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Human resources are a company's most dominant asset, because they can play an important role in the company's business development. An outsourcing company is a legal entity and is obliged to comply with business licenses issued by the Central Government. The eligibility process for new employees is supported based on the level of ability and competency determined by the company. The existence of difficulties for companies in determining the eligibility of new employees which makes the reason for the ineffectiveness of the processes carried out by the company at this time, is used as a goal for the authors for the purposes of a study. By using the classification method in a data mining with the C4.5 algorithm (Decision Tree) and a RapidMiner application as a tool in the analysis process carried out to find a factor supporting the process of a new employee eligibility. With the data of 960 applicants used as a sample, this data was taken from 2021-2022. From the data divided into several attributes used, the highest Gain value obtained from these attributes through the results of Test 2 of 0.417152421 which will be used as the root in the process of determining employee eligibility and has the highest accuracy value of 98.44%.

The design of a village fund assistance information system using the C4.5 algorithm aims to optimize the selection process for prospective aid recipients at the Ogan Ilir Regency Social Service (DINSOS). Currently, the village fund assistance selection process often takes a long time and is prone to inaccuracy and unfairness due to the limitations of the manual system. The C4.5 algorithm was chosen to build an effective decision tree in classifying based on predetermined criteria, such as income, number of dependents, employment, housing status, and expenses. By utilizing the Gain or Gain Ratio value of each attribute, the C4.5 algorithm is able to produce a clear decision tree, which makes it easier for DINSOS to make decisions objectively and transparently. This information system is designed with an easy-to-use user interface and a structured database to facilitate the management of aid recipient data. The results of the implementation of this system show increased accuracy in determining prospective aid recipients and time efficiency in data processing, thus supporting efforts to evenly distribute village fund assistance in Ogan Ilir Regency in a targeted manner.

Credit approval prediction is one of the critical challenges in the financial industry, where the accuracy and efficiency of credit decision-making can significantly affect business risk. This study proposes an outlier detection method using the Gaussian Mixture Model (GMM) combined with Extreme Gradient Boosting (XGBoost) to improve prediction accuracy. GMM is used to detect outliers with a probabilistic approach, allowing for finer-grained anomaly identification compared to distance- or density-based methods. Furthermore, the data cleaned through GMM is processed using XGBoost, a decision tree-based boosting algorithm that efficiently handles complex datasets. This study compares the performance of XGBoost with various outlier detection methods, such as LOF, CBLOF, DBSCAN, IF, and K-Means, as well as various other classification algorithms based on machine learning and deep learning. Experimental results show that the combination of GMM and XGBoost provides the best performance with an accuracy of 95.493%, a recall of 91.650%, and an AUC of 95.145%, outperforming other models in the context of credit approval prediction on an imbalanced dataset. The proposed method has been proven to reduce prediction errors and improve the model's reliability in detecting eligible credit applications.

Dengue Hemorrhagic Fever (DHF) is a disease caused by the Dengue virus and has a significant impact on public health, especially in tropical areas. Early diagnosis and prediction of DHF risk are essential to prevent complications and improve medical care. This study aims to develop a DHF risk prediction model using the Decision Tree method based on clinical symptoms and laboratory data. The data used include symptoms such as fever, joint pain, rash, and laboratory results such as platelet count and hematocrit. The Decision Tree model was chosen because of its ability to handle data with various variables and provide easy-to-understand interpretations. The research data were taken from patients diagnosed with DHF in several hospitals during a certain period. The dataset was then analyzed to find relevant patterns that could predict a high risk of DHF. The model training and testing process was carried out using cross-validation techniques to ensure prediction accuracy. The results showed that the Decision Tree model had an accuracy rate of 96.95% and consistent results from cross-validation which produced an average accuracy of 92.8%,, with good sensitivity and specificity in predicting DHF risk based on a combination of clinical symptoms and laboratory data. Factors such as low platelet count and fever symptoms lasting more than three days were found to be significant predictive variables. In conclusion, this Decision Tree model has the potential to be used as a tool in early prediction of DHF risk, which can help medical personnel in clinical decision making and patient management. Further development can be done by adding other variables such as epidemiological data to improve model performance.

Dengue Hemorrhagic Fever (DHF) is a disease caused by the Dengue virus and has a significant impact on public health, especially in tropical areas. Early diagnosis and prediction of DHF risk are essential to prevent complications and improve medical care. This study aims to develop a DHF risk prediction model using the Decision Tree method based on clinical symptoms and laboratory data. The data used include symptoms such as fever, joint pain, rash, and laboratory results such as platelet count and hematocrit. The Decision Tree model was chosen because of its ability to handle data with various variables and provide easy-to-understand interpretations. The research data were taken from patients diagnosed with DHF in several hospitals during a certain period. The dataset was then analyzed to find relevant patterns that could predict a high risk of DHF. The model training and testing process was carried out using cross-validation techniques to ensure prediction accuracy. The results showed that the Decision Tree model had an accuracy rate of 96.95% and consistent results from cross-validation which produced an average accuracy of 92.8%,, with good sensitivity and specificity in predicting DHF risk based on a combination of clinical symptoms and laboratory data. Factors such as low platelet count and fever symptoms lasting more than three days were found to be significant predictive variables. In conclusion, this Decision Tree model has the potential to be used as a tool in early prediction of DHF risk, which can help medical personnel in clinical decision making and patient management. Further development can be done by adding other variables such as epidemiological data to improve model performance.

A good sleep pattern is very important for our body's health both physically and mentally, while lifestyle habits such as physical activity and diet play a big role in influencing sleep quality. By using a decision tree, researchers aim to predict whether we have a healthy sleep pattern or not based on lifestyle. Healthy sleep patterns are regular and quality sleep habits to maintain our physical health. Healthy sleep patterns generally involve sleeping 8 hours – 9 hours per night, having a regular and consistent sleep time. The decision tree model was chosen because of the decision tree's ability to provide accurate predictions and produce rules that are easy to understand. This model can help us raise awareness of the importance of a healthy lifestyle in maintaining sleep quality.

A good sleep pattern is very important for our body's health both physically and mentally, while lifestyle habits such as physical activity and diet play a big role in influencing sleep quality. By using a decision tree, researchers aim to predict whether we have a healthy sleep pattern or not based on lifestyle. Healthy sleep patterns are regular and quality sleep habits to maintain our physical health. Healthy sleep patterns generally involve sleeping 8 hours – 9 hours per night, having a regular and consistent sleep time. The decision tree model was chosen because of the decision tree's ability to provide accurate predictions and produce rules that are easy to understand. This model can help us raise awareness of the importance of a healthy lifestyle in maintaining sleep quality.

The detection of computer network attacks is becoming increasingly important as the complexity and frequency of cyber-attacks threatening information systems and network infrastructure continue to rise. These attacks may lead to severe consequences, including data breaches, service disruptions, and financial losses. To address these challenges, artificial intelligence techniques have become a major focus in the development of more effective, adaptive, and reliable intrusion detection systems. Among various classification algorithms, the C4.5 decision tree has demonstrated strong performance due to its simplicity, interpretability, and high classification accuracy. This study aims to apply the C4.5 algorithm for network attack detection using a comprehensive dataset that includes multiple categories of attacks and normal network activities. The proposed methodology consists of several stages, including data preprocessing, feature selection, decision tree model construction, and performance evaluation using standard metrics such as accuracy, precision, recall, and F1-score. Data preprocessing is performed to handle missing values, normalize data, and reduce noise, thereby improving the overall quality of the dataset and enhancing classification results. The experimental results indicate that the C4.5 decision tree algorithm effectively classifies network traffic into attack and normal categories with a satisfactory level of accuracy. The model successfully identifies attack-related patterns and highlights significant features that influence detection performance. Further analysis reveals that appropriate feature selection and parameter tuning significantly contribute to improving model reliability and robustness. This research provides a valuable contribution to the development of efficient, accurate, and practical network intrusion detection systems. The proposed approach is expected to strengthen information security frameworks and support proactive defense strategies against increasingly sophisticated cyber threats, thereby enhancing the protection of critical network infrastructures.

Admission of new students to an educational institution is an activity that is always carried out every new academic year, where prospective new students always increase from year to year (Muwardah and Pramunendar, 2015). Admission of students can be held from elementary to middle school, from middle school to high school / vocational school. The focus of this research is the registration of new students at SMK. As is known, SMK is a Vocational High School or abbreviated as (SMK) and where there are many majors provided which ultimately makes prospective new students confused about which major is right for them because will take a long time.. Based on C4.5 as a Classification Algorithm: C4.5 is a popular algorithm for building decision trees. It works by dividing a dataset into smaller subsets based on attribute values, thus forming an easy-to-understand tree structure. Classification results using decision trees provide a clear visualization of the decision-making process and the variables that contribute to student choices.

Eligibility for new employees includes individuals who have skills appropriate to the position they are applying for, have a high willingness to learn, communicate well, and have integrity and good work ethics. They must also be able to adapt to the work environment and team quickly, but determining the suitability of new employees is quite difficult given the competencies of each division, therefore the use of data mining is very suitable for determining the suitability of new employees according to the needs of the company which uses them. decision tree algorithm (C4.5), the results obtained from the decision tree algorithm process show the truth tree for classifying new employees and a high level of accuracy with a percentage of 98.44% based on test 2.

Critical Control Point (CCP) a point, stage, or procedures where the danger that related with food can be prevented, eliminated or reduced to the point that can be accepted (allowed or secure point). The purpose of this research is to know the level of the determination of Critical Control Point (CCP) on the process of fish canning sardinella,SP and know the critical control point on the process of fish canning sardinella,SP. This study uses qualitative data obtained from interviews, surveys, observations and documentation using data reduction analysis, data presentation, verification and decisionmaking trees. The results of this study show that the Application of Critical Control Point (CCP) in the Lemuru Fish Canning Process (Sardinella Lemuru) at PT Sarana Tani Pratama has been effective even though there are several elements that need more attention so that its application can be in accordance with the guidelines for good food production methods. Critical control points (CCP) in the process of canning lemuru fish (Sardinella, sp), are as follows: 1) Acceptance of raw materials (frozen & fresh), 2) Closure of cans (seammer), 3) Sterelization / Retort.

According to academic data, student math ability tests at MTSS PAB 5 Klambir Lima yield mixed results. There are students who understand math well, but there are also those who have difficulty understanding the mathematical concepts themselves. Math teachers at this school have difficulty designing lessons that can meet the needs of students with different levels of understanding. So, it is necessary to group student data to produce educational decision-making and improve learning effectiveness, such as through data mining. Data mining is a semi-automated process that uses machine learning techniques, mathematics, statistics, and artificial intelligence to identify and organize information contained in large databases. The process of finding information can be done by determining the decision rule based based on the level of student understanding in mathematics lessons using the Decision Tree Algorithm C4.5 method. The use of the Decision Tree algorithm C4.5 aims to make it easier to determine decision rules based on gender, Predicate, teacher teaching methods, student learning interest, and level of understanding. Based on the results of the study, it was found that if the teacher's teaching method is good, the predicate value is B, the student's learning interest is less interested, and the gender is male, then the student's level of understanding in mathematics lessons is not understood.

The advancement of e-commerce has changed the way people shop. However, there is a mismatch between the actual quality of a product and the seller’s description. Product reviews are an important source of information for making purchasing decisions. However, processing large numbers of reviews manually is difficult. This research aims to detect emotions in Indonesian language product review texts using contextual embeddings. The public dataset used was PRDECT-ID, which comprises five emotion labels. The methods used include data preprocessing, feature extraction using contextual embeddings such as Bidirectional Encoder Representations from Transformers (BERT), and classification using Decision Tree, Naïve Bayes, and k-Nearest Neighbors (KNN). Among the compared models, the KNN model demonstrated the highest improvement, achieving a 15.09% enhancement over the decision tree results. This research provides insights into the effectiveness of contextual embeddings in detecting emotions in Indonesian language product review texts.

This study aims to apply the C4.5 algorithm in classifying athlete performance based on the 2023 award recipient list. The C4.5 algorithm was chosen for its ability to construct decision trees that can identify patterns and characteristics distinguishing high-performing athletes. The data used in this study includes various attributes such as gender, age, sport, number of medals, and level of competition participation. The results show that the C4.5 algorithm can classify athletes with high accuracy. The resulting decision tree provides valuable insights into the key factors contributing to athlete performance. The implementation of this algorithm is expected to assist sports organizations in more effectively identifying and developing potential talents.    

This study aims to apply the C4.5 algorithm in classifying athlete performance based on the 2023 award recipient list. The C4.5 algorithm was chosen for its ability to construct decision trees that can identify patterns and characteristics distinguishing high-performing athletes. The data used in this study includes various attributes such as gender, age, sport, number of medals, and level of competition participation. The results show that the C4.5 algorithm can classify athletes with high accuracy. The resulting decision tree provides valuable insights into the key factors contributing to athlete performance. The implementation of this algorithm is expected to assist sports organizations in more effectively identifying and developing potential talents.

Customer attrition has become the focus of many businesses today – since the online market space has continued to proffer customers, various choices and alternatives to goods, services, and products for their monies. Businesses must seek to improve value, meet customers' teething demands/needs, enhance their strategies toward customer retention, and better monetize. The study compares the effects of data resampling schemes on predicting customer churn for both Random Forest (RF) and XGBoost ensembles. Data resampling schemes used include: (a) default mode, (b) random-under-sampling RUS, (c) synthetic minority oversampling technique (SMOTE), and (d) SMOTE-edited nearest neighbor (SMOTEEN). Both tree-based ensembles were constructed and trained to assess how well they performed with the chi-square feature selection mode. The result shows that RF achieved F1 0.9898, Accuracy 0.9973, Precision 0.9457, and Recall 0.9698 for the default, RUS, SMOTE, and SMOTEEN resampling, respectively. Xgboost outperformed Random Forest with F1 0.9945, Accuracy 0.9984, Precision 0.9616, and Recall 0.9890 for the default, RUS, SMOTE, and SMOTEEN, respectively. Studies support that the use of SMOTEEN resampling outperforms other schemes; while, it attributed XGBoost enhanced performance to hyper-parameter tuning of its decision trees. Retention strategies of recency-frequency-monetization were used and have been found to curb churn and improve monetization policies that will place business managers ahead of the curve of churning by customers.

This study employed knowledge discovery in databases (KDD) to extract and discover knowledge from the Benue State Polytechnic (Benpoly) admission database and used a structural causal model (SCM) ontological framework to represent the admission process in the Nigerian polytechnic education system. The SCM ontology identified important causal relations in features needed to model the admission process and was validated using the conditional independence test (CIT) criteria. The SCM ontology was further employed to identify and constrain input features causing bias in the local interpretable model-agnostic explanations (LIME) framework applied to machine learning (ML) black-box predictions. The ablation process produced more stable LIME explanations devoid of fairness bias compared to LIME without ablation, with higher prediction accuracy (91% vs. 89%) and F1 scores (95% vs. 94%). The study also compared the performance of different ML models, including Gaussian Naïve Bayes, Decision Trees, and Logistic Regression, before and after ablation. The limitation is that the SCM ontology is qualitative and context-specific, so the fair-LIME framework can only be extrapolated to similar contexts. Future work could compare other explanation frameworks like Shapley on the same dataset. Overall, this study demonstrates a novel approach to enforcing fairness in ML explanations by integrating qualitative SCM ontologies with quantitative ML/LIME methods.

The Family Hope Program (PKH) is a program that provides attention to the community, especially the health category, education category and social welfare category for poor families. The Family Hope Program (PKH) aims to reduce poverty and improve the welfare of the Indonesian population. Due to the large number of residents who want to register themselves as PKH recipients, there are residents who manipulate data or claim to be poor people in order to get PKH. If this continues to happen, and there is no preventive action, it is not impossible that many residents are not right in receiving PKH provided by the Government. One of the efforts that can be made is to test the classification of prospective PKH recipients in Bah Sorma Village. This study aims to classify prospective recipients of the Family Hope Program in Bah Sorma Village. The dataset used is data on prospective PKH recipients in Bah Sorma Village, Pematang Siantar City. This research is a comparative study of previous research using the Naïve Bayes method. The method used in this research is Data Mining with the C4.5 method which is used to see the accuracy of the best method than previous research. The accuracy result obtained by this research is 98.18%. Based on the results obtained, research with the case of classification of prospective PKH recipients in Bah Sorma Village using the C4.5 Algorithm gets better accuracy than previous research using Naïve Bayes obtaining an accuracy of 80%.

Skin diseases are highly prevalent and transmissible. It has been one of the major health problems that most people face. The diseases are dangerous to the skin and tend to spread over time. A patient can be cured of these skin diseases if they are detected on time and treated early. However, it is difficult to identify these diseases and provide the right medications. This study's research objectives involve developing an ensemble machine learning based model for classifying Erythemato-Squamous Diseases (ESD). The ensemble techniques combine five different classifiers, Naïve Bayes, Support Vector Classifier, Decision Tree, Random Forest, and Gradient Boosting, by merging their predictions and utilizing them as input features for a meta-classifier during training. We tested and validated the ensemble model using the dataset from the University of California, Irvine (UCI) repository to assess its effectiveness. The Individual classifiers achieved different accuracies: Naïve Bayes (85.41%), Support Vector Machine (98.61%), Random Forest (97.91%), Decision Tree (95.13%), Gradient Boosting (95.83%). The stacking method yielded a higher accuracy of 99.30% and a precision of 1.00, recall of 0.96, F1 score of 0.97, and specificity of 1.00 compared to the base models. The study confirms the effectiveness of ensemble learning techniques in classifying ESD.

The rapid growth of digital technologies has significantly increased the complexity and frequency of cyber threats, making network security a critical concern in modern information systems. Traditional security approaches, such as rule-based and signature-based systems, are often limited in detecting sophisticated and unknown attacks. Therefore, this study proposes an Anomaly-Based Intrusion Detection System (AbIDS) utilizing machine learning and deep learning techniques to enhance detection capabilities. The research adopts a Design Science Research approach, involving stages of problem identification, data collection, preprocessing, model development, system implementation, and evaluation. Several models, including Decision Tree (DT), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), are implemented and compared. The results indicate that deep learning models, particularly LSTM and CNN, outperform traditional machine learning methods in terms of accuracy, precision, recall, and F1-score, while maintaining a lower false positive rate. Additionally, the integration of incremental learning enables the system to adapt to new attack patterns without requiring complete retraining, improving scalability and real-time performance. Despite the promising results, challenges such as computational complexity and false positives remain. Overall, the proposed IDS model demonstrates strong potential as an effective and adaptive solution for enhancing network security in dynamic environments.