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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat Jurnal Sisfokom (Sistem Informasi dan Komputer) JISKa (Jurnal Informatika Sunan Kalijaga) Variance : Journal of Statistics and Its Applications
Miftahurrohmah, Brina
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science Mathematics Social Sciences

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Journal : Variance : Journal of Statistics and Its Applications

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LSTM MODELING WITH AN AUTOREGRESSIVE APPROACH FOR DAILY TEMPERATURE PREDICTION IN GRESIK REGENCY Ifadah, Azlia Septy; Miftahurrohmah, Brina; Amelia, Putri; Firmansyah, Ardhi Dwi
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 2 (2024): 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/variancevol6iss2page175-182

Abstract

The zero-hunger program is one of the primary goals of the SDGs, especially in large countries like Indonesia, where hunger remains a serious issue. The agricultural sector plays a crucial role in addressing this problem. However, the effectiveness of this sector is highly dependent on climate changes, such as temperature. Therefore, this research aims to develop a daily temperature prediction model in Gresik Regency using the LSTM method with an autoregressive approach. This model is expected to assist farmers in optimizing planting and harvesting times. The autoregressive approach is applied by analyzing the ACF and PACF plots to determine the lags used as lookback parameters. The research results show that the LSTM model with five lookbacks and 150 epoch parameters provides the best outcomes, with an RMSE value of 0.50, MAE of 0.39, R2 of 0.69, and MAPE of 0.01.
FILLING THE PRECIPITATION GAPS: ACCURATE IMPUTATION WITH SUPPORT VECTOR REGRESSION IN NORTH SULAWESI Cahyaning, Angelin; Miftahurrohmah, Brina; Prassida, Grandys Frieska; Tikno, Tikno
VARIANCE: Journal of Statistics and Its Applications Vol 6 No 2 (2024): 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/variancevol6iss2page183-194

Abstract

Incomplete precipitation data poses major challenges in accurate precipitation predictions, hindering the effectiveness of water resource management and disaster risk mitigation efforts in North Sulawesi, Indonesia. This research aims to develop a precipitation prediction model using Support Vector Regression (SVR) to handle missing data. The precipitation data used comes from BMKG and ERA5 stations. The results show that using the RBF kernel with parameters ∁ = 1000, ɛ = 0.1, γ = 100 produces the best predictions, except Dtatiun Meteorologi Naha with γ = 1000. The best model is shown in the model evaluation RMSE of 0.099, MAE of 0.099, and R² of 0.999. The ability of SVR to capture precipitation trends is shown in the model evaluation results. The best model obtained is used for the missing data imputation process.
Co-Authors Amelia, Putri Azzahrah, Dewi Ayu Fitria Bariyah, Taufiqotul Cahyaning, Angelin Catur Wulandari Dharmawan, Yogantara Setya Faria, Niswatun Firmansyah, Ardhi Dwi Grandys Frieska Prassida Ifadah, Azlia Septy Iqbal, Arif Muhammad Jannah, Kharisma Roudlotul Jannah, Nur Alfiyah Miftakhul Sisprasojo, Nova Ridho Sukhna, Isa Kholifatus Tikno, Tikno Tyas, Naomi Ayu Citra Ning