; Julia Dewi Ma’rifah; Taukhid Wisnu Broto; Fitri Indah Puspitaningsih
G-Tech: Jurnal Teknologi Terapan Vol 9 No 4 (2025): G-Tech, Vol. 9 No. 4 October 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i4.8090

Abstract

The furniture industry plays a crucial role in Indonesia’s manufacturing sector, which contributed 18.98% to the national GDP in 2024 with a growth rate of 4.43%. However, increasing market demand and product variety have created significant challenges in achieving efficient and timely production scheduling. Inefficient manual scheduling processes have been shown to cause substantial delays, with approximately 31% of production orders experiencing lateness due to poor scheduling and machine utilization. This study aims to design and implement a Decision Support System (DSS) based on the Weighted Product (WP) method to objectively determine production scheduling priorities using order quantity, processing time, and profit value as key criteria. The DSS was developed using Microsoft Visual Basic 6.0 and Microsoft Office Access 2016, utilizing secondary data from a furniture manufacturing company. Validation results show that the DSS achieved a 99.998% accuracy rate compared to manual Excel calculations, indicating correct implementation of the WP computational algorithm. The system effectively enhances production planning by providing objective, data-driven scheduling recommendations, reducing delays, optimizing resource utilization, and improving operational efficiency. Overall, the DSS serves as an effective managerial tool to support decision-making consistency and competitiveness in the manufacturing industry.
A hybrid DEA-BPNN framework for performance modelling of Indonesian listed furniture and wood processing firms Amarta, Zain; Etruly, Niki; Ma'rifah, Julia Dewi
Manajemen dan Bisnis Vol 25, No 2 (2026): July 2026 (Online First)
Publisher : Department of Management - Faculty of Business and Economics. Universitas Surabaya.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24123/mabis.v25i2.1113

Abstract

This study proposes a hybrid Data Envelopment Analysis-Backpropagation Neural Network (DEA-BPNN) framework to evaluate and predict the efficiency performance of furniture and wood processing firms listed on the Indonesia Stock Exchange (IDX). As a strategic manufacturing sector, firm performance in this industry is frequently challenged by cost volatility, scale inefficiencies, and fluctuating market demand, while conventional efficiency methods remain limited in capturing nonlinear relationships and predictive insights. To address these limitations, the study integrates frontier-based efficiency measurement with machine learning-based prediction. Using panel data from six IDX-listed firms over the 2020-2024 period, efficiency scores are first estimated through CCR and BCC DEA models, with total assets, cost of goods sold, and operating expenses as inputs, and revenue and profit as outputs. The results reveal notable heterogeneity in efficiency performance, where several firms achieve full BCC efficiency, indicating strong pure technical efficiency, while variations in CCR efficiency highlight the presence of scale inefficiencies. In the second stage, a BPNN model is developed to predict CCR and BCC efficiency scores. The optimized 5-8-2 network architecture demonstrates strong predictive performance, achieving a Mean Squared Error (MSE) of 0.0145, low Mean Absolute Percentage Error (MAPE) values of 1.22% (CCR) and 0.89% (BCC), and high Pearson correlation coefficients of 0.94 and 0.96. Overall, the findings confirm that the hybrid DEA-BPNN framework provides a robust tool for efficiency evaluation and prediction, supporting performance monitoring and strategic decision-making in Indonesia’s furniture and wood processing industry.
Doomscrolling dalam Sistem Manufaktur: Dampak pada Kewaspadaan dan Kinerja Operasional Nukhbah Sany; Julia Dewi Ma’rifah
Jurnal Kalibrasi Vol 24 No 1 (2026): Jurnal Kalibrasi
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/kalibrasi/v.24-1.3382

Abstract

This study discusses the impact of doomscrolling on vigilance and operational performance within manufacturing systems. In production environments that demand high levels of attention, repeated exposure to negative digital content has the potential to disrupt workers’ cognitive stability and increase the risk of operational errors. The research employed a qualitative approach through a narrative-critical literature review with interdisciplinary integration covering industrial ergonomics, human factors, occupational psychology, and workplace safety. The research stages included literature searches in major scientific databases, selection based on relevance to digital distraction and work performance, and thematic analysis to identify the main mechanisms linking digital behavior with operational performance. The analysis results indicate that doomscrolling contributes to attention fragmentation, increased emotional burden, a higher probability of errors in high-risk tasks, and decreased sustained performance due to attentional residue. These findings suggest that doomscrolling can reduce situational awareness and disrupt the stability of manufacturing production systems. Practically, this study emphasizes the importance of managing digital distractions as part of industrial safety and operations management systems. The study also proposes a managerial checklist framework as a guideline for reducing attention-based error risks in manufacturing environments.
Co-Authors Amarta, Zain Dian Eko Hari Purnomo Fesa Putra Kristianto Fesa Putra Kristianto Fitri Indah Puspitaningsih Kristianto, Fesa Putra Maharsi Anis Sabila Niki Etruly Nukhbah Sany Nukhbah Sany, Nukhbah Prasetya, Juliasari Rahayu, Putri Sabila, Maharsi Anis Salwa Islami Athirah Sunardiansyah, Yogi Akbar Taukhid W Broto Yessi Nasia Ulfia Yogi Akbar Sunardiansyah