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All Journal Journal of Mechanical Engineering Education (Jurnal Pendidikan Teknik Mesin) Jurnal Mantik Journal of Artificial Intelligence and Digital Business
Miftakhudin, Muhammad
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Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mechanical Engineering

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DETERMINING STANDARD TIME OF PRACTICUMS IN MAINTENANCE DOMESTIC AIR CONDITIONING SYSTEM FOR VOCATIONAL SCHOOLS STUDENT Miftakhudin, Muhammad; Suherman, Amay; Berman, Ega Taqwali
Journal of Mechanical Engineering Education Vol 7, No 1 (2020): Juni
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jmee.v7i1.29437

Abstract

Maintenance of the domestic air-conditioning system is a basic competency that exists in the refrigeration and air-conditioning expertise program. At present there is no set standard for the time students have to take in one practice training in maintaining the domestic air conditioning system. This study aims to obtain standard time practicum in maintenance domestic air conditioning systems for vocational scholls student. This study describes the standard time results of maintenance work on the domestic air conditioning unit using a quantitative approach with pre experimental design. Data collection uses test and observation instruments, where time measurement uses the stopwatch. The results showed that the standard time for maintaining an ideal domestic air conditioning system for students to achieve competence was 49.18 minutes. The standard timecan be reached after students repeat the practice of maintaining the domestic air conditioning system three times. This research complicates the students' self-confidence is increasing in the practice of maintaining the domestic air conditioning system.
Application of artificial neural network with optimization of genetic algorithms for weather prediction Gunawan, Gunawan; Miftakhudin, Muhammad; Arif, Zaenul
Jurnal Mantik Vol. 8 No. 1 (2024): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v8i1.5225

Abstract

This research integrates Artificial Neural Network (ANN) with Genetic Algorithm Optimization (GA) to improve the accuracy of weather prediction. This method utilizes ANN-optimized GA, creating a model that can adapt to the dynamics of weather patterns. Using a dataset that includes meteorological variables such as temperature, humidity, and precipitation from January 1, 2023, to October 28, 2023, the model was tested for its ability to predict weather conditions accurately. The process begins with data preprocessing, ANN training, and GA optimisation. The evaluation showed that the optimized model was able to reduce the Mean Absolute Error (MAE) from 1.6865 to 0.8701, the Mean Absolute Percentage Error (MAPE) from 5.9864 to 3.1408, and the Root Mean Squared Error (RMSE) from 2.253 to 1.039, signalling a significant improvement in prediction accuracy and efficiency. This research confirms the potential of ANN and GA integration in improving weather prediction, providing new insights for developing more accurate and reliable prediction models for various applications, from agriculture to disaster management.
Integrasi Artificial Neural Network dan Algoritma Genetika untuk Prediksi Bencana Banjir Pesisir Kota Tegal Miftakhudin, Muhammad; Murtopo, Aang Alim; Arif, Zaenul
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 3 (2025): Agustus - October
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i3.2068

Abstract

Banjir merupakan ancaman rutin di Kota Tegal yang disebabkan oleh curah hujan tinggi, buruknya drainase, dan kenaikan muka air laut. Penelitian ini bertujuan mengembangkan model prediksi banjir berbasis kecerdasan buatan menggunakan Artificial Neural Network (ANN) yang dioptimasi dengan Algoritma Genetika (AG). Data cuaca harian tahun 2024–2025 dari BMKG digunakan sebagai basis pelatihan, mencakup variabel seperti temperatur, curah hujan, kelembapan, dan kecepatan angin. Model ANN bertipe Multilayer Perceptron (MLP) digunakan untuk mengenali pola non-linier, sementara AG mengoptimasi hyperparameter penting guna meningkatkan akurasi. Evaluasi kinerja model dilakukan menggunakan metrik Mean Absolute Error (MAE) dan Root Mean Squared Error (RMSE). Hasil menunjukkan bahwa model ANN yang telah dioptimasi dengan AG mengalami peningkatan akurasi signifikan dibandingkan model baseline tanpa optimasi, dengan penurunan MAE sebesar 19,63% dan RMSE sebesar 26,31%. Temuan ini menunjukkan bahwa pendekatan hibrida ANN-AG efektif digunakan dalam prediksi bencana banjir berbasis data cuaca. Model ini berpotensi diimplementasikan dalam sistem peringatan dini banjir yang adaptif dan akurat di wilayah pesisir
Co-Authors Aang Alim Murtopo Amay Suherman Arif, Zaenul Ega Taqwali Berman Gunawan