PENERAPAN ALGORITMA K-MEANS CLUSTERING UNTUK PERANCANGAN WEB APLIKASI DAILY REPORTING PRODUCTION PRESS (PT ASTRA DAIHATSU MOTOR)

PAMBAGYO, DALU MUKTI (2024) PENERAPAN ALGORITMA K-MEANS CLUSTERING UNTUK PERANCANGAN WEB APLIKASI DAILY REPORTING PRODUCTION PRESS (PT ASTRA DAIHATSU MOTOR). S1 thesis, Universitas Mercu Buana - Menteng.

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Abstract

Seiring perkembangan tenknologi yang makin pesat, keamanan suatu informasi sangat lah penting. Beberapa macam cara dilakukan untuk mendapatkan sebuah informasi ataupun data valid, realtime, akurat, dan tentunya dari sumber yang valid, salah satunya K-Means Clustering. K-Means Clustering adalah salah satu algoritma unsurprised learning yang termasuk ke dalam analisis klaster (Cluster Analysis) non hirarki yang digunakan untuk mengelompokkan data berdasarkan variable atau feature. K-Means Clustering bertujuan mendapatkan kelompok data dengan memaksimalkan kesamaan karakteristik dalam klaster dan memaksimalkan perbedaan antar klaster. Algoritma K-means clustering mengelompokkan data berdasarkan jarak antara data terhadap titik centroid klaster yang didapatkan melalui proses berulang. Analisis perlu menentukan jumlah K sebagai input algoritma. K-means clustering dapat kita gunakan jika kita melakukan pengelompokkan data berdasarkan variabel tertentu, di mana kita belum dapat menentukan kelas outputnya seperti apa (unsupervised learning). Algoritma ini dapat kita gunakan jika kita dihadapkan pada maslah yang penyelaesaiannya membutuhkan proses segmentasi atau pengelompokkan menjadi subgroup tertentu, seperti analisis pasar dan pelanggan. Algoritma ini dapat kita gunakan saat melakukan pembacaan intuitif terhadap data yang baru kita dapatkan. Selain itu, kita juga dapat memanfaatkan algoritma k-means clustering dalam proses Exploratory Data Analysis (EDA) untuk melengkapi proses analisis statistika deskriptif dan visualisasi data. Along with the rapid development of technology, information security is very important. Several ways are used to obtain valid, real-time, accurate information or data, and of course from valid sources, one of which is K-Means Clustering. K-Means Clustering is one of the elemental prioritized learning algorithms that is included in the non-hierarchical cluster analysis which is used to group data based on variables or features. K-Means Clustering aims to get groups of data by maximizing the similarity of characteristics within clusters and maximizing differences between clusters. The K-means clustering algorithm groups data based on the distance between the data and the cluster centroid points obtained through an iterative process. The analysis needs to determine the number of K as input to the algorithm. We can use K-means clustering if we group data based on certain variables, where we cannot determine what the output class is like (unsupervised learning). We can use this algorithm if we are faced with a problem whose solution requires a process of segmentation or grouping into certain subgroups, such as market and customer analysis. We can use this algorithm when making intuitive readings of the data we just got. In addition, we can also utilize the k-means clustering algorithm in the Exploratory Data Analysis (EDA) process to complete the descriptive statistical analysis process and data visualization.

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