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Journal articles on the topic 'Student clustering'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 July 2025

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1

Bani Riyan, Ade, Mochamad Fikri Rifai, and Christina Juliane. "Analysis and Design of Student Point Systems to Improve Student Achievement using The Clustering Method." Journal of World Science 2, no. 3 (2023): 597–603. http://dx.doi.org/10.58344/jws.v2i3.155.

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Introduction: The student points system is an application for recording students' achievement and offense points. The lack of recording and dissemination of information on achievement results makes students less motivated to improve achievement, and the distribution of scholarships for outstanding students is inappropriate. To improve student achievement, an application program is needed that can record and disseminate student achievement data in real-time, accurate, and effective. So, the purpose in this study is to know and analyze the design of the student point system to improve student ac
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Mawarni, Qorik Indah, and Eko Setia Budi. "Implementasi Algoritma K-Means Clustering Dalam Penilaian Kedisiplinan Siswa." Jurnal Sistem Komputer dan Informatika (JSON) 3, no. 4 (2022): 522. http://dx.doi.org/10.30865/json.v3i4.4242.

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Education has a very important role for students, not just potential but noble character in the form of discipline, therefore it is necessary to group each school based on student discipline. Implementing a clustering system with the K-means method which is used to classify and determine the value of student discipline which produces a clustering output of student discipline that is beneficial for the school to prevent students from misbehaving early on. Analysis of data needs used in this study in the form of primary data obtained from a questionnaire given to students. The attributes used ar
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Qomariyah and Maria Ulfah Siregar. "Comparative Study of K-Means Clustering Algorithm and K-Medoids Clustering in Student Data Clustering." JISKA (Jurnal Informatika Sunan Kalijaga) 7, no. 2 (2022): 91–99. http://dx.doi.org/10.14421/jiska.2022.7.2.91-99.

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Universities as educational institutions have very large amounts of academic data which may not be used properly. The data needs to be analyzed to produce information that can map the distribution of students. Student academic data processing utilizes data mining processes using clustering techniques, K-Means and K-Medoids. This study aims to implement and analyze the comparison of which algorithm is more optimal based on the cluster validation test with the Davies Bouldin Index. The data used are academic data of UIN Sunan Kalijaga students in the 2013-2015 batch. In the k-Means process, the
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Agung, Triayudi, Oktri Widyarto Wahyu, Kamelia Lia, Iksal, and Sumiati. "CLG clustering for dropout prediction using log-data clustering method." International Journal of Artificial Intelligence (IJ-AI) 10, no. 3 (2021): 764–70. https://doi.org/10.11591/ijai.v10.i3.pp764-770.

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Implementation of data mining, machine learning, and statistical data from educational department commonly known as educational data mining. Most of school systems require a teacher to teach a number of students at one time. Exam are regularly being use as a method to measure student’s achievement, which is difficult to understand because examination cannot be done easily. The other hand, programming classes makes source code editing and UNIX commands able to easily detect and store automatically as log-data. Hence, rather that estimating the performance of those student based on this lo
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Li, Li, Xiangfeng Luo, and Haiyan Chen. "Clustering Students for Group-Based Learning in Foreign Language Learning." International Journal of Cognitive Informatics and Natural Intelligence 9, no. 2 (2015): 55–72. http://dx.doi.org/10.4018/ijcini.2015040104.

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Big data make it possible to mine learning information for insights regarding student performance in foreign language learning (FLL). Group-based learning is a usual method to improve FLL, whose effectiveness is greatly influenced by student groups. The general grouping method is to divide students into groups by their teacher manually, which is not timely or accurate. To overcome the shortcomings of manual methods, this paper proposes an automatic grouping method based on clustering technologies. First, the student profile is built to model the student's knowledge level, which can be updated
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Yin, XueHong. "Construction of Student Information Management System Based on Data Mining and Clustering Algorithm." Complexity 2021 (May 18, 2021): 1–11. http://dx.doi.org/10.1155/2021/4447045.

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Data mining is a new technology developed in recent years. Through data mining, people can discover the valuable and potential knowledge hidden behind the data and provide strong support for scientifically making various business decisions. This paper applies data mining technology to the college student information management system, mines student evaluation information data, uses data mining technology to design student evaluation information modules, and digs out the factors that affect student development and the various relationships between these factors. Predictive assessment of knowled
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Asroni, Asroni, Dita Kurniasari, and Aprilia Kurnianti. "The Implementation of Clustering Method With K-Means Algorithm In Grouping Data of Students’ Course Scores at Universitas Muhammadiyah Yogyakarta." Emerging Information Science and Technology 1, no. 3 (2020): 75–83. http://dx.doi.org/10.18196/eist.v1i3.13172.

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Student grades can be a reference. A large number of student grade data in a university causes data accumulation; thus, data are grouped with data mining. This study aims to classify student grade data in the second semester. Grouping student grade data was performed using the clustering method with the K-means algorithm. The research data were derived from the database of Universitas Muhammadiyah Yogyakarta. The data were students’ grades in the academic years of 2010/2011, 2011/2012, 2012/2013, 2013/2014, and 2014/2015. The analysis process was carried out using WEKA software, SQL Server 201
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Wang, Zhihui. "Higher Education Management and Student Achievement Assessment Method Based on Clustering Algorithm." Computational Intelligence and Neuroscience 2022 (July 4, 2022): 1–10. http://dx.doi.org/10.1155/2022/4703975.

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Monitoring and guiding instructional management require student performance evaluation. Traditional evaluation and analysis methods based on absolute scores, on the other hand, have certain flaws and are unable to fully reflect the information contained in student performance, thus limiting the impact of student performance evaluation on teaching and learning management. Data mining is regarded as the backbone technology for future information processing, and it introduces a new concept to the way humans use data. Schools must analyse and evaluate the performance of students in the same grade
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Purba, Winda Nia, Rinaldi Syahputra, Fine Reza Nainggolan, and Gabriel Immanuel Manullang. "IMPLEMENTASI DATA MINING CLUSTERING DALAM MENGUKUR KEPUASAN TERHADAP PELAYANAN PERPUSTAKAAN DI UNIVERSITAS PRIMA INDONESIA." Jurnal Teknik Informasi dan Komputer (Tekinkom) 7, no. 1 (2024): 318. https://doi.org/10.37600/tekinkom.v7i1.1213.

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This research aims to measure student satisfaction with Prima Indonesia University library services using data mining methods. Clustering techniques are used to group student satisfaction data based on various attributes such as service quality, resource availability, facility comfort, and interaction with library staff. Data was collected through questionnaires distributed to students. The clustering results revealed significant patterns in student satisfaction, which were analyzed to identify key factors influencing satisfaction levels. These results provide library managers with valuable in
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Li, Guozhang, Rayner Alfred, and Xue Wang. "Student Behavior Analysis and Research Model Based on Clustering Technology." Mobile Information Systems 2021 (November 5, 2021): 1–6. http://dx.doi.org/10.1155/2021/9163517.

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Now, entering the 21st century, with the continuous improvement of my country’s higher education level, the enrollment rate of all colleges and universities across the country is increasing year by year. Faced with the information management of a large number of students, the workload and work pressure of consultants at various universities have doubled. The rapid and effective development of modern computer software and hardware has also initiated and effectively developed the informatization process of universities. The student management system is the core and foundation of the entire schoo
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Sheng Cao, Sheng Cao, Songdeng Niu Sheng Cao, Guanghao Xiong Songdeng Niu, Xiaolin Qin Guanghao Xiong, and Pengfei Liu Xiaolin Qin. "Student Model and Clustering Research on Personalized E-learning." 網際網路技術學刊 22, no. 4 (2021): 935–47. http://dx.doi.org/10.53106/160792642021072204020.

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Allen, Joseph, Xudong Liu, Karthikeyan Umapathy, and Sandeep Reddivari. "Clustering Partial Lexicographic Preference Trees (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 18 (2021): 15751–52. http://dx.doi.org/10.1609/aaai.v35i18.17872.

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In this work, we consider distance-based clustering of partial lexicographic preference trees (PLP-trees), intuitive and compact graphical representations of user preferences over multi-valued attributes. To compute distances between PLP-trees, we propose a polynomial time algorithm that computes Kendall's Tau distance directly from the trees and show its efficacy compared to the brute-force algorithm. To this end, we implement several clustering methods (i.e., spectral clustering, affinity propagation, and agglomerative nesting) augmented by our distance algorithm, experiment with clustering
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Azzahra, Chairunisa, and Sriani Sriani. "Clustering of High School Students Academic Scores Using K-Means Algorithm." Journal of Information Systems and Informatics 7, no. 1 (2025): 572–86. https://doi.org/10.51519/journalisi.v7i1.1029.

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The clustering of student subject scores in senior high school is conducted using the K-Means Clustering algorithm. The issue addressed in this study is how to optimally group students based on their academic scores to help schools understand the distribution of student abilities. This clustering is essential as a foundation for evaluating and improving the learning system. The research methodology includes data collection and preprocessing, determining the optimal number of clusters using the Davies-Bouldin Index (DBI), and applying the K-Means Clustering algorithm. The analysis results indic
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Huda Aminuddin, Fattachul, Teuku Djauhari, and Arnol Arjansyah. "PENENTUAN JURUSAN PADA SMKN 1 MUARO JAMBI DENGAN METODE K-MEANS CLUSTERING." JURNAL AKADEMIKA 15, no. 1 (2022): 76–82. http://dx.doi.org/10.53564/akademika.v15i1.846.

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This research is motivated by the lack of structure in determining the majors of class X students at SMKN 1 Muaro Jambi, because in the current majors they still use the manual system, namely written exams, this makes it difficult for students to determine majors according to their abilities. The problem in this study is how to apply data mining with the K-Means method in the application of student majors at SMKN 1 Muaro Jambi. How to design an application program for determining student majors at SMKN 1 Muaro Jambi using the K-Means method. This study uses the K-Means Clustering method to obt
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Ouassif, Kheira, Benameur Ziani, Jorge Herrera-Tapia, and Chaker Abdelaziz Kerrache. "Empowering Education: Leveraging Clustering and Recommendations for Enhanced Student Insights." Education Sciences 15, no. 7 (2025): 819. https://doi.org/10.3390/educsci15070819.

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This paper introduces an unsupervised machine learning approach for student clustering and personalized recommendations in education. We employ the K-means clustering algorithm to identify distinct student groups based on behavioral engagement metrics. Unlike previous studies that relied on predefined categories, our methodology validated the number of clusters using both the elbow method and silhouette analysis, which ensured an optimal grouping structure. The clustering phase served as a foundation for deriving insights into student learning behaviors. To assess the clustering quality, we ap
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Andini, Ratih Friska Dwi, Febri Liantoni, and Aris Budianto. "Clustering Student Competencies Using the K-Means Algorithm." Ultimatics : Jurnal Teknik Informatika 17, no. 1 (2025): 99–106. https://doi.org/10.31937/ti.v17i1.4071.

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This study aims to evaluate the effectiveness of the K-Means algorithm in clustering student competencies. The subject of the study is students of the Informatics and Computer Engineering Education study program at a public university in Indonesia, with course score data representing various areas of competence as features. The K-Means algorithm is used to group student data into several clusters based on academic grade patterns. The results show that the K-Means algorithm is quite effective in identifying the initial pattern of student competence, with a Silhouette Score of 0.3489, which fall
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Meng, Xiang, Qi He, Yanhua Dong, and Hongyu Sun. "Analysis of College Students’ Consumption Behavior Data Based on Fractional-Order Firefly Optimization Clustering Algorithm." Applied Sciences 15, no. 14 (2025): 7723. https://doi.org/10.3390/app15147723.

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Data mining-based student consumption behavior analysis is an important part of smart campus construction, which could find students’ eating patterns and consumption levels. Therefore, data mining-based student consumption behavior analysis became a hot topic both in research and industry areas. For an increasing amount of data, traditional data mining algorithms are not suitable. The clustering algorithm is becoming more and more important in the field of data mining, but the traditional clustering algorithm does not take the clustering efficiency and clustering effect into consideration. In
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Elista, Kiki, Riska Evangelina Hutabarat, Jefri Ricardo Doloksaribu, and Desiana Bondar. "EFFECT OF USING CLUSTERING TECHNIQUE ON THE STUDENTS ACHIEVEMENT IN WRITING RECOUNT TEXT." ELT (English Language Teaching Prima Journal) 1, no. 2 (2020): 59–88. http://dx.doi.org/10.34012/eltp.v1i2.1352.

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ABSTRACT
 This research was conducted to investigate the effect of using clustering techniques on the student achievement in writing recount text. Therefore, an experimental research was conducted to obtain the data, the population of this study were the first school year in the academic year 2019/2020 of SMA DHARMAWANGSA Medan which has two classes consisted of 80 students. They were taken as the sample. 40 students in the experimental group were taught using clustering techniques while the other 40 students in the control group were taught using free writing technique. Writing test was
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Kurniawan, Muhammad Faizal, Devi Sugianti, Arief Soma Darmawan, Ari Putra Wibowo, and Widiyono Widiyono. "IMPLEMENTASI CLUSTERISASI UNTUK PENGELOMPOKKAN GAYA BELAJAR MAHASISWA DENGAN METODE K MODES." METHOMIKA Jurnal Manajemen Informatika dan Komputerisasi Akuntansi 8, no. 1 (2024): 20–25. http://dx.doi.org/10.46880/jmika.vol8no1.pp20-25.

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One of the keys to success in learning is determining student learning styles. Learning styles are grouped into 3, grouping learning styles based on the characteristics of students. Sample data was used for 3 classes in the artificial intelligence course with total data of 83 students who answered 36 questions. To be able to carry out student mapping using the k modes method for clustering. The K modes method is used because the data used is categorical. K modes can be used for multi-dimensional clustering and shorter computing times. With the clustering application for grouping student learni
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Kurniawan, Taufik Rahmat, Endang Chumaidiyah, and Luciana Andrawina. "Application of the k-means clustering method and simple linear regression to new student admissions as a promotion method." JURNAL INFOTEL 15, no. 1 (2023): 25–33. http://dx.doi.org/10.20895/infotel.v15i1.860.

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At private label universities in Indonesia, new students are still the main thing in terms of achieving university operational income. This study intends to group the data of ITTelkom Surabaya students by utilizing the data mining process using the k-means clustering method, then the results of the clustering are forecasted using simple linear regression to be able to predict the achievement of new students as the effect variable and year as the causative variable. The results of this study consist of 5 variables, namely student province, student study program, income of student parents, stude
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Hardika, Khusnuliawati, and Riskiana Putri Dhian. "Hybrid clustering based on multi-criteria segmentation for higher education marketing." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 19, no. 5 (2021): 1498–506. https://doi.org/10.12928/telkomnika.v19i5.18965.

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Market segmentation in higher education institutions is still rarely applied although it can assist in defining the right strategies and actions for the targeted market. The problem that often arises in market segmentation is how to exploit the preferences of students as customers. To overcome this, the combination of hybrid clustering method with multiple criteria will be applied to the case of the market segmentation for students in higher education institutions. The integration of geographic, demographic, psychographic, and behavioral criteria from students is used to get more insightful in
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Mohd Talib, Nur Izzati, Nazatul Aini Abd Majid, and Shahnorbanun Sahran. "Identification of Student Behavioral Patterns in Higher Education Using K-Means Clustering and Support Vector Machine." Applied Sciences 13, no. 5 (2023): 3267. http://dx.doi.org/10.3390/app13053267.

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In many academic fields, predicting student academic success using data mining techniques has long been a major research issue. Monitoring students in higher education institutions (HEIs) and having the ability to predict student performance is important to improve academic quality. The objective of the study is to (1) identify features that form clusters that have holistic characteristics and (2) develop and validate a prediction model for each of the clusters to predict student performance holistically. For this study, both classification and clustering methods will be used using Support Vec
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Felnditi, Isaias F. X. F. A., Desi Arisandi, and Tri Sutrisno. "PERANCANGAN SISTEM REKOMENDASI PENJURUSAN PADA SEKOLAH MENENGAH KEJURUAN SANTO PAULUS MENGGUNAKAN METODE K-MEANS CLUSTERING." Jurnal Ilmu Komputer dan Sistem Informasi 8, no. 1 (2020): 74. http://dx.doi.org/10.24912/jiksi.v8i1.11472.

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Santo Paulus Vocational School in Jakarta, is one of the Private Schools in Central Jakarta which holds majors for grade X (ten) students who will continue their studies to grade XI (eleven). The obstacle that is often found in the majors in SMK Santo Paulus Jakarta is the difficulty in determining which students meet the criteria to occupy certain majors. This is because the majors process is still done manually, so it requires quite a long time and is considered inappropriate. This study, purpose to the K-Means Clustering algorithm for the decision support system of student majors at SMK San
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Salwana, Ely, Suraya Hamid, and Norizan Mohd Yasin. "Student Academic Streaming Using Clustering Technique." Malaysian Journal of Computer Science 30, no. 4 (2017): 286–99. http://dx.doi.org/10.22452/mjcs.vol30no4.2.

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Arfyanti, Ita, Tommy Bustomi, and Ivan Haristyawan. "Implementasi Data Mining dengan Menerapkan Algoritma K-Means Clustering untuk Memberikan Rekomendasi Jurusan Kuliah Bagi Mahasiswa Baru." JURNAL MEDIA INFORMATIKA BUDIDARMA 8, no. 2 (2024): 988. http://dx.doi.org/10.30865/mib.v8i2.7429.

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At the tertiary level, a student studies in a field of expertise or major that suits his or her area of talent and interest. Choosing an inappropriate college major will have consequences for the future of the prospective new student. In choosing a major, a prospective new student should choose a major that suits his abilities, both academically and his talents. One way to overcome prospective students who are wrong in choosing this major is to use the K-Means Clustering method. The K-Means algorithm is part of clustering data mining which has the role of forming new groups based on cluster fo
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Toha, Muhamad Toha, Diana Yusuf, and Vany Terisia. "Penyebaran Mahasiswa ITB Ahmad Dahlan Jakarta Menggunakan Algoritma Clustering." Jurnal Teknologi Informasi (JUTECH) 5, no. 1 (2024): 35–46. http://dx.doi.org/10.32546/jutech.v5i1.2450.

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The development of information technology has made it possible to collect and analyze large and complex data. In the context of higher education, information about student distribution patterns can be of added value for institutions to plan and manage their resources effectively. This research aims to design a web mining system that uses a clustering algorithm to grouping the distribution of students at ITB Ahmad Dahlan. In this study, we collected data from open web sources that are relevant to students, such as student profiles, academic preferences, and extracurricular activities. The data
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Amartya Ghosh. "Evaluating Clustering Algorithms for Educational Performance." Dandao Xuebao/Journal of Ballistics 37, no. 1 (2025): 132–37. https://doi.org/10.52783/dxjb.v37.184.

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Analyzing student performance is a vital undertaking within the realm of educational data mining (EDM). This process empowers academic institutions to uncover significant trends, pinpoint students who may be struggling, and formulate effective support strategies. This scholarly article delves into the application of clustering methodologies to classify students based on various performance metrics, such as academic grades, attendance records, engagement levels, and involvement in extracurricular activities. By segmenting students into distinct groups, educators can gain a clearer understanding
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Mayasari, Eka. "THE CONTRIBUTION OF CLUSTERING TECHNIQUE IN DEVELOPING IDEAS ON A SHORT PARAGRAPH FOR THE FIRST YEAR ENGLISH EDUCATION STUDENTS OF STAI MIFTAHUL ULUM TANJUNGPINANG." EXPOSURE : JURNAL PENDIDIKAN BAHASA INGGRIS 7, no. 2 (2018): 117–26. http://dx.doi.org/10.26618/exposure.v7i2.1609.

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In writing students usually difficult in developing and expressing their ideas, they have not various sentences in writing a paragraph; they also sometimes confuse to start to make their paragraph pursuant to the topic of which given from teacher. This matter which make blase and student bored in writing. Hence from that, the researcher used a clustering technique to assist students in developing and expressing their paragraph, and help the students more variety in writing a paragraph. So that their interesting and nattier paragraph, directional and also its development pursuant to the topic o
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Mohamad Faezal Fauzan Nanda and Zaehol Fatah. "PENINGKATAN EFISIENSI PEMANTAUAN KEHADIRAN SISWA MENGGUNAKAN CLASTERING K-MEANS PADA MADRASAH I'DADIYAH SALAFIYAH SYAFI'IYAH." Jurnal Ilmiah Multidisiplin Ilmu 2, no. 1 (2025): 127–36. https://doi.org/10.69714/87vcvz50.

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This research aims to increase efficiency in monitoring student attendance at Madrasah I'dadiyah Salafiyah Syafi'iyah by utilizing the K-Means Clustering analysis method. Monitoring student attendance is still carried out conventionally, so it often takes time and is less effective in identifying overall student attendance patterns. For this reason, in this research, student attendance data collected from the madrasa attendance system was analyzed using K-Means Clustering, a machine learning technique that can group students based on their attendance patterns. This process produces several gro
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Hariyanto, Rudi, and Mohammad Zoqi Sarwani. "OPTIMIZING K-MEASN ALGORITHM USING PARTICLE SWARM OPTIMIZATION TO GROUP STUDENT LEARNING PROCESSES." Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi 6, no. 1 (2021): 65–68. http://dx.doi.org/10.25139/inform.v6i1.3459.

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In the implementation of learning, there are several factors that affect the student learning process, including internal factors, external factors, and learning approach factors. Internal factors (factors within students), for example: the physical and spiritual condition of the student. Namely: physiological aspects (body, eyes and ears) and psychological aspects (student intelligence, student attitudes, student talents, student interests and student motivation). External factors (factors from outside students), for example: environmental conditions around students. Namely: social environmen
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Diana, Anita, Atik Ariesta, Arief Wibowo, and Diva Ajeng Brillian Risaychi. "NEW STUDENT CLUSTERIZATION BASED ON NEW STUDENT ADMISSION USING DATA MINING METHOD." Jurnal Pilar Nusa Mandiri 19, no. 1 (2023): 1–10. http://dx.doi.org/10.33480/pilar.v19i1.4089.

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The process of admitting new students to the Faculty of Information Technology (FTI) at Universitas Budi Luhur produces a large amount of student data in the form of student profile data and other data. This happens causing a buildup of new student data, thus affecting the search for information on that data. This study aims to classify regular undergraduate admissions data at the Faculty of Information Technology (FTI) Universitas Budi Luhur by utilizing the data mining process using the clustering technique. The algorithm used for clustering is the K-Means algorithm. K-Means is a non-hierarc
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Yih, Jeng Ming. "Supervised Clustering Algorithm for University Student Learning Algebra." Advanced Materials Research 542-543 (June 2012): 1376–79. http://dx.doi.org/10.4028/www.scientific.net/amr.542-543.1376.

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The popular fuzzy c-means algorithm based on Euclidean distance function converges to a local minimum of the objective function, which can only be used to detect spherical structural clusters. Gustafson-Kessel clustering algorithm and Gath-Geva clustering algorithm were developed to detect non-spherical structural clusters. However, Gustafson-Kessel clustering algorithm needs added constraint of fuzzy covariance matrix, Gath-Geva clustering algorithm can only be used for the data with multivariate Gaussian distribution. In GK-algorithm, modified Mahalanobis distance with preserved volume was u
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Fahrillah Fahrillah and Zaehol Fatah. "PENGELOMPOKAN DATA NILAI SISWA MADRASAH TA’HILIYAH MENGGUNAKAN METODE K-MEANS CLUSTERING." Jurnal Riset Sistem Informasi 2, no. 1 (2025): 53–59. https://doi.org/10.69714/0v1pkz05.

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Data mining, or data mining is the process of collecting and processing data to extract important information. The stages in the data mining process are useful for finding a particular pattern from a large amount of assessment data. This goal is to find out and form student data clusters based on grades so that they become a cluster, so that the results of student clusters can be a reference in improving student grades in the next learning process. The results of the evaluation and assessment of students are carried out by teaching staff or teachers in conducting assessments during the learnin
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Peerbasha, S., and M. Mohamed Surputheen. "Study and Analysis of Data Mining Algorithms for Identifying the Students’ for Psychology Motivation." Asian Journal of Computer Science and Technology 8, S2 (2019): 83–87. http://dx.doi.org/10.51983/ajcst-2019.8.s2.2018.

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The development of many educational institutions is based on the performance of students learning and understanding capabilities. Here, we analyzed their academic profile with their grades and various cumulative attributes. The academic performance in learning their subjects could be improved by motivational approach. The analysis of student performance is carried out through knowledge-based data mining process. But, the problem is arrived by a probability of information prediction accuracy from student data set which is not accurate. Here, we propose a novel machine learning algorithm based o
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Mansur, Andi Besse Firdausiah, and Norazah Yusof. "Comparative classification of student’s academic failure through Social Network Mining and Hierarchical Clustering." Computer Engineering and Applications Journal 6, no. 2 (2017): 79–86. http://dx.doi.org/10.18495/comengapp.v6i2.204.

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Student academic failure are caused by several factors such as: family relationship, study time, absence, parent education, travel time and etc. This study observe several factors which are related to student academic failure by calculating the centrality degree between students to find the correlation between failure factors for each students. Furthermore, each student will be measured by measuring the geodesic distance for each factors for hierarchical clustering. The flow betwenness measure and hierarchical clustering show the promising result, where students who has similar factors value a
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Choque-Soto, Vanessa Maribel, Victor Dario Sosa-Jauregui, and Waldo Ibarra. "Characterization of the Dropout Student Profile Using Data Mining Techniques." Revista de Gestão Social e Ambiental 19, no. 2 (2025): e011306. https://doi.org/10.24857/rgsa.v19n2-067.

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Objective: One of the primary concerns in Educational Data Mining is student dropout rates. This study aims to investigate student dropout rates in higher education by identifying and analyzing the demographic and academic characteristics of university students who discontinue their studies. Theoretical Framework: Based on Educational Data Mining with clustering techniques, this study utilizes pattern recognition and data segmentation models to analyze dropout behavior within Informatics programs. Method: Data mining techniques were applied to a dataset that contained demographic and academic
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Nella Ane Br Sitepu, Agnesia Rointan Sijabat, Cindy Rounali Limbong, Lenny Evalina Pasaribu, Einson O.B Nainggolan, and Michael Manulang. "K-Means Clustering of Student Mid-Term and Final Exam Score Data." Journal Of Data Science 2, no. 02 (2024): 92–98. https://doi.org/10.58471/jds.v2i02.5417.

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Clustering is a method in data mining that aims to group data based on similar characteristics. This research utilises the k-means clustering algorithm to group students based on their UTS and UAS scores, making it easier for lecturers to identify students' academic abilities. With the application of this method, it is expected to form groups of students who are intelligent, less intelligent, and moderate. In addition, this research also addresses the challenges in observing student grades which are often done manually, resulting in wasted time and effort. Through the k-means clustering approa
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Maulana, Indra, and Moh Agri Triansyah. "MAPPING STUDENT LOG FILES WITH K-MEANS CLUSTERING." Asian Journal of Social and Humanities 1, no. 01 (2022): 15–25. http://dx.doi.org/10.59888/ajosh.v1i01.4.

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One of the important characteristics of an e-learning platform is that students can take the course at any time, and they are not required to complete all the available learning activities at one time. In moodle, data logs are valuable information that contains activities from course users and course teachers. The data recorded in the moodle data log can be in the form of activity data, assignment time (assignment timestamp), and ranking value or final grade (grade). Data log exploration of educational data mining can be used to facilitate monitoring and see what activities are often carried o
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Qusyairi, Muhammad, Zul Hidayatullah, and Arnila Sandi. "Penerapan K-Means Clustering Dalam Pengelompokan Prestasi Siswa Dengan Optimasi Metode Elbow." Infotek: Jurnal Informatika dan Teknologi 7, no. 2 (2024): 500–510. http://dx.doi.org/10.29408/jit.v7i2.26375.

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Student achievement is very important and meaningful in the world of education, this picture can be seen from the grouping of classrooms, determining talents and interests as well as the level of ability and willingness of students according to the abilities of each individual. This is why researchers conducted research at MIS NW 03 Pancor with data from class 4 as many as 23 students, class 5 as many as 27 students, and class 6 as many as 25 students which were related to variables in student learning achievement. In determining this matter, the school still uses conventional decision-making
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R, Jayasree, and A. Sheela Selvakumari N. "Analyzing Student Performance using Fuzzy Possibilistic C-Means Clustering Algorithm." Indian Journal of Science and Technology 16, no. 38 (2023): 3230–35. https://doi.org/10.17485/IJST/v16i38.226.

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Abstract <strong>Objectives:</strong>&nbsp;This work is to propose a more effective Fuzzy C-means clustering algorithm for predicting student performance based on their health.&nbsp;<strong>Methods:</strong>&nbsp;The standard dataset is collected from UCI repository. This study proposes FPCM-SPP clustering algorithm which is compared with traditional algorithms like K-Means, K-Medoids, and Fuzzy C-Means using student data from secondary education at two Portuguese institutions (2008). Based on the clustering accuracy, mean squared error, and cluster formation time, the performance of the clust
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Jayasree, R., and N. A. Sheela Selvakumari. "Analyzing Student Performance using Fuzzy Possibilistic C-Means Clustering Algorithm." Indian Journal Of Science And Technology 16, no. 38 (2023): 3230–35. http://dx.doi.org/10.17485/ijst/v16i38.226.

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Perani Rosyani and Fabian Syawali. "Application of Advanced Class Determination System Using K-Means Clustering Method (Case Study: SMK Al-Badar Balaraja)." International Journal of Integrative Sciences 2, no. 10 (2023): 1557–70. http://dx.doi.org/10.55927/ijis.v2i10.6347.

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This research develops an information system and application program to classify advanced students at SMK Al-Badar Balaraja using K-Means Clustering. The main problems addressed are unclear classification and the absence of criteria for advanced classes. The research aims to design the system, develop the app, and determine advanced classes based on report grades, attendance, achievements, extracurricular activities, and student organization involvement. The K-Means Clustering algorithm is used to classify student data into clusters based on final evaluations. Focusing on 180 grade 11 Office A
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Nelson, Butarbutar, Perdana Windarto Agus, Hartama Dedy, and Solikhun. "KOMPARASI KINERJA ALGORITMA FUZZY C-MEANS DAN K-MEANS DALAM PENGELOMPOKAN DATA SISWA BERDASARKAN PRESTASI NILAIAKADEMIK SISWA." Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) 1, no. 1 (2016): 46–55. https://doi.org/10.5281/zenodo.546761.

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Various efforts have been undertaken by schools to improve academic achievement students in an effort to achieve national education standards. One of which is by doing tutoring for each student, but the results have not been so satisfying. This is due to the particular school education section did not fully understand each student's ability to master an eye teaching core subjects, especially the UN. To overcome this by utilizing clustering techniques will do the data grouping students based on merit value academic sources of data obtained directly from the education department. With use cluste
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Wang, Huihui. "Research on the Construction of Portrait of Higher Vocational College Students' Groups Based on Clustering Ensemble." Advances in Education, Humanities and Social Science Research 11, no. 1 (2024): 147. http://dx.doi.org/10.56028/aehssr.11.1.147.2024.

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The rapid development of higher vocational education necessitates personalized teaching tailored to individual students. This paper presents an ensemble optimized clustering framework to classify higher vocational college students accurately. By integrating multiple clustering algorithms and employing optimization mechanisms, the framework enhances clustering quality. Experiments on real student data show that this method excels in clustering performance and adapts to various feature data types, yielding results aligned with actual situations. This research provides a data foundation for perso
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Yusoff, Marina, Muhammad Najib Bin Fathi, and . "Evaluation of Clustering Methods for Student Learning Style Based Neuro Linguistic Programming." International Journal of Engineering & Technology 7, no. 3.15 (2018): 63. http://dx.doi.org/10.14419/ijet.v7i3.15.17408.

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Students’ performance is a key point to get a better first impression during a job interview with an employer. However, there are several factors, which affect students’ performances during their study. One of them is their learning style, which is under Neurolinguistic Programming (NLP) approach. Learning style is divided into a few behavioral categories, Visual, Auditory and Kinesthetics (VAK). This paper addresses the evaluation of clustering methods for the identification of learning style based on system preferences. It starts with the distribution of questionnaires to acquire the informa
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Maulana, Indra, and Utami Rosalina. "CLUSTERING DATA NILAI UJIAN AKHIR SEMESTER MENGGUNAKAN ALGORITMA DATA MINING K-MEANS." PERISKOP : Jurnal Sains dan Ilmu Pendidikan 1, no. 2 (2020): 76–85. http://dx.doi.org/10.58660/periskop.v1i2.10.

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Analyzing student performance in education is an active and fairly new area of educational research. Mapping student performance is useful for both teachers and students. However, the factors that affect student performance need to be identified first to build the initial predictive model. The clustering of UAS scores acts as an indicator of whether the spread of knowledge from teachers to students is evenly distributed or not. This is very relevant to the state of the covid pandemic that is happening in Indonesia and the world, we can know which clusters of students are truly self-study or no
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Sreelatha, Gavini, Veeresh Dachepalli, and Gudur Sahiti. "DATA ANALYSIS FOR STUDENTS BASED ON GEOLOCATION APPROACH." International Journal of Interpreting Enigma Engineers 01, no. 03 (2024): 33–41. http://dx.doi.org/10.62674/ijiee.2024.v1i03.005.

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It is quite important for students, particularly those who are new to strange places, to find appropriate housing. Some viable solutions to this difficulty can be found in advanced analytical approaches such as geolocational analysis and clustering algorithms. The optimal student housing options in any city can be found by thoroughly exploring geolocational data, as demonstrated in this study. In order to organise housing possibilities according to criteria such as budget, proximity to amenities, and availability, we employ the popular K-Means Clustering technique. Our objective is to provide
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Triayudi, Agung, Wahyu Oktri Widyarto, Lia Kamelia, Iksal Iksal, and Sumiati Sumiati. "CLG clustering for dropout prediction using log-data clustering method." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 3 (2021): 764. http://dx.doi.org/10.11591/ijai.v10.i3.pp764-770.

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&lt;span lang="EN-US"&gt;Implementation of data mining, machine learning, and statistical data from educational department commonly known as educational data mining. Most of school systems require a teacher to teach a number of students at one time. Exam are regularly being use as a method to measure student’s achievement, which is difficult to understand because examination cannot be done easily. The other hand, programming classes makes source code editing and UNIX commands able to easily detect and store automatically as log-data. Hence, rather that estimating the performance of those stude
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Ranjan Banerjee. "Understanding Student Performance Through Clustering in Educational Data Mining." Dandao Xuebao/Journal of Ballistics 37, no. 1 (2025): 120–31. https://doi.org/10.52783/dxjb.v37.183.

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Understanding how students perform is crucial in educational data mining (EDM). By analyzing performance, academic institutions can identify important patterns, detect students who might be struggling, and develop effective support systems. This paper explores using clustering techniques to group students based on various performance indicators, including grades, attendance, engagement, and participation in extracurricular activities. Dividing students into distinct groups allows educators to better understand their learning behaviors, allocate resources more efficiently, and implement tailore
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Mao, Wei, Guihong Wan, and Haim Schweitzer. "Graph Clustering Methods Derived from Column Subset Selection (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (2024): 23573–75. http://dx.doi.org/10.1609/aaai.v38i21.30479.

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Spectral clustering is a powerful clustering technique. It leverages the spectral properties of graphs to partition data points into meaningful clusters. The most common criterion for evaluating multi-way spectral clustering is NCut. Column Subset Selection is an important optimization technique in the domain of feature selection and dimension reduction which aims to identify a subset of columns of a given data matrix that can be used to approximate the entire matrix. We show that column subset selection can be used to compute spectral clustering and use this to obtain new graph clustering alg
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