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

In the digital age, efficient sales transaction management is crucial for business success. Point of Sale (POS) systems, such as Wingpos, are a solution for transaction recording, inventory management, sales reporting, and data analysis. However, software errors can disrupt operations and compromise security aspects. This research aims to assess the functionality and quality of the Wingpos website by using the Black Box Testing method, which tests software by comparing actual outputs to expected results based on given inputs. The Equivalence Partitioning technique was applied to focus on testing functional aspects of the login and register features. Through this testing, technical insights into the quality of the Wingpos software were gained, as well as a systematic approach to web-based application testing. The results of the study are expected to improve user experience and serve as a reference in the development of similar systems in the future.

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.