Impaired blood flow caused by coronary artery occlusion due to thrombus can cause damage to the heart muscle which is often called Myocardial Infarction (MI). To avoid the complexity of MI diseases such as heart failure or arrhythmias that can cause death, it is necessary to diagnose and detect them early. An electrocardiogram (ECG) signal is a diagnostic medium that can be used to detect acute MI. Diagnostics with the help of data science is very useful in detecting MI in ECG signals. The purpose of study is to propose an automatic classification framework for Myocardial Infarction (MI) with 15 lead ECG signals consisting of 12 standard leads and 3 Frank leads. This research contributes to the improvement of classification performance for 10 MI classes and normal classes. The PTB dataset trained with the proposed 1D-CNN architecture was able to produce average accuracy, sensitivity, specificity, precision and F1-score of 99.98%, 99.91%, 99.99%, 99.91, and 99.91%. From the evaluation results, it can be concluded that the proposed 1D-CNN architecture is able to provide excellent performance in detecting MI attacks.

Rekam Medis Elektronik (RME) merupakan bagian dari SIM-RS yang berisi catatan atau rekam medis pasien yang dibuat menggunakan sistem komputerisasi serta diperuntukkan bagi penyelenggaraan Rekam Medis di setiap fasilitas layanan kesehatan. RSUD Siti Fatimah merupakan Rumah Sakit milik Pemerintah Provinsi Sumatera Selatan, resmi berdiri pada tahun 2018. Sistem Infomasi RME di RSUD Siti Fatimah saat ini sudah dikembangkan menjadi sistem informasi yang lebih luas dan terintegrasi dengan berbagai aplikasi pendukung, sehingga perlu adanya kegiatan pelatihan baik teori maupun praktik untuk pihak-pihak terkait yang menggunakan sistem ini. Pelatihan ini bertujuan untuk meningkatkan kualitas dan pengetahuan SDM RS tentang penggunaan RME, agar dapat meningkatkan profesionalisme dan pelayanan RS kepada masyarakat. Kegiatan pelatihan ini dilaksanakan secara tatap muka. Metode pelaksanaan kegiatan menggunakan pendekatan pelatihan dalam bentuk ceramah, praktik dan tanya jawab dengan menggunakan lima tahapan dari metode Action Research yaitu, diagnosa, perencanaan, pelaksanaan, evaluasi kegiatan dan pembelajaran. Hasil dari kegiatan pelatihan ini berdampak positif bagi peserta kegiatan yang dapat dilihat dari antusiasme peserta serta hasil wawancara dan tanya jawab dengan hasil 90% peserta pelatihan dapat memahami dan mampu menggunakan sistem informasi RME, baik secara teori maupun praktik.

Myocardial Infarction (MI) is a leading cause of global mortality, necessitating efficient diagnostic methods. This study develops a simplified one-dimensional Convolutional Neural Network (1D-CNN) model for classifying MI using 12-lead ECG signals from the PTB-XL dataset. The research focuses on reducing computational complexity by limiting convolutional layers while maintaining high accuracy. The proposed model processes ECG signals of varying lengths (600–1000 samples), identifying 700 samples as optimal, achieving an average accuracy of 96.18%, sensitivity of 82.84%, specificity of 97.63%, precision of 84.13%, and an F1-score of 82.68%. Leads V5 and V6 demonstrate superior performance in detecting MI, while other leads, such as I and AVL, require further optimization. By combining precise signal segmentation and an efficient CNN architecture, this model minimizes computational load without compromising performance, making it a strong candidate for real-time clinical applications. The findings highlight the importance of signal length optimization and simplified architecture in enhancing early MI detection.

Employee performance appraisal is one of the efforts made by the company so that employees work well and produce high quality and dedicated employees. However, the assessment process that does not use assessment criteria and parameters is considered less objective because it assesses the subjectivity of each employee in the process of selecting the best employee. Decision Support System (SPK) is a decision-making process assisted by a computer system. The Simple Additive Weighting (SAW) method is one of the methods in SPK that can evaluate employees based on alternatives and criteria and predetermined weights. The SAW method can process large and unlimited amounts of data. SAW method only gives the maximum value or the highest value as the best. The calculation process using SAW on the selection of the best employee is based on five criteria, namely attendance, responsibility, initiative, teamwork, and compliance. This Decision Making System (SPK) uses the Python programming language which is implemented using Google Collaboratorry so that it can be used anywhere as long as it is connected to the internet.