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p-Index From 2021 - 2026
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All Journal Journal Collabits Technologia Journal
Foezi Arisandi SJ
Politeknik Sukabumi
Computer Science & IT Engineering

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Cloud Computing Integration in the Digital Transformation of MSMEs: A Case Study on the Retail Sector Nila Natalia; Errysa Subekthi; Febri Dolis Herdiani; Foezi Arisandi SJ
Technologia Journal Vol. 2 No. 4 (2025): Technologia Journal-November
Publisher : Pt. Anagata Sembagi Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62872/jkbsjt34

Abstract

This study aims to analyze the role of cloud computing integration in supporting the digital transformation process of Micro, Small, and Medium Enterprises (MSMEs) in the retail sector in Indonesia. Digital transformation has become a strategic necessity in facing the dynamics of global competition and changes in technology-based consumer behavior. This study uses a qualitative method with a case study approach on several retail MSMEs that have adopted cloud computing services. Data were obtained through in-depth interviews, field observations, and documentation, then analyzed descriptively through the stages of data reduction, data presentation, and conclusion drawing. The results show that the implementation of cloud computing can improve operational efficiency, strengthen data management, and accelerate the decision-making process based on real-time information. MSMEs that adopt this technology experience a reduction in IT infrastructure costs of up to 40% and an increase in employee productivity of up to 30%. However, challenges remain such as limited digital literacy, uneven internet infrastructure, and concerns about data security. This study confirms that cloud computing functions not only as a technological solution, but also as a catalyst for changing work culture and business management systems towards sustainable digitalization.
Data Cleaning and Exploratory Data Analysis for Heart Disease Prediction Foezi Arisandi SJ; Dendy Jonas Managas
Journal Collabits Vol. 3 No. 2 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i2.37650

Abstract

Cardiovascular disorders remain a major cause of death worldwide, emphasizing the importance of accurate and timely diagnostic support. This study presents a data-driven framework for predicting heart disease by integrating structured data cleaning procedures, exploratory data analysis (EDA), and supervised machine learning classification. The Heart Statlog (Cleveland-Hungary) dataset, which contains a range of clinical attributes associated with cardiac conditions, is used as the primary data source. To ensure data quality, several preprocessing steps are applied, including treatment of missing values, elimination of duplicate records, correction of inconsistent entries, and transformation of categorical variables into numerical representations. EDA is subsequently employed to explore feature distributions and inter-variable relationships using statistical measures and visualization techniques. Logistic Regression and Random Forest algorithms are implemented to construct predictive models and assess classification performance. The experimental results indicate that chest pain category, ST-segment slope, exercise-induced angina, and maximum heart rate are among the most influential predictors of heart disease. Furthermore, the Random Forest classifier achieves higher overall performance than Logistic Regression, suggesting its stronger capability in modeling complex clinical patterns. These findings confirm that rigorous preprocessing combined with appropriate machine learning methods can significantly enhance predictive accuracy and support the development of reliable clinical decision-support systems.
Co-Authors Dendy Jonas Managas Errysa Subekthi Febri Dolis Herdiani Natalia, Nila