Analisis Peramalan Persediaan Bahan Baku Pada Usaha Tahu Murni Desa Tuhemberua Ulu Kota Gunungsitoli
Keywords:
Forecasting, Moving Average, Exponential Smoothing
References
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Gopalakrishnan, V., Pethe, S., Kefayati, S., Srinivasan, R., Hake, P., Deshpande, A., Liu, X., Hoang, E., Davila, M., Bianco, S., & Kaufman, J. H. (2021). Globally local: Hyper-local modeling for accurate forecast of COVID-19. Epidemics, 37, 100510. https://doi.org/10.1016/j.epidem.2021.100510
Hemalatha, C., Sankaranarayanasamy, K., & Durairaaj, N. (2021). Lean and agile manufacturing for work-in-process (WIP) control. Materials Today: Proceedings, 46, 10334–10338. https://doi.org/10.1016/j.matpr.2020.12.473
Jaidi, N., Siswantoyo, Liu, J., Sholikhah, Z., & Andhini, M. M. (2022). Ambidexterity Behavior of Creative SMEs for Disruptive Flows of Innovation: A Comparative Study of Indonesia and Taiwan. Journal of Open Innovation: Technology, Market, and Complexity, 8(3), 141. https://doi.org/10.3390/joitmc8030141
Khare, V., & Jain, A. (2023). Predict the performance of driverless car through the cognitive data analysis and reliability analysis based approach. E-Prime - Advances in Electrical Engineering, Electronics and Energy, 6(November), 100344. https://doi.org/10.1016/j.prime.2023.100344
Khare, V., Jain, A., & Bhuiyan, M. A. (2023). Assessment of hydro energy potential from rain fall data set in India through data analysis. E-Prime - Advances in Electrical Engineering, Electronics and Energy, 6(August), 100290. https://doi.org/10.1016/j.prime.2023.100290
Legaki, N. Z., Karpouzis, K., Assimakopoulos, V., & Hamari, J. (2021). Gamification to avoid cognitive biases: An experiment of gamifying a forecasting course. Technological Forecasting and Social Change, 167, 120725. https://doi.org/10.1016/j.techfore.2021.120725
Lin, W., & Wei, Y. (2024). Economic forecasting with big data: A literature review. Journal of Management Science and Engineering, 9(2), 254–270. https://doi.org/10.1016/j.jmse.2024.01.003
Mizoue, Y., Sencer, B., & Beaucamp, A. (2020). Enhancing tropical cyclone intensity forecasting with explainable deep learning integrating satellite observations and numerical model outputs. International Journal of Machine Tools and Manufacture, 103648. https://doi.org/10.1016/j.isci.2024.109905
Neubauer, L., & Filzmoser, P. (2024). Improving forecasts for heterogeneous time series by “averaging”, with application to food demand forecasts. International Journal of Forecasting, xxxx. https://doi.org/10.1016/j.ijforecast.2024.02.002
Nzengue, A. G. B., Mpofu, K., Mathe, N., Muvunzi, R., & Oyesola, M. (2023). An Integrated Value-Addition in Supply Chain Network for Metal-based Additive Manufacturing. Procedia CIRP, 120, 892–897. https://doi.org/10.1016/j.procir.2023.09.095
Peng, Y., Wang, T., Wang, H., Wang, L., Zhang, H., & Wu, S. (2023). Impact of digital filtering as a weak constraint on 4DVar to predict and perturb typhoons in WRF model. Atmospheric Research, 284(December 2022), 106578. https://doi.org/10.1016/j.atmosres.2022.106578
Rahmawati, A., Wahyuningsih, S. H., & Garad, A. (2023). The effect of financial literacy, training and locus of control on creative economic business performance. Social Sciences and Humanities Open, 8(1), 100721. https://doi.org/10.1016/j.ssaho.2023.100721
Sroginis, A., Fildes, R., & Kourentzes, N. (2023). Use of contextual and model-based information in adjusting promotional forecasts. European Journal of Operational Research, 307(3), 1177–1191. https://doi.org/10.1016/j.ejor.2022.10.005
Syberg, M., West, N., Lenze, D., & Deuse, J. (2023). Framework for predictive sales and demand planning in customer-oriented manufacturing systems using data enrichment and machine learning. Procedia CIRP, 120, 1107–1112. https://doi.org/10.1016/j.procir.2023.09.133
Szalkowski, G. A., & Mikalef, P. (2023). Understanding digital platform evolution using compartmental models. Technological Forecasting and Social Change, 193(December 2022), 122600. https://doi.org/10.1016/j.techfore.2023.122600
Thomakos, D., & Xidonas, P. (2023). The origins of forward-looking decision making: Cybernetics, operational research, and the foundations of forecasting. Decision Analytics Journal, 8(July), 100284. https://doi.org/10.1016/j.dajour.2023.100284
Villarroya, C., Calafate, C. T., Onaindia, E., Cano, J. C., & Martinez, F. J. (2022). Neural Network-based Model for Traffic Prediction in the City of Valencia. Procedia Computer Science, 207(Kes), 552–562. https://doi.org/10.1016/j.procs.2022.09.110
Yusuf, N., Govindan, R., & Al-Ansari, T. (2024). Energy markets restructure beyond 2022 and its implications on Qatar LNG sales strategy: Business forecasting and trend analysis. Heliyon, 10(7), e27682. https://doi.org/10.1016/j.heliyon.2024.e27682
Gopalakrishnan, V., Pethe, S., Kefayati, S., Srinivasan, R., Hake, P., Deshpande, A., Liu, X., Hoang, E., Davila, M., Bianco, S., & Kaufman, J. H. (2021). Globally local: Hyper-local modeling for accurate forecast of COVID-19. Epidemics, 37, 100510. https://doi.org/10.1016/j.epidem.2021.100510
Hemalatha, C., Sankaranarayanasamy, K., & Durairaaj, N. (2021). Lean and agile manufacturing for work-in-process (WIP) control. Materials Today: Proceedings, 46, 10334–10338. https://doi.org/10.1016/j.matpr.2020.12.473
Jaidi, N., Siswantoyo, Liu, J., Sholikhah, Z., & Andhini, M. M. (2022). Ambidexterity Behavior of Creative SMEs for Disruptive Flows of Innovation: A Comparative Study of Indonesia and Taiwan. Journal of Open Innovation: Technology, Market, and Complexity, 8(3), 141. https://doi.org/10.3390/joitmc8030141
Khare, V., & Jain, A. (2023). Predict the performance of driverless car through the cognitive data analysis and reliability analysis based approach. E-Prime - Advances in Electrical Engineering, Electronics and Energy, 6(November), 100344. https://doi.org/10.1016/j.prime.2023.100344
Khare, V., Jain, A., & Bhuiyan, M. A. (2023). Assessment of hydro energy potential from rain fall data set in India through data analysis. E-Prime - Advances in Electrical Engineering, Electronics and Energy, 6(August), 100290. https://doi.org/10.1016/j.prime.2023.100290
Legaki, N. Z., Karpouzis, K., Assimakopoulos, V., & Hamari, J. (2021). Gamification to avoid cognitive biases: An experiment of gamifying a forecasting course. Technological Forecasting and Social Change, 167, 120725. https://doi.org/10.1016/j.techfore.2021.120725
Lin, W., & Wei, Y. (2024). Economic forecasting with big data: A literature review. Journal of Management Science and Engineering, 9(2), 254–270. https://doi.org/10.1016/j.jmse.2024.01.003
Mizoue, Y., Sencer, B., & Beaucamp, A. (2020). Enhancing tropical cyclone intensity forecasting with explainable deep learning integrating satellite observations and numerical model outputs. International Journal of Machine Tools and Manufacture, 103648. https://doi.org/10.1016/j.isci.2024.109905
Neubauer, L., & Filzmoser, P. (2024). Improving forecasts for heterogeneous time series by “averaging”, with application to food demand forecasts. International Journal of Forecasting, xxxx. https://doi.org/10.1016/j.ijforecast.2024.02.002
Nzengue, A. G. B., Mpofu, K., Mathe, N., Muvunzi, R., & Oyesola, M. (2023). An Integrated Value-Addition in Supply Chain Network for Metal-based Additive Manufacturing. Procedia CIRP, 120, 892–897. https://doi.org/10.1016/j.procir.2023.09.095
Peng, Y., Wang, T., Wang, H., Wang, L., Zhang, H., & Wu, S. (2023). Impact of digital filtering as a weak constraint on 4DVar to predict and perturb typhoons in WRF model. Atmospheric Research, 284(December 2022), 106578. https://doi.org/10.1016/j.atmosres.2022.106578
Rahmawati, A., Wahyuningsih, S. H., & Garad, A. (2023). The effect of financial literacy, training and locus of control on creative economic business performance. Social Sciences and Humanities Open, 8(1), 100721. https://doi.org/10.1016/j.ssaho.2023.100721
Sroginis, A., Fildes, R., & Kourentzes, N. (2023). Use of contextual and model-based information in adjusting promotional forecasts. European Journal of Operational Research, 307(3), 1177–1191. https://doi.org/10.1016/j.ejor.2022.10.005
Syberg, M., West, N., Lenze, D., & Deuse, J. (2023). Framework for predictive sales and demand planning in customer-oriented manufacturing systems using data enrichment and machine learning. Procedia CIRP, 120, 1107–1112. https://doi.org/10.1016/j.procir.2023.09.133
Szalkowski, G. A., & Mikalef, P. (2023). Understanding digital platform evolution using compartmental models. Technological Forecasting and Social Change, 193(December 2022), 122600. https://doi.org/10.1016/j.techfore.2023.122600
Thomakos, D., & Xidonas, P. (2023). The origins of forward-looking decision making: Cybernetics, operational research, and the foundations of forecasting. Decision Analytics Journal, 8(July), 100284. https://doi.org/10.1016/j.dajour.2023.100284
Villarroya, C., Calafate, C. T., Onaindia, E., Cano, J. C., & Martinez, F. J. (2022). Neural Network-based Model for Traffic Prediction in the City of Valencia. Procedia Computer Science, 207(Kes), 552–562. https://doi.org/10.1016/j.procs.2022.09.110
Yusuf, N., Govindan, R., & Al-Ansari, T. (2024). Energy markets restructure beyond 2022 and its implications on Qatar LNG sales strategy: Business forecasting and trend analysis. Heliyon, 10(7), e27682. https://doi.org/10.1016/j.heliyon.2024.e27682
Published
2024-04-30
How to Cite
LaoliI. E., KakisinaS. M., HarefaI., & GeaJ. B. I. J. (2024). Analisis Peramalan Persediaan Bahan Baku Pada Usaha Tahu Murni Desa Tuhemberua Ulu Kota Gunungsitoli. Jurnal Ilmiah Metansi (Manajemen Dan Akuntansi), 7(1), 209-214. https://doi.org/10.57093/metansi.v7i1.269
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