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All Journal VARIANSI: Journal of Statistics and Its Application on Teaching and Research Eksponensial
Wiwit Pura Nurmayanti
Universitas Mulawarman
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Other

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Comparative Analysis of DES-Brown and DES-Holt Methods in Forecasting the Stock Price of PT Telekomunikasi Indonesia Tbk Dela Juliarsih Rahman; Wiwit Pura Nurmayanti; Thesya Atarezcha Pangruruk; Erlyne Nadhilah Widyaningrum; Siti Hadijah Hasanah
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 8 No. 1 (2026)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm486

Abstract

This study aims to dermine the best forecasting method for the stock price of PT Telekomunikasi Indonesia Tbk using the Double Exponential Smoothing (DES) Brown and DES-Holt methods. The data used consist of stock prices from January 2019 to September 2025. The DES-Brown method employs a single parameter, while DES-Holt uses two parameters. Forecasting accuracy is evaluated using Mean Absolute Deviation (MAD), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results indicate that the DES-Brown method with a smoothing parameter produces the smallest forecasting errors compared to the DES-Holt method, with MAD, RMSE , and MAPE . Therefore, it can be concluded that the DES-Brown method is the most suitable approach for forecasting the stock price of PT Telekomunikasi Indonesia Tbk.
Implementasi Model Hybrid Autoregressive Fractionally Integrated Moving Average-Neural Network (ARFIMA-NN) pada Peramalan Indeks Harga Saham Gabungan Khairunnisa Avrilia; Desi Yuniarti; Wiwit Pura Nurmayanti; M. Fathurahman; Sri Wahyuningsih
VARIANSI: Journal of Statistics and Its application on Teaching and Research Vol. 8 No. 1 (2026)
Publisher : Program Studi Statistika Fakultas MIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/variansiunm487

Abstract

Fenomena fluktuasi ekstrem pada harga penutupan Indeks Harga Saham Gabungan (IHSG) di Bursa Efek Indonesia (BEI) menciptakan ketidakpastian yang sulit diprediksi, sehingga peramalan pada data harga penutupan IHSG dapat membantu investor untuk mengantisipasi risiko investasi dan mempermudah investor untuk menentukan strategi investasi pada periode mendatang. Model hybrid Autoregressive Fractionally Integrated Moving Average-Neural Network (ARFIMA-NN) diimplementasikan karena model ini mampu menangani karakteristik long memory dan memiliki kemampuan menangkap pola non-linier, yang diharapkan dapat meningkatkan akurasi pada peramalan. Berdasarkan hasil analisis, diperoleh hasil peramalan menggunakan model hybrid ARFIMA-NN dengan 1 hingga 3 neuron yang menunjukkan bahwa nilai MAPE berada di bawah 10% atau peramalan sangat baik. Selanjutnya berdasarkan model hybrid ARFIMA(1;0,51;4)-NN 2 menggunakan data IHSG periode Januari 2005 hingga dengan Desember 2024 diperoleh IHSG periode Januari hingga Desember 2025 yang meningkat setiap bulannya.
Peramalan Nilai Transaksi Uang Elektronik Di Indonesia Menggunakan Double Exponential Smoothing Brown Dengan Optimasi Nonlinier Anna Putri Aritonang; Meiliyani Siringoringo; Wiwit Pura Nurmayanti; Sri Wahyuningsih; Suyitno Suyitno
EKSPONENSIAL Vol. 17 No. 1 (2026): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/8hjsey81

Abstract

The Double Exponential Smoothing (DES) Brown method is one of the forecasting methods used for data that exhibited a trend pattern, in which the smoothing process was performed twice. The determination of the optimal smoothing parameter in the DES Brown method is usually carried out through a trial-and-error process. Another way to obtain the optimal smoothing parameter value more quickly and accurately is by using nonlinear optimization. In this study, two optimization methods were used: the Golden Section and the Levenberg-Marquardt methods. The objectives of this research were to obtain the optimal smoothing parameter of the DES Brown method using the Golden Section and Levenberg-Marquardt optimizations, to forecast the value of electronic money transactions in Indonesia for the period of January to March 2025 using the DES Brown method with the optimal smoothing parameter, and to identify the best optimization method for determining the optimal smoothing parameter of DES Brown method were obtained based on the MAPE value. The results of the study showed that the optimal smoothing parameter of the DES Brown method using the Golden Section optimization was 0.4634178 and the Levenberg-Marquardt optimization was 0.3498674. 
Co-Authors Anna Putri Aritonang Dela Juliarsih Rahman Desi Yuniarti Erlyne Nadhilah Widyaningrum Hasanah, Siti Hadijah Khairunnisa Avrilia M. Fathurahman Meiliyani Siringoringo Pangruruk , Thesya Atarezcha Pangruruk Sri Wahyuningsih Suyitno Suyitno