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All Journal JURIKOM (Jurnal Riset Komputer) JSAI (Journal Scientific and Applied Informatics)
Tia Arlin Dita
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Analisis Sentimen Aplikasi MPStore Menggunakan Algoritma Logistic Regression dan LDA Tia Arlin Dita; Ali Ibrahim; Rizka Rahmadhani; Mira Afrina
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9557

Abstract

The rapid growth of the digital economy encourages user satisfaction as the key to successful application innovation. Within technopreneurship, understanding user sentiment is essential for sustainable product development. This study aims to analyze sentiment and identify the deter-minants of user satisfaction regarding the MPStore application based on reviews from the Google Play Store. Review data were collected via scraping and analyzed using Logistic Regression (LR) for sentiment classification (positive, negative, neutral) also Latent Dirichlet Al-location (LDA) for satisfaction topic extraction. The result shows that the LR model achieved an accuracy of 88.5%. The LDA analysis also successfully revealed eight main topics, includ-ing ease of use, transaction speed, and technical obstacles (errors, login, balance issues). Over-all, a majority of users hold a positive perception of MPStore's efficiency and ease of transac-tions. This study concludes that the combination of sentiment analysis and topic modeling is effective for explaining the level of user satisfaction and providing a strategic foundation for digital application developers.
Integrasi Faktor Iklim dan Lingkungan untuk Prediksi Risiko DBD di Kota Palembang Menggunakan Pendekatan GeoAI Berbasis LSTM Tia Arlin Dita; Ali Ibrahim
JURNAL RISET KOMPUTER (JURIKOM) Vol. 13 No. 2 (2026): April 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i2.9407

Abstract

Dengue Hemorrhagic Fever (DHF) remains a significant vector-borne disease threat to public health in Palembang. This study aims to analyze the environmental and demographic factors influencing DHF risk and predict risk trends using a GeoAI approach. Four primary variables land surface temperature, rainfall, population density, and residential area were integrated to develop a DHF risk index for the 2020 - 2025 period. The analysis reveals that the risk index consistently falls within the high category across all regions, showing a gradual upward trend from 0.517 in 2020 to 0.527 in 2025. To project future risks, a Long Short-Term Memory (LSTM) model was employed. Model evaluation demonstrated robust performance with a Mean Squared Error (MSE) of 0.0028, a Root Mean Squared Error (RMSE) of 0.052, and a Mean Absolute Error (MAE) of 0.031, indicating low error rates and stable predictive capability. Prediction results suggest that DHF risk is expected to continue increasing through 2029, particularly in sub-districts with high population density and expanding residential areas. This research provides a scientific contribution by developing a predictive model that is more adaptive and precise than conventional statistical approaches. Through the integration of artificial intelligence and spatial data (GeoAI), this model effectively captures non-linear patterns and spatio temporal dynamics, serving as a sustainable early warning system.
Co-Authors Ali Ibrahim Mira Afrina Rizka Rahmadhani