Pengelompokan Incident dalam Lingkungan Kerja Warehouse Menggunakan Text Mining

Syaani, Fiika Arma`atus (2019) Pengelompokan Incident dalam Lingkungan Kerja Warehouse Menggunakan Text Mining. Undergraduate thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Kecelakaan dapat terjadi kapan saja dan dimana saja baik disengaja maupun tidak disengaja khususnya pada sektor industri. Sebagai tindakan pencegahan, diperlukan sebuah analisis guna mengetahui pola-pola yang terbentuk berdasarkan informasi pada data incident yang dilaporkan. Tujuannya agar perusahaan mampu mengidentifikasi dan melakukan tindakan penanganan secara cepat apabila terjadi hal yang serupa. Dilakukan pendekan Text Mining dengan menerapkan teknik bigram untuk membentuk pola-pola incident tersebut. Metode analisis yang digunakan adalah K-Means dan Hierarchical Clustering. Selain itu, digunakan pula feature selection dengan menerapkan metode Genetic Algorithm untuk mendapatkan feature optimal. Hasil yang diperoleh menyatakan bahwa proses feature selection sangat berpengaruh terhadap pembentukan klaster incident. Jika dibandingkan, metode K-Means maupun Hierarchical Clustering memberikan pengaruh yang berbeda pada setiap sektor warehouse. Hasil terbaik pada Sektor Life Style dan Technology terbentuk dengan menggunakan metode K-Means Clustering sedangkan pada Sektor Consumer, Retail, dan SPL didapatkan hasil klaster terbaik berdasarkan metode Hierarchical Clustering.
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Incidents can occur anytime and anywhere whether intentional or unintentional, especially in the industrial area. To prevent the incident, an analysis is needed to find out the patterns that formed based on the information from incident reports. The goal for companies are to be able to identify and take action to deal quickly if some incident happens. Text Mining is done by applying the bigram technique to form the incident patterns. The analytical method used is K-Means Clustering and Hierarchical Clustering. In addition, a feature selection is also used by applying the Genetic Algorithm method to obtain optimal features. The results obtained state that the feature selection process is very influential on the formation of incident clusters. When compared, the K-Means Clustering and Hierarchical Clustering methods have different effects on each warehouse sector. The best results in the Sector of Life Style and Sector of Technology are formed by using the K-Means Clustering method while in the Sector of Consumer, Retail and SPL the best cluster results are obtained based on the Hierarchical Clustering method.

Item Type:	Thesis (Undergraduate)
Additional Information:	RSSt 519.53 Sya p-1 2018
Uncontrolled Keywords:	Bigram, Genetic Algorithm, Incident Report, Text Clustering, Warehouse
Subjects:	Q Science Q Science > QA Mathematics > QA278.55 Cluster analysis Q Science > QA Mathematics > QA402.5 Genetic algorithms. Interior-point methods. Q Science > QA Mathematics > QA76.9.D343 Data mining. Querying (Computer science) Q Science > QA Mathematics > QA9.58 Algorithms
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49201-(S1) Undergraduate Thesis
Depositing User:	Syaani Fiika Arma'atus
Date Deposited:	23 Nov 2021 07:55
Last Modified:	23 Nov 2021 07:55
URI:	http://repository.its.ac.id/id/eprint/61540

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