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All Journal Jurnal Teknologi Dan Sistem Informasi Bisnis Indonesian Journal of Pharmacology and Therapy
Juwita Permata Sari
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Computer Science & IT Electrical & Electronics Engineering Immunology & microbiology Medicine & Pharmacology

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Effectiveness of nifedipine compared with other antihypertension on hypertension during pregnancy Juwita Permata Sari; Aisyah Nur Sapriati; Cyndi Yulanda Putri; Satya Prima Kustanto; Umu Kholifah
Indonesian Journal of Pharmacology and Therapy Vol 3 No 1 (2022)
Publisher : Faculty of Medicine, Public Health, and Nursing Universitas Gadjah Mada and Indonesian Pharmacologist Association or Ikatan Farmakologi Indonesia (IKAFARI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijpther.3248

Abstract

Hypertension is the most common complication of pregnancy. It is a major cause of maternal, fetal, and neonatal morbidity and mortality. In this article, the effectiveness of nifedipine compared with other antihypertensives in pregnant women with hypertension was reviewed. The randomized control trial (RCT) of nifedipine and other antihypertension in pregnancy without complications published from 2016 to 2021 in Google Scholar, Cochrane and PubMed were gathered. It was reported that antihypertensives administration to pregnant women with hypertension was very meaningful both for the mother herself and for the fetus or baby. Furthermore, nifedipine has better effectiveness in lowering blood pressure compared to other antihypertensives such as IV labetalol, oral labetalol, IV hydralazine, methyldopa in the treatment of preeclampsia, severe preeclampsia, severe pre-eclampsia/eclampsia, chronic hypertension, hypertension emergency, and severe hypertension.
Algoritma K-Means Menggunakan Metode Elbow Untuk Mengelompokkan Kinerja Performance Index Berdasarkan Dataset Absensi Pegawai Bunga Sabila; Juwita Permata Sari; Kholis; Darman Sahputra Harefa; Dedy Hartama
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2119

Abstract

Attendance is quantitative data that reflects the level of employee discipline and has the potential to be used as a basis for performance evaluation. However, in many agencies, attendance data has not been optimally utilized in making decisions related to employee management. This study aims to classify employee performance based on attendance data using the K-Means Clustering algorithm with the Elbow Method approach. The data analyzed consisted of 57 employees with attributes of the number of tardiness, absence, and blank absences. The research process starts from data collection, pre-processing, to clustering process. Determination of the optimal number of clusters is done with the Elbow Method using Python, which shows the best K value is four. Clustering was done using RapidMiner software, by applying a visual-based workflow to group employees based on similarity in attendance patterns. The final results show the formation of four clusters with different Key Performance Index (KPI) characteristics: excellent, good, fair, and poor. These clusters reflect the level of employee discipline that can be used as a basis for objective managerial evaluation and decision-making. This research shows that the application of data mining-based clustering methods can be a tool in analyzing employee performance in an organizational environment.
Co-Authors Aisyah Nur Sapriati Bunga Sabila Cyndi Yulanda Putri Darman Sahputra Harefa Dedy Hartama Kholis Satya Prima Kustanto Umu Kholifah