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All Journal Al-Hijr: Journal of Adulearn World Techno Agriculturae Studium of Research
Ibrahim, Nour
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Agriculture, Biological Sciences & Forestry Computer Science & IT Education Languange, Linguistic, Communication & Media Other

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AN IOT-BASED WEARABLE SENSOR SYSTEM FOR MONITORING THE HEALTH, RUMINATION, AND ESTRUS CYCLE OF DAIRY COWS IN INDONESIA Al-Sayid, Nisreen; Ibrahim, Nour; Al-Attar, Hassan
Techno Agriculturae Studium of Research Vol. 2 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/agriculturae.v2i6.2951

Abstract

The rapid development of Internet of Things (IoT) technology offers significant opportunities for improving livestock management, especially in dairy farming systems in developing countries like Indonesia. Traditional methods of monitoring dairy cow health, behavior, and estrus cycles rely on manual observation, which can be time-consuming, subjective, and inaccurate. These limitations lead to delayed disease detection, suboptimal reproductive performance, and reduced milk productivity. This study aims to design and evaluate an IoT-based wearable sensor system for continuous monitoring of dairy cow health, rumination patterns, and estrus cycles in Indonesian dairy farms. A research and development approach combined with field testing was employed. The system integrates wearable sensors attached to cows, collecting data on movement, body temperature, and rumination activity. Data is transmitted in real-time via IoT networks to a cloud platform for processing and visualization. System performance was assessed through accuracy testing, reliability analysis, and farmer feedback. The results show that the system effectively detects changes in rumination behavior, identifies early health issues, and predicts estrus cycles with high consistency compared to traditional methods. Farmers reported improved decision-making efficiency and reduced labor intensity. The IoT-based wearable sensor system demonstrates potential as an innovative solution for enhancing dairy cow health monitoring and reproductive management in Indonesia, supporting sustainable dairy farming practices.
TRANSFORMING TRADITIONAL TEACHING MODELS WITH ARTIFICIAL INTELLIGENCE: INNOVATIONS IN EDUCATION Pahmi, Pahmi; Ibrahim, Nour; Nizam, Zain
Al-Hijr: Journal of Adulearn World Vol. 5 No. 1 (2026)
Publisher : Sekolah Tinggi Agama Islam Al-Hikmah Pariangan Batusangkar, West Sumatra, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/alhijr.v5i1.1212

Abstract

The rigidity of traditional standardized education often fails to address the diverse cognitive needs of modern learners, necessitating a paradigm shift toward more adaptive instructional models. This study investigates the transformative potential of integrating Artificial Intelligence (AI) into secondary curricula to facilitate the transition from mass instruction to personalized pedagogy. Utilizing a mixed-methods quasi-experimental design, we evaluated the academic and operational impact of an AI-driven adaptive learning framework on a cohort of 600 students and 30 educators over a twelve-week intervention. The research benchmarked an AI-augmented experimental group against a control group receiving traditional direct instruction using standardized assessments and telemetry data. Empirical results demonstrate that the AI-integrated model yielded a statistically significant 11.4% increase in concept mastery (p<0.001) and substantially compressed the achievement gap within the classroom. Furthermore, the automation of administrative tasks reclaimed five hours of weekly instructor time, facilitating a strategic redistribution of labor toward high-value mentorship. We conclude that AI acts as a critical force multiplier that does not replace the teacher but fundamentally restructures the instructional core, validating a “Symbiotic Intelligence” approach that couples machine efficiency with human empathy to optimize educational outcomes.
Co-Authors Al-Attar, Hassan Al-Sayid, Nisreen Nizam, Zain Pahmi Pahmi