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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Edu-Ling: Journal of English Education and Linguistics
Firdaus, Muhammad Alif
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Arts Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Other

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Improving English Descriptive Writing Skill Through Brainstorming Technique Masitah, Siti; Marita, Yosi; Rakhmanina, Lisa; Melati, Melati; Firdaus, Muhammad Alif
Edu-Ling: Journal of English Education and Linguistics Vol. 8 No. 1 (2024): December
Publisher : English Education Study Program Faculty of Teacher Training and Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32663/edu-ling.v8i1.4698

Abstract

Generally, the purpose of  this research was to improve the writing learning process, namely how to implement English descriptive writing teaching using brainstorming techniques in the English Language Education Study Program, Prof. University. Dr. Hazairin S.H involving lecturers and students. Furthermore, the specific technique of this research aims to describe the use of brainstorming in writing descriptive English teaching and to found out the results of descriptive writing produced by students after using this brainstorming technique. This research was mixed research (mixed methods) with an Action Research design, where qualitative data mining will first be carried out to explore in depth how Brainstorming techniques can be applied in improving students' descriptive writing skills. Then proceed with quantitative data collection. The research results showed that there was an increase in students' English descriptive writing skills. This can be seen from the increase in scores from the pre test, Post test cycle I, and post test cycle II. The students' average score increased from 68.54 in the first cycle post test to 73.00 in the second cycle post test. From these results it can be concluded that the brainstorming technique is effective in improving students' English descriptive writing skills.
Voting classifier in pain points identification Miftahuddin, Yusup; Firdaus, Muhammad Alif
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp3915-3925

Abstract

A successful app understands and addresses the needs of its users. Pain points-specific difficulties and frustrations that users experience while using an application-are crucial for understanding user expectations and improving user experience. Google Play Store reviews can be a valuable source for identifying these pain points, but this raw data requires processing to be useful for developers. This study develops a model to automatically classify reviews as either containing pain points or not. We chose the voting classifier as our primary algorithm because of its proven ability to produce models with high accuracy through combining the strengths of multiple classifiers. After evaluating 5 different classifier methods, our research shows that the optimal model combines XGradient boosting, multinomial naïve Bayes, and logistic regression-with each contributing unique strengths in text classification. This combination achieves 90% accuracy and a 90% F1-Score, outperforming previous studies that used neural networks (which achieved 80% accuracy). The model successfully identifies user frustrations from app reviews, providing developers with actionable insights to improve their applications. 
Co-Authors Marita, Yosi Melati Melati Miftahuddin, Yusup Rakhmanina, Lisa Siti Masitah