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All Journal Jurnal Pengabdian Informatika (JUPITA) Jurnal Nasional Teknologi Informasi dan Aplikasinya
I Made Surya Adi Palguna
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PENGEMBANGAN APLIKASI PEMBAYARAN MASYARAKAT MENGGUNAKAN REACT NATIVE DAN GOLANG DI GANESHCOM STUDIO I Made Surya Adi Palguna; Agus Muliantara
Jurnal Pengabdian Informatika Vol. 3 No. 3 (2025): JUPITA Volume 3 Nomor 3, Mei 2025
Publisher : Jurusan Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana

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Abstract

Jurnal ini membahas pengembangan aplikasi pembayaran masyarakat yang menggunakan teknologi React Native dan bahasa pemrograman Golang di GaneshCom Studio. Aplikasi ini bertujuan untuk memudahkan proses pembayaran berbagai layanan publik dan komersial bagi masyarakat. Penelitian ini mencakup perancangan sistem, pengembangan aplikasi, serta evaluasi performa dan keamanan. Hasil dari penelitian ini menunjukkan bahwa penggunaan React Native dan Golang dapat menciptakan aplikasi yang responsif, efisien, dan aman, memenuhi kebutuhan pembayaran masyarakat dengan baik.
Analisis Sentimen Twitter Pengaruh Tokoh Politik dengan Menggunakan Metode K-Nearest Neighbor I Made Surya Adi Palguna; Ngurah Agus Sanjaya ER
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 2 (2024): JNATIA Vol. 2, No. 2, Februari 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i02.p25

Abstract

Public opinion towards political figures can consist of positive and negative sentiments. Besides that, social media has developed which can be used as a forum for public opinion, one of which is Twitter. From this public opinion, sentiment analysis is formed which uses a classification algorithm. This work leverages the K-Nearest Neighbor (KNN) algorithm, which classifies data based on its similarity to existing data points. Tweets undergo preprocessing, followed by TFIDF weighting for keyword importance and confusion matrix calculations for calculate the evaluation of algorithm. By analyzing the nearest neighbors, sentiment values are assigned. The KNN model achieved an accuracy of 84,06% for k = 5, precision of 86,70% for k = 5, recall of 95,89% for k = 7, and F1-score of 90,93% for k = 5, demonstrating its effectiveness in assessing sentiment and influence through Twitter data. This research contributes to the field of political communication by offering a robust method for analyzing public opinion and gauging the influence of political figures on social media platforms. 
Co-Authors Agus Muliantara Ngurah Agus Sanjaya ER