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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Riset Kimia AL ISHLAH Jurnal Pendidikan Jurnal Kajian Bahasa, Sastra dan Pengajaran (KIBASP) TOTOBUANG Communications in Science and Technology SeBaSa: Jurnal Pendidikan Bahasa dan Sastra Indonesia Jurnal Informatika Ekonomi Bisnis Bulletin of Computer Science Research Bulletin of Information Technology (BIT) Gunung Djati Conference Series Jurnal Informatika Ekonomi Bisnis Jurnal Pengabdian Masyarakat Mandira Cendikia (JPKM-MC) Jurnal Ilmiah Multidisiplin Keilmuan Mandira Cendikia (JIMK-MC)
Jasril Jasril
STKIP YDB Lubuk Alung Sumatera Barat
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Journal : Bulletin of Computer Science Research

 1 
Klasifikasi Sentimen Pada Dataset yang Terbatas Menggunakan Algoritma Convolutional Neural Network Saputra, M Ridho; Surya Agustian; Jasril; Novriyanto
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.613

Abstract

This study aims to analyze public responses to the appointment of Kaesang Pangarep as the Chairman of the Indonesian Solidarity Party (PSI) using a sentiment classification approach based on the Convolutional Neural Network (CNN) algorithm. The primary dataset consists of 300 Indonesian-language tweets categorized into three sentiment classes: positive, negative, and neutral. The limited size of the training data presents a major challenge, as it can hinder the model's ability to generalize. To address this issue, data augmentation was carried out by incorporating external datasets with Covid-19 and Open Topic themes. The preprocessing stages include text cleaning, normalization, and tokenization. The developed CNN model utilizes a layered architecture and applies regularization techniques such as L2 and dropout to reduce the risk of overfitting. Accuracy, F1-score, precision, and recall were used as performance evaluation metrics. Experimental results show that the best performance was achieved when the Kaesang and Covid-19 datasets were combined, yielding an F1-score of 0.62 on the validation set and 0.51 on the test set. These findings indicate that adding external data can improve classification accuracy, even under limited data conditions. This study contributes to the development of deep learning-based sentiment classification methods for Indonesian-language texts.
Penggunaan Convolutional Neural Network NASNetLarge Dalam Klasifikasi Citra Daging Babi dan Sapi Aqilah, M Alfandri; Jasril; Sanjaya, Suwanto; Insani, Fitri
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.666

Abstract

The adulteration of beef with pork is a serious issue in Indonesia, particularly for Muslim consumers who are required to consume halal products. According to a Kompas (2020) report, a case of meat adulteration involving 100 kilograms of mixed meat sold as beef was discovered in Tangerang City. This practice not only violates religious laws but also poses threats to public health and consumer trust. To address this challenge, this study adopts a deep learning approach using NASNetLarge for the classification of pork, beef, and mixed meat images. Unlike previous research that utilized EfficientNet-B2 and achieved an accuracy of 98.23%, this study’s NASNetLarge approach produced a comparably competitive accuracy of 98.03%. The dataset used consists of 1,932 images sourced from the Kaggle platform, which were processed through preprocessing and augmentation stages. The data were then split into two distribution scenarios: the entire dataset and a balanced class dataset with 90:10 and 80:20 ratios. Evaluation results show that the best parameter combination was achieved in the first scenario with a 90:10 ratio using augmented images, a learning rate of 0.001, 128 dense units, and the Adam optimizer. The model achieved the highest accuracy of 98.03%, with a precision of 98.63%, recall of 98.40%, and an F1-score of 98.50%. These results indicate that NASNetLarge is effective in accurately and consistently classifying meat images. Image augmentation significantly improved model performance, and the 90:10 data ratio yielded more optimal results compared to 80:20. These findings have the potential to support food surveillance efforts by enabling rapid and accurate detection of meat adulteration.
Co-Authors Agustian, Surya Ahmad Fauzan Akrajas Ali Umar Aqilah, M Alfandri Asmawati Asmawati Asmawati Dadang Sunendar Desi Fitria Desi Fitria Efrianto Erman Taer Fitri Insani Julian Chandra Kurnia Rahman, Fikri Laspida Hartati Laspida Harti Laspida Harti Lestari Handayani Lira Hayu Afdetis Mana Mega Silfina Mimi Jamilah Nazruddin Safaat Novriyanto Pangestu, Yoga Rahmi Eka Putri Rati, Yolanda Saputra, M Ridho Sholihah, Putri Mar Atus Sitorus, Afrida Helena Sulistyo Rini, Ari Sumiyadi Sumiyadi Surya Agustian Suwanto Sanjaya Taufiq, M. Aries Winda Azmi Yasnur Asri Yuda Teruna, Hilwan Zulkarnain Usman Zulmy, Winda Permata