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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer JTAM (Jurnal Teori dan Aplikasi Matematika)
Rozy, Agus Fachrur
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Computer Science & IT Engineering Mathematics

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Optimalisasi Prediksi Harga Ihsg Menggunakan Hybrid Weighted Fuzzy Time Series Hidden Markov Model Dengan Algoritma Evolusi Differensial Syalsabilla, Alya Fitri; Astutik, Suci; Rozy, Agus Fachrur
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 4: Agustus 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.1148867

Abstract

Perdagangan saham berdasarkan Indeks Harga Saham Gabungan (IHSG) di Indonesia adalah area dinamis dan kompleks. Prediksi pergerakan harga IHSG memiliki volatilitas pasar saham yang tinggi. Penggunaan Hybrid Weighted Fuzzy Time series Hidden Markov Model (WFTS-HMM) dengan Algoritma Evolusi Diferensial (DE) menjanjikan solusi dengan pendekatan terbaru. Penelitian ini bertujuan untuk meningkatkan akurasi prediksi harga IHSG melalui optimasi model hybrid.. Penelitian menggunakan data IHSG tiap bulan dari Januari hingga Desember 2023 dari situs www.yahoo.finance.com. Prediksi yang dihasilkan dari Model Hybrid WFTS-HMM dioptimasi dengan Algoritma ED memiliki tingkat kesalahan prediksi yang lebih rendah (1.45%) dibandingkan dengan model tanpa DE (1.49%).   Abstract   Stock trading based on IHSG in Indonesia is a dynamic and complex area. Predicting IHSG price movements entails high stock market volatility. Utilizing the Hybrid WFTS-HMM Model with the DE Algorithm promises a cutting-edge approach. This research aims to enhance the prediction of IHSG price through hybrid model optimization and performance evaluation. The study employs IHSG monthly data from January to December 2023 from www.yahoo.finance.com. Forecasting from the Hybrid WFTS-HMM Model with the DE Algorithm has lower prediction error (1.45%) compared to the model without DE (1.49%).
Development of Accuracy for the Weighted Fuzzy Time Series Forecasting Model Using Lagrange Quadratic Programming Rozy, Agus Fachrur; Solimun, Solimun; Wardhani, Ni Wayan Surya
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 4 (2023): October
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i4.16783

Abstract

Limitation within the WFTS model, which relies on midpoints within intervals and linguistic variable relationships for assigning weights. This reliance can result in reduced accuracy, especially when dealing with extreme values during trend to seasonality transformations. This study employs the Weighted Fuzzy Time Series (WFTS) method to adjust predictive values based on actual data. Using Lagrange Quadratic Programming (LQP), estimated weights enhance the WFTS model. MAPE assesses accuracy as the model analyzes monthly IHSG closing prices from January 2017 to January 2023.The MAPE value of 0.61% results from optimizing WFTS with LQP. It utilizes a deterministic approach based on set membership counts in class intervals, continuously adjusting weights during fuzzification, minimizing the deviation between forecasted and actual data values.The Weighted Fuzzy Time Series Forecasting Model with Lagrange Quadratic Programming is effective in forecasting, indicated by a low MAPE value. This method evaluates each data point and adjusts weights, offering reliable investment insights for IHSG strategies..
Peningkatan Akurasi Metode Weighted Fuzzy Time Series Forecasting Menggunakan Algoritma Evolusi Differensial dan Fuzzy C-Means Rozy, Agus Fachrur; Solimun, Solimun; Wardhani, Ni Wayan Surya
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 5: Oktober 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023107505

Abstract

Prediksi adalah suatu pendekatan yang digunakan untuk mengantisipasi ketidakpastian masa depan. Metode prediksi konfensional terkendala pada penyesuaian data terhadap asumsi yang digunakan sehingga diperlukan Metode Weighted Fuzzy Time Series. Meskipun metode WFTS telah terbukti efektif terdapat tantangan dalam meningkatkan akurasi peramalan yang dihasilkan. Dua teknik yang sering digunakan dalam konteks ini adalah Algoritma Evolusi Differensial (ED) dan Fuzzy C-Means (FCM). Data yang digunakan pada penelitian ini adalah Jakarta Islamic Index (JKII) per bulan dari bulan Agustus 2018 hingga Juli 2023. Data yang digunakan adalah data sekunder yang diperoleh dari situs www.yahoo.finance.com. Analisis dilakukan untuk meningkatkan akurasi dari metode peramalan WFTS dengan klasifikasi FCM dan proses optimalisasi menggunakan hasil forecasting dengan Algoritma Evolusi Diffensial (DE).Hasil klasifikasi dengan Fuzzy C-Means, ditemukan 7 klaster dengan jumlah keanggotaan yang berbeda. Perhitungan nilai peramalan dilkakukan dengan defuzzyfikasi dengan mengubah variabel linguistik menjadi bilangan real. Proses transformasi ini melibatkan perkalian antara bobot yang diperoleh dari estimasi Fuzzy C Means dengan nilai titik tengah pada setiap cluster. Proses optimalisasi hasil dilakukan dengan menggunakan algoritma DE dapat meningkatkan akurasi dari forecasting. Kesimpulan yang didapat yaitu algoritma evolusi differensial dapat meningkatkan akurasi forecasting dari metode weighted fuzzy time series dengan kombinasi pembentukan kelas interval menggunakan metode fuzzy c-means. Hal ini dikarenakan nilai MAPE yang dihasilkan dari algoritma evolusi differensial lebih kecil daripada model weighted fuzzy time series.   Abstract Prediction is a form of approach in anticipating future uncertainties. Conventional prediction methods encounter difficulties in adapting data with the assumptions used, necessitating the application of the Weighted Fuzzy Time Series (WFTS) method. Although the WFTS method has proven effective, there are challenges in improving the accuracy of the generated forecasts. There are two commonly applied approaches: the Differential Evolution (DE) algorithm and Fuzzy C-Means (FCM). The data used in this research is the Jakarta Islamic Index (JKII) on a monthly basis from August 2018 to July 2023. The information collected is secondary data obtained from the website www.yahoo.finance.com. The analysis conducted involves performing FCM classification to form interval classes and optimizing the forecasting results of the WFTS method with DE. The Fuzzy C-Means classification resulted in finding 7 clusters with different membership counts. Forecasting values are calculated through defuzzification by converting linguistic variables into real numbers. This transformation process involves multiplying the weights obtained from the Fuzzy C-Means estimation with the mid-point values of each cluster.The optimization process is performed using the DE algorithm. The research findings conclude that the use of the differential evolution algorithm improves the accuracy of the forecasting from the Weighted Fuzzy Time Series method with the approach of combining interval class formation through the Fuzzy C-Means method. The DE algorithm works by seeking the best solution in a complex parameter space through iterations and performance evaluations, thereby significantly enhancing the performance of the forecasting model.
Co-Authors Ni Wayan Surya Wardhani Solimun, Solimun Suci Astutik Syalsabilla, Alya Fitri