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All Journal AdMathEduSt: Jurnal Ilmiah Mahasiswa Pendidikan Matematika Variance : Journal of Statistics and Its Applications Jurnal Laut Pulau
Syahrin, Khairummin Alfi
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Mathematics

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Prakiraan Perubahan Suhu Permukaan Laut dengan Neuralprophet di Taman Laut Bunaken Syahrin, Khairummin Alfi; Disera, Tiara Emanuella; Nesty Youwe, Angelina Serena Gracella; Merdeka, Juang; Saputra, Agung Hari; Norman, Yosik; Nugraheni, Imma Redha
Jurnal Laut Pulau: Hasil Penelitian Kelautan Vol 3 No 2 (2024): Jurnal Laut Pulau
Publisher : Prodi Ilmu Kelautan Fakultas Perikanan dan Ilmu Kelautan Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/jlpvol3iss2pp42-50

Abstract

Penelitian ini mengkaji prediksi suhu permukaan laut (SPL) di Taman Laut Bunaken yang diproyeksikan meningkat secara signifikan, dengan dampak yang berpotensi besar bagi sektor pariwisata dan ekonomi. Metode yang digunakan adalah kuantitatif deskriptif dengan model Neuralprophet, yang merupakan pengembangan dari Facebook Prophet dan menunjukkan peningkatan kinerja prediksi. Tujuan penelitian ini adalah memprediksi SPL hingga tahun 2032, serta mengidentifikasi konfigurasi hyperparameter Neuralprophet yang memberikan performa optimal. Model Neuralprophet dilatih menggunakan 80% data, menghasilkan nilai MAE training sebesar 0.204115, RMSE training sebesar 0.258052, dan Loss training sebesar 0.004066. Pada tahap pengujian dengan 20% sisa data, model menghasilkan MAE validasi sebesar 0.216127, RMSE validasi sebesar 0.27317, dan Loss validasi sebesar 0.003463. Hasil prediksi menunjukkan adanya peningkatan rata-rata SPL sebesar 0.003815°C per bulan selama 120 bulan, dengan estimasi total peningkatan SPL sebesar 0.4578°C pada tahun 2032.
PERFORMANCE COMPARISON OF DECISION TREE MODELS FOR PM10 PREDICTION IN JAKARTA Syahrin, Khairummin Alfi; Saputra, Agung Hari
VARIANCE: Journal of Statistics and Its Applications Vol 7 No 1 (2025): VARIANCE: Journal of Statistics and Its Applications
Publisher : Statistics Study Programme, Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/variancevol7iss1page11-20

Abstract

PM10 are airborne particulates that have a diameter of ≤10 μm. The potential hazards of PM10 particulates are an issue that is being intensified by many researchers. This research utilizes PyCaret, a library to accelerate the process of modeling and experimentation in the field of machine learning (ML) and data science. This research compares the performance of three decision tree-based models Extra Trees, Random Forest, and XGBoost in predicting PM10 particulate levels, presenting data and visualizations for each models predictions. The data used is ISPU data at five air quality monitoring stations in Jakarta, with the main dataset of PM10 in 2021. The forecast results show an increasing graph pattern, with higher fluctuations in XGBoost. The Extra Trees model produces the best performance, with MASE 0.8808, RMSSE 0.8113, MAE 12.6173, RMSE 14.7436, MAPE 0.2433, SMAPE 0.207, and R² -1.2013.
PERFORMANCE COMPARISON OF RANDOM FOREST, DECISION TREE, AND EXTRA TREES MODELS FOR RAINFALL PREDICTION IN JAKARTA Syahrin, Khairummin Alfi; Tiara Emanuella Disera; Juang Merdeka; Alamin, Mirza Virgiansah; Yosik Norman
AdMathEduSt: Jurnal Ilmiah Mahasiswa Pendidikan Matematika Vol. 13 No. 1 (2026)
Publisher : Universitas Ahmad Dahlan

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Abstract

Accurate rainfall prediction is important for weather monitoring and flood management in urban areas. This study evaluates the performance of three decision tree-based models, Random Forest, Extra Trees, and Decision Tree, for predicting daily rainfall in Jakarta using data from the BMKG Tanjung Priok observation station for the year 2025. The dataset, expressed in millimeters per day, was preprocessed to handle missing values and ensure consistency, and analyzed using the PyCaret library in the Jupyter Notebook environment. Model training was conducted with optimized hyperparameters, and performance was assessed using MASE, RMSSE, MAE, RMSE, SMAPE, and R². All models produced similar overall trends, although the Extra Trees model showed slightly higher fluctuations. Comparative evaluation indicated that the Random Forest model achieved the best performance, with MASE of 0.7925, RMSSE of 0.6373, MAE of 9.51 millimeters, RMSE of 14.06 millimeters, SMAPE of 1.8287, and R² of -0.8227, demonstrating superior accuracy in capturing rainfall patterns. These results suggest that Random Forest is the most suitable model for daily rainfall forecasting in Jakarta, providing reliable predictions that can support meteorologists and policymakers in improving forecast accuracy and planning.
Co-Authors Agung Hari Saputra Alamin, Mirza Virgiansah Disera, Tiara Emanuella Juang Merdeka Merdeka, Juang Nesty Youwe, Angelina Serena Gracella Nugraheni, Imma Redha Tiara Emanuella Disera Yosik Norman Yosik Norman