Document Type : Original Paper
Authors
Department of Statistics, Faculty of Mathematics and Natural Sciences, Hasanuddin University, Makassar, 90245 Indonesia Makassar, Indonesia
Abstract
The Jakarta Composite Index (JCI) is a pivotal benchmark for evaluating the performance of all stocks listed on the Indonesia Stock Exchange (IDX). Given the inherent complexity, nonlinearity, and non-stationarity of stock market data, selecting robust forecasting methods is essential. This study compares the performance of the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) in forecasting JCI movements. The researcher assessed prediction accuracy using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The training phase revealed that the optimal ANFIS model employed the generalized bell membership function, outperforming trapezoidal and Gaussian alternatives. Concurrently, the best SVM configuration utilized a linear kernel (cost = 10), demonstrating superior performance compared to radial basis function (RBF) and sigmoid kernels. In the testing phase, ANFIS achieved an RMSE of 39.894 and MAPE of 0.4647, while SVM recorded an RMSE of 38.728 and MAPE of 0.4516. These results underscore the superior predictive capabilities of SVM, positioning it as a reliable tool for stock market forecasting. The study’s findings provide valuable insights for investors and policymakers in navigating market uncertainties and optimizing investment strategies.
Keywords
- Forecasting
- Support Vector Machine
- Jakarta Composite Index
- Adaptive Neural-based Fuzzy Inference System
Main Subjects
- Bursa Efek Indonesia. Bursa Efek Indonesia (Internet). 2023 Mar 4 (cited 2024 Nov 25). Available from: https://www.idx.co.id/id/produk/saham
- Arviana, Nerissa, Geofanni. Sebelum Investasi, Pahami Dulu Apa Itu IHSG dan Berbagai Fungsinya [Internet]. Glints.com. 2023 Jan 19 [cited 2024 Nov 30]. Available from: https://glints.com/id/lowongan/apa-itu-ihsg/#.ZAMkgh_P3IU
- Wang WC, Chau KW, Cheng CT, Qiu L. A comparison of performance of several artificial intelligence methods for forecasting daily discharge time series. J Hydrol. 2009;368(1–4):294–306.
- Al-qaness MA, Fan H, Ewees AA, V DY. Improved ANFIS model for forecasting Wuhan City Air Quality and analysis COVID-19 lockdown impacts on air quality. Environ Res. 2021;194:110607.
- Tarigan IA, Bayupati IP, Putri GA. Komparasi model Support Vector Machine dan Backpropagation dalam peramalan jumlah wisatawan mancanegara di Provinsi Bali. J Teknol Syst Komput. 2021;9(2):90–5.
- Makridakis S, Wright S, McGee VE. Metode dan aplikasi peramalan. 2nd ed. Jakarta: Binaputra Aksara; 1999.
- Widyapratiwi LK, Mertasana IP, Arjana IG. Peramalan beban listrik jangka pendek di Bali menggunakan pendekatan Adaptive Neuro-Fuzzy Inference System (ANFIS). Teknol Elektro. 2012;11(1):7–13.
- Widodo PP. Penerapan soft computing dengan Matlab. Bandung: Rekayasa Sains; 2012.
|
- Wulandari A, Rosita, Gernowo R. Metode Autoregressive Integrated Moving Average (ARIMA) dan metode Adaptive Neuro Fuzzy Inference System (ANFIS) dalam analisis curah hujan. Berkala Fisika. 2019;22(1):41–8.
- Jang JS, Sun CT, Mizutani E. Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. New Jersey: Prentice Hall; 1997.
- Vapnik VC. Support-vector networks. Mach Learn. 1995;20(3):273–97.
- Santoso B. Data mining teknik pemanfaatan data untuk keperluan bisnis. Yogyakarta: Graha Ilmu; 2007.
- Campbell C, Ying Y. Learning with Support Vector Machine. Morgan & Claypool Publishers; 2011.
- Albert S, Shantika M, Syahru R. Sistem informasi peramalan tren pelanggan dengan menggunakan metode double exponential smoothing di MESS GM. J Komput Apl. 2020;10(4):237–46.
- Sezer OB, Gudelek MU, Ozbayoglu AM. Financial time series forecasting with deep learning: a systematic literature review (2005–2019). Appl Soft Comput. 2020;90:106181.
- Novitasari HR. Weather parameters forecasting as variables for rainfall prediction using Adaptive Neuro Fuzzy Inference System (ANFIS) and Support Vector Regression (SVR). In: International Conference on Science & Technology (ICoST 2019). 2020. p. 1–6