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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 953 Documents
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Optimasi Deteksi Intrusi Jaringan Menggunakan Hybrid Model Autoencoder dan Random Forest Nanda, Afri; Nasution, Torkis
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.v7i4.9309

Abstract

Conventional Intrusion Detection Systems often suffer from performance degradation due to their inability to handle the complexity of high-dimensional data and class imbalance in modern network traffic. This study aims to optimize the Network Intrusion Detection System (IDS) by addressing the limitations of the Random Forest algorithm in handling high-dimensional data and its lack of model transparency (black-box). The proposed method is a Hybrid model integrating an Autoencoder as a non-linear feature extractor and Random Forest as a classifier. The Autoencoder is trained using a semi-supervised strategy to generate latent features and Reconstruction Error (MSE), which serves as a robust anomaly indicator. Additionally, the Synthetic Minority Over-sampling Technique (SMOTE) is applied to address class imbalance in the NSL-KDD dataset. To address the challenge of interpretability, SHAP-based Explainable AI (XAI) is strategically implemented to elucidate the complex interactions between the Autoencoder-compressed latent features and the final classification decisions, thereby transforming this hybrid architecture into a transparent system. Evaluation results demonstrate that the Hybrid Autoencoder-Random Forest model outperforms the Random Forest Baseline, achieving an Accuracy increase of 2.54% (to 77.61%) and a Recall increase of 3.96% (to 62.31%). The significant improvement in the Recall metric empirically validates the effectiveness of hybrid features, specifically the Reconstruction Error, in detecting Zero-Day attacks characterized by unknown patterns. Furthermore, SHAP visualization successfully reveals the contribution of latent features, providing crucial transparency for network security forensic analysis.
Deep Fake Image Detection Using Vision Transformer with Random Oversampling Technique Tirto Prakoso, Dipo Paudro; 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.v7i4.9316

Abstract

Recent developments in deep learning have facilitated the generation of visually convincing deepfake images, creating serious concerns for the reliability and security of digital media content. The primary challenge lies in detecting these sophisticated manipulations while handling imbalanced datasets, a common issue in deepfake detection research. This research focuses on designing a robust deepfake image classification model based on the Vision Transformer (ViT) architecture to differentiate between authentic and manipulated images. The main objectives are to: (1) adapt and fine-tune a pre-trained Vision Transformer for binary classification, (2) evaluate the effectiveness of Random Oversampling in addressing class imbalance while preventing data leakage, and (3) assess model performance using comprehensive metrics. Methods: A pre-trained Vision Transformer model (Deep-Fake-Detector-v2-Model) was adapted and fine-tuned using a dataset consisting of 190,335 images. To overcome the issue of class imbalance, a Random Oversampling strategy was applied exclusively to the training set after dataset splitting to prevent data leakage. The dataset was divided into training and testing subsets using an 80:20 ratio. During the training phase, data augmentation techniques such as image rotation, sharpness variation, and pixel normalization were employed. The model was trained for four epochs with a learning rate of 1×10⁻⁶ and a batch size of 32. Results: Experimental evaluation demonstrates that the proposed model achieves a classification accuracy of 94.46% on the test dataset. The model demonstrates high precision of 97.56% for fake images and 91.74% for real images, with corresponding recall rates of 91.21% and 97.72% respectively. The F1-score reaches 94.46% for both classes, indicating balanced performance. Novelty: This research presents a novel application of Vision Transformer architecture for deepfake detection, combining efficient transfer learning with strategic oversampling to handle imbalanced datasets while preventing data leakage. The study demonstrates that ViT-based models can effectively capture subtle manipulation artifacts in deepfake images, achieving superior performance compared to traditional convolutional neural network approaches.
Analisis Kinerja Decision Tree dan Naïve Bayes Pada Klasifikasi Tingkat Kepuasan Masyarakat Fitaria, Nora; Darwis, 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.v7i4.9325

Abstract

The Public Satisfaction Survey (SKM) is an official instrument used by the government to evaluate public service performance as stipulated in Regulation of the Minister of State Apparatus Empowerment and Bureaucratic Reform (PermenPANRB) Number 14 of 2017. However, the use of SKM data in many government agencies is still limited to calculating satisfaction index values without further predictive analysis. This study aims to classify the level of satisfaction of service users of the Metro City Investment and Integrated Services Agency (DPMPTSP) using the Decision Tree and Naïve Bayes algorithms. The data used is SKM data from 2025 to the fourth quarter, consisting of 2,760 respondents, which consists of nine service elements (U1–U9) with satisfaction categories as class variables. The research process includes data pre-processing, classification modeling using RapidMiner, and model evaluation based on confusion matrix, accuracy, precision, and recall. The results showed that the Naïve Bayes algorithm produced an accuracy rate of 95.04%, higher than the Decision Tree, which obtained an accuracy of 84.46%, and had a better recall value in the dominant class (recall of the Satisfied class was 98.16%). These advantages demonstrate the efficiency of the Naïve Bayes probabilistic approach in handling categorical features in public service elements. This study proves that the application of Data mining techniques to SKM data can support data-based public service evaluation.
Performance Analysis of Quantum Long Short-Term Memory (QLSTM) Models for TLKM Stock Price Prediction Vhazira, Nasya; Hikmah, Irmayatul; Afandi, Mas Aly
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.v7i4.9337

Abstract

Stock price prediction is a challenging task due to its nonlinear, dynamic, and temporal characteristics, yet accurate forecasting models are crucial for decision-making in volatile stocks such as PT Telkom Indonesia Tbk (TLKM). Despite the rapid adoption of AI-based forecasting methods, several research gaps remain. Empirical studies on Quantum Long Short-Term Memory (QLSTM) are still relatively limited compared to classical LSTM variants, particularly for emerging market datasets. Existing research also tends to emphasize architectural comparisons rather than systematically analyzing training configurations. The joint effects of optimizer selection, epoch number, and hidden unit size on QLSTM performance have not been comprehensively evaluated, and many studies rely on limited evaluation metrics, reducing the strength of robustness assessment. To address these gaps, this study applies a QLSTM model to predict stock opening prices using historical time-series data and systematically evaluates the impact of different optimizers. The model is trained using Adam, Nadam, RMSprop, and SGD with epoch variations (50–250) and hidden units (8, 16, 32). Performance is measured using accuracy, MAE, MSE, RMSE, MAPE, and R² to ensure a comprehensive evaluation. The results indicate that adaptive optimizers consistently outperform SGD, with Adam providing the most stable and accurate predictions, highlighting the importance of optimizer choice and hyperparameter configuration in QLSTM-based stock forecasting.
Optimizing Ensemble Learning Models with SMOTE-ENN for Early Stroke Detection in Imbalanced Clinical Datasets Nurmala, Dina; Santoso, Angga Bayu
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.v7i4.9347

Abstract

Stroke remains a leading cause of mortality and long-term disability worldwide, including in Indonesia, highlighting the urgent need for early risk identification. Machine learning models for stroke prediction often suffer from severe class imbalance, where stroke cases constitute only 4.9% of clinical datasets, leading to biased predictions that favor the majority class. This study evaluates three ensemble and kernel-based algorithms Random Forest, XGBoost, and Support Vector Machinecombined with two resampling strategies (SMOTE and SMOTE-ENN) using the Healthcare Stroke Dataset (5,110 records, 11 clinical attributes). To prevent data leakage, resampling was strictly applied within each training fold of 5-fold stratified cross-validation, while all evaluations were conducted on the original imbalanced test set. The results demonstrate that XGBoost integrated with SMOTE-ENN achieved the highest minority-class sensitivity, improving PR-AUC by 23.5% (0.1537 vs. 0.1244 with SMOTE alone), while detecting 24% of stroke cases (12 out of 50) in the test set. Although cross-validation results indicate strong class discrimination with AUC-ROC values above 0.98, the low PR-AUC reflects the operational challenge of extreme class imbalance and the inevitable trade-off between recall and precision, resulting in an increased number of false positives. Consequently, the proposed model is best positioned as a first-tier population screening tool that flags high-risk individuals for confirmatory clinical diagnostics, rather than as a standalone diagnostic system. The approach maintains computational efficiency (training time < 0.12 seconds) and substantially improves model stability, evidenced by a 73% reduction in cross-validation variance. These findings support the integration of hybrid resampling techniques with ensemble learning as a practical and scalable framework for early stroke risk screening in resource-constrained primary healthcare settings.
Machine Learning Comparative Analysis of SVR Method with RBF Kernel and Random Forest for Bitcoin Price Prediction Pratama, Miko Septa; 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.v7i4.9348

Abstract

This study aims to determine how accurate machine learning predictions are for predicting Bitcoin prices using the SVR With RBF Kernel and Random Forest methods. This study was conducted because Bitcoin’s volatility is so high that it is difficult to predict. Therefore, this study uses two different methods to allow for a more objective evaluation of model characteristics on volatile data. The dataset was obtained through Kaggle with a Bitcoin price dataset from 2018 to October 2025, totaling 2,856 datasets in CSV format. After training both methods on the same dataset, price prediction results were obtained. Support Vector Regression (SVR) With RBF Kernel achieved a relatively high data evaluation result with an MAE of 10866.882878735294, MSE of 204836847.5591309, and RMSE of 14312.12239883138, while the Random Forest method achieved a low data evaluation result with an MAE of 19342.47, MSE of 659671833.13, and RMSE of 25684.08. The result of these two methods show a significant difference, with Random Forest more closely aligning with the acual data, with a lower evaluation value and producing values closer to the actual data. This research was conducted to determine the accuracy of the Support Vector Regression (SVR) with RBF Kernel and Random Forest algorithms. It is concluded that both methods make good predictions, only the Random Forest method is closer to the actual Bitcoin price.
Market-Adaptive Stock Trading through B-WEMA Driven Proximal Policy Optimization Ichsan, Mulia; Zahra, Amalia
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.v7i4.9349

Abstract

Developing automated trading strategies that achieve stable returns while controlling risk remains a central threat in quantitative finance. Many reinforcement learning-based trading systems focus on reward maximization but provide limited justification for the choice of forecasting indicators and often lack comprehensive benchmarking against alternative strategies and risk measures. This essay addresses the problem of integrating a statistically grounded price-smoothing technique with a policy optimization scheme to improve sequential trading decisions under market uncertainty. We propose a hybrid model that combines Brown’s Weighted Exponential Moving Average (B-WEMA) as a trend-sensitive forecasting indicator with a Deep Reinforcement Learning agent trained using Proximal Policy Optimization (PPO). The role of B-WEMA is to provide structured price signals that reduce noise sensitivity, while PPO determines buy and sell actions through policy updates constrained for stable learning. The performance of the proposed model is evaluated over a 10-month trading horizon and compared with a buy-and-hold benchmark and an alternative reinforcement learning method, Advantage Actor-Critic (A2C), both implemented under the same experimental conditions. Empirical results show that the proposed B-WEMA-PPO framework achieved a cumulative return of 23.43% over the test period, outperforming both the benchmark and the A2C-based agent. In addition to cumulative return, risk-adjusted performance metrics, namely volatility and maximum drawdown, are reported to provide a balanced assessment of profitability and risk exposure. These findings suggest that incorporating structured exponential smoothing into policy optimization may enhance the stability and effectiveness of reinforcement learning-based trading strategies.
Optimasi Hyperparameter Random Forest untuk Klasifikasi Depresi Mahasiswa Menggunakan GridSearchCV dan RandomizedSearchCV Utami, Eka Wahyu; Kurniawan, Defri
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.v7i4.9366

Abstract

Student mental health is an important issue that requires a data-driven approach to support the classification process of student depression. This study aims to analyze the factors that cause depression and optimize the performance of the classification model by applying the Random Forest algorithm. The data used in this research is secondary data from the Student Depression Dataset obtained from the Kaggle platform, with a total of 27,901 data points. The research stages begin with data collection followed by Exploratory Data Analysis (EDA), which includes descriptive statistical analysis and correlation between variables using a heatmap. Data preprocessing involves removing irrelevant features, handling missing values, encoding categorical data, and splitting the data into training and testing sets. Model development is carried out through three scenarios: a baseline model, hyperparameter optimization using GridSearchCV, and RandomizedSearchCV. Model performance evaluation is measured using a Confusion Matrix to analyze accuracy, precision, recall, and F1-score. The results show that all models produce relatively stable accuracy in the range of 0.84–0.85. The model with GridSearchCV optimization provides the best performance with a recall value of 0.8869 and an F1-score of 0.8719. This increase in recall is important to minimize the risk of false negatives in identifying students experiencing depression. It is hoped that these findings can contribute as a decision support system for educational institutions in more accurately detecting and managing students' mental health.
Implementasi Arsitektur MobileNetV2 Berbasis Citra untuk Deteksi Penyakit Dropsy dan Popeye pada Ikan Cupang Musyaffa, Fadhilah Rafi; Mahfudh, Adzhal Arwani; Subowo, Moh Hadi
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.v7i4.9379

Abstract

The identification of diseases in betta fish based on visual symptoms remains a challenge, particularly for beginners who lack experience in recognizing disease characteristics. This study aims to implement an image-based MobileNetV2 architecture as a diagnostic support system to detect dropsy and popeye diseases in betta fish that have already exhibited visual symptoms. The dataset used in this study consists of 600 betta fish images divided into three classes: healthy, dropsy, and popeye, with 200 images in each class, collected from the internet. Data preprocessing was conducted through image ratio adjustment, normalization, and data augmentation to increase data variability. A transfer learning approach was applied by freezing most layers of the MobileNetV2 feature extractor and fine-tuning several of the final layers. Model evaluation was performed using 5-Fold Cross Validation to ensure experimental stability and reproducibility. The best model from each fold was then combined using an ensemble method based on average probability to improve prediction performance on the test dataset. Experimental results show that the average 5-Fold Cross Validation accuracy reached 74.71% with a standard deviation of ±4.57%, while the Macro-F1 score achieved ±74.43%. The ensemble approach produced a test accuracy of 85.56% with balanced classification performance across all classes. Grad-CAM visualizations indicate that the model is able to focus on image regions relevant to disease symptoms. These findings demonstrate that the MobileNetV2 architecture is effective as an image-based diagnostic support tool for betta fish diseases.
Comparative Analysis of K-NN and Naïve Bayes Algorithms for Early-Stage Chronic Kidney Disease Classification Rahma, Intan Dwi; Furqan, Mhd; Triandi, Budi
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.v7i4.9392

Abstract

Chronic Kidney Disease (CKD) is a global health issue characterized by low early detection rates and high diagnostic costs. Artificial intelligence, particularly machine learning, offers a promising solution as a rapid and cost-effective decision support system. This study aims to comprehensively analyze and compare the performance of two simple and interpretable classification algorithms, K-Nearest Neighbor (K-NN) and Naïve Bayes (NB), for predicting CKD based on clinical data. The dataset was sourced from the UCI Machine Learning Repository, comprising 400 instances and 25 clinical attributes such as blood pressure and serum creatinine. The methodology included data preprocessing (median imputation for numerical features, mode imputation for categorical features), encoding, Min-Max normalization, data splitting (70:30 ratio), model training, K parameter optimization for K-NN via 5-fold cross-validation, and evaluation using accuracy, precision, recall, F1-Score, and Confusion Matrix metrics. Experimental results demonstrated that the Naïve Bayes algorithm achieved superior performance with an accuracy of 95.83%, precision of 95.95%, recall of 97.26%, and F1-Score of 96.60%. The K-NN algorithm with an optimal K=5 attained an accuracy of 91.67%. Statistical analysis using a paired t-test (α=0.05) with p-value=0.012 confirmed that this performance difference was significant. It is concluded that Naïve Bayes is more effective for this CKD dataset, likely due to its robustness in handling feature independence assumptions and varied data scales. This model holds strong potential for development into an early-stage CKD screening tool to assist healthcare professionals.

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