Nita Mirantika
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PERBANDINGAN METODE K-NEAREST NEIGHBOR DAN NAIVE BAYES UNTUK REKOMENDASI PENENTUAN MAHASISWA PENERIMA BEASISWA PADA UNIVERSITAS KUNINGAN Sumiah, Aah; Mirantika, Nita
BUFFER INFORMATIKA Vol 6, No 1 (2020)
Publisher : TI S1 FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/buffer.v6i1.2907

Abstract

ABSTRAKBeberapa tahun terakhir,  data semakin heterogen dan kompleks dengan volume yang meningkat cepat hingga diperkirakan akan mencapai 44 zettabyte di tahun 2020 (turner 2014). Hal ini sering disebut dengan Big data. Era Big data menghasilkan data yang menumpuk sehingga perlu dilakukan pengolahan untuk mencari knowladge dari tumpukan data tersebut menggunakan data mining.  Penelitian ini merupakan penelitian lanjutan dari penelitian sebelumnya yang berjudul “ Implementasi data Mining untuk Rekomendasi Penentuan Mahasiswa Penerima Beasiswa menggunakan Metode Naive Bayes Studi Kasus Universitas Kuningan”.Pada penelitian ini mencoba membandingkan dua agoritma untuk mengetahui algortima mana yang paling cocok digunakan untuk rekomendasi penentuan mahasiswa penerima beasiswa pada universitas kuningan menggunakan algoritma K-Nearest Neighbor dan algoritma Naive Bayes. Metode ini di pilih karena kedua algoritma merupakan algoritma yang populer digunakan dalam proses pengklasifikasian data Hasil analisis data di implementasikan menjadi sebuah sistem informasi menggunakan visual basic.net dan sql server yang dapat digunakan oleh bagian akademik sebagai rekomendasi dalam proses seleksi penerimaan beasiswa.Keyword :  sistem informasi, data mining, beasiswa, naive bayes classifier, K-Nearest Neighbor, visual basic.net, sql server  ABSTRACTIn recent years, data has become increasingly heterogeneous and complex with volumes increasing rapidly until it is estimated to reach 44 zettabytes by 2020 (turner 2014). This is often referred to as Big Data. The era of Big Data generates data that is piling up, so it needs to be processed to find knowladge from the data stack using data mining. This research is a continuation of previous research entitled "Implementation of Mining Data for Recommendations for Determining Scholarship Recipients using the Naive Bayes Method of Case Study at Kuningan University". In this study, trying to compare two agorithms to find out which algorithm is the most suitable for the recommendation of determining scholarship recipients at a brass university using the K-Nearest Neighbor algorithm and the Naive Bayes algorithm. This method was chosen because both algorithms are popular algorithms used in the process of classifying data.The results of data analysis are implemented into an information system using visual basic.net and SQL Server that can be used by the academic department as a recommendation in the selection process for scholarship acceptance. Keywords: Information systems, data mining, scholarships, naive bayes classifier, K-Nearest Neighbor, visual basic.net, sql server
ANALISIS TINGKAT KESIAPAN PENGGUNA E-LEARNING UNIVERSITAS KUNINGAN DENGAN MENGGUNAKAN MODEL TECHONOLOGY READINESS INDEX (TRI) Yusuf, Fahmi; Syamfithriani, Tri Septiar; Mirantika, Nita
NUANSA INFORMATIKA Vol 14, No 2 (2020)
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (123.252 KB) | DOI: 10.25134/nuansa.v14i2.2991

Abstract

Readiness in adopting new technology, especially in Online Learning in a certain University, is determined by user’s readiness i.e. the User in Online Learning.  In Kuningan University, we have concern in e-class: Online learning System, which is categorized as a new technology. The research is to analyze the user’s readiness and to measure the success of the e learning applied in our University. The method that we use in this research to measure user’s readiness level is the Technology Readiness Index (TRI). TRI is an index to measure the User’s readiness in using new technology to achieve the daily learning. The measurement is done by using four variables ; optimism, innovativeness, discomfort and insecurity. We use SPSS 21 application to analyze our data. The Research data is collected by giving Questioners to 371 respondents in Kuningan University (UNIKU). After we had calculated the data, we got the result that the TRI total value was 2.81 (categorized as Low Technology Readiness Index) and the user group was categorized as Skeptics Group (the optimism, innovativeness, discomfort and insecurity were low. These made the students of Kuningan University had uncertain feeling towards the e-class.Key words: Technology readiness index, e-learning, LMS
PENGARUH PENGGUNAAN MEDIA PEMBELAJARAN E-LEARNING TERHADAP MOTIVASI DAN PRESTASI BELAJAR MAHASISWA ( Studi Eksperimen pada Mata Kuliah Komputasi Paralel Mahasiswa Angkatan III Program Studi Teknik Informatika di FKOM UNIKU ) Nita Mirantika
NUANSA INFORMATIKA Vol 8, No 1 (2013)
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/nuansa.v8i1.15

Abstract

The presence of parallel computing courses in Informatics Engineering program is very relevant, applicable and can provide great inspiration for learners. But in reality, this parallel computing courses many students complain because the perceived difficult to learn. Course materials are always on the up grade in accordance with the technological developments that are in. Conventional teaching methods are still the favorite method is given, can not be a good solution to the above problems. because conventional study focused only on the educators in this faculty, so that students are passive, not pushing for creative thinking. Therefore we need another method, which can provide insight and inspiration to the students so that learning can take place effectively and in accordance with technological developments. One of these alternatives is the use of e-learning media.To assess the use of e-learning media are expected to be an alternative solution to the above problems, the authors conducted a study on Information Technology Student III level UNIKU who took a course parallel computing using three classes. The method used is the experimental method. With a variety of considerations, which became the experimental class is TI A class and TI C while the control class is TI B class. Data research conducted through written tests and questionnaires. Data were analyzed using a computerized program SPSS version 16 at the level of 95%. The results showed that the use of e-learning media can increase student motivation and achievement compared to conventional learning.  Keywords: E-learning, Learning Media, Experimental Study, learning motivation, learning achievement.  Â