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All Journal Jurnal Informatika Terpadu
Afdhal Kurniawan
Bina Nusantara University
Computer Science & IT Decision Sciences, Operations Research & Management Education

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Perancangan Alat Tekanan Darah Otomatis secara Berkala menggunakan Sensor MPX5050DP untuk Pasien Hipertensi Afdhal Kurniawan; Karli Eka Setiawan
Jurnal Informatika Terpadu Vol 9 No 2 (2023): September, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v9i2.993

Abstract

Regular blood pressure monitoring is crucial in caring for outpatient patients undergoing treatment. Hypertension, commonly known as high blood pressure, is a prevalent medical condition that can increase the risk of heart disease, stroke, and other vascular disorders. Patients with hypertension often require regular blood pressure measurements to monitor their health status and evaluate the effectiveness of ongoing treatment. To mitigate the risks associated with hypertension, routine health check-ups, including periodic blood pressure measurements at home, can be taken. This research aims to make a regular automatic blood pressure meter to help hypertensive patients. The design of an automatic periodic blood pressure measurement device using the MPX5050DP sensor for hypertensive patients is expected to facilitate regular monitoring and assist doctors in making accurate diagnoses. Research results indicate a larger error percentage for diastolic values than systolic values. Specifically, the error percentage is 3.45% for systolic values and 7.17% for diastolic values.
Pengelompokan Rumah Sakit di Jakarta Menggunakan Model DBSCAN, Gaussian Mixture, dan Hierarchical Clustering Karli Eka Setiawan; Afdhal Kurniawan
Jurnal Informatika Terpadu Vol 9 No 2 (2023): September, 2023
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v9i2.995

Abstract

It should be recognized that after the COVID-19 pandemic, the distribution of hospital facilities should be a primary concern in meeting society's fundamental rights to local and national governments. Therefore, it is necessary to analyze data that describes the current conditions regarding the distribution of hospital facilities, especially in Jakarta. Data analysis using a machine learning technique can provide several benefits, such as understanding the distribution of health workers, providing insight into human resource planning, monitoring hospital performance, and even becoming a reference for future targets by predicting the need for health workers. The research proposed three models of unsupervised learning, such as the density-based spatial clustering of applications with noise algorithm (DBSCAN), the Gaussian mixture model, and the agglomerative hierarchical model, to cluster a database of hospitals in Jakarta, Indonesia, that contains information on the number of medical workers in the hospital and its bed facilities. This data set was obtained from a web scraping process on the Ministry of Health of the Republic of Indonesia website in 2022. The comparison of the third unsupervised model showed that the Gaussian Mixture model produced the smallest Davies-Bouldin value with a value of 0.645713. This study is an extension of our previous investigation, in which the study is, as far as we know, the first to discuss the classification of the data on the list of hospitals in Jakarta based on information on the number of medical personnel and the number of bed facilities that make up the contribution of this research.
Co-Authors Karli Eka Setiawan