GUI MATLAB UNTUK KOMBINASI METODE ANALYTIC HIERARCHY PROCESS (AHP) DAN TOPSIS DALAM PEMILIHAN CAFE TERFAVORIT (STUDI KASUS : Pemilihan Cafe Terfavorit di Daerah Tembalang, Semarang)

Putri Aulia Netra; Tatik Widiharih; Hasbi Yasin

doi:10.14710/j.gauss.5.3.515-524

DOI: https://doi.org/10.14710/j.gauss.5.3.515-524

GUI MATLAB UNTUK KOMBINASI METODE ANALYTIC HIERARCHY PROCESS (AHP) DAN TOPSIS DALAM PEMILIHAN CAFE TERFAVORIT (STUDI KASUS : Pemilihan Cafe Terfavorit di Daerah Tembalang, Semarang)

*Putri Aulia Netra -

Tatik Widiharih -

Hasbi Yasin -

BibTex Citation Data :

@article{J.Gauss14708,
    author = {Putri Netra and Tatik Widiharih and Hasbi Yasin},
    title = {GUI MATLAB UNTUK KOMBINASI METODE ANALYTIC HIERARCHY PROCESS (AHP) DAN TOPSIS DALAM PEMILIHAN CAFE TERFAVORIT (STUDI KASUS : Pemilihan Cafe Terfavorit di Daerah Tembalang, Semarang)},
    journal = {Jurnal  Gaussian},
  volume = {5},
    number = {3},
    year = {2016},
    keywords = {AHP, TOPSIS, cafe, favorite, preference},
    abstract = { Tembalang is an area that has many culinary business. One of them is cafe bussiness. This condition causes high competition in attracting consumers to gain profit. According to this situation, we need a method to asses the most favourite cafe based on consumer taste to create cafe as they expected. The methods used in choosing the most favourite cafe are  Analytic Hierarchy Process  (AHP) and  Technique for Order Preference by Similarity to Ideal Solution  (TOPSIS). Both of method are the methods used to solve the  Multi-Attribute Decision Making  (MADM) problem. AHP is used as a method of weighting each criteria by forming pairwise comparison matrix, normalizing pairwise comparison matrix, weighting and testing the consistency of the weight that was gained. Whereas TOPSIS is used to rank the most favorite cafe by calculating the weighted-normalized decision matrix MADM, determining the positive and negative ideal solution, calculating the distance between each alternative with positive and negative ideal solution and calculating the value of preference for each alternatives. There are eight cafes and fourteen criterias. The criterias are the taste of foods and drinks, price, site accessibility, wifi, the neatness of waiters, the hospitality of waiters, waiters’s knowledge about menu, the accuracy of the preparation of the foods and drinks, transaction convenience, varian of menu, the safety and cleanliness of area, handling against misstatement, layout and decoration, and serving. The result of this research is: the most preferred cafe has 0.84322 of preference value.  Preference value which calculated manually has similar result with  Graphical User Interface  (GUI)  Matlab.   Keywords : AHP, TOPSIS, cafe, favorite, preference },
   issn = {2339-2541},   pages = {515--524}  doi = {10.14710/j.gauss.5.3.515-524},
    url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/14708}
}

Citation Format:

Abstract

Tembalang is an area that has many culinary business. One of them is cafe bussiness. This condition causes high competition in attracting consumers to gain profit. According to this situation, we need a method to asses the most favourite cafe based on consumer taste to create cafe as they expected. The methods used in choosing the most favourite cafe are Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Both of method are the methods used to solve the Multi-Attribute Decision Making (MADM) problem. AHP is used as a method of weighting each criteria by forming pairwise comparison matrix, normalizing pairwise comparison matrix, weighting and testing the consistency of the weight that was gained. Whereas TOPSIS is used to rank the most favorite cafe by calculating the weighted-normalized decision matrix MADM, determining the positive and negative ideal solution, calculating the distance between each alternative with positive and negative ideal solution and calculating the value of preference for each alternatives. There are eight cafes and fourteen criterias. The criterias are the taste of foods and drinks, price, site accessibility, wifi, the neatness of waiters, the hospitality of waiters, waiters’s knowledge about menu, the accuracy of the preparation of the foods and drinks, transaction convenience, varian of menu, the safety and cleanliness of area, handling against misstatement, layout and decoration, and serving. The result of this research is: the most preferred cafe has 0.84322 of preference value. Preference value which calculated manually has similar result with Graphical User Interface (GUI) Matlab.