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Journal : Technomedia Journal

Pengukuran Kesiapan Transformasi Digital Smart City Menggunakan Aplikasi Rapid Miner Pascalina, Donna; Widhiastono, Raymondhus; Juliane, Christina
Technomedia Journal Vol 7 No 3 Februari (2023): TMJ (Technomedia Journal)
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (280.526 KB) | DOI: 10.33050/tmj.v7i3.1914

Abstract

Digital transformation of organizational change to be more effective and efficient in a city, digital transformation in the city is not yet ready, to determine readiness for change it is necessary to measure readiness in the Smart City Digital Transformation using quantitative data from Human Resources on readiness measurements carried out directly through surveys to all OPD Semarang City. Researchers use Data Mining and Decision Tree C4.5 Algorithm to examine the data, Research uses RapidMiner. The results of this study have an accuracy rate of 82.05% from 36 OPD agencies with 2 rules, namely ready and not ready in the city of Semarang, which are declared not ready with 3 enabler parameters, namely Understanding of Transformation, Cultural Transformation, Competence and Basic Knowledge.
Evaluasi Tingkat Kepuasan Mahasiswa Terhadap Pelayanan Akademik Menggunakan Metode Klasifikasi Algoritma C4.5 Widiastuti, Tri; Karsa, Koko; Juliane, Christina
Technomedia Journal Vol 7 No 3 Februari (2023): TMJ (Technomedia Journal)
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (400.773 KB) | DOI: 10.33050/tmj.v7i3.1932

Abstract

The purpose of this study was to determine the effect of academic services on student satisfaction so that students do not feel disappointed with academic services. This study measures the level of student satisfaction with the existing academic services at Jenderal Achmad Yani University, Cimahi. The data set from the survey results of student satisfaction with academic services at Unjani is used to generate models, rules and accuracy scores for student satisfaction using the Decision Tree C4.5 algorithm data mining classification method, to see the results of the accuracy values ​​of several attributes, namely tangible, empathetic, responsiveness. , reliability and assurance. The results of the tests carried out with the rapidminer application, the accuracy value of the 7 (Seven) Faculties testing at Unjani resulted in a value above 90%, which means that this value indicates that the service that has been running so far is considered very good. Testing student satisfaction surveys must of course be carried out continuously to be able to continue to improve academic services to students for the better.