Article Per Year (5 Year)
p-Index From 2021 - 2026
1.704
P-Index
This Author published in this journals
All Journal Jiko (Jurnal Informatika dan komputer) JURTEKSI International Journal of Basic and Applied Science Jurnal Sains Teknologi dan Sistem Informasi Prosiding Snastikom INTERNATIONAL JOURNAL OF MECHANICAL COMPUTATIONAL AND MANUFACTURING RESEARCH Journal of Computer Science and Research
M. Safii
STIKOM Tunas Bangsa
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Library & Information Science Mechanical Engineering Physics

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : International Journal of Basic and Applied Science

 1 
Application Of K-Means Clustering Algorithm On Population Growth In Simalungun Regency Murniati Rambe; M. Safii; Irawan Irawan
International Journal of Basic and Applied Science Vol. 10 No. 2 (2021): September: Basic and Applied Science
Publisher : Institute of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v10i2.55

Abstract

Population growth is a condition when the population increases from previous years. Population growth has several variables, namely birth, death and migration rates. Positive population growth indicates an increase in population and vice versa. Population growth is caused by a high birth rate with a decrease in the death rate. The high rate of population growth and occurs in a fast period of time is what triggers a population explosion which is closely related to an increase in poverty, unemployment, crime, slum settlements, hunger and other social problems. An increase in the poverty rate occurs when high population growth is not matched by good economic growth accompanied by equitable distribution of income. An increase in unemployment occurs if the increase in population with reduced availability of adequate employment can lead to an increase in criminal cases. By knowing these problems, Data Mining is needed to classify aid receipts, build jobs. by using the K-Means method in clustering the population growth rate. The K-Means method can assist the Government in making decisions and the information needed to solve the problem of population growth and record all densely populated areas in an appropriate way.
A Oil Palm Harvest Grouping Using K-Medoids Algorithm Dessy Dwi Angraini; M. Safii; Fitri Anggraini
International Journal of Basic and Applied Science Vol. 10 No. 2 (2021): September: Basic and Applied Science
Publisher : Institute of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v10i2.56

Abstract

Oil palm (Elaies Guinnnsiss Jacq) is one of the important industrial crops producing cooking oil, industrial oil, and fuel. Indonesia is the largest palm oil producer in the world. The rest of the processing of oil palm fruit is called janjang. Janjang also serves to be used as compost. The data that is processed in this research is the harvest data at PT. Surya Intisariraya Mandau. Data mining is the process of looking for patterns or information in selected data using certain techniques or methods. The processing steps are grouped using the K-Medoids method and then the data will be processed using RapidMiner tools. Where this grouping is done to minimize the amount of similarity of data and appropriate so that it becomes more valid data. This study aims to simplify the grouping of harvest data based on high, medium and low clusters.
Application Of Naive Bayes Algorithm In Classification Of Child Nutrition At The Simalungun Health Office Susi Septi Hardiani; M. Safii; Dedi Suhendro
International Journal of Basic and Applied Science Vol. 10 No. 3 (2021): December: Basic and Applied Science
Publisher : Institute of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v10i3.57

Abstract

Toddlers are among the most vulnerable groups to nutritional problems when viewed from the point of view of health and nutrition problems, while at this time they are experiencing a cycle of relatively rapid growth and development. .7% is quite high where the number of births is relatively large. Researchers try to classify 10 toddlers using WEKA to find out whether they have nutritional disorders or are normal by using 5 attributes as system input and a class namely nutrition which divides this class into 4 namely bad, less, good and more with the amount of training data 219 data then data compared with the actual nutritional conditions and obtained an accuracy of 60% and an error of 40% with these results it can be concluded that the accuracy is not too good. Based on this, it is hoped that the results of this classification can help further research in classifying the nutrition of children under five.
Co-Authors Abdi Rahim Damanik Afifah Wulandari Akbar Fahri Hambali Aron Saputra Sirait Bahrudi Efendi Damanik Dedi Suhendro Dessy Dwi Angraini elfrida lucyana hutahaean Fitri Anggraini Indra Gunawan Irawan Irawan Lena Sapura Muhammad Rafai Murniati Rambe Saputra Ramadani Sundari Retno Andani Susi Septi Hardiani Syahrial Azmi Pohan Tri Febri Damayanti Wanto, Anjar