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All Journal Innovation in Research of Informatics (INNOVATICS) Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA)
Elmeftahi, Souhayla
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Comparison of Machine Learning Algorithms in Detecting Contaminants in Drinkable Water Elmeftahi, Souhayla; Rakhman, Maulana Decky; Rahmatulloh, Alam
Innovation in Research of Informatics (Innovatics) Vol 6, No 1 (2024): March 2024
Publisher : Department of Informatics, Siliwangi University, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/innovatics.v6i1.10385

Abstract

Water, a vital natural resource essential for human existence, is a fundamental human right, indispensable for a dignified life. Despite its significance, the quality of water is often compromised by a myriad of harmful substances, minerals, and contaminants stemming from various sectors like industry, agriculture, residential, and energy. Traditional methods such as WQI and STORET, relying on manual inspection, prove time-consuming. Thus, the integration of machine learning emerges as a pivotal solution to swiftly assess water quality.Numerous studies have explored this challenge using various algorithms; however, a definitive comparison is elusive due to the abundance of existing methods. In response, this research undertakes a meticulous evaluation of seven algorithms to ascertain the optimal approach for water quality classification, employing metric values as benchmarks. Notably, the Random Forest algorithm emerges as the most effective, achieving an impressive accuracy of approximately 84.8%. Following closely are the XGBoost and CatBoost algorithms, showcasing commendable performance with accuracies of 82.9% and 80.2%, respectively. Subsequent rankings include the Decision Tree algorithm at 77.3%, SVM at 72.3%, K-NN at 70.6%, and AdaBoost with the lowest accuracy at 63.33%. This comparative analysis contributes valuable insights for informed decision-making in water quality assessment.    
Bidirectional Encoder Representations from Transformers Fine-Tuning for Sentiment Classification of Cek Bansos Reviews Haerani, Erna; Rahmatulloh, Alam; Elmeftahi, Souhayla
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 4 No. 1 (2025): March 2025
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v4i1.4981

Abstract

Social assistance programs are essential government initiatives aimed at supporting underprivileged communities. One such program is facilitated through the Cek Bansos application, which enables users to check their eligibility for social aid. However, user experiences with the application vary, leading to various sentiments in their reviews. Understanding these sentiments is crucial for improving the application’s functionality and user satisfaction. This study focuses on sentiment analysis of user reviews of the Cek Bansos application by leveraging a fine-tuned Indonesian-language Bidirectional Encoder Representations from Transformers (BERT) model. This research aims to evaluate the BERT model's effectiveness in classifying sentiments in user reviews and provide insights that could improve the Cek Bansos application. This research method is the BERT model was fine-tuned using hyperparameters such as a learning rate of 3e-6, batch size of 16, and 9 epochs. The dataset consisted of 8,000 reviews, divided into training (70%), validation (20.1%), and test (9.9%) sets. Review scores were manually categorized, where ratings of 1 to 2 were classified as negative sentiment, 3 as neutral, and 4 to 5 as positive. The results of this research are as follows: the fine-tuned model achieved an accuracy of 77%, with additional evaluation metrics such as precision, recall, and F1 score, demonstrating the model's effectiveness in identifying positive, negative, and neutral sentiments separately. This study concludes that the BERT model provides a reliable method for sentiment classification of user reviews, which could support developers and policymakers in refining the Cek Bansos application to enhance user experience. Additionally, a web-based application developed using Streamlit allows government officials to visualize sentiment trends in real time, improving their understanding of user feedback. Future research could further explore alternative machine learning models and additional linguistic features to improve sentiment classification accuracy and the overall user experience.
Co-Authors Alam Rahmatulloh Haerani, Erna Rakhman, Maulana Decky