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All Journal INSERT: Information System and Emerging Technology Journal Journal of Information System and Computer
Sabrina, Dinta
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Computer Science & IT Engineering

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ASISTEN DIGITAL CEPAT DAN PRAKTIS CHATBOT PMB MENGGUNANKAN ALGORITMA NEURAL NETWORK Sabrina, Dinta; Arina Zulfa; Heru Saputro; Alzena Dona Sabilla
Journal of Information System and Computer Vol. 4 No. 2 (2024): Desember 2024
Publisher : Universitas Islam Nahdlatul Ulama Jepara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34001/jister.v4i2.1216

Abstract

Demand for accurate information services, and responsiveness is increasing in the modern era, especially in the process of receiving new students. The limitations of human resources that provide information services in a direct way cause user delays and dissatisfaction. Therefore, an automatic solution that can provide efficient and effective information services, is the chatbot service (PMB) using AI to make it easier for prospective students and educational institutions to communicate. The study created a chatbot that could understand a better natural language by combining the neural convolutional network (CNN) and long short-term memory (LSTM) supported by embedding gloves. To ensure that the neural network's models can process text optimally, development processes involve important stages such as tokenization, padding, and the formation of the embedding matrix. Test results show that models have high training accuracy, but validation charts show overfitting, which is indicated by the big difference between losing training and losing validation. Embedding gloves, however, successfully enhance word representation and help people better understand the context of the text included. The CNN-LSTM PMB chatbot aims to provide a faster, more, relevant, and accurate service to prospective students
Kombinasi Vader Lexicon Dan Support Vector Machine Untuk Klasifikasi Sentimen Komentar Aplikasi Blu Bca Sabrina, Dinta; Sabilla, Alzena Dona; Azizah, Noor
INSERT : Information System and Emerging Technology Journal Vol. 6 No. 1 (2025)
Publisher : Information System Study Program, Faculty of Engineering and Vocational, Undiksha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/insert.v6i1.86240

Abstract

Aplikasi perbankan yaitu Blu BCA menjadi layanan perbankan yang mudah digunakan bagi masyarakat. Komentar yang terdapat di Google Play Store terkait aplikasi Blu BCA sangat beragam, yang mencerminkan berbagai sentimen pengguna terhadap aplikasi ini. Dalam penelitian ini, bertujuan untuk melakukan klasifikasi sentimen terhadap komentar aplikasi Blu BCA guna memahami pola sentimen pengguna secara mendalam. Tahapan penelitian ini dimulai dengan mengumpulan data melalui web scraping, kemudia tahap preprocessing, pelabelan data, SVM dan evaluasi. Hasil pelabelan data dengan VADER Lexicon menunjukkan hasil sentimen positif sebanyak 636 komentar, untuk sentimen negatif sebanyak 215 komentar, sedangkan sentimen netral sebanyak 148 komentar. Metode SVM digunakan untuk mengklasifikasi, sementara pelabelan dilakukan menggunakan VADER Lexicon. Hasil menunjukkan model SVM mencapai tingkat akurasi sebesar 85%. Selain itu, keluhan umun pada sentimen negatif dilakukan pengelompokan frasa yang mencakup “cutomer service”, “face verification”, serta masalah performa aplikasi seperti “really slow” dan “application error”. Penelitian ini menunjukkan bahwa metode SVM dengan dukungan VADER Lexicon dapat digunakan secara efektif untuk mengklasifikasikan sentimen pengguna terhadap aplikasi Blu BCA.
Co-Authors Alzena Dona Sabilla Arina Zulfa Heru Saputro Noor Azizah Sabilla, Alzena Dona