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Analisis Hasil Tracer Study Terhadap Alumni Universitas Telkom Dengan Menggunakan Minimum Spanning Tree (mst). Dika Rizky Nurcholis; Rian Febrian Umbara; Indwiarti Indwiarti
eProceedings of Engineering Vol 5, No 3 (2018): Desember 2018
Publisher : eProceedings of Engineering

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Abstract

Abstrak Tracer Study merupakan survey alumni yang dilaksanakan oleh sebuah perguruan tinggi. Tujuan dilaksanakannyatracerstudyadalahuntukmengetahuiprosespenyerapan,danposisilulusandalamduniakerja. Pada tugas akhir ini dilaksanakan penelitian untuk mengetahui topologi jaringan pada kompetensikompetensi alumni Telkom University menggunakan metode Minimum Spanning Tree (MST) dengan Algoritma kruskal untuk mengetahui kompetensi yang paling penting dan berpengaruh pada alumni UniversitasTelkom. JenisdatayangdigunakanadalahdatatracerstudyalumniTelkomUniversitytahun2015. Macam-macamsentralitasmenentukankepentinganrelatifuntuksetiapkompetensitertentu. Limamacam sentralistas akan dibahas yaitu, sentralitas derajat, sentralitas keantaraan, sentralitas kedekatan, sentralitas vektor eigen dan sentralitas keseluruhan. Sentralitas keseluruhan dapat digunakan untuk meringkas informasipentingyangterkandungdalamjejaringsosial. Hasilkompetensiyangpalingpentingdanberpengaruh terdiri dari enam kompetensi, yaitu kompetensi (1) Pengetahuan di bidang atau disiplin ilmu, (22) Kemampuan dalam memegang tanggung jawab, (4) Keterampilan Internet, (3) Pengetahuan umum , (8) KemampuanBelajar,dan(6)BerpikirKritis.Katakunci: CentralityMeasure,TracerStudy,MST,AlgoritmaKruskal.Abstract Tracer Study is an alumni survey that conducted by a university. The purpose of this Tracer Study is to figure out the process of absorption, and position of graduates in the world of work. In this final project, a research was conducted to find out the network topology on Telkom University alumni competencies using Minimum Spanning Tree (MST) method with kruskal algorithm to find out the most important and influentialcompetenciesofalumniinTelkomUniversity. Thetypeofdatausedisdatatracerstudyalumni Telkom University in 2015. Centrality measures determine the relative importance for each particular competence. Five kinds of centrality will be discussed degree centrality, betweenness centrality, closeness centrality,eigenvectorcentralityandoverallmeasurecentrality. OverallMeasurecanbeusedtosummarize importantinformationthatcontainedinsocialnetworking. Themostimportantandinfluentialcompetency results there are six competencies, namely competency (1) Knowledge in the field or discipline, (22) Ability to hold responsibilities, (4) Internet skills, (3) General knowledge, (8) Ability Learning, and (6) Critical Thinking.Keywords: CentralityMeasure,TracerStudy,MST,KruskalAlgorithm.