Briliantara, Muhammad Kharisma Akbar (2024) Klasifikasi Timah Batangan Pada Proses Casting Ingot Metal Lead Recovery (MLR) Area Menggunakan Metode Artificial Neural Network (ANN). Diploma thesis, Institut Teknologi Sepuluh Nopember.
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2040201144-Undergraduate_Thesis.pdf - Accepted Version Restricted to Repository staff only until 1 October 2026. Download (7MB) | Request a copy |
Abstract
PT. GS Battery, sebagai produsen aki pertama di Indonesia dengan lisensi dari Japan Storage Battery Co. Ltd., memproduksi timah batangan berbahan dasar asam (H2SO4) dan timbal (Pb). Pada area Metal Lead Recovery (MLR), proses pembuatan timah batangan sangat memengaruhi kualitas produk akhir. Standarisasi ketinggian dan massa timah batangan ditetapkan pada 76-77 mm dan 26-27 kg. Namun, proses Quality Control yang dilakukan secara manual menyebabkan ketidakefisienan waktu dan akurasi klasifikasi yang rendah. Dari Maret hingga September 2023, 1% dari total 111280 pcs timah batangan tidak terklasifikasi dengan baik, mempengaruhi kualitas bahan baku baterai.Proyek akhir ini bertujuan mengembangkan sistem klasifikasi timah batangan berbasis pengukuran ketinggian dan massa dengan implementasi metode Artificial Neural Network (ANN). Sistem ini dapat meningkatkan efisiensi waktu, memudahkan man power dalam proses klasifikasi, serta meningkatkan akurasi klasifikasi standar atau grade timah batangan. Pengujian sistem sebelum pengembangan menunjukkan akurasi klasifikasi 88.50% dengan cycle time 107 menit. Setelah pengembangan, akurasi meningkat menjadi 94.60% dengan cycle time 65 menit. Penggunaan metode ANN lebih lanjut meningkatkan akurasi klasifikasi hingga 98.42% dengan cycle time 73 menit. Peningkatan ini menunjukkan bahwa metode ANN secara signifikan meningkatkan kinerja sistem klasifikasi timah batangan.
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PT. GS Battery, as the first battery manufacturer in Indonesia licensed by Japan Storage Battery Co. Ltd., produces lead ingots based on sulfuric acid (H2SO4) and lead (Pb). In the Metal Lead Recovery (MLR) area, the process of manufacturing lead ingots significantly affects the quality of the final product. The standardization of the height and weight of lead ingots is set at 76-77 mm and 26-27 kg. However, the manual Quality Control process leads to inefficiencies in time and low classification accuracy. From March to September 2023, 1% of a total of 111,280 lead ingots were not classified correctly, affecting the quality of battery raw materials. This final project aims to develop a lead ingot classification system based on height and weight measurements with the implementation of the Artificial Neural Network (ANN) method. This system is expected to improve time efficiency, facilitate manpower in the classification process, and increase the accuracy of standard or grade classification of lead ingots. Testing the system before development showed a classification accuracy of 88.50% with a cycle time of 107 minutes. After development, the accuracy increased to 94.60% with a cycle time of 65 minutes. The use of the ANN method further improved classification accuracy to 98.42% with a cycle time of 73 minutes. This improvement indicates that the ANN method significantly enhances the performance of the lead ingot classification system.
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