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Validity and Reliability of the Objective Measure of Ego Identity Status Adaptation: The Rasch Model Approach Umar, Nur Fadhilah; Syahril, M. Fiqri; Nasution, Salsabila; Ardis, Nurfaidah; azzahrah, Humairah; Rafli, Muhammad
Bulletin of Counseling and Psychotherapy Vol. 7 No. 3 (2025): Bulletin of Counseling and Psychotherapy
Publisher : Kuras Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51214/002025071583000

Abstract

Ego identity plays a crucial role in shaping an individual’s self-concept, career decisions, and social relationships. Accurately measuring ego identity status is essential for understanding identity formation and promoting healthy psychological development. This study examines the validity and reliability of the adapted Objective Measure of Ego Identity Status (OMEIS) using the Rasch Model, a contemporary psychometric approach that enhances measurement precision. A sample of 431 university students participated in this study, with data collected through an online questionnaire. Rasch analysis was employed to assess item fit, rating scale effectiveness, person reliability, and item difficulty levels. The results indicate that the adapted OMEIS demonstrates strong structural validity and high item reliability (0.99), although person reliability (0.63) requires improvement. The Wright Map analysis confirms the instrument’s ability to capture variations in ego identity, while item difficulty analysis highlights areas for potential refinement. Findings suggest that the Rasch Model provides a robust framework for validating psychological instruments, ensuring their applicability across diverse populations. This study contributes to the refinement of identity measurement tools and underscores the importance of advanced psychometric methodologies in psychological research
Penerapan Metode Segmentasi Warna HSV untuk Deteksi Objek Berbasis Warna pada Citra Digital Rizka Rizka; Nasution, Salsabila; Aulia, Fatwa; Supiyandi Supiyandi
Router : Jurnal Teknik Informatika dan Terapan Vol. 3 No. 4 (2025): Desember : Router : Jurnal Teknik Informatika dan Terapan
Publisher : Asosiasi Profesi Telekomunikasi dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v3i4.706

Abstract

This study discusses the application of the HSV color segmentation method for color-based object detection in digital images. The data used consist of digital images in JPG, PNG, or WebP format containing various colored objects, including red tomatoes, yellow bananas, green apples, orange oranges, purple akebia, brown sapodilla, and blue blueberries. The research process involves converting images from BGR to HSV, determining HSV ranges for each color, creating masks, performing segmentation, analyzing pixels, detecting contours, and visualizing results using bounding boxes. The results show that the HSV method effectively detects objects, separates them from the background, and provides quantitative information, including pixel count, area percentage, and average HSV values for each color. Red, yellow, green, orange, purple, brown, and blue colors were successfully segmented, displaying clear and accurate objects, both for single and multiple objects, under various sizes and lighting conditions. These findings confirm that the HSV method is a simple, fast, and effective approach for color-based image analysis.
Parking Route Modeling Using the A* Algorithm for Density Reduction at the Faculty of Science and Technology, State Islamic University of North Sumatra Berutu, Asro Hayati; Nasution, Salsabila; Rahmadani, Suci
JITCoS : Journal of Information Technology and Computer System Vol. 1 No. 2 (2025): Volume 1 Number 2, December 2025
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v1i2.46

Abstract

The increasing number of vehicles on university campuses has led to significant congestion, particularly around parking areas. This study aims to design an intelligent parking route model using the Density-Aware A* algorithm to minimize vehicle congestion within the Faculty of Science and Technology (FST) at UIN North Sumatra. The proposed approach represents the internal campus network as a weighted graph, where each edge integrates both spatial distance and a density penalty that reflects the occupancy-to-capacity ratio of each parking area. The algorithm was implemented and simulated using Python and the NetworkX library within Google Colab. The results show that the system accurately identifies the optimal parking route based on vehicle type and real-time occupancy data. For motorcycles, the optimal path is A > B > F with a total cost of 23.06, while for cars, the most efficient path is A > B > H with a total cost of 18.21. The findings indicate that incorporating density-based cost adjustments effectively balances travel efficiency and vehicle distribution, contributing to overall congestion reduction in the FST–FKM corridor. Future research should focus on integrating live sensor data and adaptive feedback mechanisms to support large-scale deployment across diverse campus environments.