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All Journal Sistemasi: Jurnal Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Jurnal Informatika dan Rekayasa Elektronik Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science TEKNO : Jurnal Penelitian Teknologi dan Peradilan The Indonesian Journal of Computer Science
Romli, Moh. Ali
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Arts Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Library & Information Science

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Comparison of Naïve Bayes Classifier and Decision Tree Algorithms for Sentiment Analysis on the House of Representatives' Right of Inquiry on Twitter Wahyuni, Putri; Romli, Moh. Ali
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8670

Abstract

This research analyzes public sentiment towards the topic of the House of Representatives' Right of Inquiry on Twitter using Naive Bayes Classifier and Decision Tree algorithms. The goal is to compare the effectiveness of the two algorithms in political sentiment analysis. . The research methodology includes data collection from Twitter, data pre-processing, sentiment classification, and result analysis. Sentiment analysis reveals the dominance of positive sentiment related to the DPR's Right of Inquiry. However, this study has limitations in terms of dataset size and depth of text-based sentiment analysis. This research contributes to a better understanding of public sentiment towards political issues in Indonesia and highlights the importance of proper algorithm selection in social media sentiment analysis.  Development suggestions include exploration of deep learning techniques, integration of multimodal analysis, data balancing (oversampling or undersampling) and improvement of pre-processing so that the model is better able to capture negative contexts. The results of the study showed excellent performance of both Naive Bayes Classifier and Decision Tree algorithms with accuracy above 95%. Decision Tree excels with an accuracy of 99%, while Naive Bayes Classifier performs better with an accuracy of 96%. The results with the Confusion Matrix test are precision 0.98, recall 1.00, and F1-Score 0.99.
Implementation of AlexNet and Xception Architectures for Disease Detection in Orange Plants Al Fatah, Venus; Romli, Moh. Ali
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8700

Abstract

Oranges are one of Indonesia's primary horticultural commodities, with production increasing each year. However, pest and disease infestations often go undetected, leading to significant reductions in crop yields. This study implements Convolutional Neural Network (CNN) technology to identify diseases in orange plants using two architectures: AlexNet and Xception. The implementation results show that the Xception architecture achieved a high accuracy of 96% after 100 training epochs, indicating its effectiveness in disease detection tasks. This research highlights the potential of integrating CNN technology, particularly the Xception model, into web-based systems for disease detection in orange plants. Such systems can assist farmers in maintaining crop health, improving productivity, and ensuring harvest quality.
Co-Authors Afika, Bintang Arda Al Fatah, Venus Firmansyah, Naufal Mubarok, Khazin Muhammad Atharsyah Nurhidayat, Helmi PUTRI WAHYUNI Rohman, Abid Taufiqur Taufiqur Rohman, Abid