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All Journal JOURNAL OF HUMANITIES, SOCIAL SCIENCES AND BUSINESS (JHSSB) Bulletin of Information Technology (BIT) Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence
Supriatna, Dahlan
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences

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Analisis Prediksi Curah Hujan di Kota Tangerang Menggunakan Metode LSTM dan GRU Supriatna, Dahlan; Anggai, Sajarwo; Tukiyat
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 5 No 2 (2025): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v5i2.1068

Abstract

Curah hujan yang tidak menentu dapat memengaruhi berbagai sektor, seperti pertanian, energi, dan infrastruktur. Akurasi prediksi curah hujan sangat penting untuk mitigasi risiko bencana banjir maupun kekeringan. Penelitian ini bertujuan untuk membandingkan akurasi prediksi curah hujan menggunakan dua algoritma deep learning, yaitu LSTM dan GRU serta dapat memberikan kontribusi pada pengelolaan sumber daya air yang lebih efektif. Model ini diterapkan pada data historis curah hujan dan variabel meteorologi terkait, data penelitian adalah data sekunder yang bersumber dari data BMKG Kota Tangerang periode Januari 2014 – Januari 2025 sebanyak 4.062 data. Evaluasi kinerja model dilakukan menggunakan metrik seperti MAE, MSE, RMSE, dan R². Hasil menunjukan Model LSTM dengan konfigurasi hyperparameter optimal—terdiri dari timesteps 36 bulan, 64 unit memori, 100 epoch pelatihan, batch size 16, dropout 0.3, dan learning rate 0.0001—menghasilkan metrik evaluasi terbaik MAE sebesar 0.08473, MSE sebesar 0.00973, RMSE sebesar 0.09863, dan R2 sebesar 0.65601. Nilai R2 yang relatif tinggi ini mengindikasikan bahwa model LSTM mampu menjelaskan sekitar 65.6% dari variabilitas dalam data curah hujan aktual. Sebagai perbandingan, model GRU dengan kinerja terbaiknya (menggunakan batch size 32) menunjukkan metrik evaluasi yang sedikit di bawah LSTM, yaitu MAE 0.08883, MSE 0.01078, RMSE 0.10383, dan R2 Score 0.61878, secara keseluruhan, LSTM terbukti lebih unggul dalam kapabilitas prediksinya.
Analysis of Stock Price Prediction for PT Mayora Indah Tbk Using ARIMA and Prophet Models Tukiyat, Tukiyat; Nuraini, Ani; Sembodo, Eko; Supriatna, Dahlan; Sova, Maya
JOURNAL OF HUMANITIES, SOCIAL SCIENCES AND BUSINESS Vol. 4 No. 4 (2025): AUGUST
Publisher : Transpublika Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55047/jhssb.v4i4.1903

Abstract

The instability of stock market prices necessitates the utilization of precise predictive models, with ARIMA and Prophet providing alternative methods for addressing patterns, seasonal variances, and changes in value. This study aims to compare the forecasting performance of ARIMA and Prophet models in predicting the stock price of PT Mayora Indah Tbk. (MYOR.JK) using daily closing price data obtained from Yahoo Finance, spanning the period from January 1, 2018, to May 2, 2025. ARIMA was employed for its robustness in handling stationary and linear time series, whereas Prophet was applied due to its flexibility in capturing nonlinear components, seasonal fluctuations, and sudden market changes. The models were developed and evaluated in RStudio, with accuracy measured using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The ARIMA (1,1,1) model produced a MAPE of 3.21% and white noise residuals, signifying reliable short-term predictions yet limited adaptability to complex long-run dynamics. Conversely, the Prophet model achieved a lower MAPE of 2.87%, exhibiting superior predictive accuracy, trend adaptability, and sensitivity to abrupt price movements. Overall, the findings indicate that Prophet outperforms ARIMA for daily stock price forecasting and underscore the importance of selecting appropriate models in financial time series analysis, while also encouraging future exploration of hybrid or deep learning-based approaches such as Long Short-Term Memory (LSTM) networks to further enhance prediction accuracy.
Komparasi Model LSTM dan CNN-LSTM untuk Peramalan Curah Hujan di Kota Tangerang Selatan Uliyatunisa; Supriatna, Dahlan
Bulletin of Information Technology (BIT) Vol 6 No 3: September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i3.2235

Abstract

This study compares the performance of Long Short-Term Memory (LSTM) and Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) models for daily rainfall forecasting in South Tangerang City using meteorological data from January 2005 to July 2025. Data from official meteorological stations was processed with mean imputation for missing values and MinMaxScaler normalization. Models were evaluated based on Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and coefficient of determination R². Results show CNN-LSTM outperforms with RMSE 0.79, MAE 0.63, MSE 0.62, and R² 0.61, compared to LSTM (RMSE 0.83, MAE 0.60, MSE 0.68, R² 0.58). Prediction visualizations confirm CNN-LSTM's accuracy in capturing extreme patterns, with statistically significant differences via t-test. The novelty lies in using a long-term (20-year) dataset for tropical Indonesia, demonstrating the hybrid model's efficacy for complex spatio-temporal predictions. Findings support flood early warning systems and water resource management, recommending additional climate variable integration for further development.
Co-Authors Anggai, Sajarwo Ani Nuraini, Ani Maya Sova Sembodo, Eko Tukiyat Tukiyat Tukiyat, Tukiyat Uliyatunisa