Treffer: Integrasi Algoritma Support Vector Machine dengan Java untuk Memprediksi Kualitas Komponen Otomotif dalam Industri 4.0.

Industry 4.0 marks a major transformation in the manufacturing sector, including the automotive industry, through the integration of intelligent technologies such as machine learning to enhance production efficiency and quality. This study examines the implementation of the Support Vector Machine (SVM) algorithm integrated with the Java programming language to accurately predict the quality of automotive components. SVM is known for its effectiveness in classifying complex data and is well-suited for production environments that require high precision. The research process involves data collection and preprocessing of component quality, training of the SVM model, and implementation of the model within a Java platform to allow seamless integration with existing industrial automation systems. Testing results show that the developed SVM model can classify component quality with high accuracy, offering significant potential in reducing defective products and improving production efficiency. Integration with Java enables this predictive system to be easily implemented into industrial software infrastructures based on Java. This study highlights that the combination of machine learning and applied programming can be a strategic solution in supporting the digital transformation of the automotive industry in the Industry 4.0 era. [ABSTRACT FROM AUTHOR]

Industri 4.0 menandai transformasi besar dalam sektor manufaktur, termasuk industri otomotif, dengan integrasi teknologi cerdas seperti machine learning untuk meningkatkan efisiensi dan kualitas produksi. Penelitian ini mengkaji penerapan algoritma Support Vector Machine (SVM) yang diintegrasikan dengan bahasa pemrograman Java untuk memprediksi kualitas komponen otomotif secara akurat. SVM dikenal efektif dalam klasifikasi data yang kompleks dan sangat cocok untuk lingkungan produksi yang memerlukan ketepatan tinggi. Proses penelitian mencakup pengumpulan dan pra-pemrosesan data kualitas komponen, pelatihan model SVM, serta implementasi model dalam platform Java guna memungkinkan integrasi dengan sistem otomasi industri yang telah ada. Hasil pengujian menunjukkan bahwa model SVM yang dibangun mampu mengklasifikasikan kualitas komponen dengan akurasi yang tinggi, memberikan potensi signifikan dalam pengurangan produk cacat dan peningkatan efisiensi produksi. Integrasi dengan Java memungkinkan sistem prediksi ini mudah diimplementasikan dalam infrastruktur perangkat lunak industri yang berbasis Java. Penelitian ini menunjukkan bahwa kombinasi machine learning dan pemrograman terapan dapat menjadi solusi strategis dalam mendukung transformasi digital industri otomotif di era Industri 4.0. [ABSTRACT FROM AUTHOR]

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