Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Infotech Journal Bulletin of Information Technology (BIT)
Sri Wahyuni
Universitas Pembangunan PancaBudi
Computer Science & IT

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

Found 2 Documents
Search

 1 
ANALISIS SENTIMEN PENGGUNA PADA APLIKASI BANK DIGITAL KROM DENGAN ALGORITMA SUPPORT VECTOR MACHINE Maulian Saputra; Sri Wahyuni
INFOTECH journal Vol. 10 No. 2 (2024)
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/infotech.v10i2.11801

Abstract

The rapid development of financial technology has driven the widespread adoption of digital banking applications, including the KROM app, among the public. This study aims to analyze user sentiment toward the KROM Digital Bank application using the Support Vector Machine (SVM) algorithm. User review data was collected from Google Play, then processed through data preprocessing steps such as text cleaning, tokenization, and removing irrelevant words. The SVM algorithm is used to classify user sentiment into positive and negative categories. The results indicate that SVM performs well in classifying user sentiment, with an accuracy of 84,38%. This analysis is expected to provide insights for app developers to improve service quality based on user perceptions and experiences.
Analisis Data Mining Pola Penggunaan Seluler dan Klasifikasi Perilaku Pengguna di Berbagai Perangkat Menggunakan Metode C4.5 Andi Ernawati; Sri Wahyuni
Bulletin of Information Technology (BIT) Vol 5 No 4: Desember 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v5i4.1689

Abstract

Along with the development of digital technology, the use of mobile devices is increasing rapidly and affects user behaviour in accessing information and interacting with digital applications. This research aims to analyse mobile device usage patterns and classify user behaviour across various devices by utilising the C4.5 data mining method. The data used in this study was obtained from the Kaggle.com platform which provides a dataset of mobile device usage patterns, including variables such as frequency of application use, duration of device use, and type of application accessed. The research stages include data collection, data pre-processing to ensure quality, and analysis using the C4.5 algorithm. The C4.5 algorithm was chosen due to its ability to build a decision tree model that can classify user behaviour with a good level of accuracy. The results of this study show that there are certain patterns in mobile device usage that can be linked to demographic characteristics and user preferences for device types and applications. The resulting decision tree model is able to classify user behaviour with an accuracy rate of 41.71%%, and shows that social media applications and streaming applications are the most frequently used categories on mobile devices. This research is expected to provide insights for app developers and digital marketers in understanding user behaviour and optimising mobile-based interaction strategies. In addition, the results of this study also contribute to the application of the C4.5 method for analysing mobile technology usage patterns in the context of big data. Keywords: Data Mining, C4.5, Mobile Usage Pattern, User Behaviour Classification,Rapidminer Decision Tree...
Co-Authors Andi Ernawati Maulian Saputra