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All Journal Jurnal Mantik Penusa Jurnal Mantik TRIDARMA: Pengabdian Kepada Masyarakat (PkM) INFOKUM BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Info Sains : Informatika dan Sains Jurnal ICT : Information and Communication Technologies
Wira Apriani
STMIK Pelita Nusantara
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CLUSTERIZATION OF DIABETES HEALTH INDICATORS WITH K-MEANS CLUSTER ALGORITHM Yuda Perwira; Wira Apriani; Ahmad Zein; Sinta Lia Alfaris
INFOKUM Vol. 10 No. 03 (2022): August, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

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Abstract

Diabetes is one of the most common chronic diseases in the world, affecting millions of people every year and placing a significant financial burden on the economy. Diabetes is a serious chronic disease in which individuals lose the ability to effectively regulate blood glucose levels, and it can lead to decreased quality of life and life expectancy.The purpose of this study is to cluster diabetes health to be able to cluster quickly whether a person has diabetes, or prediabetes or is free from diabetes so that diabetes can be anticipated as early as possible. The data used in this study is the result of a survey from the US Behavioral Risk Factor Surveillance System (BRFSS) in 2015 which contains a net data collection of 253,680 survey responses to the CDC's 2015 BRFSS. The target variable Diabetes_012 has 3 classes. 0 for no diabetes or only during pregnancy, 1 for prediabetes, and 2 for diabetes.The method used in this study is the K-Means Clustering method where this method has been quite successful and is widely used by many researchers to cluster and predict, indicators of a person's diabetes health can be grouped into 3 groups, namely the health of people without diabetes, the health of people with prediabetes. and the health of people with type 2 diabetes, as for the results of the clustering of 2349 data, there are 235 people with health without diabetes, 1816 people with prediabetes health conditions and 298 people with type 2 diabetes.
PREDICTION OF STUDENTS WORKING ACCORDING TO COMPETENCY WITH THE C4.5 ALGORITMA ALGORITHM wira apriani; Yuda Perwira
INFOKUM Vol. 10 No. 4 (2022): October, computer, information and engineering
Publisher : Sean Institute

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In this digital era, data is very important to be extracted in order to mine knowledge in it, the resulting data is then used to make preparations, or decisions for the future in order to get the best planning, in universities there are a lot of data that can be mined, one of which is alumni tracer data, alumni tracer data can be used to determine the characteristics of students who work according to their competencies or not by using data mining techniques, the purpose of this study is to determine the characteristics of students who after graduation can work according to their competencies , if these characteristics or criteria can be known well, then the student department can direct students who are actively learning to be able to pursue these criteria so that later these students can work according to their competencies and according to their interests. commitment to produce graduates as superior human resources who work according to their competencies. The stages of this method are collecting data from the alumni tracer questionnaire from STMIK Pelita Nusantara then processing it with data mining techniques, namely data selection, data cleaning, data transformation, then the data is processed by data mining technique classification decision tree algorithm C4.5 then evaluation and simulation with rapid miner to validate manual calculations then build the system and implement the system.
Co-Authors Agustina Simangunsong Ahmad Zein Bosker Sinaga Daulay, Suandi Ellisa Purba Finna Maulidina Fiqri Irhami Humala Simangunsong Kicky Maulana Nuraisana Nuraisana Sinta Lia Alfaris Sulastri Sulastri Yuda Perwira Yuni Endah Kurniati