Implementasi Klasterisasi K-Means pada Master-Slave Genetic Algorithm untuk Menurunkan Waktu Komputasi: Studi Kasus Optimasi Mesin CNC Batik Tulis
Submission Date: 2022-06-21 09:26:46
Accepted Date: 2022-09-29 17:21:24
Abstract
Mayoritas penelitian mengenai otomasi proses pembatikan menggunakan mesin Computer Numerical Control (CNC) terfokus pada aspek kualitas. Tantangan selanjutnya adalah menentukan bagaimana cara menurunkan waktu permesinan. Penelitian sebelumnya telah berhasil menurunkan waktu permesinan dengan mengoptimalkan urutan pembatikan menggunakan Master-Slave Genetic Algorithm sehingga dapat meminimalkan total jarak tempuh pembatikan. Namun, penelitian tersebut masih memiliki kekurangan yaitu proses komputasi yang lama, yang dikarenakan banyaknya instances atau jumlah segmen batik yang harus dioptimasi. Penelitian ini bertujuan untuk menurunkan waktu komputasi dengan teknik klasterisasi K-means, sehingga jumlah segmen yang dioptimasi pada setiap klaster menjadi lebih sederhana dan proses komputasi menjadi lebih efisien. Hasil penelitian menunjukkan bahwa teknik klasterisasi dapat menurunkan waktu komputasi secara signifikan dengan hasil yang lebih baik.
Keywords
Genetic Algorithm; K-Means; Klasterisasi; Mesin CNC Batik; Optimasi
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