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All Journal JURNAL TEKNOLOGI DAN OPEN SOURCE Rengganis Jurnal Pengabdian Masyarakat
Rian Rafiska
Institut Agama Islam Negeri Kerinci
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Education Mathematics Social Sciences

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Pemberdayaan Mandiri Karang Taruna Dalam Inovasi Kerajinan Bambu Hutan Adat Temedak untuk Meningkatkan Keterampilan Pemuda Desa Kabupaten Kerinci Anggi Desviana Siregar; Ravico Ravico; Reri Seprina Anggraini; Rhomiy Handican; Rian Rafiska
Rengganis Jurnal Pengabdian Masyarakat Vol. 2 No. 2 (2022): November 2022
Publisher : Pendidikan Matematika, FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/rengganis.v2i2.243

Abstract

The purpose of this service activity is to develop skills for youth villager who are involved in Karang Taruna in managing natural products in the Temedak Customary forest, Keluru Village, Kerinci Regency, Jambi Province. One of the many natural products found in the Temedak customary forest is Forest Bamboo which can be managed by the surrounding community. This mentoring activity was carried out with the support of the government and BumDes, the surrounding community and the assistance of IAIN Kerinci's Kukerta Students. In addition, this service is expected to provide knowledge in several fields, namely production, sales promotion, and marketing of a product. The method used is the guidance and evaluation method. The results of innovations made by youth organizations by processing bamboo into products that have selling and innovative values such as decorative lamps, tissue holders, and others.
The Implementation of Data Mining in Measuring Student Satisfaction Levels at IAIN Kerinci Rian Rafiska
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i1.3565

Abstract

Measuring student satisfaction is crucial to consider, given the high level of competition in the education sector alongside the advancement of knowledge and technology. It is essential to ensure that the services expected by students align with what they actually receive. Measuring student satisfaction can significantly assist higher education institutions in improving the quality of services, which in turn can impact the increase in student enrollment. This study employs a quantitative method, utilizing one of the data mining techniques, namely classification with the C4.5 algorithm, to measure student satisfaction levels. The population in this study consists of active students at IAIN Kerinci, with a sample size of 100 respondents. These students act as subjects who provide assessments or opinions on variables characterized by Tangible, Reliability, Responsiveness, Assurance, and Empathy. The data is then processed using data mining classification techniques, and testing is performed with the aid of RapidMiner software. The calculation and testing results demonstrate that data mining successfully classifies the variables in measuring student satisfaction with excellent performance, producing 10 rules from the decision tree with an accuracy rate of 98.22%. These rules are expected to serve as a basis for decision-making to determine actions that need to be taken to enhance student satisfaction.
The Implementation of Data Mining in Measuring Student Satisfaction Levels at IAIN Kerinci Rian Rafiska
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i1.3565

Abstract

Measuring student satisfaction is crucial to consider, given the high level of competition in the education sector alongside the advancement of knowledge and technology. It is essential to ensure that the services expected by students align with what they actually receive. Measuring student satisfaction can significantly assist higher education institutions in improving the quality of services, which in turn can impact the increase in student enrollment. This study employs a quantitative method, utilizing one of the data mining techniques, namely classification with the C4.5 algorithm, to measure student satisfaction levels. The population in this study consists of active students at IAIN Kerinci, with a sample size of 100 respondents. These students act as subjects who provide assessments or opinions on variables characterized by Tangible, Reliability, Responsiveness, Assurance, and Empathy. The data is then processed using data mining classification techniques, and testing is performed with the aid of RapidMiner software. The calculation and testing results demonstrate that data mining successfully classifies the variables in measuring student satisfaction with excellent performance, producing 10 rules from the decision tree with an accuracy rate of 98.22%. These rules are expected to serve as a basis for decision-making to determine actions that need to be taken to enhance student satisfaction.
Co-Authors Anggi Desviana Siregar Desviana Siregar, Anggi Desviana Siregar Desviana Ravico Ravico, Ravico Reri Seprina Anggraini Rhomiy Handican