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All Journal FORUM STATISTIKA DAN KOMPUTASI CAUCHY: Jurnal Matematika Murni dan Aplikasi Desimal: Jurnal Matematika Xplore: Journal of Statistics Journal of Mathematics, Computation and Statistics (JMATHCOS) Indonesian Journal of Statistics and Its Applications
Aam Alamudi
Department Of Statistics, Bogor Agricultural University (IPB)
Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Mathematics Social Sciences Other

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Comparison Between SARIMA and DeepAR with Optuna Hyperparameter Optimization for Estimating Rice Production Data in Indonesia Zahid, Muhammad Farhan; Fitrianto, Anwar; Silvianti, Pika; Alamudi, Aam
Indonesian Journal of Statistics and Applications Vol 8 No 2 (2024)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v8i2p95-111

Abstract

Forecast is a prediction of future events that had taken a significant role in our society especially when facing time-sensitive issues like food availability. Food is a critical aspect in ensuring people's welfare, especially in a country like Indonesia with a large population. Availability and access to rice are a vital need for the people of Indonesia. Rice is not only the main source of carbohydrates, but also has a central role in the cultural and social aspects of Indonesian society. Forecasting can be a strategy to anticipate fluctuations in food demand and supply. Forecasting can be an important instrument for the government and stakeholders to make the right and effective decisions. The growing period of rice which is heavily influenced by seasonality makes DeepAR and SARIMA techniques a good solution to solve this problem. Both methods offer the ability to address features in rice production such as trends, seasonality, and anomaly effects. This study demonstrates that DeepAR, especially when optimized with Optuna, outperforms SARIMA in forecasting rice production in Indonesia, as evidenced by superior performance in key evaluation metrics such as Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE).
OPEC Crude Oil Price Forecasting Using ARIMA with Ensemble Empirical Mode Decomposition Lutfiah Adisti, Tiara; Soleh, Agus M; Alamudi, Aam; Rahardiantoro, Septian; Rizki, Akbar
Indonesian Journal of Statistics and Applications Vol 9 No 2 (2025)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v9i2p230-239

Abstract

World crude oil prices fluctuate every day. One source of crude oil traded is oil from crude oil exporting countries that are members of the Organization of the Petroleum Exporting Countries (OPEC). In the total of 40% of world crude oil is produced by OPEC. This makes forecasting the price of crude oil OPEC’s policy very necessary in order to maintain world oil market stability. Fluctuating oil price data is made simpler and easier to interpret by applying the Ensemble Empirical Mode Decomposition (EEMD) method. The EEMD method decomposes the data into a number of Intrinsic Mode Functions (IMF) and residual of the IMF. In this study, the ARIMA forecasting model is compared using the original data and the decomposition results in the form of IMF components and IMF residuals. The comparison of the two methods is seen based on the overall and average MAPE value of the forecasting results in five time ranges. The EEMD-ARIMA method has an average MAPE value of 9.09% and standard deviation MAPE value of 7.39%. OPEC crude oil price forecast in January-August 2021 ranges from $42.22 to $60.6 per barrel. The final result of the analysis in this study shows that the ARIMA method with decomposition data (EEMD-ARIMA) is better than the ARIMA method using original data
PENDUGAAN PARAMETER FUNGSI COBB-DOUGLAS GALAT ADITIF DENGAN ALGORITME GENETIKA Hanif, Iqbal; Soleh, Agus M; Alamudi, Aam
Indonesian Journal of Statistics and Applications Vol 1 No 1 (2017)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v1i1.54

Abstract

Cobb-Douglas function with additive errors is a function which can be used to analyse the relationship between production output and production factors. The method commonly used to estimate the parameter of that function is Nonlinear Least Square (NLS) and a common algorithm for this method is Gauss Newton iteration (NLS-GN). However, NLS-GN method has less-optimum results when analysing multicolinearity data. A possibly better method for this analysis is Genetic Algorithm (NLS-GA). The purpose of this study is to analyse the use of Genetic Algorithm to estimate parameters of Cobb-Douglas function with additive errors. The results show that NLS-GA method could not produce a better parameter estimator than NLS-GN method does but it produced a better parameter estimator in analysing multicolinearity data. NLS-GA method is capable of producing a better model with predictive ability than NLS-GN method does with real data. Keywords: cobb-douglas function, genetic algorithm, nonlinear least square
CONSTRUCTING EARTHQUAKE DISASTER-EXPOSURE LIKELIHOOD INDEX USING SHAPLEY-VALUE REGRESSION APPROACH Anisa, Rahma; Sartono, Bagus; Silvianti, Pika; Alamudi, Aam; IJSA, Indonesian Journal of Statistics and Its Applications
Indonesian Journal of Statistics and Applications Vol 3 No 1 (2019)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v3i1.198

Abstract

Indonesia is very prone to earthquake disaster because it is located in the Pacific ring of fire. Therefore, a reference level of earthquake disaster exposure likelihood events in Indonesia is needed in order to increase people's awareness about the risks. This study aims to determine the index that describes the risk of possible future earthquake disaster. As initial research, this study is focus on earthquake disasters in Java region, as it has the largest population in Indonesia. Several indicators that are related to the severity of earthquake disaster impact, were used in this study. The weights of each indicators were determined by considering its shapley-value, thus all indicators gave equal contribution to the proposed index. The results showed that shapley-value approach can be utilized to construct index with equal contribution of each indicators. In general, the resulted index had similar pattern with the number of damaged houses in each districts.
Co-Authors . Aunuddin Agus M Soleh Agus M Soleh Agus Mohamad Soleh Agus Soleh Akmala Firdausi Anik Djuraidah Anisa, Rahma Aulia Dwi Oktavia Ayoe Indria Winuri Azira Irawan Bagus Sartono Budi Susetyo Dhika Firmansyah Dian Kusumaningrum Else Virdiani Eria, Raisa Nida Erlinda Widya Widjanarko Farit Mochamad Afendi Fitrianto, Anwar Hanifatun Nufusia Hiola, Yani Prihantini IJSA, Indonesian Journal of Statistics and Its Applications Indahwati Indonesian Journal of Statistics and Its Applications IJSA Iqbal Hanif Iqbal Hanif, Iqbal Itasia Dina Sulvianti Kusman Sadik Lutfiah Adisti, Tiara Mahdiyah Riaesnianda Manaf, Silmi Annisa Rizki Maya Deanti Mega Maulina Nadhif Nursyahban Ni Kadek Manik Dewantari Nisa Nur Aisyah Pika Silvianti Putri, Oktaviani Aisyah Rahardiantoro, Septian Rahma Anisa Resty Fanny Riansyah, Boy Rizki, Akbar Tania Amalia Darsono Utami Dyah Syafitri Yasmin Erika Faridhan Yenni Angraini Zahid, Muhammad Farhan