Article Per Year (5 Year)
p-Index From 2021 - 2026
0.778
P-Index
This Author published in this journals
All Journal IJISTECH (International Journal Of Information System & Technology) Journal Sensi: Strategic of Education in Information System IJISTECH Journal Collabits
Dendy Jonas Managas
Universitas Raharja
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Social Sciences

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : journal collabits

 1 
Comparative Analysis of Public Sentiment Towards Sri Mulyani and Purbaya as Finance Ministers on the X Platform Using the Indobertweet Model Muhammad Aryaka Zamzami; Siti Maesaroh; Dendy Jonas Managas
Journal Collabits Vol. 3 No. 1 (2026)
Publisher : Journal Collabits

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/collabits.v3i1.37962

Abstract

The development of social media has positioned platform X (Twitter) as a primary source for expressing public opinion toward government figures and policies. This study aims to analyze public sentiment toward two Indonesian public figures, Sri Mulyani Indrawati and Purbaya Yudhi Sadewa, by utilizing the transformer-based IndoBERTweet model. The data were collected from January 1, 2025, to November 1, 2025. A total of 11,000 tweets related to Sri Mulyani were collected; however, only 2,500 tweets were used for data processing and model training, with a maximum limit of 1,000 tweets per month. Meanwhile, 650 tweets were obtained for Purbaya Yudhi Sadewa. This research employs a supervised learning approach with labeled data consisting of positive, negative, and neutral sentiment classes. Minimal preprocessing was applied, considering that IndoBERTweet is specifically designed to handle the characteristics of social media text. The model was trained for five epochs and evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the IndoBERTweet model can classify sentiment effectively, particularly on the Sri Mulyani dataset, which contains a larger volume of data and achieves an accuracy of over 82%. In contrast, the model’s performance on the Purbaya Yudhi Sadewa dataset shows a lower accuracy of 71%, influenced by the limited amount of data. This study confirms that the quantity and distribution of data significantly affect the performance of transformer-based sentiment analysis models. Based on the sentiment classification results, public sentiment toward Sri Mulyani Indrawati tends to be dominated by negative and neutral sentiments, while sentiment toward Purbaya Yudhi Sadewa shows a distribution dominated by neutral and positive sentiments.
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 Arsi Yulianjani Dedy Prasetya Kristiadi F Ferry Foezi Arisandi SJ Fredy Susanto Muhammad Aryaka Zamzami Siti Maesaroh Tatu Solihat