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All Journal Jurnal Simantec Elsains : Jurnal Elektro Infotronik : Jurnal Teknologi Informasi dan Elektronika Jurnal TAMBORA Building of Informatics, Technology and Science TeknoIS : Jurnal Ilmiah Teknologi Informasi dan Sains Jurnal Informatika dan Rekayasa Elektronik Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) Respati Jurnal Teknimedia: Teknologi Informasi dan Multimedia Media Jurnal Informatika Bima Abdi: Jurnal Pengabdian Masyarakat JUTECH : Journal Education and Technology JURNAL REKAYASA INFORMASI SWADHARMA (JRIS) JURNAL ELEKTRO DAN INFORMATIKA SWADHARMA (JEIS) Bulletin of Information Technology (BIT) Jurnal Informatika Teknologi dan Sains (Jinteks) Digital Transformation Technology (Digitech) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Informatika: Jurnal Pengembangan IT Jurnal Pengabdian Rekayasa Sistem Jurnal Teknologi Informatika dan Komputer Jurnal Gemilang Informatika (GIT)
Eri Sasmita Susanto
Universitas Teknologi Sumbawa
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Other

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Journal : Respati

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PREDIKSI KELULUSAN MAHASISWA MAGISTER TEKNIK INFORMATIKA UNIVERSITAS AMIKOM YOGYAKARTA MENGGUNAKAN METODE K-NEAREST NEIGHBOR Susanto, Eri Sasmita; Kusrini, Kusrini; Fatta, Hanif Al
Jurnal Teknologi Informasi RESPATI Vol 13, No 2 (2018)
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (357.085 KB) | DOI: 10.35842/jtir.v13i2.260

Abstract

INTISARIPenelitian ini difokuskan untuk mengetahui uji kelayakan prediksi kelulusan mahasiswa Universitas AMIKOM Yogyakarta. Dalam hal ini penulis memilih algoritma K-Nearest Neighbors (K-NN) karena K-Nearest Neighbors (K-NN) merupakan algoritma  yang bisa digunakan untuk mengolah data yang bersifat numerik dan tidak membutuhkan skema estimasi parameter perulangan yang rumit, ini berarti bisa diaplikasikan untuk dataset berukuran besar.Input dari sistem ini adalah Data sampel berupa data mahasiswa tahun 2014-2015. pengujian pada penelitian ini menggunakn dua pengujian yaitu data testing dan data training. Kriteria yang digunakan dalam penelitian ini adalah , IP Semester 1-4, capaian SKS, Status Kelulusan. Output dari sistem ini berupa hasil prediksi kelulusan mahasiswa yang terbagi menjadi dua yaitu tepat waktu dan kelulusan tidak tepat waktu.Hasil pengujian menunjukkan bahwa Berdasarkan penerapan k=14 dan k-fold=5 menghasilkan performa yang terbaik dalam memprediksi kelulusan mahasiswa dengan metode K-Nearest Neighbor menggunakan indeks prestasi 4 semester dengan nilai akurasi= 98,46%, precision= 99.53% dan recall =97.64%.Kata kunci: Algoritma K-Nearest Neighbors, Prediksi Kelulusan, Data Testing, Data Training ABSTRACTThis research is focused on knowing the feasibility test of students' graduation prediction of AMIKOM University Yogyakarta. In this case the authors chose the K-Nearest Neighbors (K-NN) algorithm because K-Nearest Neighbors (K-NN) is an algorithm that can be used to process data that is numerical and does not require complicated repetitive parameter estimation scheme, this means it can be applied for large datasets.The input of this system is the sample data in the form of student data from 2014-2015. test in this research use two test that is data testing and training data. The criteria used in this study are, IP Semester 1-4, achievement of SKS, Graduation Status. The output of this system in the form of predicted results of student graduation which is divided into two that is timely and graduation is not timely.The result of the test shows that based on the application of k = 14 and k-fold = 5, the best performance in predicting the students' graduation using K-Nearest Neighbor method uses 4 semester achievement index with accuracy value = 98,46%, precision = 99.53% and recall = 97.64%.Keywords: K-Nearest Neighbors Algorithm, Graduation Prediction, Testing Data, Training Data
Co-Authors Abi Aulia Adi Ardiansyah, Adi Akbar, Muhammad Ilham Aldrin, Aldrin Anggia Purbadhanti Anjarsari, Meri Apriana Wulandari Ardi Susanto Arman Diansyah Arsi Dwi Septiarini Azzam, Ahmad Bahtiar, Syamsul Bajri, Febrianisa Barokah, Alfin Budikusuma, Iqbal Bulan, Santi Cahyono, Tomy Dwi Cece Listiana Umbara Dahalia Susanti Darmawan, Indra Dinola, Dinola Fahri Hamdani Fahrul Irwansyah Fajriansyah, Ananda Farida Idifitriani Farida Idifitriani Ghifron Akhiru Syahroni Gutari, Intan Mei Hanif Al Fatta Harfina Aprianti Haryanti, Eka Herfandi Herfandi Idifitriani, Farida Ikram, Fadhli Dzil Ismail Budi Aswin Jahrawati, Jahrawati Julkarnain, M. Juniardi Akhir Putra Karisma, Yana Kevin Noanda Aditya Rudy Yanto Kusrini Kusrini Lilis Hartina M. Julkarnain M. Zammul Aditya Fu’ani M. Zayyan Musoffa Mariati Mazmur Triputra Meri Anjarsari Miftahul Haq Mila Sejahtera Mohammad Taufan Asri Zaen Muhammad Firmansyah, Muhammad Muhammad Ilham Akbar Muhammad Ilyas Muhammad Rizky muhammad rizky, muhammad Mujahidin Patti Peiloh Mujianto, Ahmad Heru Mulyanto, Yudi NABILA OPER Nabila Oper Nawassyarif Novitasari Novitasari Nurmala Nurmala Nurmayani, Heni Nuryadi, Halid Nuryansah, Fikri Oktavia, Siska Atmawan Purbadhanti, Anggia Putra, Juniardi Akhir Putra, Zainur Minullah Qanita, Shindyka Syalu Rajid Andi Tuntantri Susanto Rodianto, Rodianto Safitri, Dindi Shinta Esabella Siti Kalkausar Djafar HS Sofya, Nora Dery Sukma Asyfinawati Sulastri SULASTRI Sultan Naufal Abdillah Susanti, Dahalia Susi Isnaeni Tari, Yuyun Triawan Adi Cahyanto W, Yunanri Wawa Marisa Wibowo, Angga Dwi Widiarta, I Made Wilia Ismiyarti Yasinta Bella Fitriana Yudi Mulyanto Yuliadi Yuliadi Yuliadi Yuliadi Yuniar Andriani