EDUSAINTEK: Jurnal Pendidikan, Sains dan Teknologi

PERANCANGAN WEBSITE LESCATUR BERBASIS VIRTUAL STUDENT-CENTERED LEARNING

2026-04-07T23:10:11+07:00

In an era of rapid technological development, chess education remains rigid and often fails to adapt to the diverse abilities of individual learners. To address this gap, Lescatur was developed as a student-centered chess learning platform that offers comprehensive features, including personal coaching. This study adopts a design-thinking approach, utilizing surveys to identify user needs and expert validation through CVR and CVI to assess the essentiality and relevance of the prototype features. A total of 73 respondents participated in the user-needs survey, focusing on expectations for digital chess learning. Expert validation involved five specialists who evaluated key features such as personal coaching, AI training modules, interactive e- books, and instructional videos. Survey findings indicate a strong demand for personalized and adaptive chess learning, positioning Lescatur as a potential solution for comprehensive and flexible digital chess education. The purposes of this study include (1) identifying user needs (empathize), (2) defining core learning problems (define), (3) generating interactive and adaptive learning concepts (ideate), (4) developing the prototype (prototype), and (5) evaluating its user experience (test). The CVR and CVI results demonstrate strong content validity, with nearly all CVR items meeting the minimum threshold of 0.99 and the S-CVI average exceeding 0.9, indicating high expert agreement regarding essentiality and relevance. These findings support the feasibility of further developing the Lescatur platform as a valid and user-centered virtual chess learning solution.

IMPLEMENTASI IMPORTANCE PERFORMANCE ANALYSIS (IPA) DALAM MENGEVALUASI KUALITAS PELAYANAN INFRASTRUKTUR RUAS JALAN NASIONAL DI KOTA KUPANG

2026-04-07T23:10:11+07:00

National road infrastructure plays a strategic role in supporting economic, social, and cultural activities within a region. The quality of road services should be reflected not only in their physical condition but also in the perceptions and satisfaction of road users as the primary beneficiaries. However, most evaluations have focused primarily on technical aspects, often overlooking user experiences and expectations. This study evaluates the quality of national road infrastructure services in Kupang City based on user perceptions and satisfaction. A quantitative approach was applied involving 200 respondents of productive age from various backgrounds. The assessment covered five main variables: road conditions, complementary structures, road equipment, roadside area utilisation, and maintenance services. Data were analysed using the Importance Performance Analysis (IPA) method to measure the level of conformity between performance and importance and to identify service improvement priorities. The findings reveal that nine service attributes require improvement due to gaps between user expectations and perceived performance. Cartesian diagram analysis further identified four priority attributes: properly functioning drainage channels to prevent flooding during rainfall, availability of safety barriers on steep road sections, presence of green trees to reduce heat and improve air quality, and timely repair of road damage.These results highlight critical service aspects most strongly perceived by users and provide a basis for road operators to develop more responsive, targeted, and sustainable infrastructure service improvement strategies.

INTEGRASI LEVEL OF SERVICE (LOS) DAN KEPADATAN UMKM SEBAGAI DASAR PENETAPAN PRIORITAS PENINGKATAN JARINGAN JALAN DI KOTA CIREBON

2026-04-07T23:10:10+07:00

The City of Cirebon is part of the Rebana Metropolitan Area and possesses significant potential in the MSME sector. Economic growth and increasing population mobility require a reliable and efficient transportation system. However, persistent transportation problems such as traffic congestion can reduce community income and hinder the distribution system of goods and services. This study aims to assess the Level of Service (LoS) of road segments in the City of Cirebon as a basis for determining priority road network improvements to support MSME development. The analytical methods employed include LoS analysis calculated in accordance with the PKJI 2023 and visualized using ArcGIS. MSME density was analyzed using KDE in ArcGIS. Furthermore, spatial overlay analysis was applied to integrate the LoS results with MSME density patterns. The findings indicate that the LoS values of the 19 observed road segments in the City of Cirebon vary considerably. Approximately 47.37% of the segments fall within LoS D-F, indicating traffic conditions ranging from approaching unstable flow to severe congestion at LoS F, where maximum vehicle speeds are limited to approximately 15 km/h. The KDE analysis reveals that the highest concentration of MSMEs is predominantly located in Kejaksan and Pekalipan Districts. Meanwhile, the spatial overlay analysis identifies priority road segments that require immediate attention to support MSME activities in the City of Cirebon, namely Wahidin Sudirohusodo Street and Sunan Gunung Jati Street in Kejaksan District, as well as Pekalipan Street and Nyi Mas Gandasari Street in Pekalipan District.

PERANCANGAN MODEL YOLOv8-LSTM UNTUK DETEKSI GERAKAN ANOMALI

2026-04-07T23:10:10+07:00

Manual monitoring of CCTV systems for detecting anomalous movements, such as criminal activity, is highly inefficient and prone to human error, thus urging the need for automated surveillance systems. A key research gap is that most object detection models (spatial in nature) fail to understand the temporal context (movement patterns over time) which is key to distinguishing normal and anomalous activities. This study proposes the design of a hybrid deep learning model YOLOv8-LSTM to address this issue. Using the 4D R&D (Define, Design, Develop) research methodology, an architecture is designed in which YOLOv8 (yolov8m) functions as a spatial feature extractor (generating a 106-dimensional vector) from each video frame. The sequence of these features is then analyzed using a Bidirectional Long Short- Term Memory (Bi-LSTM) equipped with an Attention Pooling mechanism to model temporal dependencies and classify movements. The prototype test results on the test set show strong performance, achieving an AUC of 0.8646 and an F1-Score of 0.6530. Qualitative analysis through 3D latent space visualization successfully demonstrated the model's effectiveness: initially overlapping spatial features (YOLOv8 input) were successfully mapped into clearly separated clusters of normal and anomalous classes (LSTM output). This study validates that the proposed hybrid architecture effectively combines spatial and temporal understanding for accurate anomalous motion detection.