Jabatan fungsional statistisi memiliki tugas utama yaitu melakukan kegiatan statistik. Kegiatan statistik ini termasuk penyediaan data dan informasi statistik serta analisis dan pengembangan statistik. Jabatan fungsional statistisi dalam menjalankan tugasnya, perlunya peningkatan kemampuan dengan mengikuti pelatihan di bidang statistika. Pelatihan ini juga perlu keluaran yang sesuai dengan yang dibutuhkan fungsional statistisi. Oleh karena itu, pengabdian ini bertujuan untuk membantu meningkatkan kompetensi fungsional statistisi pada berbagai instansi di Jawa Timur dalam mengolah data dan publikasi, diharapkan dengan adanya pelatihan ini bisa meningkatkan kebergunaan informasi yang diperoleh agar dapat tersalurkan dengan baik. Materi yang disampaikan yaitu Statistika Data Driven, Visual dan Analisis Data, serta Karya Ilmiah dan Publikasi. Setelah adanya pelatihan, dilanjutkan dengan pendampingan terhadap fungsional statistisi dalam pengolahan data dan pembuatan karya ilmiah yang disusun oleh fungsional statistisi. Selain itu, manfaat yang dapat diperoleh yaitu terjalinnya kerja sama yang baik antara fungsional statistisi di Jawa Timur dan Departemen Statistika di Institut Teknologi Sepuluh Nopember.
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 goals of company 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.
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