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All Journal Jurnal Ilmiah Sinus
Yovita Kinanti Kumarahadi
STMIK Sinar Nusantara
Computer Science & IT

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Technology Acceptance Model pada Sistem Informasi Akademik berbasis Web Yovita Kinanti Kumarahadi; Kumaratih Sandradewi
Jurnal Ilmiah SINUS Vol 19, No 2 (2021): Vol. 19 No. 2, Juli 2021
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/sinus.v19i2.534

Abstract

Academic information system is an important system in students learning’s activities because it’s used as connector between students and lecturers. In addition, academic information system also acts as a liaison between lecturers and other relevant external parties. Academic information system provides several services, both for students and lecturers. These facilities include the provision of information on study plans, grades, schedules, and courses. However, only a few facilities are frequently used so that the use of the academic information system is not optimal.The problem in this study is the need for an evaluation to determine the acceptance of the academic information system by users, so that the system can be improved and developed. This study aims to determine the acceptance of web-based academic information system by users at STMIK Sinar Nusantara Surakarta with the Technology Acceptance Model or TAM. 
Clustering Pelaksanaan Vaksinasi di Jawa Tengah Menggunakan Metode K-Means Yovita Kinanti Kumarahadi; Brigitta Melati Kumarahadi; Kumaratih Sandradewi
Jurnal Ilmiah SINUS Vol 20, No 2 (2022): Vol. 20 No. 2 Juli 2022
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/sinus.v20i2.620

Abstract

Vaccines are a form of government responsibility in guaranteeing citizens' rights to health. In its implementation, the government seeks to fulfill the availability of vaccines for at least 208,265,720 residents. This number is the minimum number to be able to achieve herd immunity. To make it easier to show the vaccine achievements of each region, clustering can be done. The K-Means method is a non-hierarchical clustering method that is performed by partitioning data into predefined clusters. The research objects are 35 cities/districts in Central Java. The results of the data processing show that there are 2 optimal clusters, with information that cluster 1 is an area with a high vaccination achievement, while cluster 2 is an area with a low vaccination achievement.
Co-Authors Brigitta Melati Kumarahadi Kumaratih Sandradewi