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Bulletin of Informatics and Data Science
Published by Asosiasi Peneliti Data Science Indonesia
ISSN : -     EISSN : 25808389     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
The Bulletin of Informatics and Data Science journal discusses studies in the fields of Informatics, DSS, AI, and ES, as a forum for expressing research results both conceptually and technically related to Data Science
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 54 Documents
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Hybrid Autoencoder and NiaARM Framework for Flash Viral Detection on YouTube Shorts Erlin Windia Ambarsari; Mercy Hermawati; Dedin Fathudin
Bulletin of Informatics and Data Science Vol 5, No 1 (2026): May 2026
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v5i1.162

Abstract

YouTube Shorts has rapidly emerged as a dominant short-form video platform, yet small creator channels often experience an unusual viral phenomenon best described as flash viral—a sudden surge of views that peaks within 24 to 48 hours and then collapses almost as quickly. Detecting and explaining this pattern is challenging because traditional statistical detectors miss multivariate signatures, while classical Association Rule Mining (ARM) such as Apriori loses information through mandatory discretization. This study proposes a hybrid framework that combines a semi-supervised Deep Learning Autoencoder with Nature-Inspired Numerical Association Rule Mining (NiaARM) using Differential Evolution and Particle Swarm Optimization. The framework is validated on six temporal snapshots of the Indonesian Boburu YouTube Shorts channel, comprising 63 unique videos (42 active) collected between February 22 and March 19, 2026. Experimental results show that the Autoencoder achieves an F1-score of 0.667 with 100% recall, matching the best classical baseline (Z-Score) while providing learnable representational capacity for future scaling. NiaARM-PSO discovered 3,115 high-quality numerical association rules with a maximum lift of 63.00, compared to only 43 rules and a maximum lift of 2.52 obtained by Apriori, an improvement of approximately 25 times. Traffic source decomposition further revealed that 99.9% of viral views originated from external platforms rather than YouTube's recommendation system, indicating that flash viral on micro-channels is externally driven. This research contributes a methodological framework that simultaneously detects and explains flash viral phenomena in short-form video analytics
Interpretable Decision Tree for Predicting High Self-Reported Happiness Using Digital Behavior and Lifestyle Indicators Rafika Damayanti Sururin Nufus; Fiandra Lazuart Adi Hafizh Arraszaq
Bulletin of Informatics and Data Science Vol 5, No 1 (2026): May 2026
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v5i1.153

Abstract

Research on digital well-being is dominated by text-derived features and post hoc explanations, leaving limited evidence on whether compact behavioral data can support an inherently interpretable happiness classifier without obscuring predictive trade-offs. This study develops and audits an inherently interpretable decision tree for classifying high self-reported happiness from age, screen time, sleep quality, stress, offline days, exercise, gender, and social-media platform. The contribution is a leakage-safe evaluation that combines nested cross-validation, comparison with Random Forest, logistic regression, and radial-basis-function support vector machine, out-of-fold discrimination and calibration, a one-standard-error tree-selection rule, bootstrap stability, and sensitivity analyses. Across five outer folds, the decision tree obtained balanced accuracy 0.779 ± 0.049, macro-F1 0.778 ± 0.050, ROC-AUC 0.869 ± 0.045, and Brier score 0.146 ± 0.028. Random Forest achieved the highest mean balanced accuracy (0.820), whereas logistic regression achieved the highest ROC-AUC (0.913) and lowest Brier score (0.122). The selected depth-three tree retained eight leaves and used daily screen time and stress as its primary decision pathways. In 200 bootstrap samples, stress was selected in 100.0% of trees and screen time in 86.5%; however, the root alternated between stress (53.5%) and screen time (45.0%), indicating stable relevance but unstable ordering. Results remained similar with the four-feature subset, while performance changed across happiness thresholds. The model therefore offers transparent decision rules at a modest predictive cost, but the cross-sectional, single-dataset design supports predictive association rather than causal or clinical interpretation
Lightweight CNNs for Eggplant Leaf Disease Classification on a Balanced Dataset: A Comparative Study of MobileNetV3 and EfficientNetV2-B2 Masparudin Masparudin; Yonky Pernando; Ihsan Verdian; Fazlul Rahman
Bulletin of Informatics and Data Science Vol 5, No 1 (2026): May 2026
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v5i1.166

Abstract

Eggplant (Solanum melongena) is a vital agricultural commodity, but its yield is highly vulnerable to foliar diseases. Early and accurate detection using deep learning is essential for effective crop management. However, deploying automated detection in real-world agricultural settings faces two primary challenges: (1) severe classification bias caused by complex background noise and data imbalance, and (2) extreme computational constraints that hinder the deployment of conventional deep learning models on farmers' edge devices. This study presents a robust methodology for classifying four conditions of eggplant leaves (Healthy, Leaf Spot, Mosaic Virus, and Insect Pest) by implementing an automated background removal technique and targeted data augmentation, resulting in a perfectly balanced dataset of 1,400 images. Furthermore, this research conducts a comparative analysis between two distinct categories of lightweight Convolutional Neural Networks (CNNs): MobileNetV3-Large (representing ultra-lightweight architectures with 224x224 input resolution) and EfficientNetV2-B2 (representing medium-lightweight architectures with 260x260 input resolution). The models were evaluated based on their accuracy, loss convergence, and computational efficiency using an 80:20 data split and early stopping callbacks to prevent overfitting. Experimental results demonstrate that both models achieved exceptional performance. EfficientNetV2-B2 exhibited superior stability and precision, achieving a peak validation accuracy of 97.50% and a validation loss of 0.073. Meanwhile, MobileNetV3-Large reached a validation accuracy of 96.07% with significantly faster training iterations. These findings indicate that while EfficientNetV2-B2 is highly recommended for precision-critical agricultural diagnostics, MobileNetV3-Large remains a formidable alternative for deployment on edge devices with extreme computational constraints
Analysis of Electricity Consumption Patterns a Hybrid Algorithm K-Means Clustering and Support Vector Machine Fika Saputri; Asminar Asminar; Tambi Tambi; Mustarum Musaruddin; Muhammad Nadzirin Anshari Nur; Adhi Setiawan Samsul
Bulletin of Informatics and Data Science Vol 5, No 1 (2026): May 2026
Publisher : PDSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61944/bids.v5i1.155

Abstract

Fluctuations in electricity consumption make it difficult to manage and control energy use efficiently. Therefore, an analytical method capable of accurately identifying electricity consumption patterns is needed. Previous studies have generally applied clustering and classification separately and relied mainly on historical data, limiting their ability to capture dynamic electricity consumption patterns and classify new observations. This study aims to analyze electricity consumption patterns using a machine learning approach based on K-Means Clustering and Support Vector Machine (SVM). The data used consist of historical data and 24-hour real-time data obtained from PT PLN (Persero) UP3 Kendari. The research stages include data preprocessing, feature engineering, standardization using Z-scores, the clustering process using K-Means, and classification using SVM in a hybrid approach. The novelty of this study lies in using K-Means cluster labels as target classes for SVM while combining historical and real-time data. The results show the formation of four clusters: Cluster 0 representing stable moderate consumption, Cluster 1 representing high consumption, Cluster 2 representing low consumption, and Cluster 3 representing fluctuating consumption. Evaluation yielded a Silhouette Score of 0.5095 and a Davies-Bouldin Index of 0.6868, indicating fairly good cluster quality. The SVM model achieved an accuracy of 99.23% with high precision, recall, and F1-score values. These results demonstrate that the hybrid approach is effective in improving analysis performance and producing a reliable model for classifying new electricity consumption data

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