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All Journal Buletin Sistem Informasi dan Teknologi Islam DBESTI: Journal of Digital Business and Technology Innovation
Pasya, Thoriq Muhammad
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Computer Science & IT Economics, Econometrics & Finance Library & Information Science

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Pengujian Black Box Testing pada Website Segitiga Motor Menggunakan Teknik Boundary Value Analysis Widiani, Hasna Nabiilah; Santoso, Budy; Pasya, Thoriq Muhammad; Yuaziva, Asa; Ningrat, Rangga Wasita; Mindara, Gema Parasti; Wicaksono, Aditya
Buletin Sistem Informasi dan Teknologi Islam Vol 5, No 4 (2024)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v5i4.2482

Abstract

Website Segitiga Motor dirancang untuk memberikan layanan digital di sektor otomotif, termasuk registrasi akun, login, dan pemesanan layanan. Penelitian ini bertujuan untuk mengevaluasi keandalan dan kualitas fitur utama pada website Segitiga Motor menggunakan metode Black Box Testing dengan teknik Boundary Value Analysis. Fokus utama pengujian adalah memastikan bahwa fitur registrasi akun, login, dan pembuatan appointment berfungsi sesuai dengan kebutuhan pengguna. Hasil pengujian menunjukkan bahwa sebagian besar fitur, seperti registrasi akun dan login, telah berfungsi dengan baik dan memenuhi ekspektasi. Namun, kelemahan ditemukan pada validasi tanggal dalam fitur pembuatan appointment, di mana sistem masih memungkinkan pengguna untuk memilih tanggal yang tidak valid. Temuan ini memberikan wawasan penting bagi pengembang untuk memperbaiki sistem dan meningkatkan pengalaman pengguna. Penelitian ini diharapkan dapat menjadi referensi dalam pengujian perangkat lunak untuk aplikasi berbasis web di sektor otomotif.
Analisis Sentimen Tagar #KaburAjaDulu Pilihan Migrasi ke Jepang pada Platform X dengan NLP Meliala, Rajhaga Jevanya; Chasanah, Nur Indah; Manik, Jonser Steven Rajali; Pasya, Thoriq Muhammad; Lestari, Humannisa Rubina
DBESTI: Journal of Digital Business and Technology Innovation Vol 2 No 1 (2025): Mei, 2025
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/dbesti.v2i1.1756

Abstract

The hashtag #KaburAjaDulu, which went viral on platform X, reflects the concerns of Indonesian society—particularly among younger generations—regarding domestic social and economic pressures, as well as an increasing interest in migrating to Japan. This phenomenon illustrates the complexity of digital public opinion, yet few studies have specifically compared the effectiveness of different sentiment analysis algorithms within this context. Therefore, this study aims to analyze and compare public sentiment toward the #KaburAjaDulu hashtag, particularly about migration to Japan, using a Natural Language Processing (NLP) approach with three sentiment analysis algorithms: VADER, TextBlob, and BERT. A total of 1000 tweets were collected using scraping techniques, and after preprocessing, 967 tweets were included in the analysis. Sentiments were categorized into three classes: positive, negative, and neutral. The results show that VADER and TextBlob tend to classify tweets as neutral or positive, while BERT reveals a dominant negative sentiment of 52.3%. These findings suggest that BERT is more sensitive to context and implicit sentiment in the informal Indonesian language. This study highlights the importance of selecting appropriate algorithms for social media sentiment analysis and contributes to a deeper understanding of digital migration aspirations within Indonesian society.
Co-Authors Aditya Wicaksono Budy Santoso Chasanah, Nur Indah Lestari, Humannisa Rubina Manik, Jonser Steven Rajali Meliala, Rajhaga Jevanya Mindara, Gema Parasti Ningrat, Rangga Wasita Widiani, Hasna Nabiilah Yuaziva, Asa