scholarly journals TOPSIS for mobile based group and personal decision support system

2021 ◽  
Vol 7 (1) ◽  
pp. 43
Author(s):  
Ratih Kartika Dewi ◽  
Eriq Muhammad Adams Jonemaro ◽  
Agi Putra Kharisma ◽  
Najla Alia Farah ◽  
Mury Fajar Dewantoro
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Recommendation System ◽  
Ideal Solution ◽  
Tourist Destination ◽  
User Choice ◽  
Alternative Design ◽  
Tourist Destinations ◽  
Personal Decision

Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is an algorithm that can be used for alternative design in a decision support system (DSS). TOPSIS provides recommendation so that users can get information that support their decision, for example a tourist wants to visit a tourist destination in Malang, then TOPSIS provides recommendations of tourist destinations in the form of ranking recommendation, with the highest rank is the most recommended recommendation. TOPSIS-based Mobile Decision Support System (DSS) has relatively low algorithm complexity. However, there are some cases that require development from personal DSS to group DSS, for example tourists rarely come alone, in which case most of them invite friends or family. For users who are more than 1 person, the TOPSIS algorithm can be combined with the BORDA algorithm. This study explains about the implementation & testing of TOPSIS and TOPSIS-BORDA as algorithms for personal and group DSS in mobile-based tourism recommendation system in Malang. Correlation testing was conducted to test the effectiveness of TOPSIS in mobile-based recommendation system. In previous study, correlation testing for personal DSS showed that there was a relationship between the recommendation and user choice, with correlation value of 0.770769231. In this study, correlation testing for group DSS showed there is a positive correlation of 0.88 between the recommendations of the group produced by TOPSIS-BORDA and personal recommendations for each user produced by TOPSIS.

Tourism Recommendation System in Bali Using Topsis and Greedy Algorithm Methods

2020 ◽  
Vol 8 (3) ◽  
pp. 277
Author(s):  
Muhammad Afif Ubaidillah ◽  
Ida Bagus Gede Dwidasmara
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Greedy Algorithm ◽  
Support System ◽  
Recommendation System ◽  
Ideal Solution ◽  
Tourist Attractions ◽  
Order Preference ◽  
The Greedy Algorithm ◽  
Selection Of

Tourism is the mainstay of the economy of the region of Bali and is an important sector in supporting the level of community welfare. The world of tourism is essentially an important symbol for Bali. Of the many tourists who come to Bali, of course not all tourists know all information about Bali, such as in terms of tourist locations, and tourist attractions closest to other tourist attractions. Therefore, the authors aim to create a recommendation system that can provide planning for the selection of tourist attractions according to the closest distance and the user's budget. This system is designed using the TOPSIS (Technique for Order Preference by Similarity To Ideal Solution) method and the Greedy Algorithm. The TOPSIS method is a SPK (Decision Making System) that will be used for the selection of tourist attractions that will help tourists to arrange vacation planning before going on a tour. While the Greedy Algorithm is used to find the closest or shortest distance between a tourist site and other tourist attractions, this algorithm will be able to determine which path will be taken first or called the local optimal path, so that all the paths are taken at the end of the trip and create a travel route shortest or called the global optimum so that it can also be the expected solution. From this it can be determined the value of the shortest travel route that starts from the location of the user's residence to the tourist attractions and to other nearby tourist attractions. The data used is data obtained from DISPARDA, namely tourist data. Research related to the selection of tourism object decisions based on the type of tourism, prices and facilities, and the results are able to provide recommendations for tourist attractions that meet these criteria. Tourism Selection Using Techniques For Order Preference By Similarity To Ideal Solution (Topsis) With Object Localization Visualization [4], Development of Decision Support System for Hotel Determination in Buleleng District Using Analytic Hierarchy Process (AHP) Method and Technique for Others Reference By Similarity To Ideal Solution (TOPSIS) [3], a Decision Support System for Determining Tourist Locations using the Top-sis Method [6]

TERAPAN KOMBINASI METODE TOPSIS DAN ANALYTICAL HIERARCHY PROCESS PADA PEREKOMENDASIAN PENERIMA BEASISWA PENINGKATAN PRESTASI AKADEMIK (Studi Kasus pada STMIK PPKIA Tarakanita Rahmawati)

2017 ◽  
Vol 8 (2) ◽  
pp. 663 ◽  
Author(s):  
Muhammad Fadlan ◽  
Muhammad Muhammad ◽  
Hadriansa
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Analytical Hierarchy Process ◽  
Ideal Solution ◽  
Microsoft Excel ◽  
Order Preference ◽  
Hierarchy Process

Beasiswa Peningkatan Prestasi Akademik (PPA) sebagai salah satu bentuk dukungan pemerintah Republik Indonesia terhadap dunia pendidikan. Beasiswa yang disalurkan oleh pemerintah melalui Perguruan Tinggi yang ada di Indonesia ini, penyeleksian dan penetapan penerimanya sepenuhnya diserahkan kepada pihak Perguruan Tinggi yang bersangkutan. Tahap inilah yang sangat rentan terjadinya kecurangan. Pada objek penelitian  yang diteliti hingga saat ini proses penyeleksian masih dilakukan dengan menggunakan Microsoft Excel, hal ini tentu saja kurang efektif dan efisien, serta rentan akan terjadinya kesalahan bahkan kecurangan. Untuk itu, diperlukan suatu metodologi dan aplikasi yang tepat dalam melakukan penyeleksian penerima beasiswa tersebut. Decision Support System digunakan sebagai solusi untuk melakukan perekomendasian penerima beasiswa, khususnya dengan menggunakan Metode Technique  for  Order  Preference  by  Similarity  to  Ideal  Solution  (TOPSIS)  dan  Analytical  Hierarcy Process (AHP). Penggunaan kombinasi dua metode tersebut dilakukan agar memiliki tingkat akurasi yang baik jika dibandingkan  dengan menggunakan satu metode. Hasilnya,  aplikasi  decision support system dengan penerapan kombinasi metode Topsis dan AHP berhasil di rancang dan di ujicoba, serta sukses dalam perekomendasian penerima beasiswa PPA dengan menghasilkan data alternatif mahasiswa yang terurut mulai dari nilai preferensi yang paling tinggi 0.764 hingga terendah 0.189. Hasil ini dapat menjadi rekomendasi bagi pengambil keputusan dalam mengambil keputusan yang efektik, efisien dan dapat dipertanggung jawabkan.

scholarly journals Clinical Decision Support System for Diabetes Based on Ontology Reasoning and TOPSIS Analysis

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Rung-Ching Chen ◽  
Hui Qin Jiang ◽  
Chung-Yi Huang ◽  
Cho-Tsan Bau
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Recommendation System ◽  
Treatment Strategies ◽  
Clinical Decision ◽  
Antidiabetic Medication ◽  
Set Up

Introduction. Although a number of researchers have considered the positive potential of Clinical Decision Support System (CDSS), they did not consider that patients’ attitude which leads to active treatment strategies or HbA1c targets. Materials and Methods. We adopted the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) published to propose an HbA1c target and antidiabetic medication recommendation system for patients. Based on the antidiabetic medication profiles, which were presented by the American Association of Clinical Endocrinologists (AACE) and American College of Endocrinology (ACE), we use TOPSIS to calculate the ranking of antidiabetic medications. Results. The endocrinologist set up ten virtual patients’ medical data to evaluate a decision support system. The system indicates that the CDSS performs well and is useful to 87%, and the recommendation system is suitable for outpatients. The evaluation results of the antidiabetic medications show that the system has 85% satisfaction degree which can assist clinicians to manage T2DM while selecting antidiabetic medications. Conclusions. In addition to aiding doctors’ clinical diagnosis, the system not only can serve as a guide for specialty physicians but also can help nonspecialty doctors and young doctors with their drug prescriptions.

scholarly journals Group Decision Support System based on AHP-TOPSIS for Culinary Recommendation System

2019 ◽  
Vol 12 (2) ◽  
pp. 85
Author(s):  
Ratih Kartika Dewi
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Recommendation System ◽  
Group Decision ◽  
User Preferences ◽  
Group Decision Support ◽  
Group Decision Support System ◽  
Topsis Method ◽  
User Groups

This paper proposes the integration of AHP and TOPSIS to generate the ranking results of culinary recommendation for a group of users to provide better recommendation results. Formerly, Group Decision Support System (GDSS) for culinary recommendations has been developed with the TOPSIS method. TOPSIS has low algorithm complexity, so it is suitable to be applied in mobile devices. However, GDSS with TOPSIS has its disadvantages, TOPSIS have not been able to facilitate the preferences of each user inside a group so the recommendation result always consist only on dominant user. TOPSIS method produces unchanging rankings, because this method recommends a food menu based on the 1 dominant user so that the ranking is always consistent. Meanwhile, this study aims to integrate AHP for weighting criteria from each user and TOPSIS for ranking culinary recommendations. Based on rank consistency testing results that conducted in 6 different user groups, unlike the previous research, AHP-TOPSIS shows inconsistency ranking, which means that changes in user preferences affect the recommendation results that are generated by application. The AHP-TOPSIS method proved can be accommodated the computation of various preferences of each user in GDSS culinary recommendation

scholarly journals The Hotel Recommendation System Using SAW (Simple Additive Weighting) And TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) Method

bit-Tech ◽  
2019 ◽  
Vol 1 (3) ◽  
pp. 131-145
Author(s):  
Aditiya Hermawan ◽  
Evan
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Recommendation System ◽  
Solution Method ◽  
Test Results ◽  
Economic Sectors ◽  
Web Based ◽  
Search Site ◽  
Simple Additive Weighting

Tourism is one of the important economic sectors in Indonesia that needs to be developed. This is based on data of the number of tourist visits from “Kementrian Pariwisata Republik Indonesia” website that the number of tourists in Indonesia is very much and continues to increase over time. Due to the increasing number of tourist visits from various places, makes many entrepreneurs are competing to establish a hotel as a place of business with variety of  price, class, facility and service. Then, the growth of the hotels became very rapid. With so many choices of the hotels, of course it will cause a problems for tourists to decide which hotel is suitable with the desired criteria. With the development of information technology in this era of globalization, technology should be utilized to solve this problem as a decision support system that can recommend which hotel is the most suitable from the tourist desires. The hotel data to be used in this research comes from hotel search site. The methods used for this decision support system are SAW and TOPSIS methods. The reason for using this methods is because SAW has the ability to assess more accurately because it is based on criteria and the computation of TOPSIS method is efficient and fast. The criteria used on choosing the hotel are price, facilities and class. The results of this research has been generated as a web-based application for hotel recommendation. Based on the test results of this hotel recommendation system application, this application works as expected.

scholarly journals Sistem Penunjang Keputusan Pemilihan Guru Terbaik dengan Metode TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) Studi Kasus: SDN Bendungan Hilir 01 Pagi Jakarta Pusat

2017 ◽  
Vol 2 (2) ◽  
pp. 89
Author(s):  
Hendri Ardiansyah
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Ideal Solution ◽  
Order Preference ◽  
Topsis Technique

Tugas utama guru adalah mendidik; mengajar; membimbing; mengarahkan; melatih; menilai; dan mengevaluasi peserta didiknya. Guru berprestasi adalah guru yang memiliki kemampuan melaksanakan tugas; keberhasilan dalam melaksanakan tugas; memiliki kepribadian yang sesuai dengan profesi guru dan memiliki wawasan kependidikan. Sistem pendukung keputusan atau Decision Support System (DSS) merupakan suatu sistem yang dapat membantu dalam pengambilan keputusan pada sebuah organisasi atau perusahaan dengan menerapkan metode yang sesuai dengan bidang keputusan yang diambil; Pengambilan keputusan secara manual tanpa bantuan SPK akan menghasilkan penilaian yang tidak objektif dan tidak tepat. Metode Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) merupakan salah satu metode pengambilan keputusan multi kriteria dengan menerapkan bobot nilai pada setiap kriterianya. Pemilihan metode TOPSIS ini dibandingkan dengan metode SPK yang lain yaitu metode ini menggunakan prinsip bahwa alternatif yang terpilih harus mempunyai jarak terdekat dari solusi ideal positif dan jarak terjauh dari solusi ideal negatif. Sistem yang dibuat memungkinkan pihak sekolah untuk menentukan aspek penilaian berdasarkan kriteria sesuai dengan kebutuhan dari sekolah tersebut sehingga lebih fleksibel. Sistem pendukung keputusan dengan metode TOPSIS ini diharapkan dapat membantu dalam pemilihan guru terbaik.

scholarly journals SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN LOKASI PEMBUKAAN CABANG USAHA ONLINE BICYCLE INDONESIA DENGAN METODE ANALYTICAL HIERARCHY PROCESS (AHP) DAN TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTION (TOPSIS)

2021 ◽  
Vol 11 (2) ◽  
pp. 469-479
Author(s):  
Rizki Adha ◽  
Tjahjanto Tjahjanto
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Analytical Hierarchy Process ◽  
Food Delivery ◽  
Ideal Solution ◽  
Fixed Costs ◽  
Topsis Method ◽  
Order Preference ◽  
Hierarchy Process

The choice of business location will affect the risks and benefits of the company as a whole. This condition occurs because the location greatly affects the fixed costs and variable costs, both in the medium term and long term. The location of the business should be taken into account at the time of planning, so that the business to be run can be organized implementation in the future. Online Bicycle Indonesia is a startup engaged in goods and food delivery services by using online-based bike, will open branches in several cities and districts in Tangerang Raya area so that the result of choosing the right branch location, required a dynamic decision support system that can later be used as consideration of managers in the process of selecting branch locations. Problems in selecting branch of this company can be used Decision Support System using AHP-TOPSIS method. Analytical Hierarchy Process (AHP) method has advantages based on pair comparison matrix and perform consistency analysis, while Technique For Order Preference by Similarity to Ideal Solution (TOPSIS) method can solve practical decision making, because the concept is simple and easy to understand, and have the ability to measure the relative performance of decision alternatives. The results of the study show Kota Tangerang Selatan is superior to the weight of 0.824, the two Kota Tangerang with a weight of 0.732 and three Kabupaten Tangerang with a weight of 0.0, The decision taken can be accounted for by the calculation of AHP-TOPSIS as a model in the decision support system

scholarly journals A Recommendation System for People Analytics

2021 ◽  
Vol 12 (2) ◽  
pp. 1-12
Author(s):  
Nan Wang ◽  
Evangelos Katsamakas
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
System Performance ◽  
Recommendation System ◽  
Business Value ◽  
Computational Experiments ◽  
Hybrid Architecture ◽  
Learning Approaches

Companies seek to leverage data and people analytics to maximize the business value of their talent. This article proposes a recommendation system for personalized workload assignment in the context of people analytics. The article describes the system, which follows a novel two-level hybrid architecture. We evaluate the system performance in a series of computational experiments and discuss future extensions. Overall, the proposed system could create significant business value as a decision support system that could help managers make better decisions. The article demonstrates how computational and machine learning approaches can complement humans in improving the performance of organizations.

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