Perancangan Sistem Pendukung Keputusan untuk Pengendalian Kualitas Produk pada Industri Bumbu
DOI:
https://doi.org/10.55606/jutiti.v5i2.5554Keywords:
Decision Support System, product weight, spice industry, FMEA, quality control, hypothesis testingAbstract
The spice industry faces significant challenges in maintaining product weight consistency as part of quality assurance and compliance with production standards. A case at PT X revealed that a newly installed filling machine produced deviations from the target weight of 50 grams, with hypothesis testing showing that out of 30 samples, 17 samples fell outside the 95% confidence interval. To mitigate this issue, this study proposes the development of a real-time data-driven Decision Support System (DSS) combined with statistical approaches. The methodology includes two-tailed hypothesis testing to detect weight deviations and Failure Mode and Effects Analysis (FMEA) to identify dominant failure causes based on high Risk Priority Numbers (RPN), such as delayed machine calibration, operator error, and worn-out machine components. These findings serve as the foundation for designing the DSS architecture, which consists of sensor input modules, statistical data processing, risk mapping, and an automated corrective recommendation engine. The system is designed to enable early detection of deviations, accelerate response time to quality issues, and support data-driven decision-making on the production floor. The study concludes that a structured implementation of DSS can be an effective strategy to improve product weight consistency and enhance operational efficiency in spice manufacturing.
Downloads
References
Canlı, H., & Toklu, S. (2019). Implementation of decision support system with data mining methods in the quality control process of the automotive sector. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 7(1), 102–114.
Camm, A. J., Cools, F., Virdone, S., Bassand, J.-P., Fitzmaurice, D. A., Arthur Fox, K. A., Goldhaber, S. Z., Goto, S., Haas, S., & Mantovani, L. G. (2020). Mortality in patients with atrial fibrillation receiving nonrecommended doses of direct oral anticoagulants. Journal of the American College of Cardiology, 76(12), 1425–1436.
Carmignani, G. (2009). An integrated structural framework to cost-based FMECA: The priority-cost FMECA. Reliability Engineering & System Safety, 94(4), 861–871.
Chopra, S., & Meindl, P. (2001). Strategy, planning, and operation. Supply Chain Management, 15(5), 71–85.
Demirel, K. C., Şahin, A., & Albey, E. (2019). A web-based decision support system for quality prediction in manufacturing using ensemble of regressor chains. In International Conference on Data Management Technologies and Applications (pp. 96–114).
Jiang, X., Sun, X., Wang, S., & Zhang, X. (2010). An intelligent decision support system for process quality control. In 2010 IEEE International Conference on Automation and Logistics (pp. 637–642).
Kuei, C.-H., Madu, C. N., & Lin, C. (2008). Implementing supply chain quality management. Total Quality Management, 19(11), 1127–1141.
Lourin, N. S., & Budi, A. (2023). Implementasi decision support system untuk menentukan ranking nilai produk cold-pressed juice menggunakan metode weighted product pada Livera Indonesia. Jurnal Informatika dan Bisnis, 12(2), 94–105.
Naini, N. F., Santoso, S., Andriani, T. S., Claudia, U. G., & Nurfadillah, N. (2022). The effect of product quality, service quality, customer satisfaction on customer loyalty. Journal of Consumer Sciences, 7(1), 34–50.
Quang, H. T., Sampaio, P., Carvalho, M. S., Fernandes, A. C., Binh An, D. T., & Vilhenac, E. (2016). An extensive structural model of supply chain quality management and firm performance. International Journal of Quality & Reliability Management, 33(4), 444–464.
Rahayu, W. P., Nugroho, G., & Situmorang, A. F. (2020). Implementasi statistical process control pada operasi pengisian produk pangan bubuk. Jurnal Mutu Pangan, 3(1), 18–27.
Ramadhan, R. F. (2023). Implementasi dan analisis metode MOORA dan SMART pada pemilihan platform jual beli online menggunakan decision support system. Komputika: Jurnal Sistem Komputer, 12(1), 63–71.
Salkind, N. J. (2017). Tests & measurement for people who (think they) hate tests & measurement. Sage Publications.
Saniga, E. M. (1993). Decision support and statistical quality control. International Journal of Quality & Reliability Management, 10(2).
Savitri, A. (2019). Revolusi industri 4.0: Mengubah tantangan menjadi peluang di era disrupsi 4.0. Penerbit Genesis.
Sidiq, M. F., Ramdhani, T. I., & Basuki, A. I. (2024). SambalChain: Revolutionizing the Indonesian chili paste supply chain through blockchain for enhanced traceability and authenticity. In 2024 2nd International Symposium on Information Technology and Digital Innovation (ISITDI) (pp. 196–201).
Singh, D., Agusti, A., Anzueto, A., Barnes, P. J., Bourbeau, J., Celli, B. R., Criner, G. J., Frith, P., Halpin, D. M. G., & Han, M. (2019). Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease: The GOLD science committee report 2019. European Respiratory Journal, 53(5).
Sri Sulistyawati, U., & Munawir. (2024). Decoding big data: Mengubah data menjadi keunggulan kompetitif dalam pengambilan keputusan bisnis. Jurnal Manajemen dan Teknologi, 1(2), 58–71. https://doi.org/10.63447/jmt.v1i2.1114
Sudipa, I. G. I., Setyawati, E., Fajriana, F., Moedjahedy, J., Adicandra, I., & Rahim, R. (2022). Optimizing decision making in manufacturing: An analysis of the effectiveness of a DSS using the weighted product method. JINAV: Journal of Information and Visualization, 3(2), 99–103.
Vrana, I., Aly, S., & Pelikan, M. (2013). A modular decomposition for approximation of the optimum DSS in multidimensional cases. Journal of Systems Integration, 4(4), 3–9.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Jurnal Teknik Informatika dan Teknologi Informasi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
