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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 1,045 Documents
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Komparasi Kinerja Arsitektur MobileNetV2 dan EfficientNetB0 Untuk Klasifikasi Penyakit Daun Tanaman Kedelai Lisdiawati Lisdiawati; Siti Mutmainah; Khairunnas Khairunnas
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10006

Abstract

Soybean leaf diseases can reduce the quality and productivity of plants, so an accurate and efficient detection method is needed. This study aims to compare the performance of the MobileNetV2 and EfficientNetB0 architectures in classifying soybean leaf diseases using a deep learning-based transfer learning approach. The dataset used consists of soybean leaf images grouped into several disease classes, then divided into training (80%), validation (10%), and testing (10%) data. The pre-processing stage includes resizing the images to 224 × 224 pixels, normalizing pixel values, and data augmentation in the form of rotation, shifting, zooming, and horizontal flipping. The training process is carried out using the Adam optimizer with a learning rate and applying Early Stopping to reduce the risk of overfitting. Model evaluation is carried out using a confusion matrix, accuracy, precision, recall, and F1-score. The results show that MobileNetV2 obtains an accuracy of 81%, higher than EfficientNetB0 which obtains an accuracy of 70%. The contribution of this study is to provide a comparative analysis of the effectiveness of both architectures in classifying soybean leaf diseases and to show that MobileNetV2 is more optimal for application to the dataset used.
Stereo Camera-Based Motor Vehicle Dimension Measurement with Luxmeter-Based Light Intensity Detection Ageng Sudarma; Muhammad Iman Nur Hakim; Nurul Fitriani; Siti Shofiah; Nanang Okta Widiandaru
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10012

Abstract

Over Dimension and Over Loading (ODOL) vehicles require accurate and efficient dimension inspection systems to support transportation safety and regulatory compliance. This study proposes an automatic motor vehicle dimension measurement system based on stereo vision integrated with YOLOv8 object detection and luxmeter-based illumination monitoring. The system was developed using a Research and Development (R&D) approach involving stereo camera calibration, hardware-software integration, experimental testing, and validation against manual measurements. Two Logitech C270 USB cameras with a fixed 50 cm baseline were calibrated using a 9 × 6 checkerboard pattern and processed using OpenCV and Python. Vehicle and wheel objects were detected using YOLOv8 models with stereo disparity estimation performed using Semi-Global Block Matching (SGBM) and triangulation methods to calculate Overall Length (OAL), Front Overhang (FOH), Wheelbase (WB), Rear Overhang (ROH), and Overall Height (OAH). Environmental lighting conditions were monitored using a luxmeter under illumination ranges of 5,000-100,000 lux. Experimental results showed a stereo calibration success rate of 96% from 100 stereo image pairs. The developed system achieved average measurement accuracies of 98.58%, 98.92%, and 98.89% at testing distances of 7 m, 8 m, and 9 m, respectively, while the highest accuracy of 99.44% was obtained at the T_8M_LC configuration under stable illumination conditions. Operational efficiency analysis showed that the automatic measurement process, including image acquisition and computational processing, reduced total measurement time from 187 seconds in manual measurements to 5 seconds in the automated system, corresponding to an efficiency improvement of 97.32%. The results show that the proposed stereo vision system provides accurate, efficient, and lighting-robust vehicle dimension measurements suitable for automated motor vehicle inspection applications.
Classification of Cancer Epitope Mutations Using Random Forest, SVM and Sequence-Derived Features Miftahurrahma Rosyda; Dinan Yulianto
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10022

Abstract

Cancer remains a major global health challenge, with increasing incidence and mortality rates worldwide. One promising approach in cancer immunotherapy is the identification of epitopes, particularly mutated epitopes that play a critical role in immune recognition. This study aims to classify cancer epitope mutations using machine learning approaches based on sequence-derived physicochemical features. A dataset consisting of 234 samples was used, comprising into tumor and mutated tumor epitopes. Feature extraction was performed using physicochemical properties such as molecular weight, isoelectric point, aliphatic index, aromaticity, and hydrophobicity. Two machine learning models, namely Support Vector Machine and Random Forest were selected due to their proven effectiveness in biological sequence classification tasks and their robustness in handling small-to-medium-sized datasets. The results show that Random Forest achieved the best performance with an accuracy of 83% and a macro average F1-score of 0.70, while consistently outperforming the Support Vector Machine model across all data partition scenarios. However, further analysis revealed that the model exhibits limitations in detecting mutated epitopes, as indicated by a relatively high false-negative rate. This issue is likely due to the use of global sequence-derived features, which may not effectively capture local variations caused by mutations. This study contributes to the field of computational immunology by providing a comparative evaluation of Random Forest and Support Vector Machine for mutation epitope classification using sequence-derived physicochemical features. In addition, the integration of machine learning analysis with structural bioinformatics interpretation offers further biological insight into mutation-associated epitopes and their potential relevance in cancer immunotherapy.
Effect of Word Embedding on Indonesian Social Media Hate Speech Classification Using Hybrid CNN-SimpleRNN Mas Muhammad Rizqi Adiguna; Yuliant Sibaroni
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10046

Abstract

The rapid rise in the number of people using social media platforms, specifically platform X, poses great difficulties for spotting any possible harm from hate speech. In particular, due to the high informality of discourse of Indonesian internet users, expressed through slang, abbreviation, and distorted spelling, automatic moderation becomes even more complicated. The current study attempts at classifying hate speech on social media platform X, proposing a hybrid model of CNN combined with a Bidirectional SimpleRNN architecture, alongside a comparative study on word embedding approaches. The combination between CNN and SimpleRNN is chosen because it allows for exploiting both CNN's capability to extract local spatial features by finding toxic n-grams and the strength of Bidirectional SimpleRNN for capturing long contextual dependency in the sequence of text data. Given that the problem of OOV is highly significant, TF-IDF, FastText, and Word2Vec have been rigorously tested not only separately but also combined in different ways. Compared to the baseline configuration using standalone TF-IDF (which achieved 84.56% accuracy), the results show that the use of the hybrid TF-IDF + FastText provided the best performance with average accuracy of 86.49%, average precision of 86.32%, recall of 86.80% and F1 score of 86.55%. Conversely, the combination of multiple dense semantic vectors (Word2Vec and FastText) led to semantic drift and feature ambiguity; this created feature overlap and computational noise that obscured classification decision boundaries, resulting in redundancy and poor performance. It shows that the combination of lexico-statistical significance and semantic subword context greatly contributes to achieving better results in the Indonesian language setting and is very resistant to slang and OOV words found in digital settings. This study contributes to the field of natural language processing by providing a lightweight, highly accurate, and computationally efficient lexico-semantic framework tailored for moderating highly unstructured Indonesian social media text.
Multiclass Herbal Plant Classification Using CNN Architectures: A Comparative Study of MobileNetV2, EfficientNetV2B0, NASNetMobile, and InceptionV3 Mechi Sakinatun Nufus; Siti Mutmainah; Fathir Fathir
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10048

Abstract

Indonesia is a country with an exceptionally rich biodiversity; herbal plants offer a wide range of benefits in the fields of health and traditional medicine. However, the process of identifying herbal leaves is still done manually and is often prone to errors due to similarities in shape, color, and texture among leaves. This study aims to develop a multi-class herbal plant leaf image classification system based on a Convolutional Neural Network (CNN) by comparing four transfer learning architectures: MobileNetV2, EfficientNetV2B0, NASNetMobile, and InceptionV3. The dataset used consists of 10 classes of herbal plant leaves. The contributions of this study include a comparative analysis of four CNN architectures for multi-class classification, an evaluation of the effectiveness of preprocessing and data augmentation on a limited dataset, and recommendations for the most optimal model based on accuracy and computational efficiency. The experimental results show that all models achieved validation accuracy above 98%. InceptionV3 delivered the best performance with a test accuracy of 97%, precision of 90%, and accuracy, recall, and F1-score of 89% respectively, demonstrating good generalization ability. Meanwhile, MobileNetV2 offers the best balance between accuracy and computational efficiency, making it a promising candidate for herbal plant identification systems based on mobile devices or in environments with limited computational resources.
Perbandingan Kinerja Model Long Short-Term Memory (LSTM) dan Gated Recurrent Unit (GRU) Untuk Prediksi Harga Cryptocurrency Hanif Alhakim; Aditia Yudhistira
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10062

Abstract

Extreme volatility in the cryptocurrency market poses substantial financial risks, necessitating precision forecasting systems. The limitations of conventional statistical models in capturing non-linear dynamics have prompted the adoption of deep learning approaches. This study evaluates the comparative performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures in predicting the closing prices of Bitcoin (BTC), Ethereum (ETH), and Solana (SOL). Experiments utilized historical data from January 2023 to April 2026, partitioned with an 80:20 train-test ratio using a 60-day sliding window sequence. Dropout-based regularization and Early Stopping were implemented to prevent overfitting. As an empirical contribution, this research univariately examines the trade-off between memory cell complexity (LSTM) and architectural efficiency (GRU) on high-volatility data. The results demonstrate that GRU consistently outperforms LSTM across all instruments, reducing the Mean Absolute Percentage Error (MAPE) to a range of 2.93%-4.89%. Regarding computational efficiency, the GRU architecture reduced training duration by 13.33% to 36.92% compared to LSTM. Practically, these findings recommend GRU as an effective and efficient algorithmic foundation for algorithmic trading systems and digital portfolio risk management.
Klasifikasi Sentimen Program Makan Bergizi Gratis di Platform X dengan TF-IDF dan Class-Weighted LinearSVC Muhammad Syihabuddin Musyaffa; Novita Kurnia Ningrum
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10095

Abstract

This study investigates the optimization of public sentiment classification related to the Free Nutritious Meal Program on Platform X by focusing on social media data that are brief, informal, noisy, and class-imbalanced. The data were collected through scraping from January 5, 2025, to February 26, 2026, producing 7,659 initial posts. After selection, preprocessing, cleaning, and labeling, 5,307 texts were used as the modeling dataset, consisting of 2,183 positive, 2,391 neutral, and 733 negative sentiments. The texts were transformed into numerical features using Term Frequency-Inverse Document Frequency with 5,000 features and unigram-bigram settings. This study evaluated Multinomial Naive Bayes, Logistic Regression, LinearSVC, and Random Forest as comparison models. Optimization was performed using GridSearchCV, while class_weight balanced was applied to improve the model’s ability to identify the smaller negative class. The evaluation results show that LinearSVC with class_weight balanced produced the most balanced performance, with an accuracy of 0.9011, macro F1-score of 0.8989, weighted F1-score of 0.9011, negative recall of 0.8367, and negative F1-score of 0.8913. The contribution of this study lies in emphasizing minority-class evaluation through negative recall and F1-score, making the model a preliminary component for digital public opinion monitoring that is more sensitive to criticism and complaints.
Perbandingan Kinerja Random forest dan SVM Pada Klasifikasi Tingkat Kekumuhan Permukiman Menggunakan SMOTE Nurika Dwi Wahyuni; Fadhilah Syafria; Novi Yanti; Surya Agustian
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10101

Abstract

Classifying slum levels is essential for a structured, data-driven analysis of settlement conditions. This study compares the performance of Random forest and Support vector machine (SVM) in classifying slum levels in Pekanbaru City across two scenarios with and without SMOTE using slum indicator scoring data. Its contributions include analyzing SMOTE's impact on model performance and evaluating the top 10 features against the full feature set. The dataset comprises 992 RT-level records from Disperkim Pekanbaru City (2020, 2021, and 2023) featuring 16 slum indicator scores based on PUPR Ministerial Regulation No. 14/2018, categorized into three classes: Non-Slum, Low Slum, and Moderate Slum. Following the KDD process (selection, preprocessing, transformation, data mining, evaluation, and analysis), the data was split 80:20 using stratified sampling and evaluated based on accuracy, precision, recall, F1-score, and confusion matrix. Results show that the Linear SVM without SMOTE achieved perfect evaluation metrics (1.0000); however, this is interpreted cautiously as the class labels derive from strict regulatory scoring rules, making class boundaries inherently linear. Random forest saw its F1-score rise from 0.9660 to 0.9700 after SMOTE, while the most significant improvement occurred in SVM RBF, jumping from 0.9214 to 0.9779. Testing the top 10 features led to a decreased F1-score across models, indicating that utilizing all 16 features remains optimal for this dataset.
Perbandingan Metode Elbow dan Silhouette Coefficient pada K-Means untuk Pengelompokan Wilayah Berdasarkan Indeks Pembangunan Manusia Shinta Zahira Hayathun Nufus; Fathir Fathir; Hilyatul Mustafidah
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10114

Abstract

One of the primary metrics for evaluating the effectiveness of efforts to improve people’s well-being through human development is the Human Development Index (HDI). Although Indonesia’s HDI has continued to improve, disparities in human development remain evident across regions, particularly in Bali, West Nusa Tenggara (NTB), and East Nusa Tenggara (NTT). Identifying regions with similar HDI characteristics is important for supporting more targeted development policies. However, the performance of K-Means clustering is highly influenced by the number of clusters used, making the selection of an appropriate cluster number essential. This study compares the Elbow Method and Silhouette Coefficient in determining the optimal number of clusters for 2024 HDI data covering 41 regencies and municipalities based on Life Expectancy, Expected Years of Schooling, Mean Years of Schooling, and Per Capita Expenditure. The results show that the Elbow Method produces three clusters, while the Silhouette Coefficient produces two clusters with a silhouette value of 0.5312. Evaluation using the Davies–Bouldin Index (DBI) indicates that the two-cluster solution achieves a lower DBI value (0.7350) than the three-cluster solution (1.0382). These findings suggest that the HDI structure in Bali, NTB, and NTT tends to form two major groups: regions with high human development and regions with medium-to-low human development. The results also indicate that the Silhouette Coefficient is more representative for determining the optimal number of clusters in HDI data with relatively similar regional characteristics. The clustering results may support policymakers in prioritizing development programs in education, health, and community welfare
Perbandingan Kinerja Model LSTM dengan dan tanpa Indeks Fear & Greed dalam Prediksi Harga Bitcoin Berbasis Data Time Series Mada Rabbani Syah; Vinna Rahmayanti Setyaning Nastiti
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.10146

Abstract

Bitcoin is a highly volatile digital asset whose price movements are influenced by historical market patterns and investor sentiment, making accurate prediction a significant challenge for conventional forecasting approaches. This study compares the predictive performance of the Long Short-Term Memory (LSTM) model in forecasting Bitcoin prices under two configurations: without and with the integration of the Crypto Fear & Greed Index as a market sentiment feature. The dataset consists of time series data including opening, highest, lowest, and closing prices obtained from the CoinGecko API, along with daily sentiment scores collected from the Alternative.me API. The data preprocessing stages include normalization using the Min-Max Scaler and sequence construction using the sliding window method, both of which are standard practices in deep learning-based time series forecasting. Model performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) as quantitative error metrics. The experimental results show that the LSTM model without the Fear & Greed Index produced an MAE of 1011.34, an RMSE of 1340.55, and a MAPE of 1.20%, while the model incorporating the Fear & Greed Index achieved an MAE of 979.95, an RMSE of 1349.07, and a MAPE of 1.18%. These findings indicate that the inclusion of market sentiment improves overall prediction accuracy, as reflected by the reduction in MAE and MAPE, despite a marginal increase in RMSE. The main contribution of this study is providing empirical evidence regarding the impact of integrating the Crypto Fear & Greed Index as a market sentiment indicator on improving the performance of LSTM models for Bitcoin price prediction, as well as offering a direct comparison between models based solely on historical price data and models that combine historical price data with market sentiment information. This study concludes that combining deep learning techniques with measurable sentiment indicators provides a more comprehensive and effective framework for Bitcoin price prediction.

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