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All Journal Journal of Artificial Intelligence and Engineering Applications (JAIEA) AMMA : Jurnal Pengabdian Masyarakat
Dini Andriyani
STMIK IKMI Cirebon
Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing

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Pengelolaan Sampah Berbasis Teknologi Informasi untuk Masyarakat Perkotaan Mulyawan; Khaerul Anam; Daffa Ezra Pratama; Dini Andriyani
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 04 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Urban waste management faces significant challenges due to the increasing volume of waste, inadequate infrastructure, low public awareness, and limited use of technology. This community service program aims to develop an information technology-based solution to create a more efficient, environmentally friendly, and community-engaged waste management system. Through the development of a mobile application, residents can report waste conditions in real-time, monitor waste sorting, and access waste collection schedules. The program also includes community training, provision of waste sorting facilities, and educational campaigns to raise environmental awareness. The implementation has shown significant improvements in public participation in waste sorting, reduction in the volume of waste sent to landfills, and overall improvement in environmental quality. Furthermore, this initiative contributes to human resource empowerment through training in technology use and waste management. The success of this program demonstrates that integrating information technology with public education can be an effective solution to urban waste management challenges.
The Effect of SMOTE Application on Support Vector Machine Performance in Sentiment Classification on Imbalanced Datasets Dini Andriyani; Ahmad Faqih; Sandy Eka Permana
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 4 No. 2 (2025): February 2025
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v4i2.742

Abstract

This research explores the effect of applying Synthetic Minority Oversampling Technique (SMOTE) on the performance of Support Vector Machine (SVM) algorithm in sentiment classification on imbalanced datasets. Public review data was collected from social media platform X (formerly Twitter) regarding the Free Lunch Program, with a total of 2,368 reviews automatically labeled using the BERT model into three categories: positive, negative, and neutral. Sentiment imbalance in the dataset was addressed by applying SMOTE to generate synthetic data on minority classes. The research method follows the stages of Knowledge Discovery in Databases (KDD), including data selection, preprocessing, labeling, transformation using TF-IDF, SVM model training, and performance evaluation. The experimental results show that the application of SMOTE successfully improves the accuracy of the SVM model by 12.48%, from 71.41% to 83.89%. Other evaluation metrics, such as precision, recall, and F1-score, also showed significant improvement from 0.69, 0.71, and 0.68 to 0.84, respectively. These findings confirm that SMOTE is effective in overcoming data imbalance, resulting in a more accurate and reliable sentiment classification model. This research contributes to the application of sentiment analysis in data-driven public policy evaluation.
Co-Authors Ahmad Faqih Daffa Ezra Pratama Khaerul Anam Mulyawan Sandy Eka Permana