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

Kombinasi Synthetic Minority Oversampling Technique (SMOTE) dan Neural Network Backpropagation untuk menangani data tidak seimbang pada prediksi pemakaian alat kontrasepsi implan

Combination of Synthetic Minority Oversampling Technique (SMOTE) and Backpropagation Neural Network to handle imbalanced class in predicting the use of contraceptive implants  Kegagalan akibat pemakaian alat kontrasepsi implan merupakan terjadinya kehamilan pada wanita saat menggunakan alat kontrasepsi secara benar. Kegagalan pemakaian kontrasepsi implan tahun 2018 secara nasional sejumlah 1.852 pengguna atau 4% dari 41.947 pengguna. Rasio angka kegagalan dan keberhasilan pemakaian kontrasepsi implan yang cenderung tidak seimbang (imbalance class) membuatnya sulit diprediksi. Ketidakseimbangan data terjadi jika jumlah data suatu kelas lebih banyak dari data lain. Kelas mayor merupakan jumlah data yang lebih banyak, sedangkan kelas minor jumlahnya lebih sedikit. Algoritma klasifikasi akan mengalami penurunan performa jika menghadapi kelas yang tidak seimbang. Synthetic Minority Oversampling Technique (SMOTE) digunakan untuk menyeimbangkan data kegagalan pemakaian kontrasepsi implan. SMOTE menghasilkan akurasi yang baik dan efektif daripada metode oversampling lainnya dalam menangani imbalance class karena mengurangi overfitting. Data yang sudah seimbang kemudian diprediksi dengan Neural Network Backpropagation. Sistem prediksi ini digunakan untuk mendeteksi apakah seorang wanita mengalami kehamilan atau tidak jika menggunakan kontrasepsi implan. Penelitian ini menggunakan 300 data, terdiri dari 285 data mayor (tidak hamil) dan 15 data minor (hamil). Dari 300 data dibagi menjadi dua bagian, 270 data latih dan 30 data uji. Dari 270 data latih, terdapat 13 data latih minor dan 257 data latih mayor. Data latih minor pada data latih diduplikasi sebanyak data pada kelas mayor sehingga jumlah data latih menjadi 514, terdiri dari 257 data mayor, 13 data minor asli, dan 244 data minor buatan. Sistem prediksi menghasilkan nilai akurasi sebesar 96,1% pada epoch ke-500 dan 1.000. Implementasi kombinasi SMOTE dan Neural Network Backpropagation terbukti mampu memprediksi pada imbalance class dengan hasil prediksi yang baik.  The failed contraceptive implant is one of the sources of unintended pregnancy in women. The number of users experiencing contraceptive-implant failure in 2018 was 1,852 nationally or 4% out of 41,947 users. The ratio between failure and success rates of contraceptive implant, which tended to be unbalanced (imbalance class), made it difficult to predict. Imbalance class will occur if the amount of data in one class is bigger than that in other classes. Major classes represent a bigger amount of data, while minor classes are smaller ones. The imbalance class will decrease the performance of the classification algorithm. The Synthetic Minority Oversampling Technique (SMOTE) was used to balance the data of the contraceptive implant failures. SMOTE resulted in better and more effective accuracy than other oversampling methods in handling the imbalance class because it reduced overfitting. The balanced data were then predicted using backpropagation neural networks. The prediction system was used to detect if a woman using a contraceptive implant was pregnant or not. This study used 300 data, consisting of 285 major data (not pregnant) and 15 minor data (pregnant). Of 300 data, two groups of data were formed: 270 training data and 30 testing data. Of 270 training data, 13 were minor training data and 257 were major training data. The minor training data in the training data were duplicated as much as the number of data in major classes so that the total training data became 514, consisting of 257 major data, 13 original minor data, and 244 artificial minor data. The prediction system resulted in an accuracy of 96.1% on the 500th and 1,000th epochs. The combination of SMOTE and Backpropagation Neural Network was proven to be able to make a good prediction result in imbalance class