Digital learning applications like HeyJapan are increasingly popular. User reviews on platforms such as Google Play Store contain valuable information on user perceptions and experiences. To process this information systematically, this study employs a Natural Language Processing (NLP) approach to analyze sentiment toward the HeyJapan application. Data was collected using web scraping techniques with Python and the google play scraper library, resulting in 1,000 latest user reviews. The analysis included data collection, preprocessing, sentiment labeling using TextBlob, visualization, modeling with Logistic Regression, and evaluation. After preprocessing, 923 valid reviews were classified into three sentiment categories based on polarity which are positive, neutral, and negative. Results showed 71.4% of reviews positive, 26.1% neutral, and 2.5% negative. Visualizations in pie charts and word clouds provided an overview of user perceptions. Modeling with TF-IDF and Logistic Regression achieved 88% accuracy with the highest f1-score in the positive sentiment category. Evaluation indicates the model is fairly reliable in classifying sentiments, especially for positive and neutral categories, though negative sentiment classification needs improvement. This study shows the NLP approach can evaluate user perceptions of educational applications based on reviews and serve as a basis for improving foreign language learning app quality.

Perkembangan teknologi informasi yang pesat telah mendorong kebutuhan akan platform pembuatan website yang profesional, terutama bagi bisnis, individu, maupun organisasi tanpa latar belakang teknis. Penelitian ini bertujuan untuk membangun sistem informasi jasa pembuatan website UpSite menggunakan metode prototyping, dengan integrasi fitur business intelligence. Metode ini dipilih karena bersifat iteratif dan memungkinkan penyesuaian sistem secara langsung berdasarkan umpan balik pelanggan. Kebaruan utama dari sistem ini adalah integrasi fitur Business Intelligence (BI) pada dashboard admin, yang menyajikan visualisasi data penjualan dan analisis layanan terlaris secara real-time untuk mendukung pengambilan keputusan. Proses pengembangan mencakup komunikasi kebutuhan, perencanaan awal, pemodelan sistem (use case diagram, activity diagram, dan class diagram), pembangunan prototipe, serta pengujian menggunakan metode black box dan User Acceptance Testing (UAT). Hasil pengujian menunjukkan bahwa seluruh fitur inti, termasuk BI, berjalan sesuai spesifikasi dan memperoleh respons sangat baik dari calon pelanggan. Dengan demikian, UpSite dinyatakan layak digunakan sebagai solusi layanan pembuatan website yang tidak hanya fungsional, tetapi juga inovatif dengan dukungan analitik berbasis data.
 
Kata Kunci - UpSite; Metode Prototyping; Pembuatan Website; Business Intelligence; Pengujian Black Box

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.

In recent years, virtual assistants have become an integral part of everyday life, simplifying routine tasks and allowing users to focus on more important matters. This research aiming to design GiggleGate, a virtual desktop assistant integrated with both face and speech recognition technology to enhance authentication security. The objective is to develop an authentication system that not only verifies user identity but also provides a more intuitive experience and seamless interaction. The research employs a development methodology to create and implement the system, which integrates face recognition via OpenCV and speech recognition via a Python library. The findings indicate that the integration of these technologies enhances security and user experience by offering dual-factor authentication. The system is expected to contribute to more secure and accessible virtual assistant applications, offering both a practical and efficient solution for users. The implications of this study suggest that the combination of face and speech recognition can provide an effective means to protect user privacy and improve the overall functionality of desktop assistants.