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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 1,083 Documents
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A Comparative Evaluation of Random Forest, SVM, KNN, Logistic Regression, and Gradient Boosting for Stress Prediction Janur Alaudin Syah; Sumiyatun Sumiyatun
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.11002

Abstract

Student stress has become a critical concern in contemporary education, necessitating early detection systems to enable timely intervention. This study presents a comprehensive comparative evaluation of five machine learning algorithms Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest, and Gradient Boosting for classifying student stress levels into three categories (low, moderate, high). Using a dataset of 14,003 students with 14 academic, demographic, and psychosocial features, the research addresses the significant class imbalance (high stress: 50.7%, moderate: 29.1%, low: 20.2%) through systematic multi-metric assessment. Random Forest with 300 trees and class_weight='balanced' demonstrated superior performance across all metrics: accuracy (93.47%), precision (93.97%), recall (93.47%), F1-score (93.43%), and ROC-AUC (0.992). Feature importance analysis identified AssignmentCompletion, Attendance, and ExamScore as the most influential predictors. The contributions of this research include: (1) a comprehensive comparative evaluation of five foundational ML algorithms on a large-scale imbalanced educational dataset; (2) the explicit demonstration of Random Forest's effectiveness in handling class imbalance through built-in class weighting; (3) the identification of key academic predictors of student stress through feature importance analysis; and (4) the provision of a scalable evidence-based framework for early stress detection with practical implementation guidance for educational institutions.
Analisis Komparasi Kinerja SVM, Random Forest, dan XGBoost untuk Diagnosis Penyakit Batu Empedu Ananda Dika Erlangga; Erliyan Redy Susanto
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.11008

Abstract

Gallstone disease is a hepatobiliary disorder that requires accurate diagnosis to support timely management and reduce the risk of complications. The availability of clinical and laboratory data provides an opportunity to apply machine learning as a supporting approach for disease classification. This study aims to compare the performance of Support Vector Machine (SVM), Random Forest, and XGBoost algorithms for gallstone disease classification using the Gallstone Dataset from the UCI Machine Learning Repository. The contribution of this study is to provide a comparative evaluation of SVM, Random Forest, and XGBoost on the Gallstone Dataset by consistently applying feature selection, feature standardization, class imbalance handling, and cross-validation to obtain a more objective comparison of the three algorithms. The dataset consists of 319 samples; therefore, model evaluation needs to consider the limited sample size and the potential risk of overfitting, which may affect model generalization. The research stages include feature selection using the ANOVA F-Value, handling class imbalance using the Synthetic Minority Over-sampling Technique (SMOTE), feature standardization, model development, and validation using Stratified 5-Fold Cross Validation. Evaluation was conducted using accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). The results show that XGBoost achieved the best performance, with an accuracy of 79.00%, precision of 80.95%, recall of 75.32%, F1-score of 78.03%, and AUC of 85.04%. Random Forest achieved an accuracy of 78.06%, while SVM achieved 74.92%. Feature importance analysis indicated that C-Reactive Protein (CRP), Vitamin D, Hyperlipidemia, Bone Mass, and Aspartate Aminotransferase (AST) were among the attributes that contributed significantly to the classification. The results indicate that XGBoost outperformed SVM and Random Forest on the dataset used and has potential as a supporting approach for developing a gallstone disease classification system; however, evaluation using larger and more diverse datasets is required to confirm its generalization capability.
Komparasi CNN dan SVM untuk Klasifikasi Penyakit Daun Jagung Berbasis CLAHE dan Explainable AI Muhammad Azka Najih Al Wafa; Adzhal Arwani Mahfudh; Masy Ari Ulinuha
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.11025

Abstract

Corn productivity losses from leaf diseases remain a serious problem, while manual observation is subjective and accurate models are hard to trust when their decisions cannot be traced. The contribution of this research lies in controlled testing that isolates the contribution of CLAHE from architectural influences, establishing preprocessing parameters based on empirical testing, and directly comparing both methods under identical data and treatment conditions. This study compares Convolutional Neural Network (CNN) and Support Vector Machine (SVM) for corn leaf disease classification under equivalent conditions, using CLAHE preprocessing and LIME. Testing used 4,188 images in four classes. The CNN based on EfficientNet-B0 reached 94.98%, outperforming the SVM based on GLCM and color histogram features at 92.46%. This 2.52-point margin is narrower than assumed, and the additional SVM errors concentrate in one class. A hybrid combining EfficientNet-B0 features with an SVM classifier reached 95.33%, indicating the CNN advantage stems from its feature representation rather than its decision layer. The SVM also predicted one hundred sixty times faster without a graphics accelerator. An ablation test shows the CLAHE benefit is method-dependent: the SVM gained 1.19 points, the CNN did not. LIME shows most influential regions fall on lesion areas, though some appear in the background. These findings keep the SVM relevant when computational resources are limited.
A Climatology-Relative Framework for Evaluating Rainfall-Condition Categories Using Observed and Random Forest-Predicted Dasarian Rainfall Selina Ayuningtyas; Yunifa Miftachul Arif; Fresy Nugroho; Usman Pagalay; Triyo Supriyatno
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.11076

Abstract

Rainfall variability strongly influences rain-fed agricultural activities in monsoonal regions, particularly across seasonal and spatially varying rainfall regimes. This study develops and applies a climatology-relative framework for evaluating rainfall-condition categories derived from observed and Random Forest (RF)-predicted dasarian rainfall for Besuki Na-Oogst tobacco in ZOM 45–49, Jember, East Java. A 30-year historical rainfall dataset (1993–2022) was used to derive ZOM- and dasarian-specific P25 and P75 relative rainfall-condition thresholds. The same fixed thresholds were then applied to observed and RF-predicted rainfall throughout January–December 2024 to classify rainfall into Dry, Normal, and Wet categories. Categorical correspondence was evaluated using percentage agreement, Cohen's kappa, and a 3 × 3 confusion matrix at the ZOM × dasarian level. Across 180 ZOM × dasarian pairs, observed rainfall produced 50 Dry, 84 Normal, and 46 Wet classifications, whereas RF-predicted rainfall produced 7 Dry, 68 Normal, and 105 Wet classifications. Overall categorical agreement was 38.33% (κ = 0.071), with variation among the five ZOMs. The most pronounced discrepancy occurred in the Dry category: none of the 50 observed Dry cases were reproduced as Dry by the RF predictions, while 36 were classified as Wet. Conversely, all seven RF-predicted Dry cases corresponded to observed Wet conditions. The continuous comparison indicated that the RF predictions generally reproduced the broad seasonal rainfall pattern but tended to exhibit smoother variations and, in several periods, lower rainfall magnitudes than observed. These differences can lead to categorical reversals when predicted values fall across fixed climatological P25 and P75 boundaries. The findings indicate that RF-derived rainfall-condition patterns may provide complementary information for climatology-relative assessment, but should not be interpreted as direct substitutes for observed rainfall conditions or as standalone planting recommendations. The proposed framework complements conventional numerical rainfall-prediction evaluation by assessing the categorical representation of predicted rainfall within a common local historical climatology at the ZOM × dasarian scale.
Perbandingan Embedding IndoBERT dan FastText dalam Deteksi Cyberbullying Berbahasa Indonesia Muhammad Ihksan Alfiansyah; Diana Diana
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.11088

Abstract

The escalation of cyberbullying on social media necessitates automated content moderation systems capable of deciphering the characteristics of non-standard digital language. This study compares the effectiveness of contextual word embeddings (IndoBERT) and static subword embeddings (FastText) in multi-class text classification (Safe, Mild, Moderate, Severe, and Very Dangerous) for Indonesian-language social media comments. The research focuses on analyzing embedding architectures: IndoBERT is integrated with a Dense Layer, while FastText is evaluated using Logistic Regression and Random Forest (15 trees). A dataset of 16,643 samples was extracted from social media platforms and processed through text cleaning and dictionary-based slang normalization stages. Test results indicate that the bidirectional contextual architecture of IndoBERT achieved the highest performance, with an accuracy of 82.15% and a Macro F1-Score of 74.97%. Regarding FastText representations, the linear Logistic Regression approach (78.70% accuracy) proved more stable than the Random Forest model (65.50% accuracy). These findings demonstrate that contextual embeddings are more adaptive than static embeddings at distinguishing sarcasm and mutated lexicons in digital text. As a research contribution, this study not only offers a precise five-level severity classification architecture for informal Indonesian but also provides a prototype automated moderation system (CyberGuard AI) ready for implementation in real-time risk mitigation.
Comparison of Global Horizontal Irradiance Prediction Using LSTM and M-LSTM Across Seasonal Conditions Rudi Kasianto; Totok Chamidy; Muhammad Faisal; M. Ainul Yaqin; M. Imamuddin
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.11094

Abstract

Short-term prediction of Global Horizontal Irradiance (GHI) is challenging because solar radiation exhibits dynamic temporal patterns and rapidly responds to changing atmospheric conditions. This study compares univariate Long Short-Term Memory (LSTM) and multivariate LSTM models using four and six predictor variables (M-LSTM-4 and M-LSTM-6) for 30-minute-ahead GHI forecasting using Automatic Solar Radiation Station data from Malang, East Java. One-minute observations were aggregated into 30-minute intervals and evaluated using sliding windows of 12, 24, and 36 timesteps. Model performance was assessed using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²) for full-year, wet-season, and dry-season datasets. For the full-year dataset, M-LSTM-6 with a 12-timestep window achieved the best performance, with an MAE of 37.700 W/m², an RMSE of 79.340 W/m², and an R² of 0.9283. In contrast, M-LSTM-4 provided the best performance for both seasonal datasets using a 36-timestep window, achieving an MAE of 36.551 W/m² and an R² of 0.9378 during the wet season, and an MAE of 39.181 W/m² and an R² of 0.9454 during the dry season. M-LSTM-6 showed substantial performance degradation with longer historical windows, particularly during the dry season. This result suggests that combining a larger number of predictors with longer historical sequences increases the amount of input information processed by the model, which may be less effective when the additional historical information does not provide proportional predictive benefit under rapidly varying irradiance conditions. The results demonstrate that the effectiveness of multivariate information depends on predictor relevance, historical window length, and seasonal conditions. The main contribution of this study is the identification of different optimal combinations of predictor complexity and historical window length between full-year and seasonal GHI forecasting. Overall, the findings indicate that input-variable complexity and window length should be jointly optimized for local short-term GHI forecasting.
Komparasi Algoritma Support Vector Machine, Random Forest, dan XGBoost: Prediksi Penyakit Huntington Berdasarkan Data Ekspresi Gen Reza Zaynury Winata; Erliyan Redy Susanto
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.11097

Abstract

Huntington's disease is a progressive neurodegenerative disorder caused by mutations in the Huntingtin (HTT) gene, and early diagnosis remains an ongoing challenge. Leveraging gene expression data through machine learning holds potential for improving the prediction accuracy of this disease; however, the characteristics of microarray data very high feature dimensionality combined with a limited sample size (small-n large-p) together with class imbalance, increase the risk of overfitting and data leakage. This study compares the performance of Support Vector Machine (SVM), Random Forest, and Extreme Gradient Boosting (XGBoost) in predicting Huntington's disease status using the GSE135589 microarray dataset. The research pipeline includes data standardization, feature selection (SelectKBest based on the ANOVA F-test), class balancing (SMOTE), and hyperparameter tuning via GridSearchCV with Stratified 5-Fold Cross-Validation, in which the number of features (k) is also tuned as a nested hyperparameter alongside other model parameters to prevent information leakage between the training and test data. All preprocessing steps were applied exclusively to the training data (fit-on-train, transform-on-test) to ensure performance estimates free from optimistic bias. The results show that the linear-kernel SVM achieved the best performance, with an accuracy of 88.89%, precision of 89.66%, recall of 96.30%, F1-score of 92.86%, and ROC-AUC of 90.95%, outperforming Random Forest and XGBoost on the independent test set. These findings indicate that regularized linear approaches outperform tree-based models under conditions where feature dimensionality far exceeds sample size. The main novelty of this study lies in tuning the number of features (k) as a nested hyperparameter within the cross-validation scheme rather than as a separate step prior to model training, as is common in most comparable studies, thereby producing a stricter, leakage-free generalization estimate, while also serving as the first comprehensive benchmark comparing the three algorithms specifically within the domain of Huntington's disease gene expression data.
Perbandingan Holt, Brown DES, dan Theta untuk Peramalan Minyak dan Gas dengan Data Terbatas Amin Padmo Azam Masa; Hamdani Hamdani; Zainal Arifin; Novianti Puspitasari
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.11105

Abstract

The decline and fluctuations in natural resource production in East Kalimantan, particularly oil, natural gas, and coal, create uncertainty regarding future production conditions, necessitating forecasting methods suited to the characteristics of time-series data. However, few studies have specifically compared Holt’s Linear Trend Method, Brown’s Double Exponential Smoothing (Brown’s DES), and the Theta Model using East Kalimantan natural resource data with a relatively limited number of annual observations. This study aims to compare the performance of these three methods in forecasting oil lifting, natural gas lifting, and coal production. The novelty of this study lies in its comparative evaluation of the three methods across three natural resource indicators with different time-series patterns, as well as its assessment of their ability to use limited historical data to generate five-year projections. The Theta Model was included as a comparative method because it can represent time-series patterns through trend decomposition and extrapolation, making it relevant for evaluating data that exhibit long-term changes. The study uses annual data for the 2016–2024 period obtained from the Statistics Indonesia Office of East Kalimantan Province and the East Kalimantan One Data portal. The use of annual data with a relatively limited number of observations was considered when selecting the methods because trend- and smoothing-based approaches such as Holt, Brown’s DES, and Theta can be applied to time series with limited sample sizes without requiring the estimation of a large number of parameters. The research stages included data collection, model development using the three forecasting methods, accuracy evaluation based on Mean Absolute Percentage Error (MAPE), and five-year forecasting. The results show that the Theta Model performed best for oil and natural gas, with MAPE values of 13.04% and 17.42%, respectively. Meanwhile, Brown’s DES produced the lowest MAPE for coal, at 37.35%. The relatively high MAPE values ​​for coal indicate that the three methods have not fully captured the characteristics of the data's fluctuations, and no single forecasting method consistently outperforms the others across all natural resource indicators; therefore, the selection of a method must take into account the pattern characteristics and levels of variation inherent in each time series. The limited accuracy of coal forecasting highlights the need to explore alternative methods capable of capturing fluctuating patterns.
Multi-Run Statistical Evaluation of Retinex-Net, SNR-Aware, and RetinexFormer for Nighttime Vehicle Re-Identification Muhammad Rafif; Radius Tanone
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.11111

Abstract

Nighttime vehicle re-identification (Re-ID) is challenging because low illumination, sensor noise, and motion blur can obscure identity-related visual features in surveillance images. Comparative studies of low-light enhancement (LLE) as a preprocessing step for Re-ID often rely on a single training run, making it difficult to distinguish genuine method effects from training stochasticity. This study evaluates Retinex-Net, SNR-Aware, and RetinexFormer as LLE preprocessing methods for TransReID with a ViT-Base/16 backbone on a stratified nighttime subset of the DN-348 dataset. The subset contains 8,255 images from 150 training identities and 120 shared gallery/query identities, with nighttime degradation simulated using gamma darkening, Poisson-Gaussian noise, and Gaussian blur. Each scenario was trained using three independent random seeds, with performance summarized across runs using mean Average Precision (mAP) and Cumulative Matching Characteristic (CMC) at Rank-1, Rank-5, and Rank-10. Additional statistical validation was performed on a representative run using bootstrap confidence intervals and paired Wilcoxon signed-rank tests with Holm-Bonferroni correction. Image quality was additionally assessed using PSNR, SSIM, and LPIPS, while SIE and JPM were examined through a factorial ablation. RetinexFormer achieved the highest mean mAP of 48.90 ± 0.11%, followed by SNR-Aware at 48.69 ± 0.21% and the baseline at 48.51 ± 0.33%. However, neither RetinexFormer nor SNR-Aware showed a statistically significant improvement over the baseline, whereas Retinex-Net performed significantly worse. RetinexFormer also produced the best image-quality metrics, indicating that stronger visual restoration does not necessarily yield better downstream Re-ID. These findings demonstrate the importance of multi-run and statistically validated evaluation when assessing LLE methods for nighttime vehicle Re-ID. Given that none of the evaluated LLE methods yielded a statistically significant mAP improvement over the no-enhancement baseline, these results indicate that LLE should not be adopted by default as a preprocessing step for nighttime vehicle Re-ID and should only be deployed after scenario-specific multi-run statistical validation.
Sistem Rekomendasi Film dengan Collaborative Filtering Berbasis SVD, KNN dan Explainable AI pada Dataset MovieLens Rizky Adhita Saputra; Nugroho Dwi Saputro; Khoiriya Latifah
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.11114

Abstract

The rapid growth of streaming platforms triggers information overload, necessitating effective recommendation systems. However, conventional Collaborative Filtering faces extreme data sparsity, reaching 98.47% in the MovieLens dataset, and operates as a black-box lacking transparent predictive rationalization. This study aims to develop a robust movie recommendation system that accurately predicts sparse data while offering novelty through predictive transparency using Explainable AI (XAI). The methodology applies Top-N Filtering to densify the matrix, followed by optimizing Singular Value Decomposition (SVD) and K-Nearest Neighbors (KNN) algorithms via GridSearchCV. Results demonstrate that matrix densification to 81.20% successfully accelerated algorithmic performance. SVD significantly outperformed KNN, not only in error reduction (RMSE 0.182090) but also in real-world relevance metrics, achieving a Precision@10 of 0.917, Recall@10 of 0.806, and F1-Score@10 of 0.858. The main contribution of this research lies in demystifying the SVD black-box using XAI. Individual-level evaluation using Local Interpretable Model-Agnostic Explanations (LIME) quantitatively proved that factorization model recommendations are driven more by universal trend sentiments (global mean contribution +0.729) than personal history matching. Furthermore, spatial exploration through Principal Component Analysis (PCA) highlighted the complexity of SVD's 100-dimensional latent space that cannot be linearly reduced, confirming the mathematical need for caution in 2D visual interpretations. In conclusion, the synergy of optimized SVD and the XAI framework produces a robust recommendation system that breaks black-box limitations, directly impacting the enhancement of transparency and user trust in digital platforms.

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