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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.
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Articles 969 Documents
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Klasifikasi Motif Batik Nitik Berbasis Fitur Ekstraksi SqueezeNet dengan Reduksi Dimensi PCA–LDA Suciani, Ratih; Sudibyo, Usman
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.v7i4.9437

Abstract

Batik nitik motif classification faces significant challenges due to high intra-class variability and complexity of geometric dot patterns, along with limited samples per class in available datasets. Previous research using handcrafted feature extraction methods such as GLCM and MTCD achieved only 53% accuracy, while BSIF with data augmentation reached 97.70%. This study aims to develop a batik nitik classification method using feature extraction based on SqueezeNet trained on ImageNet to achieve superior accuracy without additional external data augmentation techniques. The Batik Nitik 960 dataset consisting of 960 images (60 classes × 16 samples) inherently contains natural visual diversity for each motif as curated by Minarno et al., enabling deep feature extraction from SqueezeNet to be optimized without extra augmentation. A 1000-dimensional feature vector extracted from SqueezeNet's pool10 layer then underwent dimensionality reduction using PCA, LDA, or PCA+LDA, and was classified with Random Forest, SVM, or KNN. These three classifiers were selected to represent distinct learning paradigms: ensemble method (Random Forest), margin-based classifier (SVM), and instance-based learning (KNN), enabling a comprehensive analysis of the extracted feature space characteristics. Experiments were conducted across various training data sizes (4-14 samples per class). Results showed that 8 out of 9 model combinations achieved perfect 100% accuracy, with LDA+SVM, LDA+KNN, PCA+LDA+SVM, and PCA+LDA+KNN requiring only 4 training samples per class. Only LDA+Random Forest failed to reach 100% (maximum 95.14%). The method's advantages lie in the deep feature extraction capability of SqueezeNet, which produces far more discriminative representations than handcrafted features, combined with the efficiency of supervised dimensionality reduction (LDA) in optimizing class separability. Inference time analysis shows that all model combinations are capable of performing predictions within the range of 0.013–0.173 ms per image, and stability evaluation using 5 random states confirms result consistency with mean accuracy ≥99.70% across 8 combinations (standard deviation ≤0.25%), confirming real-time implementation feasibility. This research establishes a new state-of-the-art for the Batik Nitik 960 dataset and opens opportunities for practical applications in authentication, quality control, and preservation of Indonesian batik cultural heritage. The primary contributions of this research encompass the application of SqueezeNet as a fixed feature extractor without fine-tuning for batik nitik classification a previously unexplored approach in this domain a comprehensive comparative analysis of nine dimensionality reduction and classifier combinations, and the establishment of a new state-of-the-art benchmark for the Batik Nitik 960 dataset, validating that CNN-based deep feature extraction surpasses handcrafted methods even with as few as four training samples per class. These findings pave the way for practical real-time batik identification systems applicable to authentication, quality control, and Indonesian cultural heritage preservation
Integrasi Model Hibrida TOPSIS-BORDA untuk Penentuan Prioritas Strategi Digitalisasi Pesantren yang Berkelanjutan Lathifah, Shofi Putri; Mahfud, Adzhal Arwani; Yuniarti, Wenty Dwi; Umam, Khotibul
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.v7i4.9439

Abstract

Digital transformation in Islamic boarding schools (pesantren) holds a specific urgency as these institutions face the challenge of integrating administrative governance modernization with the preservation of salaf traditions, a dilemma rarely found in general formal education. Resource limitations and preference differences among leaders and administrators often trigger strategic deadlocks. This study aims to determine sustainable digitalization strategies at Pondok Pesantren YPMI Al-Firdaus Semarang by integrating the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Borda Count. The TOPSIS model is utilized to objectively evaluate the technical feasibility of seven criteria, while BORDA facilitates quantitative deliberation to accommodate the preferences of five decision-makers. The analysis results indicate that alternative A1 (Digitalization of Administrative Management) becomes the main priority with a relative closeness value of 0.738 and a BORDA consensus score of 8.7664. This figure significantly outperforms other alternatives, proving that the improvement of basic administration is the most urgent and mutually agreed-upon foundation before the pesantren advances to more complex digitalization. Although this study is on a local scale, the integration of these methods proves effective in mapping social and technical compromises, and can be adapted by other pesantren with similar characteristics.
Optimasi Deteksi Malware Android pada Dataset Drebin Menggunakan Ensemble Learning Usmany, Haidar Nafiis; Ghozi, Wildanil
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.v7i4.9443

Abstract

The increasing number and complexity of Android malware require detection systems that are accurate, efficient, and capable of handling high-dimensional data. Machine learning–based approaches have become one of the widely adopted solutions in cybersecurity research. However, the performance of classification models is often affected by feature redundancy and suboptimal hyperparameter configurations. This study aims to evaluate the effectiveness of combining Random Forest–based feature selection with modern boosting classification algorithms for Android malware detection. The dataset used in this study is the Drebin 215 dataset, which was selected because it is one of the most widely used benchmark datasets for Android malware detection based on static analysis, enabling more objective comparison with previous studies. Feature selection was performed using the Random Forest feature importance method to reduce data dimensionality prior to the classification stage. The classification models employed include XGBoost, Light Gradient Boosting Machine (LightGBM), and CatBoost. The experiments were conducted under two scenarios: without hyperparameter optimization (non-tuning) and with hyperparameter optimization using the Grid Search method. Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC metrics, as well as computational time analysis. The experimental results show that all models achieved very strong classification performance on the Drebin benchmark dataset, with accuracy values exceeding 0.98. Among the evaluated models, LightGBM achieved the best performance, with an accuracy of 0.9900 and an F1-score of 0.9865. This performance advantage is likely influenced by the efficiency of its histogram-based learning mechanism and leaf-wise tree growth strategy, which enables faster and more effective learning on high-dimensional data. Nevertheless, the high performance observed on this benchmark dataset still requires further evaluation on more diverse datasets or dynamic environments to ensure the generalization capability of the model in real-world scenarios. The findings of this study indicate that the combination of Random Forest–based feature selection and boosting algorithms can serve as an effective approach for improving the efficiency and performance of Android malware detection systems.
Perbandingan Kinerja Model ARIMA dan LSTM dalam Peramalan Harga Crypto Solana (SOL-USD) Berbasis Data Yahoo Finance Wadiyan, Wadiyan; Permata, Permata; Priandika, Adhie Thyo; Gunawan, Rakhmat Dedi
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.v7i4.9444

Abstract

The extreme volatility and non-linear patterns of Solana (SOL) data, driven by its unique consensus mechanism and massive transaction volume, demand accurate forecasting methods to mitigate investment risks. This study compares the statistical method Autoregressive Integrated Moving Average (ARIMA) and Deep Learning Long Short-Term Memory (LSTM) using daily closing price data of SOL-USD from April 2020 to March 2025 obtained from Yahoo Finance. The ARIMA model was developed with optimal parameters (0,1,0), while the LSTM architecture utilized 50 hidden layer units with a 60-day timestep. Evaluation results indicate that the LSTM model significantly outperforms ARIMA, achieving an RMSE of 13.1352 and a MAPE of 6.07% (classified as highly accurate), compared to ARIMA's RMSE of 31.1241 and MAPE of 14.03%. The study concludes that neural network approaches are more effective and adaptive than traditional statistical methods in capturing the highly volatile price dynamics of crypto assets.
Perbandingan Naïve Bayes dan Support Vector Machine Berbasis Term Frequency−Inverse Document Frequency pada Analisis Sentimen Ulasan Produk Afiliasi Lintas Platform TikTok dan Shopee Putri, Clara Indriani; Yudhistira, Aditia
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.v7i4.9454

Abstract

The growth of affiliate marketing on digital platforms, particularly TikTok and Shopee, has led to a rapid increase in consumer reviews that can be leveraged as actionable insights for businesses. However, reviews across platforms exhibit different linguistic characteristics: Shopee reviews tend to be more repetitive and transactional, whereas TikTok reviews are more informal, rich in slang, and noisier. This difference creates a research gap because sentiment classification performance may vary across platforms, while comparative studies on cross-platform affiliate reviews remain limited. This study aims to analyze and compare the performance of Multinomial Naïve Bayes and Support Vector Machine in identifying positive and negative sentiment polarity in TikTok and Shopee affiliate product reviews. Data were collected via web scraping during December 2025–January 2026, yielding 5,502 raw reviews. After text preprocessing (case folding, regex-based cleaning, normalization, stopword removal, and stemming using Sastrawi), 4,593 clean reviews were obtained. Lexicon-based automatic labeling with negation handling produced a binary dataset of 3,314 reviews (2,729 positive and 585 negative), indicating class imbalance; therefore, no data balancing was applied and evaluation emphasized precision, recall, and F1-score in addition to accuracy. Feature representation used Term Frequency–Inverse Document Frequency, and the dataset was split using an 80:20 hold-out scheme (2,651 training and 663 testing instances). Experimental results show that the Support Vector Machine achieved higher performance (95.93% accuracy; 0.81 negative-class F1) than Multinomial Naïve Bayes (89.14% accuracy; 0.12 negative-class F1). This superiority is related to the ability of Support Vector Machine to learn a maximum-margin hyperplane in the high-dimensional and sparse Term Frequency–Inverse Document Frequency feature space, making it more robust to linguistic variation and noise than the probabilistic Naïve Bayes approach, which is more sensitive to majority-class dominance.
Comparative Analysis of VGG16 Transfer Learning Fine-Tuning Strategies for Automated Concrete Crack Classification Juantoro, Adwinof Akmal; Sugiyanto, Sugiyanto
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.v7i4.9468

Abstract

Identifying cracks in concrete structures is critical for structural health monitoring, as undetected cracks can lead to catastrophic infrastructure failure. Conventional manual inspections are labour-intensive, subjective, and costly, necessitating automated solutions capable of consistent and scalable deployment. This paper presents a systematic comparative study of four VGG16 transfer learning strategies for automated binary classification of concrete surface cracks. VGG16 was selected for its proven effectiveness in binary image classification tasks, well-established pre-trained feature representations from ImageNet, and low trainable parameter count that reduces overfitting risk on domain-specific datasets. A dataset of 40,000 concrete surface photographs was utilised, divided 80:20 for training and validation. Four training configurations were evaluated: Baseline CNN, Full Freeze, Partial Fine-Tuning, and Full Fine-Tuning, all trained using the Adam optimiser (learning rate 0.001), binary cross-entropy loss, and early stopping. Partial Fine-Tuning achieved the highest accuracy at 99.90%, followed by Full Freeze (99.84%) and Baseline CNN (99.69%). Full Fine-Tuning collapsed to 50.00% due to catastrophic forgetting. The best-performing Partial Fine-Tuning configuration achieved an AUC of 0.9998, precision of 0.9990, recall of 0.9990, and F1-score of 0.9990, with only 15 misclassifications out of 8,000 validation samples. These results confirm that Partial Fine-Tuning is the recommended strategy for concrete crack classification in structural health monitoring application.
Analisis Ketahanan Model ResNet-50 pada Klasifikasi Bahasa Isyarat Arab terhadap Degradasi Citra Bawah Air Ilham, Muhammad; Rakhmadi, Aris
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.v7i4.9479

Abstract

Automatic sign language recognition using deep learning, particularly Convolutional Neural Networks (CNNs), has shown significant potential. The ResNet architecture, through transfer learning, is frequently reported to achieve high accuracy for Arabic Sign Language Alphabet classification under ideal conditions. However, the robustness of these models against real-world visual distortions remains a significant, yet under-explored challenge. This research aims to develop a ResNet-50-based classification model while comprehensively analyzing its robustness. The primary contribution of this research is mapping the tolerance limits and the extent of performance degradation of the ResNet architecture when facing image degradation. Evaluation was conducted on both ideal test data and test data digitally modified to simulate underwater visual effects. This underwater simulation was selected as an extreme stress test scenario because it technically represents an accumulation of simultaneous real-world optical distortions, such as contrast reduction, turbidity (haziness), and light refraction. Quantitative evaluation results show that the model performs excellently with an accuracy of 96.95% under ideal conditions. However, exposure to underwater distortion resulted in an accuracy drop of 4.24%, reducing it to 92.71%. Despite this noticeable performance reduction, the model maintained an F1-Score of 92.79%. These findings provide empirical evidence regarding the capability limits of the ResNet architecture when facing visual degradation, while also emphasizing the importance of robustness testing before deep learning models can be reliably deployed in non-ideal environments full of visual uncertainties.
Hybrid DBSCAN - K-Means Clustering for Financial Loss Identification in INA-CBG Claims Based on Medical Treatment Patterns Dianqori, Muhammad Fajar; Fudholi, Dhomas Hatta; Utomo, Galih Aryo; Paputungan, Irving Vitra
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.v7i4.9483

Abstract

Hospital financial deficits due to INA-CBG claim discrepancies pose a critical challenge to healthcare sustainability in Indonesia. The difference between hospital operating costs and INA-CBG rates often results in significant financial deficits, which can threaten the sustainability of healthcare providers, especially hospitals. However, existing studies lack a systematic approach to identify distinct patterns of financial losses based on clinical treatment characteristics. This study aims to identify clusters of patients with different financial loss characteristics using a hybrid DBSCAN-K-Means clustering approach based on medical procedure frequency patterns. The DBSCAN algorithm was employed to detect and separate noise from data, while K-Means was used to identify medical treatment patterns. The data were obtained from electronic medical records of inpatients during the 2023–2024 period at a private hospital (N = 6,021 cases). The final clustering results revealed two main clusters with a highly significant difference in deficits between clusters (p = 6.21 × 10⁻³⁸, Cliff's Delta = −0.216). Cluster 0 represents patients with intensive care who have a higher frequency of routine procedures, with an average deficit of 1.51 times (51.3% greater) and an average length of stay of 1.76 times (76% longer) than Cluster 1. Cluster 1 represents patients with a focus on obstetrics/neonatology with a predominance of Doppler procedures. Meanwhile, the noise cluster (13.39%) represents more extreme cases with an average loss of −7.82 million IDR and high mortality. Of the total 315 treatment features, 114 were confirmed to be statistically significant. This study contributes a novel hybrid clustering framework for identifying financial loss patterns in INA-CBG claims, providing actionable insights for hospital management to optimize service utilization, adjust procedure fees for complex cases, and strengthen financial risk management strategies.
Analisis Performa K-Nearest Neighbor dengan Optimasi F1-Score dan Teknik SMOTE dalam Klasifikasi Risiko Serangan Jantung Pratama, Fikri Luqman; 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.v7i4.9493

Abstract

Heart attack is one of the leading causes of death worldwide, making early risk prediction essential for improving patient outcomes. However, many medical datasets suffer from class imbalance, where the number of high-risk cases is significantly smaller than normal cases. This condition may cause machine learning models to be biased toward the majority class and reduce their ability to detect high-risk patients. This study aims to analyze the performance of the K-Nearest Neighbor (KNN) algorithm optimized using F1-score and combined with the Synthetic Minority Over-sampling Technique (SMOTE) for heart attack risk classification. The dataset used is the Heart Attack Dataset, which consists of numerical and categorical features. The research applies an experimental approach by developing a machine learning pipeline that includes data preprocessing, missing value handling, feature standardization, oversampling using SMOTE, and hyperparameter optimization through GridSearchCV with F1-score as the main evaluation metric. Model evaluation is conducted using Stratified 5-Fold Cross-Validation with accuracy, precision, recall, F1-score, and ROC-AUC metrics. The results show that the baseline KNN model achieves an accuracy of 98.50%, precision 95.27%, recall 81.47%, and ROC-AUC 0.9278. Meanwhile, the KNN model integrated with SMOTE attains a recall of 87.27% and ROC-AUC of 0.9484, indicating improved detection of heart attack cases and a reduction in false negatives by 31%, although precision decreases to 72.15%. These findings demonstrate that the integration of SMOTE and hyperparameter optimization effectively improves model sensitivity, making it more suitable for medical applications that prioritize patient safety.

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