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All Journal Media Statistika Jurnal Teknologi Informasi dan Ilmu Komputer BAREKENG: Jurnal Ilmu Matematika dan Terapan Jurnal Bumigora Information Technology (BITe) Jurnal Ilmiah Matematika Journal of Applied Data Sciences Jurnal Pengabdian kepada Masyarakat STATISTIKA Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) International Conference on Digital Advanced Tourism, Management, and Technology Radiant : Journal of Applied, Social, and Education Studies

Winita Sulandari

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Author-ID : 390829

Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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ESTIMASI PARAMETER MODEL MIXTURE AUTOREGRESSIVE (MAR) MENGGUNAKAN ALGORITMA EKSPEKTASI MAKSIMISASI (EM) Asrini, Mika; Sulandari, Winita; Wiyono, Santoso Budi
MEDIA STATISTIKA Vol 6, No 1 (2013): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Mixture autoregressive (MAR) Model is a mixture of Gaussian autoregressive (AR) components. The mixture model is capable for modelling of nonlinear time series with multimodal conditional distributions. This paper discusses about the parameters estimation using EM algorithm. All possible models are then applied to national maize production data. In this case, the BIC is used for the MAR model selection. Keywords : Mixture Autoregressive, EM Algorithm, BIC, Maize Production

PERAMALAN PENGGUNAAN BEBAN LISTRIK JANGKA PENDEK GARDU INDUK BAWEN DENGAN DSARIMA Saptyani, Marita; Sulandari, Winita; Pangadi, Pangadi
MEDIA STATISTIKA Vol 8, No 1 (2015): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Bawen substation is a part of electrical distribution system. Forecasting load demand is required for power planning. Data used in this research are an hourly load demand of Bawen, Salatiga for 3 months, from February 2, 2013 to April 29, 2013, measured in Megawatt (MW).A half hourly load demand forecasting is needed for real time controlling and short-term maintenance schedulling. Since the data have two seasonal periods, i.e. daily and weekly seasonality with length 48 and 336 respectively, the model of double seasonal ARIMA (DSARIMA) is proposed as the most appropriate model for the case. Initial model is determined by the pattern of the data, based on the autocorrelation function plot. Some experiments was done by choosing several periods data. The most suitable model is chosen based on the outsample mean absolute percentage error (MAPE). The current study shows that the DSARIMA (0, 1, [1, 20, 47])(0, 1, 1)48(0, 1, 0)336 is the best model to forecastÂ 336 next period.Â Keywords: DSARIMA, MAPE, Electricity, Bawen

PENERAPAN MODEL HYBRID ARIMA BACKPROPAGATION UNTUK PERAMALAN HARGA GABAH INDONESIA Janah, Sufia Nur; Sulandari, Winita; Wiyono, Santoso Budi
MEDIA STATISTIKA Vol 7, No 2 (2014): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Hybrid model discussed in this paper combining ARIMA and backpropagation is applied to grain price forecasting in Indonesia for period January 2008 until April 2013. The grain price time series consists of linear and nonlinear patterns. Backpropagations can recognize non linear patterns that can not be done by ARIMA. In order to find the best model, some combinations of prepocessing transformations, the number of input and hidden units, and the activation function were applied in the contruction of the network structure. Based on the experiments, it can be showed that ARIMA backpropagation hybrid model provides more accurate results than ARIMA model. Â The hybrid model would rather be used in the short-term forecasting, no more than three periods.Â Keywords: ARIMA, Backpropagation, Hybrid, Grain Price

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

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.

IS THE BOX-COX TRANSFORMATION NEEDED IN MODELING TELKOM’S STOCK PRICE USING NNAR AND DESH METHODS? Noven, Michela Sheryl; Respatiwulan, Respatiwulan; Sulandari, Winita
MEDIA STATISTIKA Vol 17, No 2 (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.17.2.185-196

Accurate stock price forecasting requires appropriate preprocessing, particularly for time series data with high variability and nonlinear patterns. This study investigates whether applying the Box-Cox Transformation (BCT) improves forecasting performance when modeling Telkom Indonesia's stock price using Neural Network Autoregressive (NNAR) and Double Exponential Smoothing Holt (DESH) methods. The NNAR model architecture is selected based on nonlinearity testing of lag variables, while DESH parameters are optimized by minimizing mean square error. Forecasting accuracy is evaluated using Mean Absolute Percentage Error (MAPE), root Mean Square Error (RMSE), and Mean Percentage Error (MPE), comparing models built with and without BCT. Results show that BCT does not enhance forecasting accuracy for either NNAR or DESH. Moreover, the NNAR model without BCT outperforms DESH, producing approximately 50% lower MAPE, RMSE, and MPE values on the testing dataset. These findings suggest that BCT may not be necessary for time series modeling in this case, and NNAR without transformation is recommended for forecasting Telkom's stock price.

Co-Authors Adi Wicaksono, Nanda Adigama Tri Nugraha Aji Susanto Ardiyani, Faradilla Fadlila Nurwanda Febrianti, Tri Kartika Fhadilla, Nahdatul Hapsari, Riskhia Husna Afanyn Khoirunissa Ilahi, Esa Permata Sari Putri Indra Raditya , Dionisius Irwan Susanto Isnaini, Bayutama Isnandar Slamet Isnandar Slamet, Isnandar Kadar, Jimmy Abdel Kamisan, Nur Arina Bazilah Kayyisa, Alfien Diva Lee, Muhammad Hisyam Marita Saptyani, Marita Mika Asrini Neyun, Ignasia N.G. Noven, Michela Sheryl Nugraha, Titis Jati Pangadi Pangadi Pardede, Hilman Ferdinandus Pramesti, Arsita Anggraeni pratiwi, hasih Respatiwulan, Respatiwulan Santoso Budi Wiyono Setiawan, Crisma Devika Sri Subanti Sucipto, Christy Sheldy Sufia Nur Janah Sugianto Sugianto Sugiyanto - Susilotomoa, Dhestahendra Citra Wati, Rosita Kurnia Wijayanti, Monica Dini Yudho Yudhanto, Yudho Yuliana Susanti Yuliana Susanti, Yuliana Zakya Reyhana Zukhronah, Etik

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