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All Journal Jurnal Sosial Ekonomi Pertanian JUSIFO : Jurnal Sistem Informasi Annual Research Seminar CCIT (Creative Communication and Innovative Technology) Journal Sriwijaya Journal of Informatics and Applications Jurnal Generic
Yunita Yunita
Universitas Sriwijaya
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Social Sciences

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Journal : CCIT (Creative Communication and Innovative Technology) Journal

 1 
Implementation of K-Nearest Neighbor Method and Weighted Product Method in Determining High School Majors Kartika Rahmayani; Yunita Yunita; Kanda Januar
CCIT Journal Vol 15 No 2 (2022): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (612.893 KB) | DOI: 10.33050/ccit.v15i2.2116

Abstract

High school education in Indonesia is divided into several majors that have been determined by the ministry of education. The Major will also have an influence on students when they will continue their education to the university level. Therefore, students must be placed in majors that are in accordance with their abilities and desires so that they can complete their education well. To assist the school in providing advice on the division of student majors and provide more accurate results, the authors conducted research using the K-Nearest Neighbor method which will classify students so that they are classified into students majoring in science and social studies. K-Nearest Neighbor is used because it can classify student testing data in the case of class 2020 by adapting solutions from student training data in cases of class 2019 based on the data they have. Furthermore, so that student data that has been classified can be sorted based on the best value so that class division can be carried out according to the results of the sequence of students in each majors, the Weighted Product method is used. The Weighted Product method sorts student data based on criteria values that have different weight values. The results in this study provide the highest accuracy value for the K-Nearest Neighbor method using the k value configuration of 88% and the accuracy value 84% for using the Weighted Product method.
Implementation of K-Means and SAW Methods in Determining Non-Cash Food Aid Recipients Yunita Yunita; Rizki Kurniati; Desty Rodiah; Allsela Meiriza; Luh Sri Mulia Eni
CCIT Journal Vol 16 No 2 (2023): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v16i2.2525

Abstract

Determination of prospective non cash food assistance recipients, especially in Air Talas village, still uses a manual system so that in the process of determining the recipient there is a risk that the recipient will be inaccurate, so that the village government needs a system that can assist the process of determining prospective non cash food assistance recipients. This study aims to implement the K-Means and SAW methods in determining recipients of non cash food assistance in Air Talas village. The benefits of this research can help the Air Talas village government in determining and recommending prospective non cash food assistance recipients in accordance with established criteria, making it easier to filter, group, and rank appropriate population data according to criteria. In addition, this research is also useful for providing convenience to the community through data collection, clustering, and ranking in a transparent, real, and fast and accurate manner using decision support system software. The K-Means clustering method and the Simple Additive Weighting Ranking method were used in this study with data collection techniques through interviewing sources, in this case the village government, the social section of the community, and through collecting village archive data and relevant journals. The research location is Air Talas village with 316 data used. The results of the study are clustering data as much as 77 data obtained from feasible clusters. The cluster data was then tested using the accuracy value and obtained a value of 80%. Then the research is also in the form of ranking data using clustered data which obtains an accuracy value of 64%.
Co-Authors Allsela Meiriza, Allsela Angga Adiningrat Mulyanata Desty Rodiah Dicky Ahmad Rizaldi Evita Hardanita Fadhlan Jiwa Hanuraga Jerry Satiamy Saputra Kanda Januar Miraswan Kartika Rahmayani Luh Sri Mulia Eni M. Fachrurrozi . Muhammad Arbi Muhammad Rizkiansyah Novi Yusliani Oktadini, Nabila Rizky Osvari Arsalan Risky Tama Putri Rizki Kurniati Rusdi Efendi Wenty Octaviani