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PEMANFAATAN INTERNET SEBAGAI MEDIA PEMBELAJARAN AKUNTANSI KEUANGAN SISWA KELAS XI AKL SMK Musdalifah; Suwardi Annas; Alin Liana
Jurnal PAJAR (Pendidikan dan Pengajaran) Vol. 6 No. 4 (2022): July
Publisher : Laboratorium Program Studi Pendidikan Guru Sekolah Dasar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33578/10.33578/pjr.v6i4.8695

Abstract

In the educational context, principals and teachers are led to pay more attention to the use of the internet as an alternative implementation of learning media in the current era. The study in this article is designed to find out the factors of internet utilization and support and the inhibiting factors of the internet as learning media for students’ financial accounting of AKL class XI at SMK. The method used was qualitative research with a descriptive approach. The subjects of the study involved the principal, teachers and four students. The data collection technique used was a triangulation technique. Data analysis techniques used qualitative analysis. The results of the study revealed that the access to deliver material in financial accounting learning was used through the digital application of google classroom and zoom cloud meeting. Supporting factors were the availability of technological devices, the skills of teachers and students, quota aid, and the presence of wifi as a facility at schools. The inhibiting factors included the decrease of wifi speed at schools, the limited amount of quota, the lack of use of digital applications, and few students who havetechnological devices like android mobile phones and laptops.
Implementation of K-Means Clustering on Poverty Indicators in Indonesia Suwardi Annas; Bobby Poerwanto; Sapriani Sapriani; Muhammad Fahmuddin S
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 2 (2022)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i2.1289

Abstract

This study aims to cluster all districts/cities in Indonesia related to poverty indicators. The attributes used are poverty gap index and poverty severity index. The data used comes from BPS. The method used is K-Means clustering, and the results show that by using the elbow and silhouette index methods, the optimal number of clusters is 2, where for cluster 1, it can be defined as a cluster with an area with a high poverty gap index and poverty severity index compared to cluster 2. As a result, cluster 1 has 42 districts/cities, and 472 for cluster 2.