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All Journal Jurnal Riset Multidisiplin Edukasi Sinergi : Jurnal Ilmiah Multidisiplin
Chintia Cantika
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Aerospace Engineering Humanities Biochemistry, Genetics & Molecular Biology Education Environmental Science Health Professions Immunology & microbiology Physics Social Sciences Other

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Analisis Ulasan Aplikasi dalam Google Play Store Menggunakan Model Naive Bayes Chintia Cantika; Mayer Dani Sitompul; Andri Wijaya
Jurnal Riset Multidisiplin Edukasi Vol. 3 No. 1 (2026): Jurnal Riset Multidisiplin Edukasi (Januari 2026) In Press
Publisher : PT. Hasba Edukasi Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71282/jurmie.v3i1.1497

Abstract

This study aims to analyze user sentiment toward mobile applications based on reviews collected from Google Play Store by applying the Naive Bayes classification model. User reviews represent an important source of information that reflects user experiences, satisfaction levels, and perceived application quality. However, the large volume and unstructured nature of textual reviews make manual analysis inefficient and subjective. Therefore, this research adopts a quantitative approach using text classification based on machine learning to automatically categorize user reviews into positive, negative, and neutral sentiment classes. The research process consists of data collection, text preprocessing, feature extraction, sentiment classification using Naive Bayes, and model performance evaluation. Text preprocessing includes case folding, tokenizing, stopword removal, and stemming to improve data quality. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The results show that positive sentiment dominates user reviews, indicating that the application is generally well received by users, although negative and neutral sentiments remain present and highlight areas that require improvement. The evaluation results demonstrate that the Naive Bayes model achieves reliable performance in classifying sentiment, with balanced evaluation metrics that indicate stable classification capability. These findings confirm that Naive Bayes remains an effective and efficient method for sentiment analysis of application reviews. This study contributes theoretically to sentiment analysis research and practically provides insights that can support application developers in evaluating user feedback and improving application quality.
Perancangan Data Warehouse (Studi kasus: Analisis Tren Penyakit Menular) Chintia Cantika; Riski Surya Saputra; Andri Wijaya
Sinergi : Jurnal Ilmiah Multidisiplin Vol. 2 No. 1 (2026): Sinergi: Jurnal Ilmiah Multidisiplin
Publisher : PT. AHLAL PUBLISHER NUSANTARA

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Abstract

Infectious diseases such as COVID-19, Tuberculosis, and Malaria remain significant global health challenges. A major obstacle in mitigating these outbreaks is the fragmentation of healthcare data, which leads to delays in analysis and decision-making. This study aims to design a Data Warehouse capable of integrating surveillance data from various heterogeneous sources to support real-time disease trend analysis. The methodology employed is a bottom-up approach utilizing a three-tier architecture. The data integration process is executed through an Extract, Transform, and Load (ETL) mechanism using Pentaho Data Integration to ensure data quality and consistency. Data storage implements a Fact Constellation Schema within a PostgreSQL database, enabling simultaneous multidimensional analysis of infection cases and mortality rates. The result of this research is a prototype of an interactive dashboard based on Tableau, which presents visualizations of geographic distribution (GIS) and temporal trend graphs. This implementation demonstrates that the centralization of healthcare data can facilitate more effective outbreak monitoring and support evidence-based public health policymaking. Keywords: Data Warehouse, Infectious Diseases, ETL, Fact Constellation Schema, Business Intelligence, Data Visualization. Abstrak Penyakit menular seperti COVID-19, Tuberkulosis, dan Malaria masih menjadi tantangan kesehatan global yang signifikan. Salah satu hambatan utama dalam mitigasi wabah ini adalah fragmentasi data kesehatan yang menyebabkan keterlambatan dalam analisis dan pengambilan keputusan. Penelitian ini bertujuan untuk merancang sebuah Data Warehouse yang mampu mengintegrasikan data surveilans dari berbagai sumber heterogen untuk mendukung analisis tren penyakit secara real-time. Metodologi yang digunakan adalah pendekatan bottom-up dengan arsitektur tiga lapisan (three-tier architecture). Proses integrasi data dilakukan melalui mekanisme Extract, Transform, and Load (ETL) menggunakan Pentaho Data Integration untuk menjamin kualitas dan konsistensi data. Penyimpanan data menerapkan Fact Constellation Schema (Skema Galaksi) pada basis data PostgreSQL, yang memungkinkan analisis multidimensi terhadap kasus infeksi dan mortalitas secara bersamaan. Hasil penelitian ini berupa purwarupa dashboard interaktif berbasis Tableau yang menyajikan visualisasi sebaran geografis (GIS) dan grafik tren temporal. Implementasi ini membuktikan bahwa sentralisasi data kesehatan dapat memfasilitasi pemantauan wabah yang lebih efektif dan mendukung perumusan kebijakan kesehatan masyarakat yang berbasis bukti (evidence-based policy). Kata Kunci: Data Warehouse, Penyakit Menular, ETL, Fact Constellation Schema, Business Intelligence, Visualisasi Data.
Co-Authors Andri Wijaya Mayer Dani Sitompul Riski Surya Saputra