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All Journal Jurnal Edukasi Elektro The Indonesian Journal of Computer Science Research
Putra, Dimas Panji Eka Jala
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Pengembangan Media Pembelajaran Interaktif Tata Surya Berbasis Augmented Reality Putra, Nanda Dwi; Rusmana, Najwa Rokhan; Muttakin, Muhammad; Irsyad, Hidayat Hatta; Syafwan, Muhammad Ikram; Putra, Dimas Panji Eka Jala
The Indonesian Journal of Computer Science Research Vol. 4 No. 2 (2025): Juli
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v4i2.227

Abstract

Pembelajaran konsep tata surya di sekolah dasar sering kali terkendala oleh keterbatasan media konvensional yang kurang mendukung pemahaman spasial dan visual siswa. Untuk mengatasi hal ini, penelitian ini bertujuan mengembangkan media pembelajaran interaktif berbasis Augmented Reality (AR) yang memungkinkan siswa berinteraksi langsung dengan visualisasi 3D planet dalam lingkungan nyata. Aplikasi dikembangkan menggunakan Unity 3D dan Vuforia SDK pada platform Android, dengan metode marker-based tracking untuk menampilkan model planet secara real-time saat marker discan. Hasil pengembangan menunjukkan aplikasi mampu menyajikan tampilan 3D planet yang informatif dan interaktif, dilengkapi narasi edukatif serta antarmuka yang mudah digunakan. Kesimpulan dari penelitian ini menunjukkan bahwa media pembelajaran berbasis AR dapat menjadi alternatif inovatif dan menarik dibanding metode konvensional, serta membantu meningkatkan pemahaman dan keterlibatan siswa. Secara aplikatif, media ini memiliki potensi besar untuk diterapkan dalam pembelajaran IPA di sekolah dasar, terutama untuk menjembatani konsep abstrak menjadi pengalaman belajar yang konkret dan menyenangkan.
Implementation of Neural Networks in Daily PV Power Output Prediction Using Bayesian Regularization Algorithms to Assist Energy Management Systems Mahmudah, Norma; Delfianti, Rezi; Sigit, Firman Matiinu; Putra, Dimas Panji Eka Jala; Nusyura, Fauzan
Jurnal Edukasi Elektro Vol. 9 No. 2 (2025): Jurnal Edukasi Elektro Volume 9, No. 2, November 2025
Publisher : DPTE FT UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/jee.v9i2.91044

Abstract

Solar power plants have several advantages, namely continuous energy production, reduced electricity demand, and low photovoltaic maintenance, so that PV power output can be optimized with reliable PV power output predictions. Implementation of Artificial Neural Network (ANN) to predict photovoltaic (PV) power output, using the Bayesian Regularization algorithm. Accurate PV power output prediction is very important in power systems. The data used are solar radiation, PV module temperature, ambient temperature, and actual PV power output, with the target being the PV power output for the next day with the PV power output output for the next day. The architecture used in this study is a Cascade Forward Neural Network (CFNN) and an Elman Neural Network (ENN). Both ANN models use daily data sets and performance evaluation using Mean Square Error (MSE). The results of the study show that ENN is more accurate than CFNN. ENN had the lowest MSE of 0.00664 at a configuration of N=8 and R of 0.9922 with a training time of 6.4 seconds, while CFNN recorded the lowest MSE of 0.024306 with N=25. ENN's ability to capture time series patterns in PV is more reliable and effective. Reliable predictions can assist in energy management systems because they help maintain supply balance, reduce the risk of failure, and improve system stability.
Co-Authors Irsyad, Hidayat Hatta Mahmudah, Norma Muttakin, Muhammad Nusyura, Fauzan Putra, Nanda Dwi Rezi Delfianti Rusmana, Najwa Rokhan Sigit, Firman Matiinu Syafwan, Muhammad Ikram