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All Journal Jurnal Pijar MIPA Frontier Advances in Applied Science and Engineering
Baihaqi, Anas
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Monthly Rainfall Prediction Using Multiple Linear Regression Method in West Nusa Tenggara Region Minardi, Suhayat; Baihaqi, Anas; Wazni, Haerul
Frontier Advances in Applied Science and Engineering Vol. 3 No. 1 (2025)
Publisher : Tinta Emas Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59535/faase.v3i1.456

Abstract

Rainfall is a crucial meteorological element in tropical regions like Indonesia. The significant influence of rainfall on various sectors of life means that rainfall predictions are necessary for making plans. This research aims to determine the accuracy of rainfall predictions using the Multiple Linear Regression method and what local factors influence rainfall in the West Nusa Tenggara Region. Multiple Linear Regression is a method that can predict monthly rainfall using more than one independent variable. There are inconsistencies in the regression analysis process, and to overcome this in this study, DMC (Double Mass Curve) was used. The data used is BMKG data from the West Nusa Tenggara (NTB) Climatology Station for 2013 - 2022. In general, the level of prediction accuracy ranges between 54.10% - 87.50%. The best correlation coefficient value for the Lombok Island Region is r = 0.79. The Sumbawa Island region is r = 0.88, and the Bima region is r = 0.83. Based on the multiple linear regression equation model obtained, the most dominant local factors influencing rainfall in the NTB region are air and sea surface temperatures.
Study of Developing Models of Crop Failure Risk Information Agustiarini, Suci; Sampelan, David; Maurits, Yuhanna; Baihaqi, Anas; Patria Megantara, Restu; Ulfah, Afriyas; Permana, Angga; Kirana, Nindya; Sulistio Adi Wibowo, Dewo; Purwaningsih, Ni Made Adi; Pamungkas, Cakra Mahasurya Atmojo; Putrantijo, Nuga; Fajariana, Yuaning
Jurnal Pijar Mipa Vol. 19 No. 1 (2024): January 2024
Publisher : Department of Mathematics and Science Education, Faculty of Teacher Training and Education, University of Mataram. Jurnal Pijar MIPA colaborates with Perkumpulan Pendidik IPA Indonesia Wilayah Nusa Tenggara Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jpm.v19i1.5981

Abstract

Climate is one factor that can influence plant growth. The risk of crop failure due to climate variability can be in the form of reduced water sources, which impact water needs in the land and the emergence of pests and diseases in plants. The risk of planting failure can impact product quality, which has the potential to decrease, higher plant handling costs, and various things that cause losses to farming businesses. The availability of climate forecast information, such as rainfall and other parameters, encourages writers to apply it to information that is easier for users to understand. One of the machine learning algorithms, Decision Tree, is used as a model in determining the risk of planting failure based on each attribute/parameter, including monthly rain, ENSO and IOD phenomena, drought, groundwater availability, and Oldeman climate type. This study aims to make a model prediction of crop failure risk potential, and the calculation is based on climate prediction data. The results of this study show differences in climatic conditions for each commodity when there is an increased potential risk of planting failure. Monthly rainfall is the most dominant factor influencing rice, maize, and soybean planting failure. Validation of the decision tree model shows that this model is quite good in determining the potential risk of crop failure in all commodities studied, with the proportion of correct proportion of more than 65%. However, the Heidke Skill Score (HSS) shows that this model is good for Paddy and Soybean; Maize shows an HSS of less than zero.
Co-Authors Agustiarini, Suci Angga Permana Fajariana, Yuaning Kirana, Nindya Maurits, Yuhanna Pamungkas, Cakra Mahasurya Atmojo Patria Megantara, Restu Purwaningsih, Ni Made Adi Putrantijo, Nuga Sampelan, David Suhayat Minardi Sulistio Adi Wibowo, Dewo Ulfah, Afriyas Wazni, Haerul