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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 926 Documents
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Komparasi Perbandingan Algoritma C4.5, Naive Bayes, K-Nearest Neighbor, Random Forest Untuk Prediksi Faktor Penyebab Penyakit Diabetes Fadli, Muhammad Bagus; Purnama, Irwan; Rohani, Rohani
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8683

Abstract

Diabetes is a chronic metabolic disease characterized by elevated blood glucose levels and can cause various serious complications and contribute to high mortality rates worldwide. The main problem in managing diabetes is the need for accurate patient status classification based on laboratory test data so that appropriate treatment can be carried out. This study aims to compare the performance of the C4.5 algorithm, Naive Bayes, K-Nearest Neighbor (KNN), and Random Forest in classifying diabetes patient data. The dataset used was sourced from Electronic Health Records (EHRs) with research subjects from Rantauprapat Regional General Hospital, totaling 10,000 data consisting of eight attributes and one class attribute, with 859 diabetes patient data and 9,141 non-diabetes patient data. The research method was carried out by dividing the data into training data and testing data using a ratio of 90:10, 80:20, and 70:30. Evaluation of model performance used accuracy parameters and Receiver Operating Characteristic (ROC) with Area Under Curve (AUC) values. The results showed that the C4.5 and Random Forest algorithms produced higher accuracy values ​​than Naive Bayes and KNN, especially at training data ratios of 90%:10% and 70%:30%. Based on the ROC evaluation, the Random Forest algorithm obtained the highest AUC values ​​at the 70%:30% ratio of 0.972 and 80%:20% of 0.970. Based on these test results, it can be concluded that the C4.5 and Random Forest algorithms have relatively better performance and are almost equivalent in classifying diabetes based on accuracy and AUC values.
Analisis Sentimen Ulasan Pengguna Aplikasi Sociolla Menggunakan Algoritma Support Vector Machine dengan Optimasi Grid Search Fitriani, Suci; Lestarini, Dinda; Seprina, Iin
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8686

Abstract

The rapid growth of digital technology has driven innovation in the beauty industry, one of which is the Soco by Sociolla platform that provides online product reviews. The increasing number of user reviews offers opportunities for conducting sentiment analysis to understand users’ perceptions of service quality. The main challenge in modeling sentiment for beauty product reviews lies in the use of highly varied, subjective, and informal language, which results in diverse distribution patterns. Therefore, this study not only applies the Support Vector Machine (SVM) algorithm for sentiment classification but also compares two kernels—Linear and Radial Basis Function (RBF)—and evaluates the effect of hyperparameter optimization using Grid Search in the context of beauty e-commerce data. A total of 3,387 reviews were collected and processed through several stages, including text preprocessing, labeling, feature extraction using TF-IDF, data splitting, model training, and evaluation. The results show that the baseline RBF kernel provides the best performance with an accuracy of 88.5%, while the baseline Linear kernel achieves an accuracy of 87.76%. Meanwhile, Grid Search optimization produces an accuracy of 86.22%, indicating that the explored hyperparameter configurations were unable to exceed the performance of the RBF baseline despite delivering stable results during cross-validation. These findings suggest that the linguistic characteristics of beauty reviews are more effectively addressed by non-linear kernels, making them superior to Linear kernels in recognizing non-linear patterns within user review data. Furthermore, the results indicate that hyperparameter optimization does not always lead to increased model accuracy, particularly when the baseline SVM configuration is already performing near optimally for the characteristics of the dataset used.
Evaluasi Komparatif Lightweight Convolutional Neural Network Untuk Klasifikasi Penyakit Daun dan Hama Tanaman Padi Julianto, Afis; Jannah, Miftahul
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8698

Abstract

Rice is a critical commodity for national food security; however, its productivity is frequently reduced due to leaf diseases and pests. Conventional identification methods that rely on visual observation are often inefficient and prone to subjectivity, particularly given the complex and variable nature of symptoms. This study to evaluate and compare the performance of several lightweight CNN architectures in accurately and efficiently detecting rice leaf diseases and pests on resource constrained devices. This study compares four CNN lightweight architectures: MobileNetV2, EfficientNetV2-B3, NasNetMobile, and a custom CNN Lightweight Architecture, all using a 13-class dataset that underwent preprocessing, augmentation, and data balancing. The models were trained for 100 epochs using the Adam optimizer. Experimental results show that EfficientNetV2B3 achieved the best performance, with 97% accuracy, precision, recall, and F1-score, followed by MobileNetV2 and NasNetMobile, which achieved 94% accuracy. The Custom CNN lightweight model produced 91% accuracy with a model size of only 0.53 MB. Overall, this study provides recommendations for developing accurate and efficient lightweight CNN models to support rice disease and pest detection on mobile devices, IoT systems, and edge computing platforms.
Dampak Penggunaan Data Augmentasi Terhadap Akurasi MobileNetV2 Dalam Deteksi Mikrosleep Berbasis Rasio Aspek Mata Maulana, Isa Iant; Riadi, Muhammad Fatah Abiyyu; Alzami, Farrikh; Naufal, Muhammad; Azies, Harun Al; Pramunendar, Ricardus Anggi; Basuki, Ruri Suko
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8719

Abstract

Detecting microsleep is important in preventing accidents caused by decreased alertness, especially in activities that require high concentration such as driving. This study aims to develop an image-based microsleep detection model using the MediaPipe FaceMesh. The EAR value is only used for the tagging process that forms the basis for dataset creation. The main problem investigated is how to produce a classification model that can accurately distinguish between normal eye conditions and microsleep conditions using image data taken from eye area snippets. To address this issue, this study applies a series of stages, starting from dataset formation, initial processing in the form of image size adjustment, normalization, and quality improvement through data augmentation, to model training using the MobileNetV2 architecture with transfer learning and fine-tuning techniques. The results of the experiment show that the use of data augmentation strategies has a significant effect on improving model performance, with the best configuration producing a test accuracy of 87.54 percent, with other high performance metrics, namely Precision of 88.64 percent, Recall (Sensitivity) of 87.14 percent, and F1-Score of 87.34 percent. These findings prove that an eye area image-based approach combined with a convolutional neural network model is capable of providing promising performance in detecting microsleep conditions. These findings prove that an approach based on eye area images combined with a convolutional neural network model can deliver promising performance in detecting microsleep. This research is expected to form the basis for the development of a more effective microsleep detection system that can be implemented in real world environments.
Disparitas Efektivitas CLAHE pada Berbagai Arsitektur Deep Learning untuk Klasifikasi Katarak Berbasis Citra Fundus Ramadhani, Frida; Paramita, Cinantya; Subhiyakto, Egia Rosi
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8725

Abstract

This study aims to highlight and compare the performance of three deep learning architectures, namely CNN, VGG16, and EfficientNet-B1, in classifying cataract conditions based on retinal fundus images. A total of 2600 fundus images of two classes (normal and cataract) were collected from open sources and processed in two versions: the original images and contrast-enhanced images using Contrast Limited Adaptive Histogram Equalization (CLAHE). Each model was tested using both versions of the dataset, with evaluation based on accuracy, precision, recall, and F1 score. The results of this experiment show that the application of CLAHE is proven to improve the accuracy of CNN from 0.89 (89%) to 0.97 (97%) and, importantly for clinical diagnosis, improve the recall for cataract class from 0.81 (81%) to 0.97 (97%) with precision 0.98 (98%), f1 score 0.97 (97%) and reduce the number of False Negatives (FN) from 9 to 6. Similarly, it improves the accuracy of VGG16 from 0.93 (93%) (with precision 0.91 (91%), recall 0.96 (96%), f1 score 0.94 (94%)) to 0.96 (96%) (precision 0.94 (94%), recall 0.98 (98%), f1 score 0.96 (96%), and also reduces the number of FN from 9 to 6, thereby improving clinical reliability. In contrast to the EfficientNet-B1 Model, CLAHE does not provide significant improvement. significant. significant, with an accuracy of 0.97 (97%), precision of 0.98 (98%), recall of 0.98 (98%), and f1 score of 0.97 (97%), the accuracy performance actually decreased to 0.96 (96%) and precision to 0.94 (94%). This shows that the effectiveness of preprocessing techniques is highly dependent on the model architecture used. CLAHE has been shown to be effective on conventional models such as CNN and VGG16, but is less optimal for complex pretrained models such as EfficientNet-B1. These findings contribute to the development of adaptive and efficient medical image classification systems, particularly in the context of automated cataract screening in primary healthcare.
Analisa Perbandingan Algoritma K-Means dan DBSCAN Untuk Klastering Wilayah Rawan Bencana Lay, Nathanael Kenneth; Arisandi, Desi; Christanto, Henoch Juli
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8727

Abstract

West Java Province exhibits high disaster vulnerability, necessitating accurate risk zone mapping for mitigation purposes. This study aims to conduct a comparative performance analysis of K-Means and DBSCAN algorithms for clustering disaster-prone areas. The comparison is important because K-Means, as a centroid-based algorithm, is sensitive to outliers, whereas DBSCAN, as a density-based method, is theoretically more suitable for complex disaster-risk data and capable of identifying anomalies. A quantitative approach was applied to secondary data on disaster incidents (Floods, Landslides, Earthquakes, Fires, and Extreme Weather) for the 2015-2024 period across 27 regencies/cities. Following normalization using StandardScaler, model performance was evaluated through Silhouette Score (SI) comparison and visual analysis of the formed cluster structures. The results reveal a paradoxical finding: although K-Means (K=2) numerically outperformed DBSCAN (0.468) with an average SI of 0.502, it demonstrated internal validity failure due to negative silhouette scores indicating misclassification in extreme regions. Conversely, DBSCAN proved superior in representing the natural data structure with its capability to isolate 6 anomalous regions as noise. Further temporal sensitivity analysis revealed significant risk dynamics, where the number of noise regions increased drastically from 5 regions in the 2015-2019 period to 10 regions in the 2020-2024 period. This indicates that disaster patterns in West Java are becoming increasingly irregular and unpredictable, positioning DBSCAN as the recommended robust method for complex risk mapping.
Pengaplikasian Convolutional Neural Network (MobileNetV3) Memanfaatkan Transfer Learning Untuk Membedakan Tanaman Cabai Berasal Dari Genus Capsicum Annuum Sujaka, Tomi Tri; Switrayana, I Nyoman; Haepa Fillah, Ibnu Mumtaz
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8740

Abstract

Accurate classification of Capsicum annuum varieties is crucial for food industry applications and agricultural research. Traditional manual classification methods are time-consuming, subjective, lack detail, and are prone to human error, requiring computer vision to automate them. This study presents learning in the form of automatic classification of nine diverse Capsicum annuum varieties using transfer learning with the MobileNetV3 architecture, which is designed to achieve high accuracy and be computationally energy efficient. The dataset consists of 4,500 images (training, testing, and validation) of 9 chili varieties: bell pepper, curly chili, cherry pepper, chiltepin, Hungarian wax, jalapeno, marconi, pequin, and Thai chili. This dataset goes through quality control, one of which is dataset balancing. The model in this study has also been optimized with Adam (Adaptive Moment Estimation). Model interpretation is also improved through Grad-CAM visualization, and model robustness has also been validated using cross-validation 5 times. This model achieved performance with a training accuracy of 97.2%, a testing accuracy of 95.1%, and a validation test of 94.8%, where 5-fold cross-validation showed consistent results (94.23% ± 1.45%). Grad-CAM analysis showed that this model focuses on structural features such as shape, surface texture, and color patterns. With the successful development of an AI system that can automatically identify chili varieties with an accuracy of 95.1%. This system works well in real conditions (90.6% accuracy) and is practical for use in agriculture and food processing. This technology can help farmers and food companies or lay people to sort chilies automatically, reduce costs, and improve quality control.
Analisis Sentimen Terhadap Ulasan Google Play Store Aplikasi Lazada, Shopee, dan Tokopedia Menggunakan Algoritma IndoBERT Aisy, Afra Rihadatul; Karyono, Giat
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8745

Abstract

The growth of e-commerce has generated many user reviews, which are an important source for understanding consumer satisfaction and perceptions. However, manual analysis of unstructured reviews that use informal language is ineffective. In addition, conventional sentiment analysis approaches are often unable to capture the linguistic variations of the Indonesian language. This study uses the IndoBERT contextual language model to classify the sentiment of e-commerce application reviews on Shopee, Tokopedia, and Lazada. Data was collected through web scraping, amounting to 12,000 data points, with 4,000 for each application, labeled based on ratings, processed through preprocessing stages, balanced using Random Oversampling, and trained for three-class sentiment classification. The evaluation showed an Macro F1-Score of 0.90, indicating strong performance across all sentiment classes, including minority classes. These results confirm the effectiveness of IndoBERT in handling data imbalance in Indonesian sentiment analysis.
Penerapan Model SVM dengan Ekstraksi Fitur ResNet50 untuk Identifikasi Sel Darah Terinfeksi Malaria Adhesyah Putra, Maulana Damar; Rakasiwi, Sindhu
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8750

Abstract

Malaria remains a major public health challenge in Indonesia, with 279,865 reported cases in 2023 and an Annual Parasite Incidence (API) of 0.99 per 1,000 population. Although microscopic examination is still considered the gold standard for malaria diagnosis, it has several limitations, including dependency on trained experts, subjective interpretation, and relatively lengthy processing time. To address these challenges, this study aims to analyze the performance of a Support Vector Machine (SVM) classifier with feature extraction based on ResNet50 in a Computer-Aided Diagnosis (CAD) system for automatic detection of malaria-infected blood cells.ResNet50 was selected for its transfer learning capability to generate high-level feature representations from medical images, while SVM was chosen due to its strong performance on high-dimensional data and limited datasets. A feature vector of 2048 dimensions produced from the global average pooling layer was classified using SVM with a Radial Basis Function (RBF) kernel. The dataset used in this study was obtained from the National Institutes of Health (NIH) and consists of 27,558 microscopic blood cell images (Parasitized and Uninfected classes). The data were partitioned using stratified sampling with an 80:20 ratio for training and testing. Preprocessing steps included pixel normalization, resizing to 224×224 pixels, and basic augmentation to improve model generalization. Experimental results show that the proposed model achieved an accuracy of 93.94%, precision of 94%, recall of 93.43% (Parasitized) and 94.46% (Uninfected), and an average F1-score of 94%. The confusion matrix indicates 2,575 true positives, 2,606 true negatives, 153 false positives, and 181 false negatives, with a false negative rate of 6.57% and a false positive rate of 5.54%. These findings demonstrate that the combination of ResNet50 and SVM has strong potential as a fast and accurate image-based malaria detection method and is suitable for implementation in healthcare settings with limited resources.
Klasifikasi Tingkat Serangan pada Log Jaringan Siber dengan Komparasi Naive Bayes dan K-Nearest Neighbor Apriliani, Evinda; Winiarti, Sri; Riadi, Imam; Yuliansyah, Herman
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8765

Abstract

The increasing threat of cybersecurity poses a significant impact on both organizations and individuals, necessitating a system capable of accurately detecting and classifying attack levels to support prioritization of responses. This study aims to analyze and compare the performance of two machine learning algorithms, Naive Bayes and K-Nearest Neighbor (KNN), in classifying cyberattack levels, and to evaluate the effect of hyperparameter tuning on improving model accuracy. The research methods included utilizing the cybersecurity_attacks dataset, data preprocessing, model training at three data split ratios (70:30, 80:20, and 90:10), and parameter optimization using Randomized Search and Grid Search. Performance evaluation was based on accuracy, precision, recall, and F1-score values. The results showed that KNN performed best, with a peak accuracy of 0.96 at the 80:20 ratio after tuning, increased from an accuracy of 0.947 before tuning, with precision, recall, and F1-score values ​​ranging from 0.95 to 0.96. Meanwhile, Naive Bayes only achieved a peak accuracy of 0.8485 at the same ratio. Although the improvement after hyperparameter tuning was not significant, this process still resulted in a more stable and consistent model. Future research is recommended to explore ensemble methods and test them on other datasets to produce more adaptive cyberattack classification models.

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