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All Journal G-Tech : Jurnal Teknologi Terapan
Totok Chamidy
Universitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering

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Forcasting Analysis of Drug Use in Hospitals Based on Multivariate Long Short-Term Memory Networks Fanny Brawijaya; Agung Teguh Wibowo Almais; Totok Chamidy
G-Tech: Jurnal Teknologi Terapan Vol 9 No 4 (2025): G-Tech, Vol. 9 No. 4 October 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i4.8244

Abstract

Effective drug inventory management is crucial for maintaining service quality and cost efficiency in hospitals. Inaccurate procurement planning can cause stockouts or overstock conditions, disrupting healthcare operations. This study presents a predictive model for outpatient drug consumption using a Multivariate Long Short-Term Memory (LSTM) network. The dataset comprises historical records from the general, pediatric, and maternity polyclinics at RSIA Fatimah Hospital, Probolinggo Regency, East Java, Indonesia, collected in January 2023. The variables include timestamp, polyclinic name, drug name, and quantity used. Data preprocessing involved cleaning, one-hot encoding for categorical features, min-max normalization, and time-based train-test splitting to avoid data leakage. The multivariate LSTM model was trained for 500 epochs under various configurations, evaluated using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). Three model groups (A, B, C) with distinct neuron counts and batch sizes were tested to assess performance variations. Model B1 achieved the best results, with the lowest MAE (10.239), MAPE (1.979%), and highest R² (0.199). Although the R² value indicates limited variance explanation, Nonetheless, the model remains useful for operational forecasting, the model effectively captures temporal patterns in drug consumption, demonstrating its potential as a decision-support tool for optimizing hospital pharmaceutical inventory management.
Analytic Predictive of Crescent Sighting Using Astronomical Data-Based Multinomial Logistic Regression in Indonesia Tomy Ivan Sugiharto; Mokhamad Amin Hariyadi; Totok Chamidy; Irwan Budi Santoso; Cahyo Crysdian; Ahmad Zarkoni; Ma'muri Ma'muri; Syahreni Syahreni
G-Tech: Jurnal Teknologi Terapan Vol 9 No 4 (2025): G-Tech, Vol. 9 No. 4 October 2025
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v9i4.8246

Abstract

This research aims to develop and validate a sophisticated crescent visibility classification model in Indonesia. Multinomial Logistic Regression (MLR) was chosen for its capability to provide clear model interpretation through coefficient analysis. Utilizing comprehensive observational data (2021-2025) from Indonesia's Meteorology, Climatology, and Geophysics Agency (BMKG), the study comprised 2210 data points. The model classifies visibility into three categories (Dark, Faint, and Bright) based on defined elongation thresholds. The final predictor variables used were azimuth difference, moon altitude, and elongation. Analysis of the optimal model's (Model A3) coefficients revealed azimuth difference and elongation as the most dominant predictors, marked by exceptionally large positive coefficients (12.050 and 12.018, respectively) for classifying the 'Faint' category. After data preprocessing and systematic optimization ('saga' solver, L2 penalty), the optimal model (A3, C=100) demonstrated exceptional performance with an outstanding F1-Score of 99.10%. These findings strongly validate MLR's effectiveness for elongation-based crescent visibility classification and highlight its substantial potential as a reliable foundation for objective decision-making.
Probabilistic Forecasting of M≥5.0 Earthquakes in East Java: A 30-Day LSTM Approach Using Seismic Feature Data Nanang Yulianto; Totok Chamidy; Mochamad Imamudin; Suhartono Suhartono; Muhammad Ainul Yaqin
G-Tech: Jurnal Teknologi Terapan Vol 10 No 2 (2026): G-Tech, Vol. 10 No. 2 April 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i2.9504

Abstract

East Java is a seismically active region where short-term earthquake forecasting remains a critical yet challenging endeavor. While deterministic prediction is inherently unfeasible, probabilistic modeling offers a practical pathway for risk mitigation. This study develops a 30-day forward-window probabilistic forecasting model for M≥5.0 earthquakes in East Java using a Long Short-Term Memory (LSTM) network framed as a binary classification task. The model is trained on 25 years of seismic data (2001–2025) from BMKG Stasiun Geofisika Pasuruan. Twenty-five seismic features were rigorously selected through correlation analysis and data-leakage prevention protocols, while class imbalance was mitigated using adaptive loss weighting. The LSTM architecture was systematically optimized via sequential hyperparameter tuning and robust validation strategies. On a hold-out test set, the model achieved an AUC-ROC of 0.752, F1-score of 0.484, and recall of 0.673, indicating the model's capacity to detect impending seismic events with reasonable sensitivity. These results confirm that deep learning can effectively capture non-linear temporal patterns in seismic sequences. The primary contribution of this work is a validated, operationally ready probabilistic forecasting framework that can be integrated into regional earthquake monitoring systems, providing actionable lead time for disaster preparedness in East Java.
Comparison of Boolean OR, AND, and OR–AND Models for Monthly Rainfall Classification in Bawean Island Rudi Kasianto; Zainal Abidin; Totok Chamidy; Mochamad Imamudin
G-Tech: Jurnal Teknologi Terapan Vol 10 No 3 (2026): G-Tech, Vol. 10 No. 3 July 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i3.10298

Abstract

Rainfall classification plays an important role in climate monitoring, water resource management, agricultural planning, and hydrometeorological disaster mitigation. While machine learning techniques have been widely used for rainfall classification, they often require substantial computational resources and complex training processes. This study proposes a simple, interpretable, and computationally efficient Boolean-based framework for monthly rainfall classification on Bawean Island, East Java, Indonesia. Monthly climatological data from 1972–2023, including rainfall, rainy days, mean temperature, and minimum temperature, were analyzed, yielding 624 observations. Rainfall was classified into three categories: low (<100 mm), moderate (100–299 mm), and high (≥300 mm). Rainy days were converted into ordinal scores, while mean and minimum temperatures were transformed into binary scores. Three Boolean-based rainfall classification models were developed and evaluated using confusion matrices, accuracy, precision, recall, and F1-score. Correlation analysis showed that rainy days had the strongest relationship with rainfall (r = 0.858), followed by minimum temperature (r = −0.592) and mean temperature (r = −0.463). The hybrid OR–AND model achieved the best overall performance, with 66% accuracy, 71% precision, 62% recall, and 61% F1-score, outperforming both the OR and AND models. These results demonstrate that the proposed Boolean-based framework provides an effective, transparent, and computationally efficient approach for monthly rainfall classification.
Co-Authors Agung Teguh Wibowo Almais Ahmad Zarkoni Ainul Yaqin Cahyo Crysdian Fanny Brawijaya Irwan Budi Santoso Ma'muri Ma'muri Mochamad Imamudin Mokhamad Amin Hariyadi Nanang Yulianto Rudi Kasianto Suhartono Suhartono Syahreni Syahreni Tomy Ivan Sugiharto Zainal Abidin