Jurnal SINTA 2
Klasifikasi Kinerja Akademik Siswa Menggunakan Neighbor Weighted K-Nearest Neighbor dengan Seleksi Fitur Information Gain
Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 9 No. 3 (2022) · pp. 605–614
- ISSN
- 2528-6579, 2355-7699
- Published
- 2022-06-20
- Publisher
- Fakultas Ilmu Komputer, Universitas Brawijaya
- Language
- id
Abstract
Kinerja akademik siswa merupakan indikator kesuksesan dari pembelajaran di sekolah. Mengukur kinerja akademik siswa dapat membantu tenaga didik mengembangkan pembelajaran yang sesuai untuk siswa sehingga meningkatkan keberhasilan pembelajaran sekolah. Kinerja akademik siswa dapat diamati melalui suatu Learning Management System bernama Kalboard 360 yaitu sistem yang berhubungan dengan perilaku siswa menggunakan alat pelacak aktivitas siswa yang memantau aktivitas pembelajaran. Data sekunder dari aktivitas tersebut dapat digunakan untuk mengetahui kinerja siswa dengan salah satu caranya adalah klasifikasi. Klasifikasi menggunakan metode Neighbor Weighted K-Nearest Neighbor dengan seleksi fitur Information Gain diterapkan pada penelitian ini untuk membantu klasifikasi kinerja siswa karena metode NWKNN mempunyai kelebihan memperhitungkan metode pembobotan kelas dan mengatasi data tidak seimbang. Seleksi fitur dengan Information Gain digunakan agar dapat mengoptimalkan hasil kerja classifier. Berdasarkan pengujian dan analisis penelitian, didapatkan nilai akurasi terbaik sebesar 0,604, dengan nilai precision adalah 0,719, nilai recall sebesar 0,676, dan nilai f-measure diperoleh adalah 0,661. Nilai tersebut dihasilkan saat menggunakan 9 fitur yaitu VisitedResource, StudentAbsenceDay, RaisedHands, AnnouncementsView, Relation, ParentsAnsweringSurvey, Discussion, NationalITy, dan PlaceofBirth dimana fitur tersebut memperoleh nilai Gain tertinggi dari urutan Gain keseluruhan fitur, dengan nilai Gain ≥ 0,1182 dan menggunakan nilai parameter optimal yaitu nilai E = 6, dan nilai K = 45. AbstractThe academic performance of students is an indicator of the success of learning in school. Measuring and understanding student performance can help for improving learning systems that are suitable for students so the success of school learning will increase. Student academic performance can be observed via Learning Management System (LMS) named Kalboard 360 dealing with student behavior through a student activity tracking device so it can monitor learning activities. In this research, the secondary data is used to determine student performance through a classification. Neighbor Weighted K-Nearest Neighbor algorithm with Information Gain method will be applied to this study to help predict student performance. NWKNN method has advantages in calculating the weight of classes and overcoming unbalanced data. Information Gain is used to optimize the classifier. Based on the research analysis, the accuracy value is 0,604, with precision value obtained is 0,719, recall value obtained is 0,676, and the f-measure value obtained is 0,661. That values is generated when using 9 features with the highest order value of all features namely VisitedResource, StudentAbsenceDay, RaisedHands, AnnouncementsView, Relation, ParentsAnsweringSurvey, Discussion, NationalITy, dan PlaceofBirth, which have Gain value≥0,1182 and using optimal parameters value, that is E = 6 and K = 45.
Collaboration network of the authors
40 authors · 75 co-authorship links · showing the 40 most connected
Hover or tap a name to highlight its co-authors. Double-click to open the author's page.
- University of Brawijaya
- Affiliation unknown
- Different organisations
- Same organisation
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- Thicker line = more joint articles · bigger circle = more articles
Show as table
| Co-author | Organisation | Joint articles |
|---|---|---|
| Fajar Pradana | University of Brawijaya | 10 |
| Putra Pandu Adikara | University of Brawijaya | 3 |
| Retno Indah Rokhmawati | University of Brawijaya | 3 |
| Fitri Utaminingrum | University of Brawijaya | 3 |
| Bayu Priyambadha | University of Brawijaya | 2 |
| Satrio Agung Wicaksono | University of Brawijaya | 2 |
| Achmad Basuki | University of Brawijaya | 2 |
| Imam Cholissodin | University of Brawijaya | 2 |
| Muhammad Wafi | University of Brawijaya | 2 |
| Novanto Yudistira | University of Brawijaya | 2 |
| Herman Tolle | University of Brawijaya | 2 |
| Aisyah Awalina | University of Brawijaya | 2 |
| Joseph Ananda Sugihdharma | University of Brawijaya | 1 |
| Rizal Setya Perdana | University of Brawijaya | 1 |
| Nathanael Victor Darenoh | University of Brawijaya | 1 |
| Aditya Rachmadi | University of Brawijaya | 1 |
| Yuita Arum Sari | University of Brawijaya | 1 |
| Aulia Akhrian Syahidi | University of Brawijaya | 1 |
| Fajerin Biabdillah | University of Brawijaya | 1 |
| Nanang Yudi Setiawan | University of Brawijaya | 1 |
| Iqbal Taufiq Ahmad Nur | University of Brawijaya | 1 |
| Indra Syahputra | University of Brawijaya | 1 |
| Bayu Sutawijaya | University of Brawijaya | 1 |
| Mochammad Dearifaldi Al Ikhsan | University of Brawijaya | 1 |
| Sulandri Sulandri | University of Brawijaya | 1 |
| Rona Salsabila | University of Brawijaya | 1 |
| Muhammad Zulfikarrahman | University of Brawijaya | 1 |
| Diva Fardiana Risa | University of Brawijaya | 1 |
| Aisyatul Maulidah | University of Brawijaya | 1 |
| Intan Yusuf Habibie | University of Brawijaya | 1 |
| Adam Syarif Hidayatullah | University of Brawijaya | 1 |
| Naziha Azhar | University of Brawijaya | 1 |
| Ovy Rochmawanti | University of Brawijaya | 1 |
| Ilham Firmansyah | University of Brawijaya | 1 |
| M. Sofyan Irwanto | University of Brawijaya | 1 |
| Budi Darma Setiawan | University of Brawijaya | 1 |
| Muhammad Nugraha Delta Revanza | Affiliation unknown | 1 |
Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.