Sales forecasting of pet food at oyen petshop using the fuzzy time series–markov chain method
Main Article Content
Abstract
Oyen Petshop faces stock management inaccuracies because sales records are still kept manually, while demand patterns are highly fluctuating and difficult to predict, often leading to overstock or stockouts that harm the business. The purpose of this study is to develop a Fuzzy Time Series–Markov Chain (FTS-MC) model to forecast pet food sales at Oyen Petshop and implement it in the form of a website. The research method applies FTS-MC to construct fuzzy intervals, generate FLR/FLRG, calculate transition probabilities, and produce forecasts based on dry-food sales data from April 2024 to March 2025. The results show that the FTS-MC model achieves a MAPE of 8.92%, with forecasted values that follow actual fluctuations and indicate a stable demand trend of 206–224 units for the next seven periods. Black Box Testing confirms that all web-based system functions operate correctly and all test scenarios pass, ensuring the system is ready for operational use. The findings indicate that the system enables more precise stock estimation, supports the establishment of safe reorder limits, and allows procurement decisions to be made faster and more consistently without manual calculations.
Downloads
Download data is not yet available.
Article Details
How to Cite
Pratama, A., & Kurniawan, R. . (2025). Sales forecasting of pet food at oyen petshop using the fuzzy time series–markov chain method . Journal of Intelligent Decision Support System (IDSS), 8(4), 198-207. https://doi.org/10.35335/idss.v8i4.327
References
Ai, C., Akaichi, F., Glenk, K., & Revoredo-giha, C. (2025). What Drives Pet Food Choices? A Systematic Literature Review. Animals, 15(11), 1–28.
Alyousifi, Y., Othman, M., Husin, A., & Rathnayake, U. (2021). A new hybrid fuzzy time series model with an application to predict PM10 concentration. Ecotoxicology and Environmental Safety, 227(12), 1–8. https://doi.org/10.1016/j.ecoenv.2021.112875
Anatan, L., & Nur. (2023). Micro, Small, and Medium Enterprises’ Readiness for Digital Transformation in Indonesia. Economies, 11(6), 1–21. https://doi.org/10.3390/economies11060156
Andriani, W., & Anandianskha, S. (2023). Comparison of three fuzzy logic algorithm methods for cellular selection. Journal of Intelligent Decision Support System (IDSS), 6(3), 129–137.
Devianto, D., Ramadani, K., Maiyastri, Asdi, Y., & Yollanda, M. (2022). The hybrid model of autoregressive integrated moving average and fuzzy time series Markov chain on long-memory data. Frontiers in Applied Mathematics and Statistics, 8(12), 1–15. https://doi.org/10.3389/fams.2022.1045241
Ese, J., & Raphael, A. (2024). Impact of Inventory Management on Small and Medium-Scale Enterprises Profitability: A Study of Supermarkets and Bottled / Sachet Water Producers in Delta State, Nigeria. International Journal of Economics and Financial Management (IJEFM), 9(9), 161–170. https://doi.org/10.56201/ijefm.v9.no9.2024.pg161.170
Fakhriyana, D., & Brilliant, I. I. (2023). Penerapan Metode Fuzzy Time Series (FTS) Cheng dan Markov-Chain. Indonesian Journal of Applied Statistics, 6(1), 44–56.
Feriyanto, E., Farikhin, & Puspita, N. P. (2024a). Monthly Rainfall Forecasting Using High Order Singh’s Fuzzy Time Series Based on Interval Ratio Methods: Case Study Semarang City, Indonesia. Asian Journal of Probability and Statistics, 26(8), 71–88. https://doi.org/10.9734/ajpas/2024/v26i8638
Feriyanto, E., Farikhin, & Puspita, N. P. (2024b). Singh’s Fuzzy Time Series Forecasting Modification Based on Interval Ratio. Jurnal Sosial Dan Sains, 4(3), 255–264. https://doi.org/10.59188/jurnalsosains.v4i3.1248
Huang, H., Huang, J., & Zulfiqar, A. (2025). Pet Food Market Analysis and Prediction Based on Multiple Regression and Machine Learning Technology. Highlights in Science, Engineering and Technology, 142, 218–228. https://doi.org/10.54097/t9dgt750
Kilay, A. L., Simamora, B. H., & Putra, D. P. (2022). The Influence of E-Payment and E-Commerce Services on Supply Chain Performance: Implications of Open Innovation and Solutions for the Digitalization of Micro, Small, and Medium Enterprises (MSMEs) in Indonesia. Journal of Open Innovation: Technology, Market, and Complexity, 8(3), 1–24. https://doi.org/10.3390/joitmc8030119
Mohite, R., Chourasiya, R., & Sharma, S. (2025). Empowering Micro, Small, and Medium Enterprises with Effective Inventory Management and Educational Interventions. Educational Innovations and Emerging Technologies, 5(2), 10–21.
Moura, H. D., de Vleeschauwer, E., Haesendonck, G., De Meester, B., D’eer, L., De Schepper, T., Mercelis, S., & Mannens, E. (2024). Performance of an End-to-End Inventory Demand Forecasting Pipeline Using a Federated Data Ecosystem †. Engineering Proceedings, 68(1), 1–10. https://doi.org/10.3390/engproc2024068033
Olamide Raimat Amosu, Praveen Kumar, Yewande Mariam Ogunsuji, Segun Oni, & Oladapo Faworaja. (2024). AI-driven demand forecasting: Enhancing inventory management and customer satisfaction. World Journal of Advanced Research and Reviews, 23(2), 708–719. https://doi.org/10.30574/wjarr.2024.23.2.2394
Ramadi, J., Walfajri, A., Arifan, D., & Oscar, R. (2025). Design and Implementation of a Web-Based Information System to Enhance Inventory and Service Management in Pet Retail Businesses. Jurnal Vokasi Informatika (JAVIT), 5(2), 329–348.
Sari, V., Alifia, N., & Halik, F. (2025). Inflation Forecasting in Indonesia Using Lee’ s Fuzzy Time Series Me thod. UNNES Journal of Mathematics, 14(1), 16–22.
Sjofjan, O., & Adli, D. N. (2022). Using fuzzy time series with and without markov chain: to forecast of edible bird nest exported from Indonesia. E3S Web of Conferences, 335(1), 6–11. https://doi.org/10.1051/e3sconf/202233500016
Stefana, E., Ramos, M., & Paltrinieri, N. (2024). Machine learning-based literature review on the concept of safety barriers against hazardous events. Journal of Loss Prevention in the Process Industries, 92(10), 1–26. https://doi.org/10.1016/j.jlp.2024.105470
Sunusi, N., Sikib, A. A., & Pasari, S. (2025). A novel hybrid CLARA and fuzzy time series Markov chain model for predicting air pollution in Jakarta. MethodsX, 14(6), 1–11. https://doi.org/https://doi.org/10.1016/j.mex.2025.103202
Surorejo, S., Mutaqin, A. F., Kurniawan, R. D., & Gunawan, G. (2024). Implementation of fuzzy mamdani method in predicting cayenne chili prices in Tegal Regency. Journal of Intelligent Decision Support System (IDSS), 7(2), 137–145. https://doi.org/10.35335/idss.v7i2.231
Telaumbanua, S. I. J., & Febrian, D. (2023). Comparison of Fuzzy Time Series Markov Chain and Fuzzy Time Series Lee in North Sumatra Import Value Forecasting. Numerical: Jurnal Matematika Dan Pendidikan Matematika, 7(1), 131–144. https://doi.org/10.25217/numerical.v7i1.2745
Upadhyay, J. P., Chaudhary, A. K., & Aryal, K. P. (2023). Modeling Daily Sales Revenue Fluctuation in Small Retail Shops Using Runge-Kutta Method for Better Cash Management Planning. Review of Contemporary Philosophy, 22(1), 6861–6870.
Wahyu, D., Fortunata, J. E., Nanda, H. D., & Oktavia, A. (2025). Empowering MSMEs Through a UI/UX-Centered IP Management Website: A Digital Approach to Economic Growth. Procedia Computer Science, 269(12), 541–550. https://doi.org/10.1016/j.procs.2025.08.306
Wulandari, R., Surarso, B., Irawanto, B., & Farikhin, F. (2021). The forecasting of palm oil based on fuzzy time series-two factor. Journal of Soft Computing Exploration, 2(1), 11–16.
Yustin, M. A., Zilrahmi, Putra, A. A., & Fitri, F. (2023). Comparison Fuzzy Time Series Cheng and Ruey Chyn Tsaur Model for Forecasting Sales at Empat Saudara Store. UNP Journal of Statistics and Data Science, 1(3), 218–225. https://doi.org/10.24036/ujsds/vol1-iss3/56
Alyousifi, Y., Othman, M., Husin, A., & Rathnayake, U. (2021). A new hybrid fuzzy time series model with an application to predict PM10 concentration. Ecotoxicology and Environmental Safety, 227(12), 1–8. https://doi.org/10.1016/j.ecoenv.2021.112875
Anatan, L., & Nur. (2023). Micro, Small, and Medium Enterprises’ Readiness for Digital Transformation in Indonesia. Economies, 11(6), 1–21. https://doi.org/10.3390/economies11060156
Andriani, W., & Anandianskha, S. (2023). Comparison of three fuzzy logic algorithm methods for cellular selection. Journal of Intelligent Decision Support System (IDSS), 6(3), 129–137.
Devianto, D., Ramadani, K., Maiyastri, Asdi, Y., & Yollanda, M. (2022). The hybrid model of autoregressive integrated moving average and fuzzy time series Markov chain on long-memory data. Frontiers in Applied Mathematics and Statistics, 8(12), 1–15. https://doi.org/10.3389/fams.2022.1045241
Ese, J., & Raphael, A. (2024). Impact of Inventory Management on Small and Medium-Scale Enterprises Profitability: A Study of Supermarkets and Bottled / Sachet Water Producers in Delta State, Nigeria. International Journal of Economics and Financial Management (IJEFM), 9(9), 161–170. https://doi.org/10.56201/ijefm.v9.no9.2024.pg161.170
Fakhriyana, D., & Brilliant, I. I. (2023). Penerapan Metode Fuzzy Time Series (FTS) Cheng dan Markov-Chain. Indonesian Journal of Applied Statistics, 6(1), 44–56.
Feriyanto, E., Farikhin, & Puspita, N. P. (2024a). Monthly Rainfall Forecasting Using High Order Singh’s Fuzzy Time Series Based on Interval Ratio Methods: Case Study Semarang City, Indonesia. Asian Journal of Probability and Statistics, 26(8), 71–88. https://doi.org/10.9734/ajpas/2024/v26i8638
Feriyanto, E., Farikhin, & Puspita, N. P. (2024b). Singh’s Fuzzy Time Series Forecasting Modification Based on Interval Ratio. Jurnal Sosial Dan Sains, 4(3), 255–264. https://doi.org/10.59188/jurnalsosains.v4i3.1248
Huang, H., Huang, J., & Zulfiqar, A. (2025). Pet Food Market Analysis and Prediction Based on Multiple Regression and Machine Learning Technology. Highlights in Science, Engineering and Technology, 142, 218–228. https://doi.org/10.54097/t9dgt750
Kilay, A. L., Simamora, B. H., & Putra, D. P. (2022). The Influence of E-Payment and E-Commerce Services on Supply Chain Performance: Implications of Open Innovation and Solutions for the Digitalization of Micro, Small, and Medium Enterprises (MSMEs) in Indonesia. Journal of Open Innovation: Technology, Market, and Complexity, 8(3), 1–24. https://doi.org/10.3390/joitmc8030119
Mohite, R., Chourasiya, R., & Sharma, S. (2025). Empowering Micro, Small, and Medium Enterprises with Effective Inventory Management and Educational Interventions. Educational Innovations and Emerging Technologies, 5(2), 10–21.
Moura, H. D., de Vleeschauwer, E., Haesendonck, G., De Meester, B., D’eer, L., De Schepper, T., Mercelis, S., & Mannens, E. (2024). Performance of an End-to-End Inventory Demand Forecasting Pipeline Using a Federated Data Ecosystem †. Engineering Proceedings, 68(1), 1–10. https://doi.org/10.3390/engproc2024068033
Olamide Raimat Amosu, Praveen Kumar, Yewande Mariam Ogunsuji, Segun Oni, & Oladapo Faworaja. (2024). AI-driven demand forecasting: Enhancing inventory management and customer satisfaction. World Journal of Advanced Research and Reviews, 23(2), 708–719. https://doi.org/10.30574/wjarr.2024.23.2.2394
Ramadi, J., Walfajri, A., Arifan, D., & Oscar, R. (2025). Design and Implementation of a Web-Based Information System to Enhance Inventory and Service Management in Pet Retail Businesses. Jurnal Vokasi Informatika (JAVIT), 5(2), 329–348.
Sari, V., Alifia, N., & Halik, F. (2025). Inflation Forecasting in Indonesia Using Lee’ s Fuzzy Time Series Me thod. UNNES Journal of Mathematics, 14(1), 16–22.
Sjofjan, O., & Adli, D. N. (2022). Using fuzzy time series with and without markov chain: to forecast of edible bird nest exported from Indonesia. E3S Web of Conferences, 335(1), 6–11. https://doi.org/10.1051/e3sconf/202233500016
Stefana, E., Ramos, M., & Paltrinieri, N. (2024). Machine learning-based literature review on the concept of safety barriers against hazardous events. Journal of Loss Prevention in the Process Industries, 92(10), 1–26. https://doi.org/10.1016/j.jlp.2024.105470
Sunusi, N., Sikib, A. A., & Pasari, S. (2025). A novel hybrid CLARA and fuzzy time series Markov chain model for predicting air pollution in Jakarta. MethodsX, 14(6), 1–11. https://doi.org/https://doi.org/10.1016/j.mex.2025.103202
Surorejo, S., Mutaqin, A. F., Kurniawan, R. D., & Gunawan, G. (2024). Implementation of fuzzy mamdani method in predicting cayenne chili prices in Tegal Regency. Journal of Intelligent Decision Support System (IDSS), 7(2), 137–145. https://doi.org/10.35335/idss.v7i2.231
Telaumbanua, S. I. J., & Febrian, D. (2023). Comparison of Fuzzy Time Series Markov Chain and Fuzzy Time Series Lee in North Sumatra Import Value Forecasting. Numerical: Jurnal Matematika Dan Pendidikan Matematika, 7(1), 131–144. https://doi.org/10.25217/numerical.v7i1.2745
Upadhyay, J. P., Chaudhary, A. K., & Aryal, K. P. (2023). Modeling Daily Sales Revenue Fluctuation in Small Retail Shops Using Runge-Kutta Method for Better Cash Management Planning. Review of Contemporary Philosophy, 22(1), 6861–6870.
Wahyu, D., Fortunata, J. E., Nanda, H. D., & Oktavia, A. (2025). Empowering MSMEs Through a UI/UX-Centered IP Management Website: A Digital Approach to Economic Growth. Procedia Computer Science, 269(12), 541–550. https://doi.org/10.1016/j.procs.2025.08.306
Wulandari, R., Surarso, B., Irawanto, B., & Farikhin, F. (2021). The forecasting of palm oil based on fuzzy time series-two factor. Journal of Soft Computing Exploration, 2(1), 11–16.
Yustin, M. A., Zilrahmi, Putra, A. A., & Fitri, F. (2023). Comparison Fuzzy Time Series Cheng and Ruey Chyn Tsaur Model for Forecasting Sales at Empat Saudara Store. UNP Journal of Statistics and Data Science, 1(3), 218–225. https://doi.org/10.24036/ujsds/vol1-iss3/56
Copyright and Licensing
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.