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All Journal Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Ilmiah Sinus
Ferdian, Praditya Rendi
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Computer Science & IT Decision Sciences, Operations Research & Management

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Mengukur Kinerja AI : Perbandingan Kepuasan Pengguna ChatGPT dan Google Gemini dalam Era Digital Buana, Pratama Angga; Abriansah, Fausta Rizky; Kurniawan, Nanda Dwi; Ferdian, Praditya Rendi; Ma’arif, Daffa Nurin Nabil; Firdaus, Azmi Maulana
Jurnal Ilmiah SINUS Vol 23, No 2 (2025): Vol. 23 No. 2, Juli 2025
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/sinus.v23i2.900

Abstract

This study aims to analyze user satisfaction with the artificial intelligence applications ChatGPT and Google Gemini using the End-User Computing Satisfaction (EUCS) method. This method evaluates five key dimensions: content, accuracy, format, ease of use, and timeliness. Data were collected from 78 respondents with diverse backgrounds using a Likert-based questionnaire. The results show that both applications fall into the "Satisfied" category, with average scores across all variables exceeding 3.70. ChatGPT scored highest on ease of use (3.93), while Google Gemini excelled in format (3.89). However, the accuracy variable received the lowest scores for both applications, at 3.57 for ChatGPT and 3.73 for Google Gemini. These findings highlight the need for improvements in information accuracy. This study is essential for providing practical insights for developers to enhance the quality of AI applications, making them more responsive to user needs.
Analisis Sentimen Algoritma Naïve Bayes, Support Vector Machine, dan Random Forest Pada Ulasan Aplikasi Ajaib Kurniawan, Nanda Dwi; Ferdian, Praditya Rendi; Hidayati, Nurtriana
Jurnal Nasional Teknologi dan Sistem Informasi Vol 11 No 1 (2025): April 2025
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v11i1.2025.87-97

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen pada ulasan pengguna aplikasi Ajaib di Google Play Store menggunakan tiga algoritma machine learning: Naive Bayes, Support Vector Machine (SVM), dan Random Forest. Data ulasan sebanyak 2.000 dikumpulkan melalui web scraping menggunakan library google-play-scraper dan diproses melalui tahap normalisasi, case folding, pembersihan, tokenisasi, dan penghilangan stopwords. Data dibagi menjadi 80% data latih dan 20% data uji dengan label sentimen diatas 3 (positif), 3 (netral), dan dibawah 3 (negatif). Hasil menunjukkan Random Forest unggul secara keseluruhan dengan recall 95% dan F1-score 91%, sementara SVM mencatatkan akurasi tertinggi 91%, dan Naive Bayes kompetitif dengan presisi 91%. Berdasarkan evaluasi terhadap keempat metrik utama, Random Forest direkomendasikan untuk analisis sentimen ulasan aplikasi Ajaib karena kemampuannya yang konsisten dalam mengidentifikasi ulasan positif. Penelitian ini memberikan panduan efektif dalam memilih algoritma machine learning untuk analisis sentimen di platform aplikasi mobile.
Co-Authors Abriansah, Fausta Rizky Firdaus, Azmi Maulana Kurniawan, Nanda Dwi Ma’arif, Daffa Nurin Nabil Nurtriana Hidayati Pratama Angga Buana