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All Journal Jurnal Kedokteran Brawijaya Gaung Informatika JMMR (Jurnal Medicoeticolegal dan Manajemen Rumah Sakit) BIO-SITE |BIOLOGI Sains Terapan JURNAL GALUNG TROPIKA Scientific Journal of Informatics Jurnal Abdi Insani JIIP (Jurnal Ilmiah Ilmu Pendidikan) Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M) TIN: TERAPAN INFORMATIKA NUSANTARA Svasta Harena: Jurnal Ilmiah Gizi Jurnal Penelitian Agama Pedagogika: Jurnal Ilmu-Ilmu Kependidikan Journal of Education, Religious, and Instructions (JoERI) Journal of Tropical Crop Science and Technology
Dwi Ratna Sari, Dwi Ratna
Universitas Diponegoro
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Journal : Scientific Journal of Informatics

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Prediction Of Student Graduation Using The K-Nearest Neighbor Method Case Study in Politeknik Negeri Tanah Laut Sari, Dwi Ratna; Julianto, Veri; Rhomadona, Herfia
Jurnal Ilmiah Informatika Vol. 8 No. 1 (2023): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v8i1.74-88

Abstract

Tanah Laut State Polytechnic as one of the universities in Indonesia has definitely paid attention to the quality of its students. One way is to predict student graduation. Graduation predictions can help study programs and academic supervisors review and pay special attention to students, especially students who are predicted to not graduate on time. Realizing one way to pay attention to the quality of students can be realized by creating a Student Graduation Prediction system using the Web-Based K-Nearest Neighbor (KNN) Method. The K-Nearest Neighbors method is an object classification method based on training data by finding the nearest neighbor value to determine the class of the new data. In the Student Graduation Prediction using the K-Nearest Neighbor Method, there is a section that can process training data, test data, the process of calculating student graduation predictions, and displaying the results obtained from the KNN calculation which has two classification classes, namely graduated and not passed. Based on the results of the study, it was found that KNN with different k values obtained different levels of accuracy, data testing with a value of k=1 obtained an accuracy rate of 83.33%, the value of k=2 obtained an accuracy rate of 79.17%, the value of k=3 to k= 8 obtained an accuracy rate of 95.83%, and the values of k=9 and k=10 obtained an accuracy rate of 91.67%. It can be concluded that the test with a value of k=3 to k=8 obtained the best or highest level of accuracy.
Co-Authors Abdul Rahim Achmad Noerkhaerin Putra Adriani Darmawati Alim, Nurmaranti Aminnudin, Muhammad Azis Nur Ansar ASTIKA, RIA Bahja Beri Candriasih, Putu Dwi Erma Kusumawati Dwi Retnoningsih Eny Fuskhah Fahmi Hafid Faisal, Elvyrah Firdaus, Lutfiyah Firdhaus Hari Saputro Al Haris Hamzah, Nadiyah Maharani Amir Harahap, Syarifah Ainun Hasyim, Mafrurrochim Hery Widijanto Irmayanti Irmayanti, Irmayanti Isbadar Nursit Kamil, Syaifuddin Kartini Kartini Komang Sutawan Kurnia Widyaningrum Mahendra, Yusril Maulana, Faiz Muzaqqi, Muhammah Ilham Nasution, Zulaini Masruro NIZMI PUTRI Nurjaya Nurmalasari Nurmalasari Pratama, Moch. Septian Yoga Rahayu, Amelia Puji Rahmawati, Tri Wahyuning Rahmawati, Triwahyuning Ratri, Devita Rahmani Rein, Rein Renaldi, Dicky Rhomadona, Herfia Ruth Dameria Haloho Sanjaya, Muhammad Fahyu sutiyono sutiyono SUYONO Tri Wahyu Sarwiyata Umaimah Usman, Al Zaima Verdy, Andika Veri Julianto Yeheskiel, Yeheskiel Yosua Yosua Zakirah Raihani Ya’la