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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

Author-ID : 2670818

Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Neuroscience Nursing Public Health Social Sciences Veterinary Other

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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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (481.224 KB)

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.

Pemasaran Tas Purun Berbasis Web Yuda Perwira; Wira Apriani; Fiqri Irhami; Kicky Maulana
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 3 No. 2.2 (2023): Jurnal Pengabdian kepada Masyarakat Nusantara
Publisher : Cv. Utility Project Solution

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In this digital era, business people must be creative and innovative in marketing their products to be able to survive against competitors and get good profits, one of which is to use information technology to transact or introduce their products. Nasti Purun is a housing business in the Perbaungan area that produces purun into bags. , hats and other products made from purun, the purpose of this service is to create a website to be able to transact online between nasti purun and consumers and create a digital catalog so that it can be easily accessed by customers without having to come to a location and can reach a wider market again, so that businesses can develop and create jobs for local residents.

Decision Support System for Determining the Best Homeroom Teacher at SMA Negeri 1 Perbaungan Using the Simple Additive Weighting Method Wira Apriani
Jurnal ICT : Information and Communication Technologies Vol. 13 No. 1 (2022): April, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Decision Support System (DSS) is a procedure-based model to process data and assist management in making decisions. Computer-based Decision Support System to improve decision-making ability to solve quasi-structured or unstructured problems. One of the methods that can be used to solve problems in this DSS is the Simple Additive Weighting (SAW) method. The SAW method is a method that uses weighted addition. With this method the optimal solution is sought from a number of alternatives and certain criteria. This application is desktop-based so that it can be used by users and aims to make it easier for users to need information and solutions to help choose the best homeroom teacher

Application Of Data Mining For Prediction Of Students Out Of College With The Method Algorithm C4.5 suandi daulay; wira apriani; yuda perwira
Jurnal ICT : Information and Communication Technologies Vol. 13 No. 1 (2022): April, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

This research was conducted to predict students dropping out of private universities, the student department needs to pay attention to students who have the potential to drop out so that they can be detected faster to make an approach with students so they don't drop out of college, with the help of data mining so that data -The data collected is useful information and with the C4.5 method so that predictions become accurate to detect students who have the potential to drop out of college. As for the results of this study, it is known that the most influential variable for students dropping out of college is marked by UKT Not Current Then Often Absent Then Gender Male whose graduation year is not recently graduated (not fresh graduate)

Analysis Of Student Sentiment On Lecturers Teaching Using The Fuzzy Tsukamoto Method Wira Apriani; Sulastri Sulastri; Finna Maulidina
Jurnal Info Sains : Informatika dan Sains Vol. 13 No. 02 (2023): Jurnal Info Sains : Informatika dan Sains , Edition September 2023
Publisher : SEAN Institute

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Analysis of student sentiment on lecturer teaching performance This study aims to analyze student sentiment on lecturer teaching performance in a good university. The method used in this research is sentiment analysis using the Fuzzy Tsukamoto Method. The data were obtained from the results of a questionnaire conducted on students regarding the teaching performance of lecturers in the last semester. The data is then processed and analyzed using a classification algorithm to classify student sentiment into positive, negative, or neutral towards lecturer teaching performance. is student sentiment towards lecturers, from the teaching quality testing data = 2 positive words (8), Material Availability = 2 neutral words (7), Student and lecturer interaction = 1 negative word (4), Lecturer Feedback Quality = 2 negative word (3) from the input the output result is a value of 3 and is in the negative set, so the result of the test is negative sentiment

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

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