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All Journal Jurnal Penelitian Pendidikan IPA (JPPIPA)
Irwandi Irwandi
STEM Center, Universitas Syiah Kuala
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Education Materials Science & Nanotechnology Physics

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Effectiveness of Block Programming and Quarky Robots to Improve Computational Abilities Thinking through the STEMC Approach Muzakiah Muzakiah; Irwandi Irwandi; Elin Yusibani; Intan Mulia Sari; Romarzila Omar; Rini Oktavia
Jurnal Penelitian Pendidikan IPA Vol 10 No 12 (2024): December
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i12.8965

Abstract

The results of PISA 2022 show that Indonesian students are still weak in science literacy, mathematics, and technology-based problem solving, which are the basis for developing computational thinking (CT) skills. CT includes four primary indicators: decomposition to break down significant problems into smaller parts, pattern recognition to find similarities in data, abstraction to filter out irrelevant information, and algorithms to design systematic steps in solving problems. There are many ways to train CT, so this study uses block programming and the Quarky robot. This approach was chosen because it is visual and interactive and makes it easier to apply CT concepts practically, making it suitable for building 21st-century skills. The study was conducted in two high schools in Banda Aceh, involving 20 students from school A and 26 students from school B. Students were divided into two study groups in each school. Learning activities were designed based on STEMC in the form of Student Worksheets (LKPD), which include interactive learning scenarios, block programming challenges, and exploration of the Quarky robot's functions to solve real problems. The activities were arranged in stages, from a basic introduction to applying CT concepts in solving complex problems. The results showed a significant increase in students' CT abilities, especially in the algorithm indicator. Although both schools progressed, school B recorded higher growth, with better pre-test and post-test results than school A. This shows that block programming-based learning and Quarky robots effectively improve CT skills, which is essential in 21st-century education
Rainfall Prediction Using Adaptive Neuro-Fuzzy Inference System Method Dedy Ardana; Irwandi Irwandi; Umar Muksin; Mochammad Vicky Idris
Jurnal Penelitian Pendidikan IPA Vol 11 No 2 (2025): February
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i2.10148

Abstract

This study analyzes rainfall prediction using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method to improve model accuracy, particularly in extreme rainfall events. The objective of this study is to evaluate rainfall prediction using the ANFIS method to enhance model accuracy, especially in predicting extreme rainfall occurrences. The results indicate a moderate positive correlation with R² of 0.55, demonstrating good model performance at low rainfall levels (<20 mm) but a tendency to underestimate high-intensity rainfall (>60 mm). Residual analysis reveals a distribution around zero without systematic bias, though significant outliers (>20 or <-20) suggest the need for accuracy improvement. Monthly RMSE exhibits fluctuations, with the best performance observed in the June-July-August (JJA) season and notable challenges in December-January-February (DJF) due to extreme variability. Annual RMSE is also higher in extreme rainfall years (2018, 2023) compared to stable years (2019, 2020). The implementation of ANFIS enhances prediction sensitivity by incorporating additional variables such as temperature and humidity, leading to more accurate forecasts, particularly in extreme weather conditions. This study is further supported by STEM research at Universitas Syiah Kuala, which emphasizes the importance of artificial intelligence in climate data analysis to improve weather prediction accuracy. The ANFIS-based approach applied in this research aligns with  STEM studies, which highlight the integration of artificial intelligence in meteorology to mitigate hydrometeorological disaster risks
Co-Authors Dedy Ardana Elin Yusibani Intan Mulia Sari Mochammad Vicky Idris Muzakiah Muzakiah Rini Oktavia Romarzila Omar Umar Muksin