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All Journal Jurnal Informatika Global Journal of Informatics, Information System, Software Engineering and Applications (INISTA)
Baldah, Azizah
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Computer Science & IT

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Analysis of Factors Affecting User Satisfaction on SinegesJuara Application Using TAM and EUCS Baldah, Azizah; Nugroho, Nicolaus Euclides Wahyu
Journal of INISTA Vol 6 No 1 (2023): November 2023
Publisher : LPPM Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v6i1.1364

Abstract

SinegesJuara is an attendance application developed to provide real-time student attendance monitoring, allowing students to access and record their school attendance flexibly. Despite its popularity, SinegesJuara faces user challenges that have prompted researchers to investigate user satisfaction levels. This research analyzed user satisfaction factors by combining the TAM and EUCS methods, aiming to understand user satisfaction with the application. The research utilized the Slovin formula to determine a minimum sample size of 91 participants. Data analysis was conducted using the SEM-PLS approach with SmartPLS 3.2.9 software. The research findings indicate that the user satisfaction level with the SinegesJuara application is 78.77%, falling into the "Satisfied" category. This demonstrates that SinegesJuara fulfills its objectives, leading to user satisfaction. The results of the TAM method show that the Perceived Usefulness variable obtained a t-statistic value of 2.081, and the Attitude Toward Using variable obtained a t-statistic value of 7.877. Similarly, with the EUCS method, the Format variable obtained a t-statistic value of 2.445. As a result, it can be concluded that these three hypotheses significantly influence user satisfaction with the SinegesJuara application. In comparison, the other five hypotheses are rejected because they do not have a significant impact.
Clustering Daerah Rawan Bencana Alam Di Indonesia Berdasarkan Provinsi Dengan Metode K-Means Baldah, Azizah; Duarisah, After Valent; Maulana, Rizky Akbar
Jurnal Ilmiah Informatika Global Vol. 14 No. 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v14i2.3186

Abstract

Indonesia is a country located in a strategic geological position and is at the confluence of three world plates. This position causes Indonesia to become a country frequently hit by natural disasters such as earthquakes, landslides and other natural disasters. Based on data obtained from the Regional Disaster Management Agency (BPBD), from year to year the number of natural disasters always increases. The increase in the occurrence of natural disasters has led to the need for further research to find out how vulnerable areas are in Indonesia and can later reduce the risks posed when natural disasters occur. Researchers will use the K-Means Clustering method to classify natural disaster areas based on their level of vulnerability to natural disasters. Clustering is a process for grouping data into several clusters or groups. K-Means is a method for grouping non-hierarchical data (partitions) which can divide data into two or more groups. The source of this dataset comes from the Central Bureau of Statistics (BPS) and consists of 12 columns and the data used are landslides and no natural disasters. Then a search for the optimal K value was carried out using the elbow method and Silhouette Analysis which produced 2 clusters namely cluster 0 and cluster 1. The results of the clustering indicated that cluster 1 was classified as a disaster-prone area in Indonesia including Aceh, North Sumatra, West Java, Central Java, East Java.
Co-Authors Duarisah, After Valent Maulana, Rizky Akbar Nicolaus Euclides Wahyu Nugroho