Comparison of Methods Fuzzy Time Series Chen And Fuzzy Time Series Cheng On the Closing Price Forecast of PT Bank Central Asia Tbk (BBCA) Shares
Abstract
Stock price forecasting is an important aspect in financial market analysis because it can support investors in making investment decisions. This study aims to forecast the closing price of PT Bank Central Asia Tbk (BBCA) shares and compare the accuracy between the Chen and Cheng Fuzzy Time Series methods. The data used are the weekly closing prices of BBCA shares for the period January 2014 to December 2024 with a total of 566 data. The forecasting process is carried out through the stages of fuzzification, formation of fuzzy logical relationships (FLR), grouping FLRs into fuzzy logical relationship groups (FLRG), and defuzzification. Accuracy evaluation is carried out using the Mean Absolute Percentage Error (MAPE). The results show that the Chen Fuzzy Time Series method produces a prediction of BBCA's closing price in the first week of January 2025 of Rp9,440.75 with a MAPE value of 6.32%, while the Cheng Fuzzy Time Series method produces a prediction of Rp9,441 with a MAPE value of 5.74%. With a smaller MAPE value, the Cheng method is proven to be more accurate than the Chen method in predicting BBCA's closing price. This shows that giving weights to fuzzy relations in Cheng's method is able to increase the accuracy of forecasting results.
References
Andini, T. D., & Auristandi, P. (2016). Forecasting the number of office stationery stocks at UD Achmad Jaya using the double exponential smoothing method. Asian Scientific Journal of Technology and Information (Jitika), 10(1).
Ariza Siregar, M., & Puspitasari, N. B. (2023). Forecasting palm oil production results of PT. Bakrie Pasaman Plantations using the Holt-Winter's exponential smoothing method.
Arum, P. R., Kintoko, Nabila, N. H. R., & Purnomo, E. A. (2023). Comparison of fuzzy time series and fuzzy time series-particle swarm optimization methods in predicting Bank BCA share price. Proceedings of the 2023 International Conference on Information Technology and Engineering (ICITE 2023), Advances in Intelligent Systems Research, 179 (pp. 5–18). https://www.atlantis-press.com/article/125995859
Chen, S.-M. (1996). Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems, 81(3), 311–319.
Cheng, C.-H., Chen, T.-L., Teoh, H. J., & Chiang, C.-H. (2008). Fuzzy time-series based on adaptive expectation model for TAIEX forecasting. Expert Systems with Applications, 34(2), 1126–1132. https://doi.org/10.1016/j.eswa.2006.12.021
Fahmuddin, M., & Rais, Z. (2021). Model hibrida dekomposisi-ARIMA untuk peramalan inflasi di Kota Makassar. Variansi: Journal of Statistics and Its Application on Teaching and Research, 3(2), 90–94. https://doi.org/10.35580/variansiunm23889
Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTexts. https://otexts.com/fpp3/
Indah Nabila, P., & Sukarsih, I. (2023). Palm oil cooking oil price prediction using average-based fuzzy time series Lee. Journal of Mathematical Research, 157–166. https://doi.org/10.29313/jrm.v3i2.2835
Khanifah, & Hendikawati, P. (2024). Comparison of the accuracy of the ANN method with the Chen, Cheng, and Markov chain FTS models for forecasting the closing price of Zoom Video Communication stocks. Journal of Multidisciplinary Inquiry in Science, Technology, and Educational Research, 2(1b), 1615–1633.
Liantoni, F., & Simanjuntak, O. (2025). Peramalan nilai saham BBCA melalui pendekatan time series menggunakan teknik exponential smoothing. JISKA (Jurnal Informatika Sunan Kalijaga), 10(3), 259–266. https://doi.org/10.14421/jiska.2025.10.3.259-266
Mar'ah, Z., Ruliana, R., & Septiana, M. (2024). Pemodelan multivariate adaptive regression spline (MARS) pada indeks harga saham gabungan (IHSG) tahun 2018–2023. Variansi: Journal of Statistics and Its Application on Teaching and Research, 6(1), 1–10. https://doi.org/10.35580/variansiunm144
Muthahharah, I., Meliyana, S. M., & Mar'ah, Z. (2025). Forecasting Indonesia's wholesale price index (WPI) using the Holt's exponential smoothing method. 6(2). https://doi.org/10.35877/454ri.qems
Nabillah, I., & Ranggadara, I. (2020). Mean absolute percentage error untuk evaluasi hasil prediksi komoditas laut. JOINS (Journal of Information System), 5(2), 250–255. https://doi.org/10.33633/joins.v5i2.3900
Namirah, S. H., & Nur, Y. (2023). Macro indicator analysis of the stock value of the LQ45 banking sector on the Indonesia Stock Exchange. https://e-jurnal.nobel.ac.id/index.php/manuver
Nugroho, K. (2016). Prediction analysis model using fuzzy time series method. Infokam, XII(1), 46–50.
Pratiwi, B., Caroline Pelawi, D., Dameria Gracia Silalahi, J., Malasari Siregar, T., & Anshari, F. (2023). Fuzzy set optimization model to determine optimal selling price of beef. Innovative: Journal of Social Science Research, 3, 3263–3275.
Qadrini, L., Asrirawan, A., Mahmudah, N., Fahmuddin, M., & Amri, I. F. (2020). Forecasting Bank Indonesia currency inflow and outflow using ARIMA, time series regression (TSR), ARIMAX, and NN approaches in Lampung. Jurnal Matematika, Statistika dan Komputasi, 17(2), 166–177. https://doi.org/10.20956/jmsk.v17i2.11803
Ramoza, I. R. (2023). Comparison of triple exponential smoothing and fuzzy time series for predicting net palm oil fresh fruit bunches (FFB).
Song, Q., & Chissom, B. S. (1993). Fuzzy time series and its models. Fuzzy Sets and Systems, 54(3), 269–277.
Sturges, H. (1926). The choice of a class interval. Journal of the American Statistical Association.
Sukartaatmadja, I., Khim, S., & Lestari, M. N. (2023). Factors affecting company stock prices. Scientific Journal of Unity Management, 11(1), 21–40. https://doi.org/10.37641/jimkes.v11i1.1627
Tricahya, S., & Rustam, Z. (2019). Forecasting the amount of pneumonia patients in Jakarta with weighted high order fuzzy time series. IOP Conference Series: Materials Science and Engineering, 546(5). https://doi.org/10.1088/1757-899x/546/5/052080
Virgianti, V., Martha, S., & Imro'ah Intisari, N. (2021). Application of fuzzy time series Chen average based on rainfall forecasting. Scientific Bulletin of Math. Stat. and Its Applications (Bimaster), 10(4).
Vivianti, V., Aidid, M. K., & Nusrang, M. (2020). Implementasi metode fuzzy time series untuk peramalan jumlah pengunjung di Benteng Fort Rotterdam. Variansi: Journal of Statistics and Its Application on Teaching and Research, 2(1), 1–12. https://doi.org/10.35580/variansiunm12904
Wibowo, E. C., & Cahyono, A. D. (2025). Comparative analysis of linear regression and neural network algorithms for stock price prediction. Sistemasi, 14(4), 1879–1896. https://doi.org/10.32520/stmsi.v14i4.5325
Wirawan, I. M., Zaeni, I. A. E., Mujaddid, U. A., & Jaya, A. S. B. M. (2021). Fuzzy time series method comparison of Chen and Cheng models to predict chile prices. 7th International Conference on Electrical, Electronics and Information Engineering: Technological Breakthrough for Greater New Life, ICEEIE 2021. https://doi.org/10.1109/iceeie52663.2021.9616907
