BIANTORO, BAMBANG (2023) ANALISA REAL-TIME DATA MINING PRODUKSI UNTUK MENINGKATKAN KUALITAS RADIAL RUN OUT BAN. S2 thesis, Universitas Mercu Buana Jakarta.
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Abstract
The new era in the industry today is the industrial revolution 4.0. The use of information technology creates faster manufacturing production processes with a high degree of automation. The use of barcode or radio frequency identification technology has made the tire manufacturing process towards the era of the industrial revolution 4.0. Production process transactions in the tire industry have switched from manual transaction to electronic systems. Production process history data is available and easy to access again in real-time. This study aims to improve the quality of the radial run out tires by analyzing historical data on real-time production processes using data mining techniques. The cluster analysis technique is used to classify the type of quality of the radial run out tires. Analysis of the factors causing the radial run out tire problem in each quality group was carried out using a decision tree analysis. Cluster analysis using the elbow method verification shows that the optimal number of clusters of radial quality run out tires is divided into 3 clusters. Two clusters show the problem of high radial run out values. The dominant factor causing the high radial run out value is the material lot variation in the Bead, Sidewall and Tread components. To solve the problem of radial run out, corrective action was taken in the cutting process length of component the Bead, Sidewall and Tread materials. The effect of action taken shows that the defect of radial defect run out the tires is decreases by 99.9% Keywords: Tires Industry, Tires Quality, Data Mining, Cluster Analysis, Decision tree Analysis Era baru dalam industri sekarang ini adalah revolusi industri 4.0. Penggunaan teknologi informasi menciptakan proses produksi manufacturing semakin cepat dengan tingkat otomatisasi yang tinggi. Penggunaan teknologi barcode atau radio frequency identification menjadikan proses manufacturing ban menuju era revolusi industry 4.0. Transaksi proses produksi di industri ban beralih dari sistem transaksi manual menjadi elektronik. Data histori proses produksi tersedia dan mudah untuk di akses kembali secara real-time. Penelitian ini bertujuan untuk melakukan perbaikan kualitas radial run out ban dengan menganalisa data histori proses produksi real-time menggunakan teknik data mining. Teknik cluster analysis digunakan untuk mengelompokkan jenis kualitas radial run out ban. Analisa faktor penyebab permasalahan radial run out ban pada masing-masing kelompok kualitas dilakukan menggunakan decision tree analysis. Cluster analysis dengan menggunakan verifikasi metoda elbow menunjukkan bahwa jumlah optimal cluster kualitas radial run out ban terbagi menjadi 3 cluster. Dua cluster menunjukkan adanya permasalahan nilai radial run out yang tinggi. Faktor dominan penyebab tingginya nilai radial run out tinggi adalah variasi lot material pada komponen Bead, Sidewall dan Tread. Untuk mengatasi permasalahan radial run out dilakukan tindakan perbaikkan pada proses pemotongan panjang material komponen Bead, Sidewall dan Tread. Pengaruh perbaikkan menunjukkan defect radial run out ban turun sebesar 99,9% . Keywords: Industri Ban, Kualitas Ban, Data Mining, Cluster Analysis, Decision tree Analysis
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