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All Journal sudo Jurnal Teknik Informatika Journal of Artificial Intelligence and Engineering Applications (JAIEA)
Rizki Agustianto
Institut Teknologi Sepuluh Nopember Surabaya
Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Analisis Sentimen Ulasan Aplikasi Maxim di Google Play Store Menggunakan Algoritma Support Vector Machine Nouval, Muhammad; Habibi, Fanza Maulana; Rahmi, Anisya; Bittaqwa, Muhammad Dawam Amru; Agustianto, Rizki; Hasan, Fuad Nur
sudo Jurnal Teknik Informatika Vol. 4 No. 4 (2025): Edisi Desember
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/sudo.v4i4.1330

Abstract

Maxim merupakan salah satu aplikasi transportasi online yang banyak digunakan di Indonesia, sehingga ulasan pengguna menjadi sumber penting untuk mengetahui kualitas layanan. Penelitian ini bertujuan untuk menganalisis sentimen ulasan aplikasi Maxim di Google Play Store menggunakan metode lexicon based dan algoritma klasifikasi Support Vector Machine dan Naïve Bayes. Data sebanyak 400 ulasan diperoleh melalui teknik scraping, kemudian dilakukan tahap pre-processing yang meliputi cleaning, case folding, normalisasi kata, tokenizing, stopword removal, dan stemming. Pelabelan data ulasan dilakukan menggunakan lexicon based dengan tiga kelas sentimen, yaitu positif, netral, dan negatif, kemudian dilakukan validasi manual untuk meningkatkan akurasi label sentimen. Representasi fitur dilakukan menggunakan TF-IDF dengan parameter unigram dan min_df=2. Pengujian dilakukan dengan tiga skenario pembagian data, yaitu 80:20, 70:30, dan 60:40. Hasil penelitian menunjukan bahwa algoritma SVM memiliki performa yang lebih stabil dibandingkan Naïve Bayes berdasarkan nilai accuracy, precision, recall, F1-score, confusion matrix, dan cross validation.
Sentiment Analysis of Indonesian National Team Failure in the 2026 World Cup Qualifications Using Support Vector Machine Algorithm Nouval, Muhammad; Habibi, Fanza Maulana; Rahmi, Anisya; Amru Bittaqwa, Muhammad Dawam; Agustianto, Rizki
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1830

Abstract

The Indonesian National Team's failure in the 2026 World Cup qualifiers has generated diverse responses on social media, particularly on Ferry Irwandi's YouTube channel. This study aims to analyze public sentiment towards the national team's performance based on YouTube user comments. The method used is a Support Vector Machine (SVM) with stages of data scraping, pre-processing (cleaning, case folding, normalization, tokenization, stopword removal, stemming), lexicon-based automatic labeling, and model evaluation using a confusion matrix. The data consists of 8,353 comments divided with a ratio of 80:20 for training and testing. The results show that the SVM algorithm is able to classify comments into two classes, positive and negative, with an accuracy of 81%, a precision of 82%, a recall of 83%, and an F1-score of 82%. These results demonstrate the effectiveness of SVM in accurately and stably identifying public opinion towards the Indonesian National Team's failure.
Co-Authors Amru Bittaqwa, Muhammad Dawam Bittaqwa, Muhammad Dawam Amru Habibi, Fanza Maulana Hasan, Fuad Nur Nouval, Muhammad Rahmi, Anisya