Quality Function Deployment and Fuzzy TOPSIS Methods in Decision Support System for Internet Service Provider Selection

Internet Service Provider (ISP) is a company or business organization that provides access to intenet and services related for individual consumer or companies. There are many ISP in Indonesia recently, and they have almost the same product to offered. This problem makes internet service provider selection become a major issue. Decision support system can be used to recommend the best ISP company based on need. The aim of this research is to used Quality Function Deployment with Fuzzy TOPSIS sequentially to select the best ISP company as needed, and implemented in decision support system for internet service provider selection. Quality Function Deployment and Fuzzy TOPSIS methods used to evaluate, and then recommend the ISP company by ranked. Quality Function Deployment method used to find out customers requirements about internet network, the weighting of the criteria and the assessment of each ISP company. Fuzzy TOPSIS used to rank ISP company. These two methods produce consistent ratings when sensitivity analysis is performed for fuzzy and crisp value. These two methods make decision support system result can be trusted

Graf Fuzzy Produk

Fuzzy graph is a graph which is consists of a pairs of vertex and edge that have degree contained on closed interval of real number [0,1] on each edge and vertex. Product fuzzy graph was defined by Dr. V. Ramaswamy and Poornima B by replacing “infimum” in definition of fuzzy graph by “product”. In this paper we study product fuzzy graph complete, in connection complement of produk fuzzy graph, join of produk fuzzy graph and multiplication of produk fuzzy graph. We show that complement of the multiplication of two product fuzzy graphs complete is a multiplication of its complement, in which this disposition produces nil graph

Dilated Convolutional Neural Network for Skin Cancer Classification Based on Image Data

Skin cancer is a disorder of cell growth in the skin. Skin cancer has a big impact, causing physical disabilities that can be seen directly and high treatment costs. In addition, skin cancer also causes death if nor treated properly. Generally, dermatologists diagnose the presence of skin cancer in the human body by using the Biopsy process. In this study, the Dilated Convolutional Neural Network method was used to classify skin cancer image data. Dilated Convolutional Neural Network method is a development method of the Convolutional Neural Network method by modifying the dilation factors. The Dilated Convolutional Neural Network method is divided into two stages, including feature extraction and fully connected layer. The data used in this study is HAM1000 dataset. The data are dermoscopic image datasets which consists of 10015 images data from 7 types of skin cancer. This study conducted several experimental scenarios of changes in the value of d, which are 2,4,6, and 8 to get the optimal results. The parameters used in this study are epoch = 100, minibatch size = 8, learning rate = 0.1, and dropout = 0.5. The best results in this study were obtained with value of d=2 with the value of accuracy is 85.67% and the sensitivity is 65.48%

Statisticam approaches for consistency index in analytical hierarchy process

This study presents a method for estimating a new Random Index (RI) value to determine the acceptance or rejection of matrices in the Analytic Hierarchy Process, using the Saaty scale. The proposed RI values are compared to the matrix consistency levels of other researchers who conducted experiments with similar numbers and matrix orders. In the case of 1000 experiments, our values showed a slight improvement compared to those of Golden and Wang. For the 2500-experiment case, our values were similar to those reported by Lane and Verdine. Lastly, in the 100,000-experiment case, our values exhibited a slight improvement compared to those obtained by Alonso and Lamata. We welcome further suggestions and encourage future research in this area

SISTEM INFORMASI PERENCANAAN PRODUKSI DAN PENJADWALAN POLA TANAM HORTIKULTURA DENGAN MODEL LINEAR PROGRAMMING DAN FUZZY TIME SERIES

Penelitian tentang perencanaan produksi telah banyak dilakukan, tesis ini menyajikan sistem informasi perencanaan produksi dan penjadwalan pola tanam yang dihadapi oleh petani hortikultura dengan mengkombinasikan dua metode. Metode fuzzy time series digunakan untuk memprediksi jumlah permintaan dan hasil dari metode fuzzy time series menjadi salah satu variabel pada perhitungan Linear Programming. Kombinasi kedua metode ini tepat mewakili dan mendukung pengambilan keputusan penentuan jadwal penanaman dalam kegiatan pertanian hortikultura dengan menggunakan variabel pendukung, data permintaan, data produksi, data jumlah tenaga kerja, data luas lahan, data keuntungan produksi, data jumlah bibit dan data lama tanam, studi kasus yang digunakan adalah tanaman jamur dengan pengambilan data di “Rumah Jamur”. Sistem informasi perencanaan produksi dan penjadwalan pola tanam ini dapat memberikan rekomendasi pola tanam dan jumlah jamur yang harus ditanam dalam satu periode oleh pemilik “Rumah Jamur”, siklus hidup jamur dalam satu periode adalah empat bulan, jumlah penanaman disesuaikan dengan jumlah permintaan yang ada yang sebelumnya telah diprediksi dengan menggunakan fuzzy time series, hasil menunjukan dari empat skenario selang tanam didapatkan nilai pada skenario pertama jarak penanaman satu bulan Rp 5.327.266,00, pada skenario kedua, jarak penanaman dua bulan Rp 6.426.950,00, nilai skenario ketiga, jarak penanaman tiga bulan dengan nilai Rp 11.200.000,00, dan jarak penanaman empat bulan dengan nilai Rp 8.742.400,00 berdasarkan hasil skenario satu, dua, tiga dan empat didapatkan nilai optimal pada skenario ke tiga Rp 11.200.000,00 dengan penanaman bibit jamur tidak semua ditanam di awal, tetapi dipecah dengan penanaman bibit berikutnya diberi jarak tiga bulan sebanyak penanaman bulan pertama 775, kedua 972, ketiga 1172, dan keempat 836. Kata Kunci : Sistem Informasi, Perencanaan Produksi, Penjadwalan Pola Tanam, Fuzzy Time Series, Linear Programming. Research on production planning has been widely performed, This thesis presents the information system production planning and planting patterns scheduling faced by horticulture farmer by combining two methods. Fuzzy time series method used to predict demand. The result of fuzzy time series method will be one of variables in Linear Programming calculation. Combination of both of these methods appropriately represent and support decision making determination of planting schedule in horticulture farming activities by using variable data demand, production, amount of farmers, size of areas, production advantage, amount of seeds and age of the plant, the case study used is mushroom plant with data collection at “Rumah Jamur”. Production planning and planting patterns scheduling information system give planting patterns recommendation and how much mushroom must be planted in one periods by the owner of “Rumah Jamur”, age of mushroom in one period is four months, planting mushroom be adjusted with demand which had previously been predicted by using fuzzy time series, the result is show for four scenario hose planting the value of profit first scenario is Rp 5.327.266,00, second scenario is Rp 6.426.950,00, third scenario is Rp 11.200.000,00, and fourth scenario is Rp 8.742.400,00, based on four scenarios the optimal profit value in third scenario Rp 11.200.000,00 with planting of mushroom divided every three months, in the first month is 775 seeds, in the second month 972 seeds, in third month 1172 seeds and the last month is 836 seeds. Keywords : Production Planning; Information System; Scheduling Planting Patterns; Fuzzy Time Series; Linear Programming

Fuzzy-AHP MOORA approach for vendor selection applications

Vendor selection is a critical activity in order to support the achievement of company success and competitiveness. Significantly, the company has some specific standards in the selection. Therefore, an evaluation is needed to see which vendors match the company's criteria. The purpose of this study is to evaluate and select the proposed vendor in a web-based decision support system (DSS) by using the fuzzy-AHP MOORA approach. The fuzzy-AHP method is used to determine the importance level of the criteria, while the MOORA method is used for alternative ranking. The results showed that vendor 4 has the highest score than other alternatives with a value of 0.2536. Sensitivity analysis showed that the proposed DSS fuzzy-AHP MOORA concept was already solid and suitable for this problem, with a low rate of change