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Human Resource Management (HRM) plays a strategic role in improving organizational competitiveness through proper management of employee placement, training, and performance evaluation. To support the achievement of these goals, a predictive model is needed that can provide an accurate picture of employee performance. This study utilizes a Human Resource Management (HRM) dataset of 1,200 data and applies several classification algorithms to compare their effectiveness, namely J48 or C4.5, Random Forest, Naive Bayes, K-Nearest Neighbor (KNN), Logistic Regression, and Support Vector Machine (SVM). To obtain more optimal results, this study uses resampling techniques and attribute selection methods with a correlation attribute eval approach, so that class distribution can be more balanced and model accuracy increases. From the test results, the Decision Tree J48 algorithm showed the best performance with an accuracy level reaching 95.41%, a kappa value of 0.8925, a mean absolute error (MAE) of 0.0432, a precision of 0.955, a recall of 0.954, and an area under the ROC curve of 0.964. These findings indicate that J48 has excellent predictive capabilities compared to other algorithms. Furthermore, this study also found that the most influential variables in determining employee performance include the percentage of the last salary increase (EmpLast Salary Hike Percent), the level of work environment satisfaction (Emp Environment Satisfaction), the length of time since the last promotion (Years Since Last Promotion), and experience in the current role (Experience Years in Current Role). Overall, the results of the study indicate that the C4.5 algorithm with the application of the resampling technique can be an optimal solution in building an employee performance prediction system. Thus, this model has the potential to be a strong basis for managerial decision-making, particularly in designing HR development strategies and policies to improve organizational performance.

This study focuses on the development of an intelligent enemy behavior system in the 2D maze game Dungeon Escape by combining the A* algorithm for pathfinding and a Behavior Tree (BT) structure for decision-making. The main objective is to enhance Non-Playable Character (NPC) intelligence, enabling more adaptive and realistic interactions with players. The A* algorithm is implemented to allow NPCs to pursue players through the shortest and most efficient paths while avoiding obstacles on a grid-based map. Meanwhile, the Behavior Tree framework is designed to manage NPC actions based on dynamic conditions, such as attacking when in close proximity, chasing players within a certain detection radius, and retreating to the original guard position when the player leaves the active zone. The research methodology involves a comprehensive literature review, system design, and implementation using the Unity game engine. Testing procedures consist of both white-box and black-box approaches to evaluate the correctness, functionality, and efficiency of the system. The results indicate that all major game features—including player navigation, combat mechanics, key collection, enemy pathfinding, and user interface interactions—operate smoothly as expected. Furthermore, NPCs exhibit adaptive behavior by dynamically switching between patrolling, chasing, and attacking modes depending on the player’s location and proximity. Performance testing shows that the integrated A* and BT system runs efficiently without significant delays or instability, even in higher-level stages with more complex layouts. The final game prototype includes seven progressively challenging levels, offering players an engaging and dynamic gameplay experience. This study demonstrates that the combination of pathfinding and decision-making algorithms provides an effective solution for designing intelligent NPCs, improving both realism and entertainment value in 2D games. The findings are expected to serve as a useful reference for future research and development in AI-based game design, particularly in the context of Unity game projects.

The transition of children from early childhood education to elementary school (SD) is a critical phase in their psychological and academic development. During this phase, children face significant challenges, including changes to a more structured learning environment and increasing academic demands. At SDN 055991 in Langkat Regency, this phenomenon is reflected in the difficulties experienced by some students, particularly with basic skills such as reading, writing, and arithmetic, as well as with socializing with peers. These difficulties can impact children's long-term academic and social development. This study aims to identify the key factors influencing children's learning readiness during this transition period, utilizing artificial intelligence (AI) technology. Specifically, this study uses Artificial Neural Networks (ANN) and Decision Trees as tools to analyze the data obtained. The use of this data-driven approach allows for a more in-depth analysis of the complex patterns and relationships between various variables that influence children's learning readiness, such as family factors, social environment, and students' basic skills. This study also references various previous studies demonstrating the effectiveness of backpropagation and Deep Learning algorithms in the context of education and student performance prediction. This approach is expected to provide more precise solutions for understanding children's learning readiness and provide a more accurate picture of the factors contributing to difficulties experienced by students in the transition to elementary school. The results of this study are expected to provide relevant recommendations for parents, educators, and education policymakers to support children's learning readiness and strengthen basic education policies that are adaptive to the needs of students in this digital era.

This study aims to analyze and compare the performance of the Naive Bayes, K-Nearest Neighbors (KNN), and Decision Tree algorithms in predicting the purchase intention of e-commerce visitors using the Online Shoppers Purchasing Intention Dataset, which consists of 12,330 records and 18 variables, with the Revenue variable serving as the classification target. The preprocessing stage involved transforming categorical and boolean variables into numerical form, standardizing features using StandardScaler, and splitting the dataset into 80 percent training data and 20 percent testing data. Model evaluation was conducted using accuracy, precision, recall, F1-score, and ROC-AUC metrics, and was further strengthened by 10-fold cross-validation to obtain more stable results. The findings indicate that KNN achieved the highest accuracy of 0.866180, while Naive Bayes produced the highest recall value of 0.690998 and the highest ROC-AUC value of 0.821696. Meanwhile, Decision Tree demonstrated relatively balanced performance with an accuracy of 0.857259 and an F1-score of 0.571776, whereas the cross-validation results identified KNN as the model with the highest average accuracy of 0.8770. These findings suggest that the selection of a classification model for purchase intention prediction cannot rely solely on a single evaluation metric, as each algorithm possesses different strengths. Therefore, a comparative approach among algorithms can help determine the most suitable model for supporting consumer behavior analysis on e-commerce platforms.

Fatigue is a critical issue in labour-intensive small industries, especially in traditional food production such as tofu manufacturing. This study aims to develop a fatigue classification model using a decision tree algorithm by integrating subjective assessments of the work system through the Macroergonomic Organizational Questionnaire Survey (MOQS) and objective physiological indicators, specifically Cardiovascular Load (CVL). The research was conducted in a tofu home industry located in Kalisari Village, Banyumas, Indonesia. Primary data were collected from 10 workers through MOQS questionnaires and heart rate measurements taken at rest and during work. CVL values were calculated and used as labels for classification into three categories: low, moderate, and high fatigue. Meanwhile, MOQS dimension scores (organization, job, personal, environment, and technology) were transformed into interval data and used as classification features. A decision tree model was built using the CART algorithm and visualized for interpretability. The results show that all workers experienced at least moderate fatigue, with 20% categorized as high fatigue. The decision tree revealed that the dimensions of organizational and personal factors were the most influential in predicting fatigue levels. The model provides a practical and interpretable tool to support decision-making in scheduling, workload balancing, and ergonomic interventions. This study demonstrates a novel approach to combining macroergonomic assessments and physiological data with machine learning for practical fatigue risk management in small-scale food production environments.

Peningkatan transaksi dalam platform e-commerce seperti Shopee menuntut adanya sistem prediksi status pesanan yang akurat, guna mengoptimalkan pelayanan dan mengurangi pembatalan maupun keterlambatan pengiriman. Penelitian ini bertujuan membangun model klasifikasi status pesanan (selesai atau batal) pada toko Stuftech.Id menggunakan algoritma C4.5. Data yang digunakan merupakan transaksi pesanan mencakup metode pembayaran, kategori wilayah pengiriman, dan ongkos kirim. Proses klasifikasi dilakukan menggunakan RapidMiner dengan tahapan preprocessing, pembangunan decision tree, dan evaluasi model. Hasil analisis menunjukkan bahwa atribut “Kategori Pulau” memiliki nilai gain tertinggi sehingga dipilih sebagai node akar. Model yang dibentuk menghasilkan akurasi sebesar 86%, dengan recall 100% untuk pesanan selesai namun hanya 6,67% untuk pesanan batal. Temuan ini mengindikasikan bahwa algoritma C4.5 efektif dalam memprediksi pesanan yang berhasil, namun perlu peningkatan dalam mendeteksi potensi pembatalan. Implementasi model ini dapat membantu pelaku usaha dalam mengambil keputusan operasional secara proaktif.

This study aims to develop an accurate predictive model for identifying students at risk of academic dropout using Decision Tree and Random Forest algorithms. The research utilizes a publicly available dataset sourced from Kaggle, which includes academic and demographic features such as GPA, attendance, credit load, financial aid status, and exam scores. The methodology involves several stages: data collection, preprocessing (handling missing values, encoding categorical variables, and feature scaling), model training, and evaluation using performance metrics such as Accuracy, Precision, Recall, F1-Score, and Confusion Matrix. Results show that the Random Forest algorithm outperforms Decision Tree in terms of accuracy and robustness, with notable feature importance on math, reading, and writing scores. The findings highlight the potential of machine learning in early detection of dropout risks and provide actionable insights for academic institutions to design timely interventions. This research contributes to the growing field of educational data mining and supports data-driven decision-making processes in higher education management.

The importance of data mining in Indonesia is increasing along with the growth of big data in various strategic sectors. Data mining plays an important role in transforming complex data into useful information to support data-driven decision making, which is urgently needed in the face of competitive challenges and operational complexity. This research aims to examine the development of data mining techniques and applications in Indonesia over the last decade (2015-2024). Through a systematic literature review approach, data was collected from academic publications in SCOPUS indexed databases. From the initial 95 papers found, a further selection was made based on accessibility, title, and abstract until 64 papers were included in the article review. The results show that techniques such as K-Means, Naive Bayes, and Decision Tree are most commonly used. In the business sector, clustering through K-Means is widely applied for market segmentation and consumer pattern analysis. The healthcare sector mainly utilizes classification techniques, such as Naive Bayes and Decision Tree, for disease risk prediction and early diagnosis. Meanwhile, the education sector uses data mining to assess student performance and predict potential dropouts, assisting institutions in optimizing learning strategies.

Coronary heart disease (CHD) remains a leading cause of mortality worldwide. Early detection is essential to reduce complications and improve patient outcomes. This study aims to develop a classification model using machine learning algorithms to predict CHD risk based on clinical symptoms. The dataset used is the Cleveland Heart Disease dataset from the UCI Machine Learning Repository, consisting of 303 patient records with 14 clinical features. The preprocessing stage involved handling missing values, normalizing features, and transforming categorical variables. Four classification algorithms were applied: K-Nearest Neighbors (K-NN), Decision Tree, Random Forest, and Support Vector Machine (SVM). Each model was trained using stratified 10-fold cross-validation to ensure generalizability. Evaluation using accuracy, precision, recall, F1-score, and ROC-AUC metrics showed that the Random Forest algorithm achieved the highest performance with 87.2% accuracy. Feature importance analysis indicated that chest pain type, resting blood pressure, cholesterol, and ST depression were the most influential indicators. These results demonstrate that machine learning, particularly Random Forest, can effectively support early diagnosis of CHD in clinical settings and has the potential to be integrated into clinical decision support systems (CDSS).

Given the increasing prevalence of mental health challenges in digital work settings, especially among IT remote workers, early detection mechanisms have become critically important. This study aims to improve the prediction accuracy of mental health conditions among IT remote workers by integrating feature engineering techniques within machine learning models. Five algorithms consisting of Random Forest, Logistic Regression, K-Nearest Neighbors, Decision Tree, and Naive Bayes were evaluated. The Random Forest model achieved the best performance, with 83% accuracy, 83% precision, 100% recall, and a 90% F1-score, followed closely by Logistic Regression with 82% accuracy. Nevertheless, the results demonstrate the feasibility of applying machine learning to support the early detection of mental health risks, offering a strong foundation for future research in predictive analytics and the development of intelligent support systems within digital work environments.

This research focuses on assessing the efficacy of a method that integrates Convolutional Neural Networks (CNN) with Decision Trees for the detection of phishing URLs. Phishing represents a major cyber threat, where cybercriminals attempt to deceive individuals into disclosing sensitive information via fraudulent websites. As the frequency of phishing attacks continues to rise, there is a pressing need for effective detection and prevention strategies. In this investigation, a dataset comprising both phishing and legitimate URLs was utilized to train a CNN-Decision Tree model. The training phase includes feature extraction from URLs using CNN, which excels at identifying intricate patterns within the data, followed by classification through Decision Trees, recognized for their capacity to deliver straightforward and comprehensible interpretations of classification outcomes. The model's performance was evaluated across nine distinct scenarios to assess its effectiveness under varying conditions. The results indicated that the hybrid CNN-Decision Tree model achieved a precision rate of 94%, a recall of 90%, and an F1-Score of 92%, with an overall accuracy of 93%. These findings suggest that the model is not only proficient in identifying phishing URLs but also maintains a commendable balance between precision and recall. This research highlights that the synergy of CNN and Decision Trees can serve as a potent solution for phishing URL detection, significantly contributing to the advancement of enhanced cybersecurity systems.

This study explores the application of machine learning algorithms, specifically Linear Regression and Decision Tree Regressor, for predicting student academic performance using academic grade data from Kaggle. The analyzed factors include attendance, assignment grades, midterm exam grades, and final exam grades. The research methodology encompasses data collection, preprocessing, model development, training, and validation. This study contributes to the field of educational data analytics by demonstrating how machine learning can provide actionable insights into students' learning patterns and academic outcomes. The findings emphasize the effectiveness of Linear Regression for linearly distributed data and Decision Tree Regressor for capturing complex, non-linear relationships. The implications of this research suggest that machine learning models can assist educators in identifying key factors influencing student performance, enabling targeted interventions to enhance learning outcomes. Future research should explore larger, more diverse datasets and incorporate ensemble methods, such as Random Forest or Gradient Boosting, to improve model generalization and prediction accuracy. Additionally, integrating socio-economic and psychological factors could provide a more holistic perspective on academic achievement.

Predicting credit worthiness is an important step for banks to reduce the risk of bad credit. This research compares the performance of four classification algorithms, namely SVM, Naïve Bayes, Random Forest and Decision Tree using simulated datasets. The results obtained on the metrics of accuracy, precision, recall, F1 score, and AUC-ROC, show that Decision Tree has the best performance with 42.5% accuracy, 48.3% precision, 47.5% recall, 47.5% F1 score, and AUC 0.60, indicating its ability to is in differentiating credit worthiness. Random Forest achieved an accuracy of 37.5% and an AUC of 0.493, while Naïve Bayes had the lowest accuracy with an accuracy of 27.5% and an AUC of 0.425. SVM gives better results than Naïve Bayes but is still inferior to Decision Tree. This research recommends implementing a Decision Tree as the main model with optimization through hyperparameter tuning, adding relevant features, and handling data accounting. These results are expected to support banking decision making more effectively and efficiently.

Education is a fundamental aspect in shaping the future of the younger generation, and choosing further studies after education at junior high school level is a crucial decision. At UPTD SMP Negeri 10 Kupang, many students, especially in class IX for the 2024/2025 academic year, face difficulties in determining their further study path. To help students plan their careers, implementing Guidance and Counseling (BK) services in the career field is very necessary, one of which is through creating a career tree. A career tree is a visual aid that depicts the career paths and educational pathways that students can pursue based on their interests and abilities. This method aims to provide students with a better understanding of the career options available, recognize their own potential, and plan strategic steps to achieve their career goals. The results of this activity show that making a career tree was successful in providing insight to students, helping them know themselves better, and motivating them to actively seek information about careers and further education. Creating a career tree has proven to be effective as a solution in planning the right career and further studies. Therefore, it is recommended to improve guidance and counseling services, implement career trees regularly, and involve parents and the community in supporting students' career development.

:Pakistan is a developing country. Its transportation infrastructure mainly consists of road network. About 95% passengers and fright is transported using the road network. This high demand on road network is because of the unreliable railway system between the cities. Due to such high demand on road network the accident involvement risk of an individual is much high as compared to developed countries. This study uses a new modeling approach to estimate road safety risk for WTP.  A correlated random parameters Tobit model (heterogeneity-in-mean) is integrated with machine learning (Decision tree).  The decision tree categorizes higher-order interactions, while the model captures unobserved correlations and heterogeneity. The framework examines WTP determinants using a representative sample of 3178 road users from Pakistan. The model estimates WTP for different (fatal and severe injury) risk reductions to monetize road traffic crash costs. Results show maximum respondents are willing to support safety improvement policies. The model reveals significant WTP heterogeneity linked to perceptions of road safety and accident risk. Systematic preference heterogeneity emerges through higher-order interactions, offering insights into WTP relationships. Marginal effects highlight varying sensitivities to explanatory variables, quantifying their impact on WTP probability and magnitude. The framework provides two key contributions. It identifies public WTP determinants, emphasizing heterogeneous effects. It also helps in prioritization safety policies by understanding public sensitivity to WTP. The insights further emphasizing on the importance of road safety interventions to the specific socio-economic profiles of road users. This study offers a significant contribution to road safety improvement by providing valuable recommendations for policy makers. By integrating detailed socio-economic factors, it also addresses the urgent need for targeted traffic safety interventions in Pakistan. These findings are expected to aid policymakers and stakeholders in developing effective strategies to enhance road safety and reduce the accident involvement risk effectively.

Abstract. From the perspective of Micro, Small, and Medium Enterprises (MSMEs), fluctuations in raw material prices are highly concerning as they can significantly impact business stability. While MSMEs may tolerate price fluctuations to some extent, from an industrial engineering perspective, such a passive approach contradicts the principles of continuous improvement. This study seeks to predict the price of large red chili peppers using five regression models implemented through Orange Data Mining: Linear Regression, Support Vector Machine, Decision Tree, k-Nearest Neighbors (kNN), and Gradient Boosting. Due to the limited availability of daily data, particularly within a daily timeframe, the study utilized weekly data spanning three years. The results of the Test and Score evaluation shows Gradient Boosting as the best-performing model, achieving a Mean Absolute Percentage Error (MAPE) of 0.7%. However, the MAPE for predictions in January 2025 increased to 15.8%. This error is expected to decrease as more weekly data becomes available to mitigate the inaccuracies inherent in this model. Keywords: prediction, red chilli, regression, supervised learning , orange data mining. Abstrak. Dalam perspektif UMKM, fluktuasi harga bahan baku adalah suatu hal yang paling ditakuti karena berakibat pada ketahanan usaha yang menjadi tidak menentu. Pada suatu kondisi, fluktuasi harga dapat diterima para UMKM, namun dalam perspektif teknik industri, sikap UMKM tersebut tidak sesuai prinsip continuous improvement. Penelitian ini mencoba untuk memprediksi harga cabai merah besar dengan menggunakan 5 model regresi dibantu Orange Data Mining. Yaitu Linear Regression, Support Vector Machine, Tree, kNN, Gradient Boosting. Data yang diperlukan sebagian besar tidak tersedia, khususnya dalam kerangka waktu harian sehingga penelitian ini menggunakan data mingguan selama 3 tahun. Hasil Test and Score menunjukkan model Gradient Boost terpilih menjadi model terbaik dengan tingkat MAPE 0.7% namun MAPE pada tahap Prediction di bulan Januari 2025 menjadi 15.8%. Error tersebut akan berkurang ketika data mingguan sudah cukup banyak untuk menambal kesalahan yang dihasilkan model ini Kata kunci: prediksi, cabai merah, regression, supervised learning , orange data mining.

Diabetes is one of the diseases that poses a significant global health challenge, with a considerable impact on quality of life and mortality rates. This study examines the use of the Support Vector Machine (SVM) algorithm for diabetes classification through a literature review. SVM was chosen due to its ability to handle imbalanced and complex data. The aim of this study is to assess the effectiveness of SVM compared to other machine learning methods in detecting diabetes. The results of the literature review indicate that SVM achieves higher accuracy than other methods such as Naïve Bayes and Decision Tree, with some studies showing accuracy above 90%. This study is expected to provide deeper insights into the development of machine learning-based diagnostic systems for diabetes.

Pakistan is a developing country. Its transportation infrastructure mainly consists of road network. About 95% passengers and fright is transported using the road network. This high demand on road network is because of the unreliable railway system between the cities. Due to such high demand on road network the accident involvement risk of an individual is much high as compared to developed countries. This study uses a new modeling approach to estimate road safety risk for WTP.  A correlated random parameters Tobit model (heterogeneity-in-mean) is integrated with machine learning (Decision tree).  The decision tree categorizes higher-order interactions, while the model captures unobserved correlations and heterogeneity. The framework examines WTP determinants using a representative sample of 3178 road users from Pakistan. The model estimates WTP for different (fatal and severe injury) risk reductions to monetize road traffic crash costs. Results show maximum respondents are willing to support safety improvement policies. The model reveals significant WTP heterogeneity linked to perceptions of road safety and accident risk. Systematic preference heterogeneity emerges through higher-order interactions, offering insights into WTP relationships. Marginal effects highlight varying sensitivities to explanatory variables, quantifying their impact on WTP probability and magnitude. The framework provides two key contributions. It identifies public WTP determinants, emphasizing heterogeneous effects. It also helps in prioritization safety policies by understanding public sensitivity to WTP. The insights further emphasizing on the importance of road safety interventions to the specific socio-economic profiles of road users. This study offers a significant contribution to road safety improvement by providing valuable recommendations for policy makers. By integrating detailed socio-economic factors, it also addresses the urgent need for targeted traffic safety interventions in Pakistan. These findings are expected to aid policymakers and stakeholders in developing effective strategies to enhance road safety and reduce the accident involvement risk effectively.

Hydroponic plant cultivation is booming, but stock and sales are hard to predict. Poor prediction can cause farmers to overstock and lose money. This study suggests a framework that uses several machine learning models, including Linear Regression (LR), Random Forest (RF), Decision Tree (DT), and Extreme Gradient Boosting. "Ensemble Learning," which combines these models, should yield more accurate and generalizable results than a single model. This framework is assessed using historical hydroponic plant sales data and related factors like price, weather, and market trends. The model's performance is measured by the difference between predictions and actual values using RMSE and MAE metrics. This framework should improve hydroponic plant stock and sales predictions. Farmers can make better production, inventory, and harvest distribution decisions. Besides reducing financial losses, this reduces food waste and improves food security.

This research aims to implement the C4.5 algorithm in predicting bad credit in digital loan systems in the FinTech industry. The C4.5 algorithm was chosen because of its ability to handle numeric and categorical attributes, as well as produce a decision tree that can be interpreted easily. This research uses a dataset containing customer transaction and profile information, such as employment status, income and payment history. Test results show that the C4.5 algorithm is able to achieve an accuracy of 89.6% in predicting the possibility of bad credit, so it can help FinTech companies manage credit risk more effectively.