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Web-Based Financial Information System Testing of PT Perta Sakti Abadi Using the Black Box Testing Method Meliala, Rajhaga Jevannya; Anggraeni, Aulia; Holik, Wildan; Manik, Jonser Steven Rajali; Hakim, Ghaeril Juniawan Parel; Mindara, Gema Parasti; Wicaksono, Aditya
International Journal of Computer Technology and Science Vol. 2 No. 1 (2025): International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v2i1.129

Abstract

Software testing is a critical phase in information system development to ensure the system's quality and reliability. This study aims to evaluate the reliability and functionality of PT Perta Sakti Abadi's financial information system using the black-box testing method with the Equivalence Partitioning (EP) technique. This technique allows input data to be grouped into valid and invalid categories, minimizing test cases without reducing testing coverage. The testing focuses on the login feature as the system's primary component by evaluating various input combinations. The testing scenarios include boundary conditions to ensure the system handles inputs correctly in various situations.The results indicate that the system successfully verifies valid credentials, rejects access with invalid data, and provides informative error messages. Additionally, the system demonstrates resilience in handling testing scenarios, including inputs with special characters and empty fields. Input validation mechanisms function optimally, supporting secure user access and ensuring the login feature aligns with functional specifications. This successful testing forms a strong foundation for testing other modules, such as multi-level authentication and data encryption. Thus, the Equivalence Partitioning technique within the black-box testing method proves effective in enhancing the quality of web-based financial information systems.
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.