Information and Communication Technology Competencies Clustering For Students For Vocational High School Students  Using K-Means Clustering Algorithm

Faisal, Muhammad; Nurdin, Nurdin; Fajriana, Fajriana; Fitri, Zahratul

doi:10.52088/ijesty.v2i3.318

Cited by 4 publications

(3 citation statements)

References 9 publications

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“…Adapun hal ini dapat dibuktikan dengan relatif cukup banyaknya siswa lulusan SMA dan SMK yang mengadu nasib dengan mengikuti Ujian Masuk Perguruan Tinggi Negeri [9]. Namun pengenalan program studi pada perguruan tinggi pada siswa kelas XII Sekolah Menengah Atas dan Sekolah Menengah Kejuruan [10] saat ini dirasa masih sangat kurang. Sedangkan proses penentuan program studi yang akan ditempuh pada saat melanjutkan pendidikan di perguruan tinggi merupakan sebuah keputusan besar, karena merupakan sebuah keputusan yang berpengaruh jangka panjang yang akan menentukan kehidupan siswa tersebut dimasa yang akan datang.…”

Section: Pendahuluanunclassified

Application of the Profile Matching Analysis Method in Decision Support Systems for Study Program Recommendations

Rasyada,

Nurdin,

Fajriana

2024

SISTEMASI

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Currently, many prospective students are still confused about which study program to choose. One of the problematic factors is the lack of references from prospective students about the contents of each study program. This research aims to build a decision support system for study program recommendations using the Profile Matching Analysis method. The benefits of this research can provide final results in the form of recommended study program scores that are most suitable for prospective students. There are 5 assessment criteria used in this research, namely Language Values, Logic/IT Values, Science Values, Practice Values, and Social Values. The methods or stages carried out in this research are: data collection, system flowchart design, application of the Profile Matching Analysis method and system implementation. In this research, the recommendation results were obtained for a student with the name Afni Ruhmini based on the results of system calculations using the Profile Matching Analysis method, obtaining recommendation results for the Public Administration study program with a score = 5.3, the Marine Science study program with a score = 5.9, and the Agribusiness study program with a score = 5.6, the Physics Education study program with a score = 5.7 and the Law study program with a score = 4.8. The Profile Matching Analysis method is very suitable to be applied to solve problems in decision support system research for study program recommendations.

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Section: Pendahuluanunclassified

Application of the Profile Matching Analysis Method in Decision Support Systems for Study Program Recommendations

Rasyada,

Nurdin,

Fajriana

2024

SISTEMASI

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“…Research conducted by (Elizawati & Lesmana, 2017) clustering method with the K-Means Algorithm can be used to group students according to their interests in productive subjects in class XII based on their report card scores when they were in class X. Another study conducted by (Faisal et al, 2022) the K-Means Clustering Algorithm, this study used 3 clusters namely very competent, competent and less competent clusters with the results of testing 10 students in cluster 1 with predicate very competent, 64 students in cluster 2 with a competent predicate, and 10 students in cluster 3 with a less competent predicate.…”

Section: Literature Reviewmentioning

confidence: 99%

Application of the K-Means Clustering Algorithm to Determine Student Academic Ability Levels during Covid-19

Bustami,

Nurdin,

Taufiq

et al. 2023

J. Adv. Zool.

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Since the existence of the COVID-19 pandemic in Indonesia, various ways and efforts have been made by the government to prevent the spread of the corona virus. One of them is by implementing an online learning system, so that it causes differences in the learning process that is usually carried out by lecturer face to face with students. Changes in the learning process are expected to be carried out effectively and efficiently. The purpose of this research is to find out how the level of students' academic ability is during online learning in the Informatics Engineering study program at Universitas Malikussaleh and aims to produce a system model that can cluster the level of student academic ability using the K-Means Clustering Algorithm. The variables used in this study are 3 parameters, namely the teaching and learning process, facilities and infrastructure and learning outcomes. The stages carried out in this study were collecting questionnaire data filled out by 200 students. The data obtained would be processed using the K-Means Clustering Algorithm and forming 3 clusters, namely low, medium and high. The clustering results of 200 data obtained 27 students having low academic ability levels (Cluster 1), 87 students having moderate academic ability levels (Cluster 2) and as many as 86 students having high academic ability levels (Cluster 3). The contribution of this research is to help the management of the Informatics Engineering Study Program at Universitas Malikussaleh to find out how the level of student academic ability is during the online learning period during the COVID-19 pandemic, so that evaluation of learning can be carried out online.

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“…K-Means is the most popular clustering method widely used in various fields because it is simple, easy to implement, has extensive data clustering capabilities, and can handle outlier data [20]. Meant to divide data into one or more groups, with similarities in one group and disparities in another [21]. The K-Means algorithm is one of the unsupervised learning algorithms and follows the most commonly used partition method for grouping because of its simplicity and efficiency [22], [23].…”

Section: K-means Algorithmmentioning

confidence: 99%

Analysis of Delivery Data by Medical Staff Using The K-Means Algorithm in Sleman District

Khasanah

Yanti

Sari

et al. 2022

Int. J. Eng. Scie. and Inform. Technology.

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The process of childbirth has many factors that result in the death of the baby and the mother during the delivery process, namely the lack of medical staff or health workers (midwife, doctor, or another paramedic). There needs to be an analysis of the delivery process assisted by medical staff. This analysis maps the readiness of medical staff with the needs in the field. Both natural and cesarean births have the same main goal, to make labor run smoothly and ensure that the mother and baby are safe. Deliveries assisted by health workers use secure, clean, and sterile equipment to prevent infection and other health hazards. The hope is to minimize the number of mothers who are not helped during childbirth. This study aims to analyze data on deliveries assisted by medical staff for case studies in Sleman District, Province of Yogyakarta Special Administrative Region, Indonesia, with the K-Means Algorithm. K-means is an unsupervised learning algorithm that has a function to group data into data clusters. This algorithm can accept data without any category labels, the learning process requires a relatively fast time, is quite easy to understand and implement, and the algorithm is quite popular. The research used 13,869 data in 2018. In 2019, the decrease in the number of mothers giving birth from 13,470 who were rescued was 13,469. The 2018 data produced 3 (three) clusters. In 2019 data produced 4 (four) clusters. With different levels of levels assisted by medical staff starting from the high, medium, and low groups.

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Information and Communication Technology Competencies Clustering For Students For Vocational High School Students Using K-Means Clustering Algorithm

Cited by 4 publications

References 9 publications

Application of the Profile Matching Analysis Method in Decision Support Systems for Study Program Recommendations

Application of the Profile Matching Analysis Method in Decision Support Systems for Study Program Recommendations

Application of the K-Means Clustering Algorithm to Determine Student Academic Ability Levels during Covid-19

Analysis of Delivery Data by Medical Staff Using The K-Means Algorithm in Sleman District

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