Comparative analysis of MCDM methods for product aspect ranking: TOPSIS and VIKOR

Alrababah, Saif A. Ahmad; Gan, Keng Hoon; Tan, Tien-Ping

doi:10.1109/iacs.2017.7921949

Cited by 19 publications

(15 citation statements)

References 12 publications

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“…dimana: * = Utility Measure terkecil − = Utility Measure terbesar * = Regret Measure terkecil − = Regret Measure terbesar = bobot maksimum group utility 1 − = bobot minimum individual regret Nilai yang biasa digunakan adalah 0,5 [13]. Nilai = 0.5 dimaksudkan untuk memaksimalkan group of benefit dan meminimalkan individual regret value [14].…”

Section: Metode Vikorunclassified

Rekomendasi Pemilihan Mobil dengan Algoritma VIKOR

Kristianto

Suryadibrata

Hansun

2021

JSI

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Penjualan mobil di Indonesia terus meningkat tiap tahunnya. Pada tahun 2009 terdapat 7,9 juta unit mobil di Indonesia dan pada 2016 telah mencapai 14,6 juta unit. Peningkatan penjualan kendaraan disebabkan oleh meningkatnya permintaan pasar. Namun demikian, tidak semua calon pembeli mengetahui mobil mana yang akan dibeli dan yang sesuai kebutuhannya. Peningkatan permintaan dari konsumen juga menyebabkan munculnya tipe-tipe mobil dengan kriteria inovatif untuk menarik calon pembeli. VIKOR merupakan algoritma yang cukup baik dalam pemecahan sistem rekomendasi dengan multi criteria. Oleh karenanya, algoritma VIKOR diterapkan di sini dengan menggunakan PHP dan framework CodeIgniter. Uji coba dilakukan dengan meminta tiga puluh responden untuk mengisi kuesioner EUCS yang mengukur kepuasan pengguna terhadap sistem yang dibangun. Hasil uji coba mendapatkan nilai persentase EUCS sebesar 77,167% yang tergolong dalam predikat baik.

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Section: Metode Vikorunclassified

Rekomendasi Pemilihan Mobil dengan Algoritma VIKOR

Kristianto

Suryadibrata

Hansun

2021

JSI

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“…Table 3. Simulation parameters For three criteria, Fuzzy AHP-Fuzzy SAW offers 29.03 %, 43.59 %, 55.55 %, 20 %, reduction in handover decision delay over the conventional SAW [4,6], TOPSIS [4,7], VIKOR [4,8] and Fuzzy SAW [4] schemes. For any number of increased inputs, Fuzzy AHP-Fuzzy SAW handover decision time will be lesser.…”

Section: Fuzzy Ahpmentioning

confidence: 99%

“…The compensatory algorithms combine multiple criteria to find the best network, whereas non-compensatory algorithms combine multiple criteria to find the acceptable network, which satisfies the minimum requirements. SAW [6], TOPSIS [7], VIKOR [8] algorithms come under compensatory category. These are popular for lower computational complexity and improved accuracy in decision making.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy based vertical handover network selection scheme for device-to-device communication

Subramani

Kumaravelu

2020

IJEECS

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<p>One of the most attractive and challenging areas in the upcoming next-generation 5G wirelessnetworkistheverticalhandover(VHO).Recently,manyoftheheterogeneous wireless communication technologies are introduced to satisfy the demands of users in all situations. Due to the deployment of heterogeneous networks, the users can access the internet anywhere, anytime through different wireless networks. To obtain seamless service and service continuity, the device should be handed over to the best wireless networks. Here, a half handover scheme for Device-to-Device (D2D) communication is implemented for the selection of the best network. The target network selection for vertical handover can be handled using multiple attribute decision making (MADM) methods. An intelligent and fast vertical handover decision is much needed, which should be reliable even for random and uncertain environments. Fuzzy logic is proved to be effective in handling imprecise data. Hence, in this work, the impact of combining fuzzy with the conventional MADM scheme, simple additive weighting(SAW)isanalyzedandthehybridschemeiscomparedwiththeconventional MADM schemes like SAW, Techniques for order preference by similarity to ideal solution (TOPSIS), VlseKriterijumska optimizacija I Kompromisno Resenje (VIKOR) in terms of handover decision delay. Since, the numbers of handovers executed are low,thehandoverdecisiondelayperformanceoftheproposedschemeissuperiorthan the considered classical MADM schemes.</p>

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“…TOPSIS was first introduced by C.LHwang and K.Yoon in 1981 [42,43]. In the case of the Multi-Criteria Decision Making (MCDM) many ranking methods can be used, one of which is a TOPSIS method [44]. MCDM was developed to provide solutions in the decision making the process [45].…”

Section: Figure 3 Hierarchy Of Purpose Selection Best Algorithmmentioning

confidence: 99%

Data mining, fuzzy AHP and TOPSIS for optimizing taxpayer supervision

Jupri

Sarno

2020

IJEECS

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The achievement of accepting optimal tax need effective and efficient tax supervision can be achieved by classifying taxpayer compliance to tax regulations. Considering this issue, this paper proposes the classification of taxpayer compliance using data mining algorithms; i.e. C4.5, Support Vector Machine, K-Nearest Neighbor, Naive Bayes, and Multilayer Perceptron based on the compliance of taxpayer data. The taxpayer compliance can be classified into four classes, which are (1) formal and material compliant taxpayers, (2) formal compliant taxpayers, (3) material compliant taxpayers, and (4) formal and material non-compliant taxpayers. Furthermore, the results of data mining algorithms are compared by using Fuzzy AHP and TOPSIS to determine the best performance classification based on the criteria of Accuracy, F-Score, and Time required. Selection of the taxpayer's priority for more detailed supervision at each level of taxpayer compliance is ranked using Fuzzy AHP and TOPSIS based on criteria of dataset variables. The results show that C4.5 is the best performance classification and achieves preference value of 0.998; whereas the MLP algorithm results from the lowest preference value of 0.131. Alternative taxpayer A233 is the top priority taxpayer with a preference value of 0.433; whereas alternative taxpayer A051 is the lowest priority taxpayer with a preference value of 0.036.

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Comparative analysis of MCDM methods for product aspect ranking: TOPSIS and VIKOR

Cited by 19 publications

References 12 publications

Rekomendasi Pemilihan Mobil dengan Algoritma VIKOR

Rekomendasi Pemilihan Mobil dengan Algoritma VIKOR

A fuzzy based vertical handover network selection scheme for device-to-device communication

Data mining, fuzzy AHP and TOPSIS for optimizing taxpayer supervision

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