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All Journal Building of Informatics, Technology and Science Journal of Social Responsibility Projects by Higher Education Forum Bulletin of Information System Research
Mesran Mesran
Sekolah Tinggi Ilmu Manajemen Sukma, Medan
Aerospace Engineering Agriculture, Biological Sciences & Forestry Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Social Sciences

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Journal : building of informatics technology and science

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Deep Learning-Based Early Detection Optimization for Rice Leaf Diseases to Support Sustainable Local Agriculture Putrama Alkhairi; Agus Perdana Windarto; Mesran Mesran; Roznim Roznim
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9338

Abstract

Rice leaf diseases such as Bacterial Blight and Blast are major threats to rice productivity that directly impact food security and the sustainability of local agriculture. This study aims to develop and optimize a deep learning-based early detection system for rice leaf diseases using a Convolutional Neural Network (CNN) architecture, specifically the Inception_v3 model. The research method includes five main stages, namely collecting rice leaf image datasets, data pre-processing (resize, normalization, and augmentation), CNN model design, model training and evaluation, and performance optimization through the application of different optimizer algorithms. Two model variants were tested and compared, namely Inception_v3 Basics with the RMSprop optimizer and Inception_v3 Optimization with the Adam optimizer. Experimental results showed that the Inception_v3 Optimization model provided the best performance, with a Precision value of 0.9672, Recall of 0.8939, F1-score of 0.9291, Balanced Accuracy of 0.9297, Matthews Correlation Coefficient (MCC) of 0.8578, Cohen's Kappa of 0.8573, and AUC ROC of 0.98. These results indicate that the Adam optimizer is able to accelerate convergence and improve model accuracy compared to RMSprop, while producing a more stable and efficient classification system. Thus, this study successfully demonstrated that the optimized Inception_v3 architecture can be used effectively for early detection of rice leaf diseases and has high potential for integration into smart farming systems to support sustainable, technology-based local agricultural practices.
Random Forest, LSTM, and IndoBERT Comparison for TikTok App Sentiment Analysis Imam Saputra; Mesran Mesran; Ruziana Mohamad Rasli
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9714

Abstract

The rapid growth of social media platforms like TikTok has generated a massive volume of user reviews on the Google Play Store, serving as a critical indicator of application service quality. However, the unstructured nature of Indonesian social media text and the significant imbalance between sentiment classes pose substantial challenges for automated classification systems. Addressing this class imbalance is highly crucial for application developers, as critical negative and neutral feedback containing essential feature complaints is easily marginalized by the overwhelming majority of positive reviews, leading to biased operational insights. This research conducted a comprehensive comparative study of three distinct computational paradigms: Random Forest, Long Short-Term Memory (LSTM), and IndoBERT, to identify the most effective model for sentiment analysis. A dataset of 5,000 TikTok reviews was meticulously processed using a negation-aware preprocessing pipeline to preserve semantic integrity. To address class imbalance, architecture-specific techniques were deployed, including SMOTE for Random Forest, Class Weighting for LSTM, and Random OverSampling for IndoBERT. The experimental results demonstrate that IndoBERT significantly outperforms other models, achieving the highest global accuracy of 81% and a Macro F1-Score of 0.56. While Random Forest and LSTM yielded lower accuracies of 75% and 71%, respectively, they exhibited stability in predicting the majority class but struggled with the inherent ambiguity of neutral sentiments. The study concludes that IndoBERT’s bidirectional self-attention mechanism provides superior contextual understanding of Indonesian slang and non-formal syntax. This research contributes a robust framework for application developers to monitor public opinion objectively. Furthermore, the findings highlight that despite advanced balancing techniques, the "neutrality bottleneck" remains a challenge, suggesting that future research should explore aspect-based sentiment analysis to enhance classification granularity in the Indonesian NLP domain.
Co-Authors Agus Perdana Windarto Akhyar Lubis Alfry Aristo Jansen Sinlae Elsy Rahajeng Fajrul Malik Aminullah Napitupulu Fatma Sari Hutagalung Imam Saputra Juanda Hakim Lubis Juni Ismail Putrama Alkhairi Riah Ukur Ginting Roznim Roznim Ruziana Mohamad Rasli Yanto Saputra Zulfikar Zulfikar