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p-Index From 2021 - 2026
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All Journal INSERT: Information System and Emerging Technology Journal Edu Komputika Journal
Gladly Caren Rorimpandey
Teknik Informatika, Universitas Negeri Manado
Computer Science & IT Education Engineering

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Ensemble RNN–Random Forest Model for Earthquake Prediction Based onSpatio-Temporal Seismic Data Meyn Choudy Riovan Kaotel; Gladly Caren Rorimpandey; Sondy Campvid Kumajas; Muhammad Zulkifli
Edu Komputika Journal Vol. 12 No. 1 (2025): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v12i1.35332

Abstract

This study proposes a hybrid Ensemble RNN–Random Forest (RNN–RF) model for short-term earthquake prediction based on spatio-temporal seismic data from the Sulawesi–Maluku region. The purpose of this research is to develop a lightweight and interpretable machine learning framework that integrates temporal and spatial features using local datasets provided by the Manado Geophysical Station of BMKG. The methodology includes six stages: data acquisition, preprocessing, feature engineering, model development, ensemble integration, and evaluation. The RNN captures sequential dependencies in seismic activity, while the Random Forest learns spatial and contextual relationships such as fault proximity and event clustering. The ensemble fuses probabilistic outputs (0.75 RNN and 0.25 RF) followed by domain-based calibration using mean magnitude, event frequency, and fault distance. Experimental results show that the proposed ensemble achieved F1 = 0.89 and AUC = 0.975, outperforming individual RNN and RF models in predictive stability and accuracy. The model demonstrates that integrating domain-specific adjustments enhances both recall and precision, while maintaining interpretability for operational deployment. This study contributes to explainable AI in seismology by bridging deep temporal modeling with geophysical reasoning, offering a scalable approach for early-warning applications in Indonesia.
Unemployment Forecasting in North Sulawesi Using a Long Short-term Memory (LSTM) Algorithm Melati Roring; Gladly Caren Rorimpandey; Kristofel Santa
INSERT : Information System and Emerging Technology Journal Vol. 7 No. 1 (2026)
Publisher : Information System Study Program, Faculty of Engineering and Vocational, Undiksha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/insert.v7i1.111436

Abstract

Unemployment remains a key socio-economic indicator that reflects both economic performance and human development conditions at the regional level. In North Sulawesi Province, unemployment levels have shown noticeable fluctuations over the past two decades, driven by changes in labour market structure, workforce characteristics, and development outcomes. This study aims to forecast the number of unemployed people in North Sulawesi by applying a multivariate Long Short-Term Memory model capable of capturing long-term temporal dependencies in time-series data. The analysis uses annual data published by Statistics Indonesia for the period 2000–2025. Several explanatory variables are selected through Pearson correlation analysis, including the Open Unemployment Rate, the number of unemployed women, the population that has never been employed, and the Human Development Index. All variables are normalised and transformed into five-year sliding windows to preserve temporal relationships. The model is constructed using a single hidden layer with 32 units and a dropout rate of 0.2 and is trained using the Adam optimisation algorithm. The evaluation results indicate that the proposed model achieves satisfactory predictive accuracy, with a Mean Absolute Error of 2,960 persons, a Root Mean Square Error of 3,454 persons, and a Mean Absolute Percentage Error of 3.56 percent. Forecasting results for the 2026–2030 period show a gradual decline in unemployment levels. These findings suggest that the model provides reliable unemployment projections and supports data-driven employment policy planning in North Sulawesi.
Co-Authors Kristofel Santa Melati Roring Meyn Choudy Riovan Kaotel Muhammad Zulkifli Sondy Campvid Kumajas