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Journal : Jurnal Ilmiah Informatika Komputer

PREDIKSI PERGERAKAN PENGGUNA MENGGUNAKAN PENDEKATAN GERAKAN TERMINAL BERGERAK Hadi, Muhammad; ., Prihandoko
Jurnal Ilmiah Informatika Komputer Vol 12, No 1 (2007)
Publisher : Universitas Gunadarma

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Abstract

Penelitian ini membahas penggunaan metode prediksi berdasarkan gerakan terminal bergerak yang digunakan untuk memprediksi pergerakan user mobile yang sedang bergerak untuk mengetahui proses selanjutnya, agar dapat disediakan sumber daya yang mencukupi untuk berlangsungnya komunikasi pada sel yang akan diuji tersebut. Aplikasi ini dibangun dengan menggunakan bahasa pemrograman Java. Dari penelitian yang dilakukan, didapatkan bahwa penggunaan pendekatan gerakan terminal bergerak dapat memberikan informasi yang cukup efektif dalam memprediksi arah gerakan terminal bergerak.Kata kunci : pergerakan, prediksi
ANALISIS SENTIMEN REVIEW PENGGUNA APLIKASI DEPOK SINGLE WINDOW DI GOOGLE PLAY MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE Arie Wijaya; Prihandoko Prihandoko
Jurnal Ilmiah Informatika Komputer Vol 28, No 1 (2023)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/ik.2023.v28i1.7902

Abstract

Technology is developing rapidly, including in the world of government. The district government makes a web or mobile-based application with the aim of helping people in getting the services that the community deserves. The Depok Regency Government created a mobile-based public service application called Depok Single Window. Due to the importance of user reviews for the continuity of the DSW application, it is required to analyze the sentiment of reviews of the Depok Single Window application on Google Play Store. Sentiment analysis is carried out using the Support Vector Machine. The data used in this study were 733 reviews obtained from the scrapping. The scrapping is carried out by utilizing python library, namely google play scrapper as access to retrieve data. The results attained from this research are an accuracy value of 89.23% for the sentiment analysis of the Depok Single Window application, which means that the Support Vector Machine is good to be used to classify the Depok Single Window application review data into positive, negative and neutral.