Student Academic Mark Clustering Analysis and Usability Scoring on Dashboard Development Using K-Means Algorithm and System Usability Scale

Amalia, Nur Laita Rizki; Supianto, Ahmad Afif; Setiawan, Nanang Yudi; Zilvan, Vicky; Yuliani, Asri Rizki; Ramdan, Ade

doi:10.21609/jiki.v14i2.980

Cited by 2 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…K-means is one part of the clustering algorithm, including on unsupervised learning (Tempola et al, 2020). K-Means is an algorithm that groups objects with the same characteristics into a cluster, which is determined repeatedly by the value of k (Amalia et al, 2021). Data mining process using K-Means is aimed at grouping data into each cluster which corresponds to the center point (centroid) from each cluster used to identify patterns (Puspitasari et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

Superior Class to Improve Student Achievement Using the K-Means Algorithm

Syahputra¹,

Hutagalung²

2022

SinkrOn

View full text Add to dashboard Cite

The accumulation of new student data every year makes searching and processing data difficult, including selecting superior class students according to their talents and abilities. Therefore, the application of the K-Means Clustering data mining method is carried out to support decisions in grouping superior classes. The report card values for each class were used as parameters with a data sample of 80 students and 3 clusters were taken which then resulted in the selection and distribution of superior classes. The purpose of the study was to classify students in the superior class so that they could improve student achievement at SMK Raksana 2 Medan. Results Based on the calculation of the variable distance at the initial centroid with a sample of 80 students and the third iteration, the WCV value is 360.9745 and the BCV value is 7.3575 with a ratio value of 0.0203. Each cluster, namely: Cluster 1 has 43 students including the superior class category. Cluster 2 has 18 students and Cluster 3 has 19 students. Clusters 2 and 3 are included in the regular class category with a total of 37 students. The web-based K-Means application can provide information and solutions needed by schools to classify and determine superior classes so that they can improve student achievement in schools. These results can be used by the school to analyze student achievement and can assist teachers in forming superior classes so as to motivate students to study harder.

show abstract

Section: Methodsmentioning

confidence: 99%

Superior Class to Improve Student Achievement Using the K-Means Algorithm

Syahputra¹,

Hutagalung²

2022

SinkrOn

View full text Add to dashboard Cite

show abstract

“…The clustering step in K-Means is to determine the k value as the number of clusters [22]. To calculate the distance of each input data to each centroid is using the Euclidean distance until the closest distance is found from each data to the centroid [23].…”

Section: K-means Clusteringmentioning

confidence: 99%

Analyze the Clustering and Predicting Results of Palm Oil Production in Aceh Utara

Ula¹,

Perdinanta²,

Hidayat³

et al. 2023

Indonesian J. Comput. Cybern. Syst.

View full text Add to dashboard Cite

PT. Perkebunan Nusantara 1 is a company that works in palm oil mills with a total land area of 1,144 Ha in Aceh Utara. This research aimed to determine the cluster of the productive palm oil production's target. The expected results of palm oil production are for the following year so that it can be used as a recommendation for the managers to maximize performance. Research data are taken from PTPTN 1 PKS Cot Girek consisting of plantation and oil palm production data. The results of PKS Cot Girek palm oil production data for 2019-2022 from January to December were 1,365,530, while in 2022, it reached 1,768,720. The overall value obtained is 4,431,180 production data. The results of a land area of 1,144 Ha got 800.4 Ha of productive land and 343.6 Ha of less effective land. The test result in the first iteration of the C-Means process is 1.87, the second iteration is 3.87, the first iteration of the K-Means is 2.27, and the seventh iteration is 4.165 with an accuracy of 0.46 and 0.295. Meanwhile, the prediction model results have an accuracy rate of 90.77%. As a comparison, the fuzzy time series' accuracy level is 81.27%.

show abstract

Student Academic Mark Clustering Analysis and Usability Scoring on Dashboard Development Using K-Means Algorithm and System Usability Scale

Cited by 2 publications

References 17 publications

Superior Class to Improve Student Achievement Using the K-Means Algorithm

Superior Class to Improve Student Achievement Using the K-Means Algorithm

Analyze the Clustering and Predicting Results of Palm Oil Production in Aceh Utara

Contact Info

Product

Resources

About