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All Journal MUQTASID Muqtasid: Jurnal Ekonomi dan Perbankan Syariah Journal of Computing Theories and Applications
Setyoko, Bimo Haryo
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Back Matter Muqtasid Vol. 10 No. 1 Setyoko, Bimo Haryo
Muqtasid: Jurnal Ekonomi dan Perbankan Syariah Vol 10, No 1 (2019): MUQTASID: Jurnal Ekonomi dan Perbankan Syariah
Publisher : IAIN Salatiga

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Abstract

Back Matter Muqtasid Vol. 10 No. 1
Front Matter Muqtasid Vol. 10 No. 2 Setyoko, Bimo Haryo
Muqtasid: Jurnal Ekonomi dan Perbankan Syariah Vol 10, No 2 (2019): MUQTASID: Jurnal Ekonomi dan Perbankan Syariah
Publisher : IAIN Salatiga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18326/muqtasid.v10i2.%p

Abstract

Back Matter Muqtasid Vol. 10 No. 2 Setyoko, Bimo Haryo
Muqtasid: Jurnal Ekonomi dan Perbankan Syariah Vol 10, No 2 (2019): MUQTASID: Jurnal Ekonomi dan Perbankan Syariah
Publisher : IAIN Salatiga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18326/muqtasid.v10i2.%p

Abstract

Front Matter Muqtasid Vol. 10 No. 2 Setyoko, Bimo Haryo
Muqtasid: Jurnal Ekonomi dan Perbankan Syariah Vol 10, No 2 (2019): MUQTASID: Jurnal Ekonomi dan Perbankan Syariah
Publisher : IAIN Salatiga

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (688.287 KB) | DOI: 10.18326/muqtasid.v10i2.%p

Abstract

Front Matter Muqtasid Vol. 10 No. 1 Setyoko, Bimo Haryo
Muqtasid: Jurnal Ekonomi dan Perbankan Syariah Vol 10, No 1 (2019): MUQTASID: Jurnal Ekonomi dan Perbankan Syariah
Publisher : IAIN Salatiga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18326/muqtasid.v10i1.%p

Abstract

Front Matter Muqtasid Vol. 11 No. 1 Setyoko, Bimo Haryo
Muqtasid: Jurnal Ekonomi dan Perbankan Syariah Vol 11, No 1 (2020): MUQTASID: Jurnal Ekonomi dan Perbankan Syariah
Publisher : IAIN Salatiga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18326/muqtasid.v11i1.%p

Abstract

Back Matter Muqtasid Vol. 11 No. 1 Setyoko, Bimo Haryo
Muqtasid: Jurnal Ekonomi dan Perbankan Syariah Vol 11, No 1 (2020): MUQTASID: Jurnal Ekonomi dan Perbankan Syariah
Publisher : IAIN Salatiga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18326/muqtasid.v11i1.%p

Abstract

Back Mater Vol. 12 No. 1 Setyoko, Bimo Haryo
Muqtasid: Jurnal Ekonomi dan Perbankan Syariah Vol 12, No 1 (2021): MUQTASID: Jurnal Ekonomi dan Perbankan Syariah
Publisher : IAIN Salatiga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18326/muqtasid.v12i1.%p

Abstract

Front Matter Vol. 11 No. 2 Setyoko, Bimo Haryo
Muqtasid: Jurnal Ekonomi dan Perbankan Syariah Vol 11, No 2 (2020): MUQTASID: Jurnal Ekonomi dan Perbankan Syariah
Publisher : IAIN Salatiga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18326/muqtasid.v11i2.%p

Abstract

Dataset Analysis and Feature Characteristics to Predict Rice Production based on eXtreme Gradient Boosting Wijayanti, Ella Budi; Setiadi, De Rosal Ignatius Moses; Setyoko, Bimo Haryo
Journal of Computing Theories and Applications Vol. 1 No. 3 (2024): JCTA 1(3) 2024
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.10057

Abstract

Rice plays a vital role as the main food source for almost half of the global population, contributing more than 21% of the total calories humans need. Production predictions are important for determining import-export policies. This research proposes the XGBoost method to predict rice harvests globally using FAO and World Bank datasets. Feature analysis, removal of duplicate data, and parameter tuning were carried out to support the performance of the XGBoost method. The results showed excellent performance based on which reached 0.99. Evaluation of model performance using metrics such as MSE, and MAE measured by k-fold validation show that XGBoost has a high ability to predict crop yields accurately compared to other regression methods such as Random Forest (RF), Gradient Boost (GB), Bagging Regressor (BR) and K-Nearest Neighbor (KNN). Apart from that, an ablation study was also carried out by comparing the performance of each model with various features and state-of-the-art. The results prove the superiority of the proposed XGBoost method. Where results are consistent, and performance is better, this model can effectively support agricultural sustainability, especially rice production.
Co-Authors Aghaunor, Tabitha Chukwudi De Rosal Ignatius Moses Setiadi Kartikadarma , Etika Octara Pribadi Ojugo, Arnold Adimabua Robet Robet Suyud Widiono Ugbotu, Eferhire Valentine Wijayanti, Ella Budi