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All Journal Semantik Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Prosiding SNATIF ANDHARUPA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Community: Jurnal Pengabdian Pada Masyarakat Scientific Journal of Informatics
Nova Rijati
Teknik Informatika Universitas Dian Nuswantoro
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Classification Model Evaluation of Public Perception Regarding Nusantara Capital Relocation Galih Sabilah Rosat; Nova Rijati
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12747

Abstract

The relocation of the Capital City of Nusantara (IKN) has triggered extensive public opinion dynamics on the X (Twitter) social media platform. This study aims to empirically map public sentiment and evaluate the comparative performance of classification machine learning algorithms: Support Vector Machine (SVM), Logistic Regression (LR), and Multinomial Naïve Bayes (MNB). The dataset comprises 2,257 unique texts spanning the 2024-2025 period, automatically categorized using a lexicon-based approach (InSet dictionary). The technical novelty of this research lies in the extraction of TF-IDF N-gram (1, 2) features, the mitigation of extreme class imbalance via the Synthetic Minority Over-sampling Technique (SMOTE) encapsulated within a cross-validation pipeline, and systematic hyperparameter optimization using GridSearchCV. The labeling results revealed a predominance of negative sentiment at 77.3% (1,745 data) compared to positive sentiment at 22,7% (512 data), reflecting high public scrutiny and skepticism. Based on the model evaluation, MNB with parameter optimization (α=0.1) emerged as the most robust model, achieving an accuracy of 80.5% and a Macro Precision of 72.3%. The performance of MNB proved capable of outperforming the architectures of LR and SVM, which struggled with multicollinearity and underfitting anomalies in high-dimensional spaces. These findings emphasize that the integration of SMOTE and the MNB algorithm is crucial in overcoming majority class bias and calibrating the limitations of static lexicons to precisely map the polarity of public opinion.
Deep Learning-Based Rice Grain Classification with Class Imbalance Handling Using Weighted Sampling and Data Augmentation Muhammad Aulia Anhar; Nova Rijati
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12930

Abstract

Rice grain quality assessment is important for maintaining product consistency and quality standards in the food industry. Manual inspection is still widely used in practice, but it is often subjective and time-consuming especially in fine-grained classification problems where the visual differences between classes are relatively subtle. Another common challenge in rice grain datasets is class imbalance where the number of normal samples is much larger than the number of defective classes. In this paper, we investigate various imbalance handling strategies for deep learning-based rice grain classification using the GrainSet dataset, which contains 30,962 rice grain images divided into eight quality classes with a highly imbalanced class distribution. The proposed approach combines weighted sampling, data augmentation, and hyperparameter optimization to address class imbalance, while Focal Loss and Class-Balanced Loss are used as comparison methods for performance evaluation. Three deep learning architectures, namely MobileNetV3, ResNet50, and ViT-Small, were evaluated using Accuracy, Macro F1-score, and Weighted F1-score metrics. Experimental results show that the proposed approach achieved the most consistent overall performance. Among the tested models, ResNet50 achieved the best result with 98.87% accuracy and a Macro F1-Score 0.9799. The results also show that convolution-based architectures are more stable than transformer-based models for texture-oriented datasets with limited training data. Furthermore, the combination of sampling-based balancing, augmentation and suitable hyperparameter configuration contributed to better recognition performance on minority classes.
Co-Authors Abdul Syukur Ajib Susanto Aris Marjuni Astuti, Yani Parti Budi Widjajanto Budi Widjajanto Budi Widjajanto Budi Widjajanto Budi Widjajanto Budi Widjajanto Budi Widjajanto Cahaya Jatmoko Christy Atika Sari Desi Purwanti Kusumaningrum Dewi Agustini Santoso Dewi Agustini Santoso Dewi Agustini Santoso Dewi Agustini Santoso Edi Sugiarto Erna Zuni Astuti Etika Kartikadarma Firdaus, Iqbal Galih Sabilah Rosat Heribertus Himawan Ifan Rizqa Marjuni, Aris Md Kamruzzaman Sarker Muhammad Aulia Anhar Nurhuda, Yeni Agus Pratama, Ahmad Sabil Deva Pratama, Yudha Hady Pratiwi, Vila Rusantia Priyo Haryoko Pujiono Pujiono Pulung Nurtantio Andono Puri Sulistiyawati Purwanto Purwanto Purwanto Purwanto Rahmasari, Erisa Adyati Sinaga, Daurat Sinaga, Daurat Sindhu Rakasiwi Soeleman, Arief Sugiyanto - Syamsiar, Syamsiar Toto Haryadi Urfiyati, Novia Wicaksana, Endra Maulia Yaacob, Noorayisahbe Mohd. Yuliman Purwanto Yuliman Purwanto Zainal Arifin Hasibuan