Pengaruh Segmentasi Pasar Terhadap Peningkatan Volume Penjualan

Penelitian ini untuk mengetahui besar pengaruh segmentasi pasar terhadap peningkatan volume penjualan. Jenis penelitian ini adalah penelitian kuantitatif. Populasi dalam penelitian ini adalah konsumen Yongky Mart Kota Gunungsitoli yang berjumlah 30 orang. Jenis data yang digunakan dalam penelitian ini adalah data primer. Teknik pengumpulan data dengan observasi, kuesioner. Hasil Segmentasi pasar berpengaruh terhadap peningkatan volume penjualan di Yongky Mart Kota Gunungsitoli sebesar 58,982% sedangkan sisanya dipengaruhi oleh faktor-faktor lain yang tidak termasuk dalam variabel penelitian ini sebesar 41,018%. Sehingga segmentasi pasar mampu mempengaruhi peningkatan volume penjualan di Yongky Mart Kota Gunungsitoli.

A Novel Technique for Finding Rough Set Based Dynamic Reducts

Volumetric increase in data along with the curse of dimensionality has diverted the recent trends of computer science. Processing such a massive amount of data is a computationally expensive job. Feature selection is the process of selecting subset of data from the entire dataset that contains most of the information. The selected subset is called Reduct. Feature selection has materialized the idea of jumbling with attributes. Subset of attributes is favored which bounces the same information as the wide-ranging set of variables. Various dynamic reduct finding algorithms have been proposed. Dynamic reducts is an extension to the idea of reduct extraction based on rough set. Sub-tables are randomly drawn from the original decision table and reducts are extracted from these sub-tables. These reducts are considered to be the stable reducts for complete dataset. However, all the existing dynamic reduct finding algorithms are computationally too expensive to be used for datasets beyond smaller size. In this research, a novel dynamic reduct finding technique based on rough set theory is proposed, where dynamic reducts and relative dependency are the two key notions. Reducts are selected, optimized and further generalized through strenuous Parallel Feature Sampling (PFS) algorithm. In-depth analysis is performed using various benchmark datasets to justify the proposed approach. Results have shown that the proposed algorithm outperforms the existing state of the art approaches in terms of both efficiency and effectiveness.