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All Journal UPEJ Unnes Physics Education Journal Jurnal Teknik Informatika (JUTIF)
Bijanto, Bijanto
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Computer Science & IT Education Physics

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Analisis Peningkatan Life skill Mahasiswa dalam Pembelajaran Metoda PJBL Pada Mata Kuliah Fisika Pada Prodi Teknik Elektro Nuri, Nuri; Bijanto, Bijanto
UPEJ Unnes Physics Education Journal Vol 10 No 2 (2021)
Publisher : UPEJ Unnes Physics Education Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/upej.v10i2.54303

Abstract

Student competencies need to be adjusted to the demands of the business and industrial world, the mental readiness of graduates to face the world of work and the needs of competence by industry that is increasingly fast. Weak afternoon graduates will face increasingly tough challenges. The writer's experience in previous research related to project learning is that life and career skills can increase student productivity. Life and career skills include competencies to be a flexible person, guide and lead others, manage time and goals, work independently, work effectively in a team. Implementing PJBL in physics subjects in the STTP electrical engineering study program is important to do as an effort to increase the competence and competitiveness of university graduates, as well as provide 21st century competence to face demographic challenges and life probabilities. This idea is stated in the Beginner Lecturer Proposal (PDP) with the title Analysis of Life and Career Skills Improvement for Electrical Engineering Students Through Methods, PJBL in Advanced Physics Subjects. The purpose of this research is to produce an appropriate PJBL implementation design in improving life and career skills for students, to analyze the values of life and career skills in students. The research method is Preexperimental Design. only involving the experimental class, without involving the control class. In general, PJBL learning can improve the five points of students' Life skill abilities with an average grade of 63.6 to 94.9 with an n-gain of 0.9 with high criteria
An Intelligent IoT-Based Hydroponic Irrigation System for Strawberry Cultivation Using Extreme Gradient Boosting Decision Model Bijanto, Bijanto; Abidin, Zainal; Asy’ari, Fajar Husain; Ali, Rabei Raad
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5173

Abstract

Most existing implementations rely on static rule-based or fuzzy logic control, which lack adaptability to dynamic environmental changes and often require manual tuning by experts. These limitations are particularly challenging for small-scale farmers who face constraints in technical knowledge, infrastructure, and operational flexibility. To address these issues, this study proposes an intelligent hydroponic irrigation system that embeds the Extreme Gradient Boosting (XGBoost) algorithm as a decision-making model. The system collects real-time sensor data including temperature, humidity, and light intensity, and uses the trained XGBoost classifier to determine irrigation needs with binary output (FLUSH or NO). The system was implemented on a vertical hydroponic setup for strawberry cultivation, and evaluated over a 21-day observation period. The results show that the XGBoost-based model was effective in maintaining consistent vegetative growth, with plants in upper-tier pipes achieving an average height above 25 cm by the end of the cycle. This demonstrates that the model could support responsive and resource-efficient irrigation control. Beyond technical performance, the research highlights the urgency of adopting data-driven smart farming systems to ensure sustainable food production, optimize limited resources, and empower small-scale farmers with accessible and scalable solutions. However, the proposed XGBoost model is still limited to local crops; therefore, when introducing new plant types or additional sensor inputs, parameter adjustments and retraining are required to maintain accuracy. Future improvements may include dynamic model retraining and integration with real-time feedback systems to enhance system autonomy and resilience in broader agricultural settings.
Co-Authors Ali, Rabei Raad Asy’ari, Fajar husain Nuri Nuri Zainal Abidin