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INDONESIA
Building of Informatics, Technology and Science
Published by Forum Kerjasama Pendidikan TInggi
ISSN : 26848910     EISSN : 26853310     DOI : -
Core Subject : Science,
Computer Science & IT
Building of Informatics, Technology and Science (BITS) is an open access media in publishing scientific articles that contain the results of research in information technology and computers. Paper that enters this journal will be checked for plagiarism and peer-rewiew first to maintain its quality. This journal is managed by Forum Kerjasama Pendidikan Tinggi (FKPT) published 2 times a year in Juni and Desember. The existence of this journal is expected to develop research and make a real contribution in improving research resources in the field of information technology and computers.
Arjuna Subject : -
Articles 953 Documents
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Analisis Sentimen Persepsi Publik Terhadap Program MBG Pada Komentar YouTube Menggunakan Naïve Bayes dan Resampling Najib, Lutfi; Mahfudh, Adzhal Arwani; Bakhri, Syaiful
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.v7i4.9400

Abstract

The Free Nutritious Meal Program (MBG), launched by the Indonesian government in 2025, has generated diverse public responses on social media, particularly on YouTube as an open digital discussion space. This study aims to analyze public perception of the MBG program through sentiment classification of YouTube comments using the Multinomial Naïve Bayes algorithm combined with Term Frequency–Inverse Document Frequency (TF-IDF) weighting. The dataset consists of 1,082 comments categorized into three sentiment classes: negative, neutral, and positive. The data distribution reveals significant class imbalance, with negative sentiment dominating at 70.61%. The baseline model achieved an accuracy of 70.67% with a macro F1-score of 27.60%, indicating bias toward the majority class. To address this imbalance, Random Oversampling (ROS) and Synthetic Minority Over-sampling Technique (SMOTE) were applied. Although overall accuracy decreased to approximately 51% after resampling, the macro F1-score improved to 36.24% (SMOTE) and 37.09% (ROS), indicating enhanced performance in detecting minority classes. In the context of public policy evaluation, improved sensitivity to minority sentiment is considered more representative than high but biased accuracy. These findings highlight the importance of handling class imbalance in social media–based sentiment analysis for public policy monitoring.
A Comparative Study of LSTM and BiLSTM Performance in Predicting XAU/USD Prices Enriko, I Ketut Agung; Gustiyana, Fikri Nizar
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.v7i4.9414

Abstract

Gold price forecasting in the XAU/USD market is challenging due to nonlinear dynamics, high volatility, and sensitivity to global macroeconomic factors. This study compares the performance of Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BiLSTM) architectures in forecasting XAU/USD closing prices using historical data from 2023–2026. Data preprocessing includes cleaning, chronological ordering, normalization, and transformation using a sliding window approach. A window size of 60 time steps is selected to represent approximately three months of daily trading activity, enabling the models to capture short- to medium-term temporal dependencies while limiting excessive noise and computational burden. The dataset is divided chronologically into training and out-of-sample testing sets to ensure proper generalization assessment. Both models employ identical architectures with two recurrent layers (50 hidden units each) and are trained using the Adam optimizer with epoch variations (20–100). Evaluation on unseen test data uses MAE, MSE, RMSE, MAPE, and R² metrics. LSTM achieves its lowest MAE of 21.26 at 40 epochs, while BiLSTM attains its best performance at 80 epochs with an MAE of 20.86 and R² of 0.9981. However, extending training to 100 epochs leads to performance degradation in BiLSTM, indicating sensitivity to overtraining. Overall, optimal performance is achieved through balanced training duration rather than increased architectural complexity.
Analisis Komparatif Arsitektur Convolutional Neural Network untuk Klasifikasi Kualitas Cabai dengan Implementasi Perangkat Mobile Ikhsanudin, Nur; Akrom, Muhamad
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.v7i4.9419

Abstract

Manual chili quality sorting is susceptible to subjectivity and inter-assessor inconsistency, which can reduce product market value. This study conducts a comparative analysis of three Convolutional Neural Network (CNN) architectures—Custom CNN, MobileNetV3-Small, and EfficientNetV2-B0 for binary chili quality classification (Good/Bad) using a primary dataset of 1,383 chili images (684 Good-class, 699 Bad-class) captured with a smartphone camera. The Good class includes chili with a smooth surface, fresh color, and no decay spots, while the Bad class includes chili showing signs of decay, physical defects, or deformation. Evaluation was conducted based on accuracy, precision, recall, F1-Score, AUC, inference time, and post-quantization model size. The results show that EfficientNetV2-B0 achieved the highest accuracy of 92.0% (precision 92.4%, recall 92.0%, F1-Score 92.0%, AUC 0.961), MobileNetV3-Small obtained an accuracy of 87.7% with the lowest server-side inference latency (2.39 ms), and Custom CNN achieved 87.3% accuracy with the most compact model size (118 KB post-quantization). All three models were integrated into a Flutter-based Android application prototype as a proof-of-concept, displaying the classification result (Good/Bad), confidence score, and inference latency, with end-to-end response times ranging from 80 to 120 ms on a Xiaomi 13T device. This study contributes empirical comparative data on three CNN architectures in the chili quality classification domain, accompanied by the construction of a local dataset and technical validation of model deployment on a mobile device. The results of this study are expected to serve as a reference in selecting CNN architecture for the development of a mobile-based chili quality classification system, particularly as a first step toward the implementation of simple small-scale sorting at the farmer level.

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