The implementation of Learning Management Systems (LMS) in higher education institutions continues to increase in line with the growing demand for flexible digital learning, with the assumption that LMS is an easy-to-use platform that will be naturally accepted by lecturers. This study aims to analyze the factors that influence the adoption of LMS among lecturers at higher education institutions in Eastern Indonesia. This study uses a quantitative cross-sectional survey. The research instrument, comprising 25 items classified into five constructs —Constructivist Pedagogical Beliefs, Traditional Pedagogical Beliefs, Perceived Ease of Use, Perceived Usefulness, and LMS Use —was administered to lecturers at a private university in North Sulawesi. Using the Partial Least Squares-Structural Equation Modeling approach, this study incorporates the Technology Acceptance Model with a constructivist and traditional pedagogical belief orientation. The results show that three of the eight variables significantly influence LMS usage. The findings indicate that constructivist pedagogical beliefs and perceived usefulness have a significant influence on LMS adoption, whereas traditional pedagogical beliefs do not have a significant impact. These results have practical implications for universities in designing training policies and strategies to optimize LMS usage.

Early detection of defects in steel plates is essential to ensure structural integrity and product quality in the metal manufacturing industry. This study compares the performance of five machine learning algorithms Support Vector Classifier (SVC), Nu-Support Vector Classifier (NuSVC), Decision Tree (DT), Random Forest (RF), and CatBoost (CB) to classify seven categories of steel plate defects using 26 technical features from a publicly available dataset on Kaggle. The preprocessing pipeline included outlier detection (IQR method), class imbalance correction using SMOTE, and feature normalization via StandardScaler. The models were evaluated using classification metrics such as Accuracy, Precision, Recall, F1-Score, ROC-AUC, and Log Loss. Results revealed that the CatBoost algorithm achieved the most balanced and consistent performance, with an AUC of 0.93, accuracy of 68.3%, and the lowest Log Loss value (0.786). In contrast, the Decision Tree showed severe overfitting with perfect training performance but poor generalization (Log Loss = 15.72). This study highlights the promise of CatBoost as an interpretable and efficient solution for automated defect detection in steel manufacturing, while also offering transparent reproducibility pathways for further research.

Melanoma is one of the most malignant forms of skin cancer, with an incidence rate of 7.9% in Indonesia. Traditional biopsy-based diagnosis, though crucial, is invasive and time-consuming, creating barriers for early detection. To address this issue, this research compares two Convolutional Neural Network (CNN) models for digital image-based melanoma classification. The study utilized a publicly available dataset from Kaggle, consisting of 17,805 images (melanoma and non-melanoma), which were divided into training, validation, and testing subsets. The models were trained using the Adamax and SGD optimizers for 100 epochs. The performance of the models was evaluated based on accuracy, loss, precision, recall, and F1-score. The CNN model with the best architecture, which consisted of two fully connected layers, achieved an accuracy of 93.18% and a loss of 0.1636, outperforming the alternative model. These results confirm the effectiveness of CNN models in classifying melanoma images and support the development of a web-based platform that allows users to upload or capture images for rapid and non-invasive detection.

The spread of misinformation on digital platforms has emphasized the urgent need for automated fact verification systems. However, selecting the most semantically relevant evidence to support or refute a claim remains a challenge, especially within the widely used FEVER dataset. Traditional approaches like TF-IDF often fall short in capturing the contextual meaning between claims and evidence. This study addresses the problem by comparing TF-IDF with Sentence-BERT (SBERT) in measuring semantic similarity. The novelty of this research lies in embedding both claims and evidence using SBERT, then calculating cosine similarity to quantify their semantic relevance. Before embedding, standard preprocessing steps were applied, including tokenization, stemming, lowercasing, and stopword removal. A quantitative approach is used to compute cosine similarity between claim-evidence pairs using both TF-IDF and SBERT embeddings. Similarity analysis, distribution statistics, and t-tests are conducted to evaluate the methods. The results show that SBERT achieves higher similarity with the “SUPPORTS” category (0.65) and stronger negative similarity with “NOT ENOUGH INFO” (-0.90), compared to TF-IDF (0.49 and -0.62, respectively). SBERT also demonstrates more stable score distributions and significantly higher t-test values across all label comparisons, indicating stronger semantic discrimination. These findings confirm that SBERT outperforms TF-IDF in identifying the most relevant evidence. The new dataset generated can serve as a foundation for future fact verification model development.

This study evaluates ensemble learning techniques for optimizing heart disease prediction, with a focus on Random Forest due to its robustness in handling complex medical data. The dataset used, "Heart Disease Prediction Dataset" from Kaggle, consists of 270 instances and 13 features like age, cholesterol, and family history. Data preprocessing involved mean imputation for missing values and min-max normalization. The study compares Random Forest with other ensemble classifiers—AdaBoost, Gradient Boosting, and XGBoost—using 10-fold cross-validation and evaluation metrics such as accuracy, precision, recall, and F1 score. Results show that Random Forest outperforms the other models with an accuracy of 87.04%, precision of 85.00%, recall of 80.95%, and F1 score of 82.93%. These findings emphasize Random Forest's ability to maintain prediction stability across various medical attributes and imbalanced data. Although the study highlights Random Forest as a promising method for early heart disease risk prediction, it remains a computational evaluation and requires clinical validation. The results aim to inform the development of predictive tools for enhancing early diagnosis and preventive strategies in healthcare systems.

As neobanks emerged as one of exciting services in banking industry in recent years and trying to penetrate young customers, this study attempts to examine whether variables in UTAUT model (performance expectancy, effort expectancy, and social influence) and additional variables such as features, curiosity, and rewards affect intention to adopt neobanks with perceived credibility as mediating variable. A quantitative survey was conducted on 359 Gen Z business students at a private university in North Sulawesi. The data collected were then analyzed using a structural equation model (SEM). The results show that only curiosity and perceived credibility have a positive effect on intention to adopt, while performance expectancy and social influence impact on perceived credibility. In addition, perceived credibility mediates the effects of performance expectancy and social influence on intention to adopt. Some recommendations are given in order to increase the Gen Z intention in adopting neobanks while at the same time improving the credibility of neobanks’ applications.

Image processing is a technique to create an image that appears and can be converted into light that describes 2 dimensions. The K-Nearest Neighbor (K-NN) algorithm is known for its efficiency and effectiveness in classification. Although K-NN is often used, a deep understanding of Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Deviation (MAD) especially in the context of image classification, still requires further exploration. This study aims to emphasize and measure the behavior of MSE, MAPE, and MAD when applied to K-NN classification results. In this study, calculating statistical features such as mean, standard deviation, skewness, and kurtosis are extracted from the histogram of chicken egg images. These features are then normalized and used as input for the K-NN algorithm. Classification performance is evaluated using MSE, MAPE, and MAD. The results show that K-NN is able to classify chicken egg images with performance measured by MSE of 0.35114462, MAPE of 0.14237803%, and MAD of 0.52930142. The differences in these values, especially the much smaller MAPE value and in percentage units, underscore the importance of selecting the right metrics according to the needs of interpretation and practical applications. This study provides a clearer understanding of how different error metrics characterize the performance of K-NN in image classification, while also highlighting the need for comparative metric considerations in future research.

This study presents an optimized text classification framework combining AdaBoost ensemble techniques with Decision Tree algorithms (ID3, C4.5, CART) to address critical challenges in small dataset scenarios (n=795 Indonesian-language reviews). Employing rigorous five-fold stratified cross-validation (random seed=42), we implemented a comprehensive preprocessing pipeline including case normalization, language-specific stemming, and TF-IDF feature extraction. The ensemble model utilized 50 AdaBoost iterations with a learning rate of 1.0, evaluated through multiple performance metrics while accounting for class imbalance effects. Key results demonstrate significant performance enhancements, with the C4.5+AdaBoost configuration achieving 96.72% accuracy (±0.88), representing a 10.6 percentage point improvement over the base C4.5 algorithm. The ensemble approach particularly improved minority class identification, boosting positive sentiment classification F1-scores by 0.28 points while maintaining exceptional neutral sentiment detection (F1-score 0.99±0.00). Comparative analysis revealed consistent advantages across all Decision Tree variants, with accuracy improvements of 18.6% for ID3, 10.6% for C4.5, and 14.2% for CART, alongside reduced performance variance (62-73% decrease). While these findings validate AdaBoost's effectiveness for enhancing Decision Tree stability in small-scale text classification, the study acknowledges limitations regarding sample size constraints and language specificity. The research contributes practical methodologies for sentiment analysis applications while emphasizing the need for validation on larger, more diverse datasets. Future work should explore comparative benchmarking against transformer architectures. Advanced feature representation techniques and multilingual generalization testing. This work provides a reproducible framework for developing robust, ensemble-based text classification systems in resource-constrained scenarios.

The ocean is the central source of livelihood for fishermen. Fishermen depend heavily on their sea catch. Fishermen often need more certainty because, as is often the case, the spot where they go for fishing is not necessarily the site of the fish. Remote sensing technology, such as the Aqua MODIS satellite, provides two vital advantages: the exact location of the fish and, importantly, guidance to the exact spot of the fish. This guidance can be a reassuring tool for fishermen. Using GIS technology on smartphones, fishermen can ensure the fish's position. The distribution of sea surface temperature and chlorophyll-a is analyzed to determine the coordinates. The purpose of this research is to use the React Native platform to create a mobile application that uses Geographic Information System (GIS) and Global Positioning System (GPS) technologies and is accessible through smartphones. This research adopts a prototyping model method consisting of several stages: Information Gathering, Quick Planning, Quick Design, Software Development, Testing and Deployment, and Results. The application developed can run smoothly from the log-in stage to predictive visualization of fish positions in the ocean. However, further research needs to be done to improve some features.

The rapid development of information technology has had a significant impact on various fields, including education. This study aims to design and develop a web-based school information system for SMK Negeri 1 Sorong to enhance efficiency in data management and the dissemination of academic and non-academic information. The methodology used follows the Waterfall system development model, which includes requirements analysis, system design, implementation, testing, and maintenance. The research results indicate that the system, developed using PHP as the programming language and MySQL as the database, operates stably and provides users with flexible access. This system facilitates the integration of student data, teacher data, class schedules, and other related information. Testing confirmed that all system functions operate according to the designed specifications. With the implementation of this system, SMK Negeri 1 Sorong can improve the quality of educational services, simplify information access, and support digital transformation in the education sector. Furthermore, the results of this study have the potential to be applied and adapted in other educational contexts, particularly in the development of web-based school information systems that can be customized according to the needs of other educational institutions.