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IoT-based mobile data acquisition for pH and temperature monitoring as an enrichment material for high school energy transformation book Hidayah, Amalia; Firmansyah, Maulana; Rahardjo, Dwi Teguh; Jamaluddin, Anif
Journal of Environment and Sustainability Education Vol. 4 No. 2 (2026)
Publisher : Education and Development Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62672/joease.v4i2.123

Abstract

Monitoring pH and temperature is critical in various fields, yet integrating such technical concepts into high school education remains a challenge. This study aims to develop and validate an IoT-based mobile data acquisition system as a practical case study for an enrichment book on the topic of energy transformation for high school students. Using a Research and Development (R&D) approach, an IoT device consisting of a pH sensor, a K-type thermocouple, and a NodeMCU ESP8266 was built. An accompanying enrichment book was developed using Canva and Heyzine Flipbook. The IoT system demonstrated good accuracy, achieving R² values for pH measurement of 0.9526 (acidic) and 0.8843 (basic). The enrichment book was validated by experts and practitioners, receiving high feasibility ratings of 92.58% and 83.01%, respectively. These findings indicate that the developed enrichment book is a highly feasible supplementary teaching resource. This study demonstrates the significant potential of integrating hands-on IoT projects into science curricula to enhance students' understanding of abstract concepts like energy transformation.
Graphene as an Active Material for Supercapacitors: A Machine Learning Approach Jamaluddin, Anif; Nursanti, Annisa Dwi; Nur'aini, Anafi; Putri, Rekyan Regasari M; Arshad, Muhammad Usama
INDONESIAN JOURNAL OF APPLIED PHYSICS Vol 13, No 2 (2023): October
Publisher : Department of Physics, Sebelas Maret University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijap.v13i2.76678

Abstract

Graphene is a promising material for supercapacitors due to its unique properties, which influence the device's supercapacitor. This study aims to investigate the key factor of graphene properties in supercapacitors (, with the goal of improving their performance. Also, we observe the machine learning models for predicting capacitance of supercapacitor including four algorithms of machine learning: Linear Regression (LR), lazy IBK, Decision Table (DT), and Random Forest (RF). Machine learning model showed that the RF model demonstrated the highest correlation value of 0.745, surpassing other models. Also, the study revealed that graphene has a high specific surface area and highly porous structure, which enhanced the high capacitance values. Finally, these machine learning models are suitable to apply in materials sciences field for understanding the materials properties in supercapacitor.