Replacement of Faulty Resistors Using Optimization of Parallel-Series-Parallel Configuration Based on Genetic Algorithm
Abstract
Resistor damage is a common problem that frequently hinders electronic device repair, especially when suitable and specific replacement components with the correct resistance values are unavailable in the market. This research proposes the use of genetic algorithms to optimize the combination of parallel-series-parallel resistor arrangements as a solution for replacing damaged resistors effectively. Genetic algorithms are chosen for their superior ability to find optimal solutions under limited data conditions. In this context, crossover probability (Pco) refers to the chance of two solutions (resistor configurations) exchanging parts of their 'genetic material' to create new solutions, while mutation (Pmu) is a random change in a solution that helps introduce solution diversity and prevents the algorithm from getting trapped in local optima. Simulations were conducted using the C programming language to generate efficient configurations for damaged resistor replacement. By utilizing five available resistance values (10Ω, 20Ω, 30Ω, 43Ω, and 51Ω), test results show that by applying Pco of 80–90% and Pmu of 10%, the genetic algorithm is capable of producing configurations that approximate or are identical to the target resistance desired within 2 to 7 generations. This success confirms that the proposed method is highly applicable and efficient in the context of electronic repairs. This research makes a significant contribution by offering a practical and effective solution for technicians and electronic service providers, especially when facing component limitations, thereby enabling quick and accurate resistor replacement in the field.
Kerusakan resistor merupakan masalah umum yang sering terjadi dan menghambat perbaikan perangkat elektronik, terutama saat komponen pengganti dengan nilai resistansi yang sesuai dan spesifik tidak tersedia di pasaran. Penelitian ini mengusulkan penggunaan algoritma genetika untuk mengoptimalkan kombinasi susunan resistor paralel-seri-paralel sebagai solusi pengganti resistor yang rusak secara efektif. Algoritma genetika dipilih karena kemampuannya yang unggul dalam menemukan solusi optimal pada kondisi data yang terbatas. Dalam konteks ini, probabilitas crossover (Pco) merujuk pada peluang dua solusi (konfigurasi resistor) bertukar sebagian 'materi genetikanya' untuk menciptakan solusi baru, sementara mutasi (Pmu) adalah perubahan acak pada sebuah solusi yang membantu memperkenalkan keberagaman solusi dan mencegah algoritma terjebak pada lokal optimum. Simulasi dilakukan menggunakan bahasa pemrograman C++ untuk menghasilkan konfigurasi efisien dalam penggantian resistor rusak. Dengan memanfaatkan lima nilai resistansi yang tersedia (10Ω, 20Ω, 30Ω, 43Ω, dan 51Ω), hasil uji menunjukkan bahwa dengan menerapkan Pco 80–90% dan Pmu 10%, algoritma genetika mampu menghasilkan konfigurasi yang mendekati atau identik dengan resistansi target yang diinginkan dalam 2 hingga 7 generasi. Keberhasilan ini menegaskan bahwa metode yang diusulkan sangat aplikatif dan efisien dalam konteks perbaikan elektronik. Penelitian ini memberikan kontribusi signifikan dengan menawarkan solusi praktis dan tepat guna bagi teknisi dan penyedia jasa servis elektronik, terutama saat menghadapi kendala keterbatasan komponen, sehingga memungkinkan penggantian resistor yang cepat dan akurat di lapangan.
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DOI: https://doi.org/10.37905/jjeee.v7i2.31388
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