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

This research explores the integration of Augmented Reality (AR) technology into custom gift products by Tricksy.co to create a more interactive and personalized gift-giving experience. By utilizing EyeJack Creator, a platform for creating AR photo markers, the system allows customers to design gifts that display personalized videos when scanned. The process includes generating AR markers, integrating QR codes, and ensuring compatibility with both iOS and Android devices. System testing confirmed that the AR markers successfully displayed the videos as intended, with smooth functionality across different platforms. However, slow internet connections impacted the speed of AR content downloading. This study highlights the potential of AR to revolutionize the gift industry by adding a unique digital layer to physical gifts, offering both emotional and interactive value.

This study focuses on black-box testing of JivaJoy software, an online product stock management and ordering system. The primary goal of this research is to evaluate the functionality and performance of the system's key features, including profile management, CRUD operations for admin and customer accounts, product stock management, shopping cart, order management, and AI counseling. Black-box testing was applied to assess whether these features meet expected operational standards and user requirements without considering the internal code structure. The test results indicate that most features function as expected. However, some issues were identified related to input validation, particularly with email format, phone number length, file uploads, and product stock management. Additionally, the order management and AI counseling features showed deficiencies in error handling and input validation. Based on these findings, the study recommends improvements in input validation, product stock management, shopping cart functionality, and AI counseling systems to enhance the reliability and performance of the JivaJoy platform. These enhancements are essential to deliver more efficient, secure, and optimal services, ensuring better user experiences and customer satisfaction.

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.