Data Mining in Personalized Travel Information System

2010 ◽  
Author(s):  
Li-Fu Hsu ◽  
Chiun-Chieh Hsu ◽  
Tsai-Duan Lin
Keyword(s):  
Data Mining ◽  
Information System ◽  
Travel Information

scholarly journals Data Mining dalam Akurasi Tingkat Kelayakan Pakai terhadap Peralatan Perangkat Keras

2020 ◽  
pp. 83-88
Author(s):  
Nurhidayat ◽  
Sarjon Defit ◽  
Sumijan
Keyword(s):  
Data Mining ◽  
Information System ◽  
Rough Set ◽  
Access Point ◽  
Learning Processes ◽  
Discernibility Matrix ◽  
Student Work ◽  
Hard Drives ◽  
Good Shape ◽  
Equivalency Class

Hardware is a computer that can be seen and touched in person. Hardware is used to support student work and learning processes. The hardware should always be in good shape. If any damage should be done quickly. The benefits of this study provide a viable level of data against hardware tools. The purpose of this study determines that hardware that is worth using quickly and precisely so easily can be repaired and replaced. Hard-processed action consists of 12 projectors, 2 units of access point, 6 units of monitors, and 20 CPU units. To see the level of appropriateness regarding hard drives requires a rough set algorithm with that stage: information system; Decision system; Equivalency class; Discernibility matrix; Discernibility Matrix module D; Reduction; Generate Rules. The results of the 40 devices of study STMIK Indonesia Padang subtract college have 10 rules of policy on whether the hardware is still viable, repaired or replaced. So using a rough set algorithm is particularly appropriate to apply in a verifiable level of accuracy to fast and precise hardware.

Rekomendasi Grup Pada Website Alumni Teknik Informatika Universitas Muhammadiyah Malang

Repositor ◽  
2020 ◽  
Vol 2 (12) ◽  
pp. 1647
Author(s):  
Hermansyah Adi Saputra ◽  
Galih Wasis Wicaksono ◽  
Yufis Azhar
Keyword(s):  
Data Mining ◽  
Information System ◽  
Information Systems ◽  
Tracer Study ◽  
Group Recommendation ◽  
Number Of Clusters ◽  
Average Value ◽  
Exchange Information ◽  
Group Object ◽  
Almost All

AbstrakBelakangan ini hampir seluruh universitas yang ada di indonesia memiliki sistem informasi alumninya sendiri-sendiri. Sistem informasi alumni mampu memberikan informasi tentang kondisi alumninya setelah menyelesaikan masa perkuliahannya. Alumni merupakan aktor yang berperan penting dalam pendidikan. Saat ini jurusan Informatika Fakultas Teknik Universitas Muhammadiyah Malang telah memiliki website alumni. Permasalahannya belum adanya sistem yang memberikan alumni rekomendasi grup pada sistem, sehingga para alumni mampu saling bertukar informasi didalamnya. Dengan adanya data alumni dan juga di dukung dengan adanya tracer study, dapat di bentuk suatu rekomendasi grup dari data tracer study. K-medoid adalah metode pengelompokan data ke  dalam  sejumlah cluster  tanpa  adanya  struktur  hirarki antara satu dengan yang lainnya. Algoritma k-medoid memiliki nilai coefficient yang lebih tinggi di bandingkan dengan k-means dalam penelitian ini. Yang mana k-medoid mendapatkan nilai rata-rata Silhouette Score 0.7325888099 dalam pengujian dengan jumlah cluster 5 dan perulangan sebanyak 10 kali. Jika dibandingkan dengan k-means yang hanya memiliki nilai rata-rata Silhouette Score 0.6872873866.AbstractLately, Almost all universities in Indonesia have their own alumni information systems. The alumni information system is able to provide information about the condition of its alumni after collage graduation. Alumni are actors who play important role in education. Currently, the Department of Informatics, Faculty of Engineering, University of Muhammadiyah Malang has an alumni website.  The problem is the absence of system that gives alumni group recommendation on the system, so that alumni are able to exchange information in this website. With the alumni data and also supported by the existence of a tracer study, it can be formed as group recommendation from the data tracer study. Clustering is one of tools in data mining that aims to group object into clusters. K-medoid is a method of grouping data into a number of clusters without hierarchical structure from one another. The k-medoid algorithm has higher coefficient value compared to k-means in this study. This K-medoid gets an average value of Silhouette Score 0.7325888099 in testing with the number of clusters 5 and repetitions 10 times. When compared with k-means which only has an average value of Silhouette Score 0.6872873866.

The Design and Implementation of Tourism Monitoring Analysis System

2014 ◽  
Vol 926-930 ◽  
pp. 4126-4129
Author(s):  
Hua Hu
Keyword(s):  
Information System ◽  
Tourism Industry ◽  
System Level ◽  
Functional Requirements ◽  
Gis Technology ◽  
Logistics Industry ◽  
Travel Information ◽  
Information Monitoring ◽  
Tourism Information ◽  
Analysis System

Abstract: In this paper, the domestic tourism industry and tourism development of information technology on the basis of the Tourism Information System on the background of the analysis，analytical focus on building a tourism information，monitoring and analysis of several important technology, which leads to the new Tourist Information system discussed in detail．In this paper, the tourism process monitoring analysis and mining of various principles and related technology solutions are discussed，by reference to the theory of GIS technology and computer technology in data mining and CRM theory in modem logistics industry, research has highlighted the tourism business focus Tourism monitoring of the RTMS system，citing the relevant functional requirements and system—level framework to achieve all the details．Finally，we outlook the future of travel information system．

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