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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA) INOVTEK Polbeng - Seri Informatika
Asro
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Education Materials Science & Nanotechnology Physics

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E-Learning Evaluation Based on Context, Input, Process, and Product (CIPP) Friadi, John; Asro; Lubis, Arina Luthfini; Yani, Dodi P.; Suroto; Bora, Ansyar; Alfiyandri
Jurnal Penelitian Pendidikan IPA Vol 10 No SpecialIssue (2024): Science Education, Ecotourism, Health Science
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10iSpecialIssue.7383

Abstract

The development of internet-based information technology has had an impact on many aspects of human life, including education. Online learning is made possible by the learning paradigm in the industrial era 4.0. During the COVID-19 pandemic, many universities implemented online learning methods using E-Learning, in line with government recommendations to implement health protocols to break the chain of the COVID-19 virus, such as wearing masks, washing hands and maintaining distance. The Faculty of Engineering, Batam University has used e-learning for learning during the pandemic and continues after the pandemic, but its implementation has never been evaluated. The aim of this research is to assess the context, input, process and product (CIPP) of e-learning at the Faculty of Engineering, Batam University. This research uses a quantitative and qualitative approach (mixed research) by distributing questionnaires to lecturers and students of study programs within the Faculty of Engineering, Batam University. Based on research conducted by evaluating E-Learning from the context aspect, both lecturers and students gave very good responses so it needs to be maintained. Furthermore, from the input aspect it provides a very good response, therefore it needs to be maintained and improved. The response from lecturers for the process aspect is in the very good category, while the response from students is in the good category, so the process element needs to be improved. Assessment of the product aspect shows that E-Learning at the Faculty of Engineering, Batam University must be improved.
Comparative Evaluation of Preprocessing Techniques in Twitter Sentiment Analysis for Indonesia’s 2024 Regional Elections Asro; Solihin
INOVTEK Polbeng - Seri Informatika Vol. 11 No. 1 (2026): February
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/tt65bb54

Abstract

The rapid expansion of social media has positioned Twitter as a critical platform for capturing public opinion during political events, including Indonesia’s 2024 Regional Elections. This study investigates the impact of preprocessing strategies and class balancing on the performance of sentiment analysis models applied to election-related tweets. An initial dataset of 9,096 tweets was collected and refined into 6,202 relevant entries from 2024–2025 through text cleaning, normalization, tokenization, and duplicate removal. Sentiment distribution analysis reveals a dominance of positive sentiment (58.4%), followed by negative (33.6%) and neutral (8.0%) expressions. Two classical machine learning classifiers—Naïve Bayes and Logistic Regression—were implemented using TF–IDF feature representation. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied exclusively to the training data, and hyperparameter optimization was conducted using GridSearchCV. Model evaluation employed an 80/20 train–test split with accuracy, precision, recall, F1-score, and confusion matrices as performance metrics. Experimental results indicate that logistic regression combined with SMOTE and hyperparameter tuning achieved the highest accuracy of 93.08%, outperforming Naïve Bayes. The findings confirm that carefully designed preprocessing pipelines and class balancing significantly enhance the reliability of sentiment classification in political social media analysis.
Co-Authors Alfiyandri Friadi, John Lubis, Arina Luthfini Solihin SUROTO Yani, Dodi P.