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INDONESIA
JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Published by Politeknik Negeri Batam
ISSN : -     EISSN : 25486861     DOI : 10.3087
Core Subject : Science,
Computer Science & IT
Journal of Applied Informatics and Computing (JAIC) Volume 2, Nomor 1, Juli 2018. Berisi tulisan yang diangkat dari hasil penelitian di bidang Teknologi Informatika dan Komputer Terapan dengan e-ISSN: 2548-9828. Terdapat 3 artikel yang telah ditelaah secara substansial oleh tim editorial dan reviewer.
Arjuna Subject : -
Articles 695 Documents
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Implementation and Comparative Analysis of CNN and Transfer Learning Models (EfficientNetB0, MobileNetV2, and ResNet50) for Rice Leaf Disease Detection Based on Digital Images Utami, Tri Wahyu; Novita, Mega; Latifa, Khoiriya
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11616

Abstract

Rice leaf diseases significantly reduce agricultural productivity, making early and accurate detection essential, particularly in rice-producing regions such as Indonesia. This study proposes an automated rice leaf disease detection system based on Convolutional Neural Networks (CNN) and transfer learning. The dataset, obtained from the Mendeley Data Repository, consists of 6,889 images classified into eight categories: Bacterial Leaf Blight, Brown Spot, Healthy Rice Leaf, Leaf Blast, Leaf Scald, Narrow Brown Leaf Spot, Rice Hispa, and Sheath Blight. The dataset was divided into 70% training, 15% validation, and 15% testing. A baseline CNN model and three pre-trained models—EfficientNetB0, MobileNetV2, and ResNet50—were evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. The baseline CNN achieved a test accuracy of 48.26%, while EfficientNetB0 achieved 58.41%. In contrast, MobileNetV2 and ResNet50 demonstrated significantly better performance, with test accuracies of 79.98% and 76.60%, respectively. MobileNetV2 exhibited the most balanced performance across all classes, showing superior generalization capability and computational efficiency. The best-performing model was integrated into a Streamlit-based application, enabling real-time rice leaf disease detection through image upload. The results confirm that transfer learning substantially improves classification accuracy and robustness compared to conventional CNNs. This study highlights the potential of lightweight deep learning models for practical implementation in smart agriculture systems and provides a reliable solution for automated rice disease detection in real-world conditions.
Evaluation of the Accuracy and Efficiency of Deep CNN Architecture in Feature Extraction for Guava Disease Classification Wicaqsana, Shiva Augusta; Luthfiarta, Ardytha; Dwi Mareta, Amalia Putri; Fitri, Maulatus Shaffira
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11655

Abstract

This study analyzes and compares several Deep Convolutional Neural Network (DCNN) architectures to evaluate the balance between classification accuracy and computational efficiency in guava fruit disease detection. A hybrid DCNN–Machine Learning (ML) approach was applied to 3,784 images from the Guava Fruit Disease Dataset using a 10-fold cross-validation scheme and undersampling techniques to address data imbalance. Six DCNN architectures were systematically tested, and the combination of ResNet50 with Artificial Neural Network (ANN) showed the best performance with an accuracy of 0.9979 and an F1-score of 0.9975, surpassing the InceptionV3 baseline (0.9974). In addition to being the most accurate, ResNet50 was also 2.5 times faster in feature extraction than DenseNet201, demonstrating an optimal balance between accuracy and time efficiency. These findings emphasize the importance of analyzing the accuracy-efficiency trade-off in selecting a DCNN architecture and open up opportunities for developing more efficient models for future agricultural image classification applications.
Match Outcome Prediction in Draft Pick and In-game Phases of MSC 2025 Mobile Legends using Random Forest and XGBoost Fadli Firmansyah, Dzaky; Prayogo Kuncoro, Adam; Riyanto, Riyanto
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11658

Abstract

Mobile Legends: Bang Bang is a widely played Multiplayer Online Battle Arena game in Southeast Asia, and its competitive ecosystem has driven the need for accurate match outcome prediction. Most existing studies analyze either the draft pick phase or the in game phase in isolation, limiting their ability to capture the full progression of a match. To address this limitation, this study evaluates the performance of Random Forest and Extreme Gradient Boosting (XGBoost) in predicting match outcomes across both phases using data from the MSC 2025 tournament. The dataset was collected from Liquipedia’s official API and match replay recordings. Draft pick features represent team composition factors such as synergy, hero strength, and patch impact, while in game features consist of statistical indicators including gold, kills, turrets, and objectives extracted from multiple time based snapshots. Both models were trained using qualification stage matches and tested on the main event. A phase separated hybrid feature engineering approach was employed to represent strategic differences between the draft pick and in game phases. Evaluation metrics include accuracy, precision, recall, F1 score, and ROC AUC. Results show that the draft pick models achieved a maximum accuracy of 57%, whereas the in game models reached 88% for Random Forest and 84% for XGBoost, with both achieving a ROC AUC of 0.94. These findings indicate that snapshot based in game features provide stronger predictive signals than draft pick composition features, which reflect only the initial strategic potential rather than actual match conditions.
Application of Multinomial Naïve Bayes for Sentiment Classification on Bukalapak Reviews Yuliawati, Dona; Faeang Ogya Widi, Musyafa
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11671

Abstract

This study investigates sentiment analysis on user reviews from Bukalapak, a major Indonesian e-commerce platform, using the Multinomial Naïve Bayes (MNB) classifier. The study focuses on tackling the challenge of data imbalance and the linguistic complexities of Indonesian, such as slang, affixes, and negation, which are common in user reviews. Data was collected through web scraping from Bukalapak's app on the Google Play Store, resulting in a dataset of 19,999 reviews. A structured preprocessing pipeline was employed, including text normalization, tokenization, stopword removal, stemming, and term frequency-inverse document frequency (TF-IDF) weighting to prepare the data. The sentiment analysis results show that the model performs well in categorizing neutral reviews (accuracy 81%), but struggles with positive and negative sentiments due to data imbalance, leading to lower accuracy for these categories. The study highlights the effectiveness of Multinomial Naïve Bayes in large-scale sentiment analysis tasks in the e-commerce domain, particularly for platforms with large volumes of user-generated content. The study also introduces SMOTE (Synthetic Minority Over-sampling Technique) for handling data imbalance and k-fold cross-validation for model evaluation, significantly improving the model’s reliability. The research concludes that sentiment analysis can greatly benefit e-commerce platforms by improving customer service, informing product management decisions, and providing valuable insights for business strategies.
Optimization of IndoBERT for Sentiment Analysis of FOMO on Social Media Through Fine-Tuning and Hybrid Labeling Adhim, Nadhif Fauzil; Cahyono, Nuri
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11686

Abstract

The rapid growth of social media in Indonesia has given rise to social phenomena such as Fear of Missing Out (FOMO). Expressions of FOMO on platforms like X (previously Twitter) often written informally, filled with abbreviations, slang, and emotional nuances, posing challenges for traditional Natural Language Processing (NLP) methods. This research aims to develop an optimized sentiment classification model for FOMO-related posts by fine-tuning the IndoBERT architecture and applying comprehensive data enhancement strategies. The study introduces three key innovations: (1) systematic text normalization to handle informal expressions, (2) a hybrid labeling framework combining automated model prediction, lexicon-based validation, and manual annotation to construct high-quality ground-truth data, and (3) hyperparameter tuning using both GridSearchCV for traditional machine learning models and Bayesian Optimization (Optuna) for deep learning models to maximize performance. The experimental results demonstrate that the optimized IndoBERT achieved superior performance with an Accuracy of 94.50%, F1-Score of 94.52%, and Macro AUC of 0.987. These results significantly surpass comparative models, including BiLSTM (Accuracy 86.60%), Support Vector Machine (88.06%), and Naive Bayes (80.73%). These results confirm that integrating hybrid labeling and fine-tuned IndoBERT significantly enhances sentiment classification performance. The findings contribute to developing reliable sentiment analysis systems for detecting social anxiety dynamics and computational social science research in Indonesian contexts.

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