cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Mesran
Contact Email
mesran.skom.mkom@gmail.com
Phone
-
Journal Mail Official
jurnal.bits@gmail.com
Editorial Address
-
Location
Kota medan,
Sumatera utara
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 27 Documents
 1   2   3 
Search results for , issue "Vol 7 No 4 (2026): March 2026" : 27 Documents clear
Prediksi Periode Fosil Trilobita Menggunakan XGBoost dengan Seleksi Fitur Geologi–Geospasial dan Hyperparameter Tuning Ramadhan, Naufal Rizky; 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.v7i4.8862

Abstract

This study investigates the application of the Extreme Gradient Boosting (XGBoost) algorithm to predict the age period of trilobite fossils based on geological and geospatial data. The challenges addressed in this research include the high complexity of paleontological data, the presence of missing values, and class imbalance in the target variable time_period, which can negatively affect predictive performance. The objective of this study is to develop an accurate and robust fossil age prediction model through systematic data preprocessing, feature selection, and model optimization. The dataset used in this research was obtained from Kaggle and consists of the attributes longitude, latitude, lithology, environment, and collection_type as the main features. The research workflow includes data cleaning, missing value imputation, categorical feature encoding, data splitting using stratified train–test split, and class imbalance handling through a class weight adjustment approach. The XGBoost model was trained on the training dataset and further optimized using RandomizedSearchCV to obtain the optimal hyperparameter configuration. Evaluation results on the testing dataset show that the tuned XGBoost model achieved an accuracy of 95%, precision of 90%, recall of 93%, and an F1-score of 91%, outperforming the model without hyperparameter tuning. These results demonstrate that the integration of geological–geospatial feature selection and hyperparameter tuning in XGBoost is effective in improving the performance of trilobite fossil age period prediction. The results of this study are expected to serve as a computational support approach in paleontology to assist fossil period determination in a more objective, efficient, and data-driven manner.
Reversible Data Hiding Citra MRI T1-Weighted Menggunakan Spatial Fuzzy C-Means dan Selective Histogram Shifting Suharyoto, Aufa Fadholi; 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.v7i4.8863

Abstract

The transmission of medical images over telemedicine networks increases the risk of data leakage and manipulation of sensitive information. This study develops a Reversible Data Hiding framework that integrates Spatial Fuzzy C-Means, Selective Histogram Shifting, and a measurable Distortion Control Mechanism for securing T1-weighted brain MRI images. The proposed method prioritizes the preservation of Region of Interest intensity characteristics and full reversibility over embedding capacity. SFCM is employed to generate Region of Interest and Non-Region of Interest mappings based on intensity distribution, with adaptive parameter adjustment for each slice. Data embedding is performed selectively on NROI using histogram shifting, while ROI areas remain unmodified. An Adaptive Feedback Control mechanism monitors image quality metrics SNR, CNR, GLCM with conservative thresholds (ΔSNR ≤ 2.0%, ΔCNR ≤ 1.0%) to ensure ROI stability. Experimental evaluation on the OASIS-1 dataset shows that the proposed method achieves an average PSNR of 54.13 dB, SSIM of 0.9996, and NCC of 0.9999, with an embedding capacity of 630 bits per slice (BPP 0.007-0.013 within NROI). Reversibility verification confirms perfect recovery (maximum difference = 0) for all samples. Batch testing on five slices demonstrates consistent performance across varying intensity characteristics, with ΔSNR and ΔCNR remaining at 0.0%. These results indicate that the method is capable of maintaining ROI technical integrity and pixel-perfect reversibility, although with a limited capacity suitable for lightweight metadata such as integrity hashes and patient identifiers. Limitations of the study include the technical-only evaluation without radiologist clinical validation and testing restricted to T1-weighted MRI modality.
Deteksi Malware Android Berbasis Ensemble Soft Voting LightGBM, Logistic Regression dan CatBoost Danendra, Ardian; 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.v7i4.8865

Abstract

The Android operating system faces serious challenges with increasingly complex and diverse malware evolution. This research proposes an Android malware detection system based on soft voting ensemble that integrates three algorithms (LightGBM, Logistic Regression, and CatBoost) to improve detection accuracy while maintaining computational efficiency. The dataset used is CCCS-CIC-AndMal-2020, which is highly imbalanced with over 400,000 Android application samples. The proposed model leverages hybrid features that combine static information (permissions, intents, API calls from the AndroidManifest) with dynamic behavior (memory activities, runtime API calls, logcat, and network traffic in an emulated environment), balancing low extraction cost with improved robustness against obfuscation. The methodology includes multi-stage preprocessing (IQR capping 40×, StandardScaler, RFE 150 features, SMOTE 30%) to improve data quality and reduce dimensionality by 56% without losing important information. The ensemble model is trained with F1-Macro-based weights (33.46% LightGBM, 30.99% Logistic Regression, 35.55% CatBoost) approximating 1:1:1 proportion. Evaluation results on the testing set demonstrate very high performance: Accuracy 95.58%, Balanced Accuracy 92.21%, F1-Macro 0.9208, True Positive Rate 100%, and False Alarm Rate 0.00%. The combination of these metrics indicates that the model can detect all malware samples without false positives on benign applications, making it suitable for production deployment. This research contributes by demonstrating the effectiveness of an efficient soft voting ensemble (only 3 models) for Android malware detection with multi-dimensional evaluation metrics representative of imbalanced data.
Perbandingan Kinerja Algoritma CatBoost, XGBoost, LightGBM dan Random Forest Dalam Memprediksi Risiko Infeksi Aids Dalam Dataset Kesehatan Yulianto, Pramudya Ridwan; Astuti, Yani Parti
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.v7i4.8975

Abstract

This study investigates the prediction of AIDS infection risk using tree-based algorithms CatBoost, XGBoost, LightGBM, and Random Forest applied to a medical and demographic dataset consisting of 2,139 observations and 23 variables. The research process includes data exploration, cleaning, handling extreme values using the interquartile range (IQR) method, normalization with RobustScaler, and class balancing using the Synthetic Minority Over-sampling Technique (SMOTE). Due to the imbalanced nature of the dataset, model evaluation emphasizes not only accuracy but also Recall, F1-Score, and AUC-ROC to better assess infected class detection. Prior to SMOTE implementation, all models achieved high accuracy but relatively low recall for the positive class; after resampling, CatBoost demonstrated the most significant improvement, with recall increasing from 63% to 77% and F1-Score from 72% to 79%, achieving an overall accuracy of 90%. In comparison, XGBoost reached an accuracy of 88.63% with a more moderate recall improvement, while LightGBM and Random Forest showed consistent yet smaller gains, indicating that the combination of SMOTE and CatBoost is more effective in minimizing False Negatives in AIDS infection cases. The main contribution of this study lies in the integration of robust outlier handling, feature normalization, and class balancing within a structured experimental framework, with a specific emphasis on sensitivity optimization to enhance early detection reliability in clinical screening contexts.
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.

Page 1 of 3 | Total Record : 27

 1   2   3 

Filter by Year

2026 2026


Reset
Filter By Issues
All Issue Vol 7 No 4 (2026): March 2026 Vol 7 No 3 (2025): December 2025 Vol 7 No 2 (2025): September 2025 Vol 7 No 1 (2025): June (2025) Vol 6 No 4 (2025): March 2025 Vol 6 No 3 (2024): December 2024 Vol 6 No 2 (2024): September 2024 Vol 6 No 1 (2024): June 2024 Vol 5 No 4 (2024): March 2024 Vol 5 No 3 (2023): December 2023 Vol 5 No 2 (2023): September 2023 Vol 5 No 1 (2023): June 2023 Vol 4 No 4 (2023): March 2023 Vol 4 No 3 (2022): December 2022 Vol 4 No 2 (2022): September 2022 Vol 4 No 1 (2022): June 2022 Vol 3 No 4 (2022): March 2022 Vol 3 No 3 (2021): December 2021 Vol 3 No 2 (2021): September 2021 Vol 3 No 1 (2021): June 2021 Vol 2 No 2 (2020): December 2020 Vol 2 No 1 (2020): June 2020 Vol 1 No 2 (2019): December 2019 Vol 1 No 1 (2019): June 2019 Vol 1 No 1 (2019): Edisi Juni More Issue