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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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Evaluasi Strategi Fine-Tuning pada ConvNeXt dan Swin Transformer untuk Klasifikasi Kanker Kulit Saputra, Ahmad Bintang; Rakasiwi, Sindhu
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.v7i4.9015

Abstract

Skin cancer is one of the diseases whose prevalence continues to increase every year, especially in areas with high exposure to ultraviolet (UV) rays. The main challenge in diagnosing skin cancer lies in the visual similarity between benign and malignant lesions, which often leads to misdiagnosis even by experienced medical personnel. The development of deep learning technology has made significant progress in medical image classification through a transfer learning approach. This study aims to compare the performance of two architectures from Transformer and CNN, namely Swin Transformer and ConvNeXt, in the task of classifying two class benign and malignant skin cancer images. Both models use pretrained from ImageNet and are applied with three different fine-tuning strategies, namely Linear Probe (LP), Full Fine-Tuning (FT), and a combination of the two previous strategies (LP-FT). The dataset used is the ISIC Archive Dataset with an 80:20 data split for training and validation, consisting of 3.297 images divided into two classes, with 1800 benign images and 1.497 malignant images. The evaluation was performed using the accuracy, precision, recall, and F1-score metrics. Swin Transformer with the LP-FT strategy achieved the best performance, with an accuracy of 92,27%, precision of 92,24%, recall of 92,17%, and an F1-score of 92,20%. These findings indicate that the two-stage fine-tuning approach can improve model stability and generalization, as well as contribute to the development of a more accurate artificial intelligence based skin cancer diagnosis system.
Analisis Sentimen Diseminasi Produk Iklim Menggunakan Metode Recurrent Neural Network (RNN) dalam Klasifikasi dan Density-Based Spatial Clustering of Applications with Noise (DBSCAN) untuk Klasterisasi Mestika, Noris; Supriyanto, Aji
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.v7i4.9025

Abstract

Climate change and extreme weather events have a significant impact on various sectors of life, making the accurate and timely dissemination of climate information crucial. Public sentiment can be an indicator of public assessment of climate dissemination. The implications of the sentiment analysis itself can be used as a communication strategy from information providers to the public. This study aims to analyze public sentiment toward the dissemination of climate products by the Central Java Climatology Station through social media platforms Instagram (@bmkgjateng) and X (@bmkg_semarang). The analysis was conducted using a hybrid framework integrating the Recurrent Neural Network (RNN) method for sentiment classification and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for topic clustering and outlier identification. A total of 12,847 comments were collected via web scraping from 2020 to 2024. The RNN classification results revealed a dominance of neutral responses (76.41%), followed by negative (13.15%) and positive (10.44%) sentiments. The model achieved high performance with 96% accuracy and a weighted average F1-Score of 0.96. DBSCAN successfully identified 82 topic clusters and classified 74.5% of the data as noise, largely consisting of non-topical interactions or spam. The validity of the cluster structure was confirmed by a Silhouette Coefficient of 0.3675, a Davies-Bouldin Index of 0.504, and a Calinski-Harabasz Index of 191.395, indicating that the formed topic clusters possess a robust structure and are distinctly separated from one another. Integrative analysis revealed that negative sentiments were consistently focused on specific issue clusters such as floods and extreme heat, whereas positive sentiments were dispersed across service appreciation. These findings suggest the necessity of implementing an automatic filtration
Stacking-Correspondence Analysis for Fuzzy Data: Computational Framework for Analyzing Complex Qualitative Survey Data Kirana, Disa Rahma; Ginanjar, Irlandia; Tantular, Bertho
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.v7i4.9054

Abstract

Bandung Regency faces a significant challenge in achieving Sustainable Development Goal (SDG) 12, marked by a critically low score of 14.53 out of 100. Uniform policies are often ineffective due to regional diversity and uncertainty in categorical survey data, which inadequately reflects real-world conditions. This study aims to identify sub-district characteristics based on consumption and production patterns to provide precise policy recommendations. The research utilizes data from the 2024 Supporting Area Survey (SWP), covering 280 villages across 31 sub-districts. A computational framework combining stacking techniques and Correspondence Analysis for Fuzzy Data (CAFD) is implemented to analyze four qualitative variables. The stacking phase transforms the multi-way data structure into a two-way structure, while CAFD effectively handles qualitative uncertainty using membership degrees. Analysis results indicate that two principal dimensions capture 73.35% of the total information variance and successfully identify 17 sub-district clusters with similar problem profiles. The fuzzy approach unveils multi-characteristic profiles, identifying both dominant and secondary traits. This research contributes a two-dimensional perceptual map, enabling the government to transition from generic policies to tailored interventions for each sub-district. This computational solution represents a concrete step toward improving the SDG 12 achievement score through data-driven strategic planning.
Explainable Aspect-Based Sentiment Analysis with Contrast-Aware IndoBERT for Indonesian Public Service Reviews Jondien, Muhammad Shihab Fathurrahman; Hariguna, Taqwa; Saputra, Dhanar Intan Surya
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.v7i4.9162

Abstract

This study presents an Explainable IndoBERT with Contrast-Aware Attention framework for Aspect-Based Sentiment Analysis (ABSA) on Indonesian public service reviews. The proposed model integrates automated aspect labeling using KeyBERT with a contrast-aware mechanism to handle mixed or opposing sentiments within a single sentence. By leveraging IndoBERT as the base transformer, the system captures context-sensitive sentiment cues while maintaining interpretability through attention-based rationale extraction. Experimental results on the SMSA dataset demonstrate an accuracy of 83.4%, with strong precision in positive and negative sentiment detection. The contrast-aware module improves clause-level understanding, while the attention-based explainability module provides transparent, token-level rationales that align with human judgments at an average rate of 87.7%. Although a modest performance decline occurs compared to non-explainable baselines, the proposed model offers significant gains in semantic transparency, making it suitable for evidence-based policy evaluation and citizen feedback monitoring. This research contributes a practical, interpretable, and linguistically grounded solution for explainable sentiment analysis in low-resource languages, advancing the application of responsible AI in public service analytics.
Klasifikasi Kesehatan Mental Menggunakan Support Vector Machine Berdasarkan Screen Time dan Interaksi Sosial Digital Pendi, Pendi; Sulistiani, Heni
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.v7i4.9189

Abstract

Mental health is an important aspect that influences the quality of life of individuals, especially in adolescents and young adults who are vulnerable to stress due to the increased use of digital devices. Technological developments have led to increased screen time and the intensity of digital social interactions, which have the potential to affect mentsal health conditions. This study aims to develop a mental health classification model using the Support Vector Machine (SVM) method with a Radial Basis Function (RBF) kernel based on digital behavior data, including daily device usage time, social media time, number of positive interactions, and number of negative interactions. The dataset used is secondary data obtained from Kaggle and goes through the stages of pre-processing, feature selection, data normalization, and division of training and test data with a ratio of 80:20. The built SVM model is able to classify mental health conditions into three classes, namely Healthy, Stressed, and Risky. The evaluation results show that the accuracy of the resulting model is 94.3%, with a precision value of 66.3%, a recall of 96.1%, and an f1-score of 74.1%. These results indicate that the variables of screen time and digital social interaction have strong potential to be used as a basis for objective and data-based mental health classification.
Penerapan Algoritma Naïve Bayes Terhadap Sentimen Ulasan Produk Skincare Pada E-Commerce Shopee Putri, Divana Wahyu; Soeleman, Moch Arief
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.v7i4.9209

Abstract

The rapid growth of the beauty industry has generated a large volume of consumer reviews, necessitating an automated processing system to understand public sentiment. This study aims to implement sentiment analysis on skincare product reviews using the Multinomial Naïve Bayes algorithm. The labeling process was conducted by converting star ratings into sentiment categories: ratings 4 and 5 were labeled as positive, ratings 1 and 2 as negative, while rating 3 was excluded to avoid data ambiguity. The feature representation stage utilized TF-IDF with an N-gram approach (unigram and bigram), generating 10,000 features from a dataset of 8,646 reviews. Based on the testing results of 1,730 test data, the model achieved an accuracy of 70%. The Confusion Matrix evaluation revealed that the model performed exceptionally well in the positive class, reaching a recall of 1.00. However, the model struggled to classify negative and neutral classes, with recall values approaching 0.00. This was caused by imbalanced data distribution, where positive reviews significantly dominated the dataset. Nevertheless, Multinomial Naïve Bayes proved efficient in handling large-scale frequency-based textual features. A weighted average F1-score of 0.58 suggests that dataset optimization is required to improve the model's ability to accurately recognize minority sentiments.
Deteksi Cyberbullying pada Komentar Media Sosial Berbahasa Indonesia Menggunakan Pendekatan Hibrida IndoBERTweet- BiLSTM Aditya, Reza Ramadhon; Syarif, Arry Maulana
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.v7i4.9249

Abstract

Cyberbullying on Indonesian-language social media has become a serious issue with significant psychological and social consequences, necessitating the development of reliable automated detection systems. However, the informal, ambiguous, and highly contextual nature of social media language, including the frequent use of slang and sarcasm, poses substantial challenges for conventional text classification approaches. This study proposes a hybrid cyberbullying detection model that integrates the domain-specific pre-trained language model IndoBERTweet with a Bidirectional Long Short-Term Memory (BiLSTM) architecture. IndoBERTweet is employed to generate contextualized semantic representations aligned with the linguistic characteristics of Indonesian Twitter data, while BiLSTM is utilized to capture bidirectional sequential dependencies at the sentence level. Experiments were conducted using a publicly available, manually annotated Indonesian Twitter dataset consisting of 13,091 samples, which were reformulated into a binary classification scheme. To address class imbalance, a combination of class weighting and label smoothing was applied during model training. Model performance was evaluated using Accuracy, Precision, Recall, F1-Score, ROC-AUC, and PR-AUC metrics. Experimental results show that the IndoBERTweet–BiLSTM model achieved the best performance with an F1-Score of 87.53%, Recall of 88.80%, Precision of 86.31%, ROC-AUC of 92.91%, and PR-AUC of 94.25%. This performance consistently outperforms baseline models based on IndoBERT and IndoBERT-p1 with identical architectural configurations. These findings highlight the critical role of domain alignment in enhancing cyberbullying detection performance for Indonesian social media text.
Implementasi Deep Learning Berbasis MobileNetV2 untuk Deteksi Real-Time Bacterial Spot dengan Pendekatan Arsitektur Lightweight Nabilul As'ad, Ahmad; Pramudya, Elkaf Rahmawan
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.v7i4.9252

Abstract

Bacterial spot caused by Xanthomonas campestris pv. vesicatoria is a critical disease in bell peppers that can reduce productivity by up to 50%. This study implements MobileNetV2 with two-stage transfer learning for real-time bacterial spot detection using lightweight architecture approach, with ResNet50 as baseline comparison. PlantVillage dataset (2,475 images) was used for training and in-domain evaluation, while India dataset (132 images) for domain shift assessment. Results demonstrate MobileNetV2 achieves 98.66% accuracy on PlantVillage test set, outperforming ResNet50 (89.78%) by 8.88 percentage points despite being 9.2× lighter (2.7 MB vs 24.3 MB TFLite) and 2.0× faster (22.4 ms vs 45.8 ms inference time). MobileNetV2 efficiency advantage is also evident in its inference memory footprint of only 107 MB RAM, significantly 2.3x lower than ResNet50(242 MB RAM), making it highly suitable for deployment on mid-range smartphones with limited RAM. External dataset evaluation reveals MobileNetV2 maintains superior robustness with 65.3% retention rate versus ResNet50's 52.3%. Trade-off analysis positions MobileNetV2 on the Pareto frontier, achieving optimal accuracy-efficiency sweet spot for plant disease detection applications. This research contributes empirical evidence for lightweight architecture superiority, comprehensive efficiency-oriented evaluation framework, ULTRA-LIGHT training strategy for addressing inverse overfitting, and realistic generalization assessment using tropical external dataset.
Analisis Pola Temporal Penyebaran Penyakit DBD dan HIV Berbasis Time Series Clustering Ramadhani, Trie Adriana; Fathoni, Fathoni
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.v7i4.9262

Abstract

In Indonesia, including in East Java Province, infectious diseases such as Dengue Fever (DHF) and Human Immunodeficiency Virus (HIV) remain public health concerns. Incidence patterns vary by region and time of year. Variations in temporal patterns among districts and cities may lead to suboptimal identification of priority intervention areas when analyses rely solely on absolute case counts. This study aims to analyze the temporal patterns of DHF and HIV case distribution in East Java Province during the 2018–2024 period in order to cluster regions based on similarities in case dynamics over time.The analysis was conducted using a time series clustering approach to group districts and cities according to the similarity of their case development patterns. Temporal similarity was measured using the Dynamic Time Warping method and subsequently clustered using Hierarchical Clustering. Prior to analysis, the data were normalized using the Z-score method to minimize the influence of differences in case scale among regions. The results show that the temporal patterns of DHF and HIV cases were each classified into three main clusters. Cluster quality evaluation using the Silhouette index yielded a value of 0.408 for DHF, indicating a relatively clear cluster structure, whereas a value of 0.197 was obtained for HIV, suggesting a weaker cluster structure due to the complexity and heterogeneity of regional-level case data. Nevertheless, the resulting clusters still provide preliminary information on variations in temporal patterns. The identified clusters represent regions with stable, fluctuating, and increasing case patterns. Several urban areas, such as Pasuruan City, Probolinggo City, and Banyuwangi Regency, tend to belong to clusters with relatively high case levels for more than one disease, indicating challenges in disease control within these regions. These findings provide an initial overview of the temporal dynamics of DHF and HIV cases in East Java, which may serve as supporting evidence for region- and time-based disease control planning.
Komparasi Algoritma Naive Bayes dan K-Nearest Neighbor untuk Analisis Sentimen Pengguna Dompet Digital pada Google Play Store Akbar, M Adhe; Ariany, Fenty
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.v7i4.9285

Abstract

The rapid growth of digital wallet users in Indonesia, reaching millions of active users, has generated a massive volume of reviews on the Google Play Store. This textual data contains crucial insights regarding customer satisfaction but is often underutilized due to challenges in processing unstructured data. This study aims to perform a comparative performance analysis between the probabilistic Naive Bayes algorithm and the distance-based K-Nearest Neighbor (KNN) in classifying user sentiment for DANA, OVO, DOKU, and LinkAja applications. This study utilizes a dataset of 18,869 reviews which exhibits a mild class imbalance with a negative sentiment dominance of 57.54%. To preserve the representation of the large original data, this research applies Stratified Sampling without synthetic data balancing techniques (such as SMOTE), followed by comprehensive preprocessing stages aided by the Sastrawi library and Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction. Model optimization was systematically conducted using GridSearchCV for Naive Bayes and the Elbow Method to determine the optimal k value for KNN. Empirical test results show that the Naive Bayes algorithm with a smoothing parameter alpha of 0.1 achieved the best performance with an accuracy of 88.5% and an AUC of 0.9237, outperforming KNN at k=27 which obtained an accuracy of 87.4%. The validity of this performance difference was confirmed to be significant through the McNemar statistical test with a p-value of 0.0045. Another crucial finding is computational efficiency, where Naive Bayes proved to be 129 times faster in the prediction process compared to KNN. Based on the significant advantages in accuracy and time efficiency, Naive Bayes is recommended as the superior method for real-time sentiment analysis in the financial technology ecosystem.

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