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All Journal Media Statistika Radiant : Journal of Applied, Social, and Education Studies
Yudho Yudhanto, Yudho
Program Studi Diploma III Teknik Informatika
Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Social Sciences Other

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IMPLEMENTATION OF PROPHET IN AMERICAN ELECTRICITY FORECASTING WITH AND WITHOUT PARAMETER TUNING Sulandari, Winita; Yudhanto, Yudho; Hapsari, Riskhia; Wijayanti, Monica Dini; Pardede, Hilman Ferdinandus
MEDIA STATISTIKA Vol 17, No 1 (2024): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.17.1.93-104

Abstract

Prophet is one of the machine learning approximation methods that accommodate trends, seasonality, and holiday impacts in time series data. Generally, the performance of machine learning models can be improved by implementing hyperparameter tuning. This study investigates whether hyperparameter tuning can improve the model's performance. To show its effectiveness, the Prophet model constructed by parameter tuning is compared to the one with fixed parameter values (namely the default model) for both the original series and the Box-Cox transformed series in terms of mean absolute percentage error (MAPE). Based on the experimental results of the twenty-four daily electricity load time series in American Electric Power (AEP). This shows that parameter tuning successfully reduces the MAPE of the default model in the range of about 3-8% for training data. However, there is no guarantee for testing data. Although, in some cases, parameter tuning can reduce the MAPE value of the default model by up to 38%, in other cases, it actually increases the MAPE of the default model by almost 15%. The experiments on testing data also show that models built from transformed data do not necessarily produce more accurate forecast values than those built from the original data.
Forecasting the Composite Stock Price Index Using Fuzzy Time Series Type 2: Peramalan Indeks Harga Saham Gabungan dengan menggunakan Metode Fuzzy Time Series Tipe 2 Pramesti, Arsita Anggraeni; Sulandari, Winita; Subanti, Sri; Yudhanto, Yudho
RADIANT: Journal of Applied, Social, and Education Studies Vol. 4 No. 2 (2023): RADIANT: Journal of Applied, Social, and Education Studies
Publisher : Politeknik Harapan Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52187/rdt.v4i2.167

Abstract

The movement and fluctuation of the IHSG are one of the references for investors in making investment decisions for buying, selling, or holding share ownership. Forecasting the value of the IHSG can assist investors in making this decision. This study used the fuzzy time series type 2 method to predict the IHSG. This study uses monthly IHSG data for 2017-2021 with 3 variables, namely close prices, high prices, and low prices. In fuzzy time series forecasting, the length of the interval affects the prediction results. This study uses a distribution and average-based method in determining the length of the interval to obtain optimal forecasting results. Based on the calculation of MAPE, the forecasting using average-based and distribution-based interval lengths had errors of 2.56% and 2.46%. The MAPE value shows that the forecasting results from the two methods of taking the length of the interval are very good. The IHSG forecasting results in January 2022 use a distribution-based interval length is 6450 while the IHSG forecasting results in January 2022 use an average-based interval length is 6510. The results of this study indicate that the interval length affects the forecasting results in fuzzy time series. Keywords: IHSG, fuzzy time series type 2, length of the interval, MAPE
Co-Authors Hapsari, Riskhia Pardede, Hilman Ferdinandus Pramesti, Arsita Anggraeni Sri Subanti Wijayanti, Monica Dini Winita Sulandari