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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Transportasi Habitat Jurnal Ilmiah Teknik Industri Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Gantang JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JET (Journal of Electrical Technology) BAREKENG: Jurnal Ilmu Matematika dan Terapan Jiko (Jurnal Informatika dan komputer) Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Jambura Journal of Mathematics Jurnal Ilmiah Ilmu dan Teknologi Rekayasa MOTIVECTION : Journal of Mechanical, Electrical and Industrial Engineering MEANS (Media Informasi Analisa dan Sistem) Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Jurnal Abdimas Bina Bangsa IJCOSIN : Indonesian Journal of Community Service and Innovation Jurnal Ilmiah Mahasiswa teknik eProceedings of Engineering Jurnal INFOTEL Abdi Masyarakat UIKA Ngabdimas Jurnal SINTA: Sistem Informasi dan Teknologi Komputasi Integrasi: Jurnal Ilmiah Teknik Industri
Ridho Ananda
Telkom University Purwokerto
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Journal : Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika

 1 
SILHOUETTE DENSITY CANOPY K-MEANS FOR MAPPING THE QUALITY OF EDUCATION BASED ON THE RESULTS OF THE 2019 NATIONAL EXAM IN BANYUMAS REGENCY Ananda, Ridho
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.8375

Abstract

Mapping the quality of education units is needed by stakeholders in education. To do this, clustering is considered as one of the methods that can be applied. K-means is a popular algorithm in the clustering method. In its process, K-means requires initial centroids randomly. Some scientists have proposed algorithms to determine the number of initial centroids and their location, one of which is density canopy (DC) algorithm. In the process, DC forms centroids based on the number of neighbors. This study proposes additional Silhouette criteria for DC algorithm. The development of DC is called Silhouette Density Canopy (SDC). SDC K-means (SDCKM) is applied to map the quality of education units and is compared with DC K-means (DCKM) and K-means (KM). The data used in this study originated from the 2019 senior high school national examination dataset of natural science, social science, and language programs in the Banyumas Regency. The results of the study revealed that clustering through SDKCM was better than DCKM and KM, but it took more time in the process. Mapping the quality of education with SDKCM formed three clusters for social science and natural science datasets and two clusters for language program dataset. Schools included in cluster 2 had a better quality of education compared to other schools.
Silhouette Density Canopy K-Means for Mapping the Quality of Education Based on the Results of the 2019 National Exam in Banyumas Regency Ridho Ananda
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Department of Informatics, Universitas Muhammadiyah Surakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.8375

Abstract

Mapping the quality of education units is needed by stakeholders in education. To do this, clustering is considered as one of the methods that can be applied. K-means is a popular algorithm in the clustering method. In its process, K-means requires initial centroids randomly. Some scientists have proposed algorithms to determine the number of initial centroids and their location, one of which is density canopy (DC) algorithm. In the process, DC forms centroids based on the number of neighbors. This study proposes additional Silhouette criteria for DC algorithm. The development of DC is called Silhouette Density Canopy (SDC). SDC K-means (SDCKM) is applied to map the quality of education units and is compared with DC K-means (DCKM) and K-means (KM). The data used in this study originated from the 2019 senior high school national examination dataset of natural science, social science, and language programs in the Banyumas Regency. The results of the study revealed that clustering through SDKCM was better than DCKM and KM, but it took more time in the process. Mapping the quality of education with SDKCM formed three clusters for social science and natural science datasets and two clusters for language program dataset. Schools included in cluster 2 had a better quality of education compared to other schools.
Co-Authors 12.5202.0161 Daniel Yeri Kristiyanto Achmad Zaki Yamani Agi Prasetiadi Al Atsary, Arry Darmawan Aliyah, Khikmatul Amalia Beladinna Arifa Anggraeni, Nadia Putri Anita Chindyana Ardian, Miko Arry Darmawan Asminah Aswan Munang Atika Ratna Dewi Auliya Burhanuddin Awanda, Yulia Bangkit Wiguna Devindra, Sheisha Nabila Dhau, Shadana Faturohman, Muhammad Iqbal Fauzan Romadlon Fipiariny Gushelmi Gushelmi Gushelmi Hadi Kurniawan Hadi Kurniawan, Hadi Isnaini Nurisusilawati Jaya, Satria Luthfi Ikhsan Al Ghani Maidawati Maidawati Maifuza Binti Mohd Amin Miftahul Huda Muhammad Afif Muhammad Ardiansyah Muhammad Zidny Naf'an Nainggolan, Racha Defitri Nurlaili Nursapia Harahap Rani Septiani Sukandar Rifki Adhitama, Rifki Rimbawati Saufik Luthfianto Saufik Luthianto Setiadi, Heri Siregar, Salsa Fadhilah Utti Marina Rifanti Wahyu Adi Prabowo Yoga, Wira Yudha Saintika Zulkarnain, Nur Afrina Huda