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All Journal Buletin Ilmiah Sarjana Teknik Elektro Jurnal Pendidikan Teknik Elektro
L. Endah Cahya Ningrum
Universitas Negeri Surabaya
Education Electrical & Electronics Engineering

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PENGEMBANGAN MODUL PEMBELAJARAN TRAINER KIT ARDUINO BERBASIS PROJECT BASED LEARNING UNTUK MENINGKATKAN HASIL BELAJAR PESERTA DIDIK PADA MATA PELAJARAN PEMROGRAMAN MIKROKONTROLER DAN ALGORITMA DI SMKN 1 SIDOARJO Ryan Rasidi Purwanto; L. Endah Cahya Ningrum; Fendi Achmad; Yulia Fransisca
Jurnal Pendidikan Teknik Elektro Vol. 16 No. 01 (2027): Jurnal Pendidikan Teknik Elektro (April 2027)
Publisher : Universitas Negeri Surabaya

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Abstract

The problems identified in this study include the limited availability of practicum media and teacher-centered learning, which results in students being less active and having difficulty understanding programming concepts in an applied manner. These limitations are found in subjects that focus on analog systems, causing students of Grade XI Audio and Video Engineering at SMKN 1 Sidoarjo to struggle to understand Microcontroller Programming and Algorithms. This study aims to develop a module and Arduino trainer kit learning media based on Project-Based Learning to improve students’ learning outcomes in the Microcontroller Programming and Algorithms course. The research employed a Research and Development (R&D) method, along with the Shapiro-Wilk test, focusing on media development and comparison between two classes. The research instruments consisted of expert validation sheets, student response questionnaires, and learning outcome tests. The data were analyzed using quantitative descriptive techniques, normality testing, t-test, and N-Gain calculation. The results showed that the validation score of the teaching module was 94%, the student worksheet was 98%, and the questionnaire was 99%, indicating that the developed module is categorized as highly valid. The practicality level reached 81%, categorized as very practical. The effectiveness test results showed an improvement in students’ learning outcomes after the implementation of the Project-Based Learning module. Based on these findings, the developed learning module is feasible and effective in improving the learning outcomes of Grade XI TAV students. Keywords: learning module, Arduino trainer kit, project based learning, learning outcomes.
Maximization Very Short-Term Forecasting of Power Photovoltaic System Using Machine Learning Based on Clearness Index Model Unit Three Kartini; L. Endah Cahya Ningrum; M. Nur Adiwana
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 3 (2026): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i3.16181

Abstract

The hybrid model for very short-term photovoltaic (PV) power forecasting, covering one hour ahead with 20-minute intervals, combines the k-nearest neighbour (k-NN) and multilayer backpropagation neural network (BP-NN) methods. The uniqueness of this model lies in integrating meteorological and the clarity index. the data preprocessing stage, the k-NN method is applied, while the multilayer BP-NN is used for forecasting. The k-NN Multilayer BP-NN algorithm calculates the nearest data points using Euclidean distance, and then processes the training and testing data through the multilayer BP-NN to generate PV power predictions. The simulation dataset was divided into 70% training data and 30% testing data, with a maximum PV power output of 611 W. The error statistical indicators of machine learning using k-NN-BP-NN model RMSE 27.44 W and MSE 1.5 W. These superior results are attributed to more stable weather patterns and consistent solar radiation. The simulation validity test demonstrated that the k-NN Multilayer BP-NN algorithm achieved better accuracy compared to the k-NN decomposition method. In addition, the model offers high computational efficiency and short inference time, making it highly suitable for real-time PV power forecasting systems.
Co-Authors Fendi Achmad M. Nur Adiwana Ryan Rasidi Purwanto Unit Three Kartini Yulia Fransisca