This study aims to determine the effect of work motivation and work discipline on employee performance at PT Tiki Jalur Nugraha Ekakurir (JNE) Kampung Lalang, a logistics and delivery service company. Optimal employee performance is the result of a combination of internal drive (motivation) and compliance with work regulations (discipline). This research uses a quantitative approach with a survey method. Data were collected through questionnaires distributed to respondents who are operational employees at JNE Kampung Lalang. Data were analyzed using multiple linear regression assisted by SPSS software. The results showed that work motivation has a positive and significant effect on employee performance with a coefficient value of 1.000 and a significance of 0.000. Conversely, work discipline has a negative but significant effect on employee performance with a coefficient value of -0.388 and a significance of 0.000. This finding indicates that increasing work motivation can encourage increased employee performance, while negative results from work discipline. This study emphasizes the importance of management in properly understanding the internal factors that influence performance so that the policies implemented do not have a counterproductive impact.
 

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).

This community service activity aims to overcome the problems of UMKM in Wonokerto village, Bandar sub-district. The main problems faced by UMKM players are packaging, lack of business permits, and digital marketing of products. This is the background for community service activities carried out by a team of accounting lecturers at the Institute of Technology and Science Nahdlatul Ulama Pekalongan. The theme of this service is the UMKM Empowerment Workshop in Wonokerto Village in the 5.0 Era Using Digital Marketing Strategies. The aim of this community service is to educate UMKM players about the concept of making attractive packaging, how to obtain business permits and how to market products digitally. The methods used in community service activities are field observation, preparing activity plans, creating activity materials, implementing activities as well as monitoring and evaluation. This service can open the minds of UMKM in Wonokerto village, Bandar sub-district, so that they can develop themselves through digital marketing training which can later improve economic prosperity in the future.