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All Journal Jurnal Pepadun
Rico Andrian
Department of Computer Science, Universitas Lampung
Computer Science & IT

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Classification of Public Sentiment towards the Performance of the Ministry of Communication and Digital regarding Online Gambling Ika Rahma Alia; Favorisen Rosyking Lumbanraja; Aristoteles Aristoteles; Rico Andrian
Jurnal Pepadun Vol. 6 No. 3 (2025): December
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/pepadun.v6i3.295

Abstract

Online gambling is a social issue currently in the spotlight in Indonesia. Although the government, particularly the Ministry of Communication and Digital (Kemkomdigi), has taken various measures, such as blocking websites and conducting digital literacy campaigns, online gambling remains rampant and has sparked various public reactions. Social media, particularly Instagram, has become a public space where people express their opinions and sentiments regarding government performance. This study aims to classify public sentiment based on comments directed at the official Kemkomdigi Instagram account regarding the issue of online gambling. This study uses two machine learning algorithms, Random Forest and XGBoost, to compare the effectiveness of the models in classifying positive and negative sentiment. A total of 724 comments were collected and manually labeled by three annotators using a voting method. Preprocessing included cleaning, case folding, tokenization, normalization, stopword removal, and stemming. Feature representation was performed using the TF-IDF method. The data was split with a 70:30 ratio and balanced using Random Oversampling. Model training used 10-fold cross-validation and hyperparameter tuning through GridSearchCV. The evaluation results showed that the tuned Random Forest performed the best, with an accuracy of 0.7082. These findings demonstrate that machine learning approaches, particularly Random Forest, are effective in automatically identifying public sentiment toward emerging public policy issues on social media.
Implementation of ResNet-50 for Solid Waste Image Classification Alfa Rizki Fadlillah; Favorisen Rosyking Lumbanraja; Rico Andrian; M. Iqbal Parabi
Jurnal Pepadun Vol. 7 No. 2 (2026): August
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/pepadun.v7i2.286

Abstract

Operational efficiency of municipal solid waste management is critically hampered by manual sorting, a process that is slow, labor-intensive, and poses occupational health risks. This study investigates the application of deep learning to automate this task, specifically by developing and accessing a classification model using Residual Network (ResNet-50) architecture. Utilizing a public dataset of 24,705 images of organic and recyclable waste, we implemented a transfer learning protocol. The pre-trained ResNet-50 model, with weights initialized from ImageNet, was fine-tuned for this binary classification problem. To ensure model robustness and prevent overfitting on the specialized dataset, a comprehensive data augmentation strategy was applied, expanding the training set to 207,522 images. Upon evaluation with a dedicated test set of 6,177 that was previously unseen, the model demonstrated high evaluation metrics, achieving an all-around classification accuracy of 95%. Another key evaluation metrics, like precision, recall (sensitivity), specificity, and F1-score, corroborated the result with a weighted average of 0.95. These findings establish that a transfer-learned ResNet-50 model provides a highly effective and reliable solution for automated solid waste classification, offering a technologically robust foundation for next-generation recycling and waste management systems.
Co-Authors Alfa Rizki Fadlillah Aristoteles, Aristoteles Ika Rahma Alia Lumbanraja, Favorisen R M. Iqbal Parabi