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All Journal Journal Of Informatics And Busisnes
Michael Imanuel
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Computer Science & IT Economics, Econometrics & Finance

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Implementasi Data Warehouse Skema Snowflake untuk Analisis Determinan Kompetensi Siswa Samuel Dimas Sutikno; Michael Imanuel; Andri Wijaya
Journal Of Informatics And Busisnes Vol. 3 No. 4 (2026): Januari - Maret
Publisher : CV. ITTC INDONESIA

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Abstract

The implementation of the National Assessment (AN) produces complex educational data volumes, covering the results of the Minimum Competency Assessment (AKM), Character Surveys, and Learning Environment Surveys. The management of transactional and scattered data often hinders the comprehensive education quality evaluation process. This study aims to design and implement a Data Warehouse using the Snowflake Schema method to analyze the influence of socio-economic status and school profiles on student literacy and numeracy achievements. Kimball's Nine-Step Methodology approach is used in data architecture design. Test results show that the Snowflake scheme is effective in handling regional and school dimension hierarchies by reducing storage redundancy. OLAP (Online Analytical Processing) analysis reveals significant gaps in literacy and numeracy scores based on school accreditation levels and student economic backgrounds, where school quality is proven to be a moderating variable in improving student achievements from low economic groups.
Analisis Sentimen Berita terhadap Harga Saham BBCA Menggunakan Naive Bayes Michael Imanuel; Samuel Dimas Sutikno; Andri Wijaya
Journal Of Informatics And Busisnes Vol. 3 No. 4 (2026): Januari - Maret
Publisher : CV. ITTC INDONESIA

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Abstract

The dynamics of the Indonesian capital market are increasingly influenced by information flow in the digital era. PT Bank Central Asia Tbk (BBCA), as a key market proxy, experienced price volatility in 2024–2025 despite solid fundamentals, indicating the influence of market psychology. This study aims to analyze the effect of stock market news sentiment on BBCA stock prices and test the effectiveness of the Multinomial Naive Bayes algorithm. Using a text mining approach, 5,000 economic news articles (2020–2025) were processed using TF-IDF and classified into positive, negative, and neutral sentiments. The results show the model achieved 92.4% accuracy with 89% precision for negative sentiment detection. Pearson correlation analysis revealed a strong positive relationship (r = 0.78) between daily sentiment scores and the following day's closing prices. The study concludes that news sentiment is a valid leading indicator for stock movements. The Naive Bayes algorithm proved efficient for financial text analysis, offering a viable tool for investor risk mitigation.
Co-Authors Andri Wijaya Samuel Dimas Sutikno