Article Per Year (5 Year)
p-Index From 2020 - 2025
0.408
P-Index
This Author published in this journals
All Journal Jurnal Pendidikan dan Teknologi Indonesia PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Setiawati, Elsa
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Analysis of Zoom App User Reviews on Google Play Store Using Recurrent Neural Networks and Gated Recurrent Unit Algorithms Muta'alimah, Muta'alimah; Setiawati, Elsa; Kwok, Josephine; Hasysya, Hauriya
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 2: PREDATECS January 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i2.1627

Abstract

The World Health Organization (WHO) declared COVID-19 a global pandemic on March 11, 2020. Technology is crucial to stop the spread of the virus. Video conferencing applications such as Zoom Cloud Meetings are essential for collaboration and communication as the government issues policies to conduct various activities from home. Zoom was released in January 2013 to become a trendy video conferencing platform until now. However, post-pandemic, the Zoom App faces challenges maintaining user satisfaction due to the reduced need for virtual meetings. This research aims to analyze user reviews of the Zoom app on the Google Play Store using the RNN and Analysis of Zoom App User Reviews on Google Play Store Using Recurrent Neural Networks (RNN) and Gated Recurrent Unit Algorithms (GRU) algorithms, determine which user reviews are positive, negative, and neutral, identify common problems with Zoom for improvement recommendations, and compare the accuracy between the RNN and GRU algorithms.  The results showed that out of 5000 reviews, 3728 sentiments were Positive, 1041 sentiments were Negative, and 231 sentiments were Neutral. The RNN algorithm achieved 86% accuracy, 86% precision, 100% recall, and 92% f1-score, while GRU achieved 83% accuracy, 87% precision, 92% recall, and 89% f1-score. Thus, RNN is superior in sentiment classification and most users are satisfied with the app, but negative reviews indicate areas that require improvement. This research provides valuable insights for developers to improve Zoom app features based on user feedback.
Evaluasi Kualitas Layanan Sistem e-Puskesmas di Kecamatan Sungai Mandau Melalui Kerangka Kerja E-SERVQUAL dan Model Kano Setiawati, Elsa; Salisah, Febi Nur; Jazman, Muhammad; Rahmawita, Medyantiwi
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 9 (2025): JPTI - September 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.978

Abstract

Permasalahan dalam penerapan sistem e-Puskesmas masing terjadi, terutama terkait kualitas layanan digital yang belom optimal. Keluhan seperti sistem tidak stabil, tampilan antarmuka membingungkan, dan kurangnya pelatihan menunjukkan perlunya evaluasi menyeluruh. Penelitian ini menggunakan pendekatan kuantitatif dengan metode E-SERVQUAL dan Model Kano. E-SERVQUAL mengukur kesenjangan antara persepsi dan harapan pengguna berdasarkan tujuh dimensi: efficiency, system availability, fulfillment, privacy, responsiveness, compensation, dan contact. Model Kano digunakan untuk mengklasifikasikan atribut layanan berdasarkan dampaknya terhadap kepuasan pengguna. Hasil menunjukkan bahwa seluruh atribut memiliki nilai GAP negative, dengan rata-rata nilai Q sebesar 0.91. ini menandakan bahwa layanan belum memenuhi harapan pengguna. Berdasarkan Model Kano terdapat 12 atribut one-dimensional, 4 attractive, 3 ust-be, dan 1 indifferent. Temuan ini menunjukkan bahwa perbaikan perlu difokuskan pada atribut yang berdampak langsung terhadap kepuasan.
Co-Authors Febi Nur Salisah, Febi Nur Hasysya, Hauriya Jazman, Muhammad Kwok, Josephine Muta'alimah, Muta'alimah Rahmawita, Medyantiwi