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All Journal Jurnal Informatika dan Teknik Elektro Terapan Register: Jurnal Ilmiah Teknologi Sistem Informasi Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Building of Informatics, Technology and Science Dinasti International Journal of Education Management and Social Science Jurnal Teknologi Dan Sistem Informasi Bisnis Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Journal of Computer System and Informatics (JoSYC) Jurnal Teknik Informatika (JUTIF) Instal : Jurnal Komputer International Journal Of Computer, Network Security and Information System (IJCONSIST) Kaunia: Integration and Interconnection of Islam and Science Journal Jurnal Sains dan Teknologi Jurnal Sistem Informasi dan Bisnis Cerdas Ekasakti Pareso Jurnal Akuntansi SmartComp Intechno Journal : Information Technology Journal Edu Komputika Journal
Pratama, Irfan
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Religion Aerospace Engineering Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences

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Journal : Edu Komputika Journal

 1 
Analysis of Forecasting Methods on Rice Price Data at Milling Level According to Quality Aulia, Indira Dhekawanti; Pratama, Irfan
Edu Komputika Journal Vol. 11 No. 1 (2024): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v11i1.4763

Abstract

Rice is a primary source of carbohydrates for many Indonesians, and its prices often surge due to uncontrolled demand. Therefore, the government is crucial in monitoring rice prices to maintain stability. Information technology, particularly data mining such as forecasting, is essential for providing accurate information on future rice prices. It will assist various stakeholders in making informed pricing policy decisions. This study employs Random Forest Regression and Gradient Boosting Regressor methods to predict rice prices using a dataset that includes monthly average rice prices at milling levels, categorized by quality (Premium and Medium), spanning from January 2013 to April 2024. The dataset consists of 136 rows, each representing a unique combination of year, month, and quality, and is stored in CSV format. Methodological steps include data collection, preprocessing, modeling, and model evaluation using monthly average rice prices at milling levels based on quality, including premium and medium grades. The results from Random Forest Regression indicate Root Mean Square Error (RMSE) values of 24.90 for premium rice and 25.47 for medium rice. The study reveals that Random Forest Regression outperforms Gradient Boosting Regressor in this context. Future research should explore additional prediction methods and consider other variables influencing rice prices to enhance model accuracy.
Feature Extraction Implementation in the Forecasting Method to Predict Indonesian Oil and Gas Exports and Imports Pradana, Michael Anggun Kado; Pratama, Irfan
Edu Komputika Journal Vol. 11 No. 1 (2024): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v11i1.7879

Abstract

Future export and import predictions can use data mining and forecasting applications of data mining. Then, normalisation is carried out using datasets taken at the centre of the statistical agency using a mix-max scaler. The normalisation results are then calculated using several forecasting methods, such as Exponential Smoothing, SARIMAX, XGBoost, and CatBoost. The accuracy of this method can be improved by using feature extraction decomposition. They are decomposing, such as trend, residue, and seasonal. The results of the decomposition then become new features that are entered into the prediction model. The prediction results are evaluated using the root mean square error (RMSE). The smaller the RMSE, the better the results. The prediction results without using the method obtained by the Exponential Smoothing method have the best level of accuracy with an average RMSE value of 0.111 and the SARIMAX method with an average RMSE value of 0.146. Meanwhile, the prediction results using the CatBoost and XGBoost feature extraction methods have the best level of accuracy with an RMSE value of 0.046. From the results of the comparison of predictions, the addition of decomposition features to most forecasting methods can significantly increase the accuracy of the calculation.
Co-Authors Ade Kurnia, Dian Akbar, Muhammad Rizky Mawandhyka Albert Yakobus Chandra Alifudin, Arif Andre Bustari Asanah, Nurul Aulia, Indira Dhekawanti Ayuningsih, Ekatri Dadang Sudrajat Dwi Krisbiantoro, Dwi Edi Wibowo Guno Wibowo, M Ioni Abdurrahman Hubu, Muhammad Rizki Iqbal, Sayid Muhammad Kurniawan, Akbar Sidiq Kurniawan, Muhammad Rifki Manurung, Syalom Kristian Nandasari, Dayang Nugroho Budi Wibowo Odi Nurdiawan Paneo, Anto Farwanto Pradana, Michael Anggun Kado Prasetyaningrum, Putri Taqwa Pratama, George Recksy Sandy Prayuda, Arya Priatama, Candra Rina Asmeri, Rina Sarmini Sarmini Suria, Ozzi Swapurba, Gangsar Taqwa Prasetyaningrun, Putri Uswatun Hasanah Waluyo, Retno Widianti, Anisa Wuri Handayani