Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JURIKOM (Jurnal Riset Komputer)
Yohanni Syahra
STMIK Triguna Dharma, Medan
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Klasterisasi Data Penanganan dan Pelayanan Kesehatan Masyarakat dengan Algoritma K-Means Yohanni Syahra; Dedi Rahman Habibie; Mardiah Nasution; Hanifah Nur Nasution; Asyahri Hadi Nasyuha
JURIKOM (Jurnal Riset Komputer) Vol 9, No 5 (2022): Oktober 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v9i5.4882

Abstract

Quality public health services are one of the characteristics of the country's successful development in the health sector. The Health Office has formulated a number of methods to determine the level of progress of health development at the center to the sub-districts. Every year the Lubuk Pakam Health Office collects public health data for processing so that it produces a ranking of regions with the predicate of healthy districts/cities. Data mining is a process used to extract and identify useful information and obtain some important information from data in analyzing public health data. Furthermore, the algorithm that will be used for data mining management in the case of analyzing public health data and used for cluster formation is the K-Means algorithm. The results obtained in the data grouping there are categories of patient assessment levels Very Satisfied, Satisfied, and Dissatisfied. From the results of the K-Means method, it can be concluded to improve services and health care as for the results of grouping the level of satisfaction
Sistem Pakar Dalam Mendiagnosis Penyakit Leishmaniasis Menerapkan Metode Case-Based Reasoning (CBR) Asyahri Hadi Nasyuha; Yohanni Syahra; Moch Iswan Perangin-Angin; Dedi Rahman Habibie; Aloysius Agus Subagyo
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.6057

Abstract

Leishmaniasis caused by protozoa of the genus Leishmania, is one of the neglected zoonoses. Sand flies (mosquitoes) of the genus Phlebotomus transmit Leishmaniasis. Leishmaniasis has attacked 98 countries and is widespread in tropical, subtropical and Mediterranean regions. Because it primarily affects endemic areas in developing countries, which often have dense populations, malnutrition, poor sanitation, and a lack of human resources for disease control, prevention, and treatment, leishmaniasis is considered a neglected tropical disease. Leishmaniasis is one of the neglected tropical diseases, it is based on the low level of public awareness and scarcity of funds for research to develop effective disease control methods. Leishmaniasis is a difficult condition to treat because the general public is not well aware of it. Based on these problems, an expert system for the diagnosis of leishmaniasis was studied. An expert system is a program that can simulate the thought process of a computer expert and solve problems that are usually handled by experts. Knowledge stored in expert systems is often obtained from human subject matter experts. By using the help of expert systems and calculations carried out using the Case-Based Reasoning (CBR) approach, this study aims to facilitate the diagnosis of Leishmaniasis based on the patient's perceived input. Approach (CBR) can facilitate diagnosis then produce more precise diagnostic results. The test results with the Case-Based Reasoning approach found that the type of disease Cutaneous Leishmaniasis had the highest similarity value with a similarity value of 73%.
Clustering Analysis of Poverty Levels in North Sumatra Province Using the Application of Data Mining with the K-Means Algorithm Widyastuti Andriyani; Asyahri Hadi Nasyuha; Yohanni Syahra; Bagas Triaji
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6867

Abstract

North Sumatra, as one of the largest provinces in Indonesia, has serious challenges related to poverty that require serious attention North Sumatra, as one of the largest provinces in Indonesia, has serious challenges related to poverty that require serious attention and in-depth analysis. Thus, research on poverty levels in this province becomes very relevant and urgent. Therefore, a more in-depth analysis is needed regarding poverty levels in various regions within this province using data mining methods. The data mining approach is a way to gain understanding from large amounts of data. In the context of the problem of poverty levels, data mining has the potential to help reveal patterns that may be hidden in economic and social data. One algorithm that is often applied in clustering analysis is the K-Means algorithm. The K-Means algorithm is a clustering method that is widely used in data analysis and allows grouping data based on similar characteristics, so that it can be used to classify areas with similar levels of poverty. The results of this research show that data mining with the application of the K-Means algorithm can help produce more effective decisions in analyzing clustering of poverty levels in North Sumatra Province involving the use of data over a ten-year period, namely from 2013 to 2022, which records the number of poor people based on District and city. 3 groups were produced, namely 3 levels of poverty, including relatively stable, very vulnerable and vulnerable. Data from 33 districts or cities in North Sumatra resulted in a poverty level clustering of 1 city that was very vulnerable, 4 cities that were vulnerable and 27 cities that were relatively stable.
Co-Authors Aloysius Agus Subagyo Andriyani, Widyastuti Asyahri Hadi Nasyuha Bagas Triaji Dedi Rahman Habibie Mardiah Nasution Moch Iswan Perangin-Angin Nasution, Hanifah Nur