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All Journal Bulletin of Information Technology (BIT) Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi)
Garda Zidane Dhamara
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Transportation

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Analisis Sentimen Aplikasi Mybca Melalui Review Pengguna Di Google Play Store Menggunakan Algoritma Naive Bayes Garda Zidane Dhamara; Danu Nur Alamsyah; Priyo Wildan Saputro; Erna Daniati; Aidina Ristyawan
Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Vol. 8 No. 2 (2024): PROSIDING SEMINAR NASIONAL INOVASI TEKNOLOGI TAHUN 2024
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/inotek.v8i2.5044

Abstract

Tujuan penelitian ini yaitu menganalisis sentimen pengguna terhadap aplikasi myBCA melalui ulasan di Google Play Store. menggunakan algoritma Naive Bayes. Data ulasan dikumpulkan dan diproses untuk menghilangkan noise dan mempersiapkannya untuk klasifikasi sentimen. Algoritma Naive Bayes kemudian digunakan untuk mengkategorikan setiap komentar sebagai positif, negatif atau netral. Hasil analisis menunjukkan bahwa mayoritas ulasan memiliki sentimen positif, menunjukkan kepuasan pengguna terhadap aplikasi myBCA. Namun, terdapat pula ulasan dengan sentimen negatif yang menyoroti beberapa kekurangan aplikasi, seperti kendala teknis dan proses yang rumit. Temuan ini dapat memberikan masukan berharga bagi BCA untuk meningkatkan kualitas aplikasi myBCA dan meningkatkan kepuasan pengguna.
Klasifikasi Genre Musik Menggunakan Machine Learning Garda Zidane Dhamara; Sucipto
Bulletin of Information Technology (BIT) Vol 6 No 3: September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i3.2021

Abstract

This study examines the implementation of music genre classification using Machine Learning to develop an accurate and efficient music recommendation application. The main problem addressed is the automatic identification of music genres to improve recommendation personalization. The method used involves applying Machine Learning algorithms to a music dataset. The objective of this research is to build a system capable of automatically classifying music genres and serving as a foundation for a smarter recommendation system. Preliminary results indicate that Machine Learning is effective in music grouping, which will contribute to increased recommendation accuracy. This research is expected to make a significant contribution to the development of intelligent music applications.
Co-Authors Aidina Ristyawan Danu Nur Alamsyah Erna Daniati Priyo Wildan Saputro Sucipto