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Community Service on Health Issue Stunting in Jelbuk Village Iqbal Sabilirrasyad; Agung Muliawan; M Faiz Firdausi; Nur Andita Prasetyo; Ferry Wiranto
RECORD: Journal of Loyality and Community Development Vol. 1 No. 1 (2024): January - April 2024
Publisher : Medikun Publisher

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Abstract

Stunting is a condition that disrupts a child's growth and development, leading to a discrepancy between their height and age. Indonesia is among the countries still grappling with this issue. Based on observational data collected during field visits to Jelbuk Village, Jember City, the researchers found that a majority of toddlers that live in the village are categorized as experiencing stunting. Consequently, the researchers organized counseling sessions and motor skills screening activities for children. The primary objective of these activities is to educate parents about stunting and how important to monitor children's development as they grow. The screening of children's development involves the use of the Pre-Developmental Screening Questionnaire (KPSP), consisting of 9-10 questions assessing a child's developmental milestones. From the screening activities using the Developmental Pre-Screening Questionnaire, the following results were obtained: out of 25 toddlers who attended the counseling, 6 were identified with developmental deviations, 4 were considered doubtful, and 15 were developing appropriately
Pattern Matching Algorithms for Optimizing the Accuracy of Optical Character Recognition in Automated Migrant Worker Registration Systems Ferry Wiranto; Muhamat Abdul Rohim
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 1 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i1.14487

Abstract

Manual registration processes for migrant workers present significant operational challenges, requiring 10-15 minutes per person with data error rates reaching 15%, hindering service efficiency in protection organizations. This research addresses these challenges by developing a domain-specific Optical Character Recognition (OCR) system optimized through multiple pattern matching algorithms tailored for Indonesian identity documents. Unlike general-purpose OCR approaches, the system implements six pattern variations for RT/RW field extraction and three hybrid strategies (direct, fuzzy, and contextual matching) for occupation fields, specifically designed to handle format inconsistencies in KTP and KK documents. Testing with 50 document samples achieved variable accuracy rates ranging from 75-95% across different field types, with the multiple pattern approach demonstrating 30.8% improvement over single-pattern methods for RT/RW fields and 20% improvement for occupation fields. Real-world deployment at Migrant Care Jember produced measurable operational improvements: 67% time reduction (12 to 4 minutes), 80% error reduction (15% to 3%), and threefold service capacity increase without additional personnel. The integrated confidence level system with visual indicators (green/yellow/red) enables non-technical users to identify fields requiring verification, enhancing practical usability. This study demonstrates that domain-specific pattern matching optimization can effectively bridge the gap between theoretical OCR advancements and practical implementation challenges in resource-constrained organizational settings, with direct implications for migrant worker protection services.