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THE EFFECT OF GROUP GUIDANCE WITH ROLE PLAYING TECHNIQUES ON INCREASING STUDENT LEARNING MOTIVATION Dimas; Muhammad Ferdiansyah; Yulianti
Jurnal Kopendik Vol 5 No 1 (2026): Maret
Publisher : Program Studi Bimbingan dan Konseling FKIP UNJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/kopendik.v5i1.53057

Abstract

This study aims to determine the effect of group guidance services with role playing techniques on increasing student learning motivation at SMA Negeri 4 Jambi City. Low student learning motivation is still found, characterized by a lack of active participation, learning discipline, and achievement motivation. This study uses a quantitative approach with a one group pretest-posttest experimental design. The research subjects were 10 students of class XI F9 who were selected by purposive sampling. Data were collected using a 25-item learning motivation questionnaire, using a Likert scale and analyzed using SPSS 27. The results of the instrument test in the initial pre-test obtained a score of 71.6, included in the moderate category with a score range of 50-66, in the post-test increased to 100.9, included in the very high category with a score range of 84-100.
IoT-enabled digital twin with renewable energy for sustainable mudless eel aquaculture Ferdiansyah, Muhammad; Mariya, Lika; Rahman, Taufik; Dwiono, Sugeng
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 3: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i3.pp912-923

Abstract

This research develops and tests a digital twin (DT)-based smart aquaculture system for mud-free eel farming through the integration of IoT sensing, artificial intelligence (AI)-based prediction, edge computing, and solar energy-based automation. The approach used is experimental systems engineering, which includes system design, hardware and software implementation, virtual replication, and physical-digital two-way synchronization. The system utilizes ESP32-based pH, temperature, dissolved oxygen (DO), ammonia (NH₃), and turbidity sensors, MQTT communication, and Raspberry Pi edge computing. Water quality prediction is performed using long short-term memory (LSTM) and random forest regression. The dataset consists of 30 days of real-time data covering water quality, actuator activity (aerator, pump, feeder), and energy production and consumption by IoT sensors and energy meters. Results show that LSTM excels by R² = 0.94; RMSE = 0.14; MAPE <5% and synchronization latency <1.5 seconds. Solar energy integration reduces energy consumption by 54 67%, whilst automation increases eel survival rate by 78% to 91%. The novelty of this research lies in the first integrated implementation of DT, AIoT, and solar energy-based automation in mud-free eel farming. The proposed framework provides a precise, scalable, and sustainable solution for the development of modern aquaculture.