Model decision tree untuk prediksi prestasi akademik matematika siswa kelas VIII SMP Frater Don Bosco Manado
DOI:
https://doi.org/10.31571/saintek.v13i2.7696Keywords:
Prediksi prestasi akademik matematika, Decision Tree, Tanpa seleksi fitur dan seleksi fitur, Feature importancesAbstract
Penelitian ini bertujuan untuk mengembangkan model Decision Tree yang dapat memprediksi prestasi akademik matematika siswa kelas VIII di SMP Frater Don Bosco Manado, serta untuk mengidentifikasi dan menganalisis faktor-faktor penting yang perlu diperhatikan oleh orang tua dalam upaya meningkatkan prestasi akademik anak mereka. Data dikumpulkan melalui dokumentasi nilai akademik siswa, catatan kehadiran, dan kuesioner yang diisi oleh siswa untuk memperoleh informasi tentang dukungan keluarga, banyaknya kegiatan ekstrakurikuler yang diikuti, lama belajar, dan tingkat pendidikan orang tua. Data tersebut dianalisis menggunakan pendekatan data mining dengan model Decision Tree. Dua model dikembangkan dan dibandingkan: model pertama tanpa seleksi fitur dan model kedua dengan seleksi fitur menggunakan metode SelectKBest. Model tanpa seleksi fitur mencapai akurasi 93,33%, sementara model dengan seleksi fitur mencapai akurasi 95,56%. Evaluasi terhadap pentingnya fitur menunjukkan bahwa tanpa seleksi fitur, nilai rapor matematika semester sebelumnya menjadi fitur yang paling dominan, diikuti oleh nilai ulangan harian dan banyaknya kegiatan ekstrakurikuler yang diikuti. Sebaliknya, dalam model dengan SelectKBest, durasi belajar menjadi fitur yang paling signifikan, diikuti oleh tingkat pendidikan ayah, dukungan keluarga, dan nilai ulangan harian. Temuan ini menunjukkan bahwa penggunaan seleksi fitur tidak hanya meningkatkan akurasi prediksi tetapi juga membantu mengidentifikasi faktor-faktor kunci yang perlu difokuskan oleh orang tua, seperti durasi belajar, pendidikan orang tua, dukungan keluarga, partisipasi dalam kegiatan ekstrakurikuler, dan nilai akademik sebelumnya, untuk meningkatkan prestasi akademik siswa.
Downloads
References
Birba, D. E. (2020). A Comparative study of data splitting algorithms for machine learning model selection. Degree Project in Computer Science and Engineering, 2020(1).
Eccles, J. S., & Roeser, R. W. (2011). Schools as developmental contexts during adolescence. Journal of Research on Adolescence, 21(1). https://doi.org/10.1111/j.1532-7795.2010.00725.x
Fauzan, A. S., Irma, A., Sari, P., & Ali, I. (2024). Analisis perbandingan algoritma decision tree dan naive bayes untuk mengevaluasi prestasi belajar siswa studi kasus: SMK Al-Musyawirin. Jurnal Mahasiswa Teknik Informatika, 8(1). https://doi.org/https://doi.org/10.36040/jati.v8i1.8403
Hanif, M. B., & Setiaji, G. G. (2022). Meningkatkan kinerja decision tree C4.5 dengan seleksi fitur korelasi pearson pada deteksi penyakit diabetes. Indonesian Journal of Computer Science. https://doi.org/https://doi.org/10.33022/ijcs.v11i2.3087
Hartanto, P. A. (2023). Penerapan algoritma decision tree untuk seleksi penerima beasiswa (studi kasus: SMPN 1 Soreang). JS: Journal of Comprehensive Science, 2(7). https://doi.org/https://doi.org/10.59188/jcs.v2i7.452
Huang, J., Li, Y. F., & Xie, M. (2015). An empirical analysis of data preprocessing for machine learning-based software cost estimation. Information and Software Technology, 67. https://doi.org/10.1016/j.infsof.2015.07.004
Jijo, B. T., & Abdulazeez, A. M. (2021). Classification based on decision tree algorithm for machine learning. Journal of Applied Science and Technology Trends, 2(01), 20–28. https://doi.org/10.38094/jastt20165
Khafid, M. (2007). Pengaruh disiplin belajar dan lingkungan keluarga terhadap hasil belajar ekonomi. Dinamika Pendidikan, 2(2), 185. https://doi.org/https://doi.org/10.15294/dp.v2i2.447
Maharana, K., Mondal, S., & Nemade, B. (2022). A review: Data pre-processing and data augmentation techniques. Global Transitions Proceedings, 3(1). https://doi.org/10.1016/j.gltp.2022.04.020
Matzavela, V., & Alepis, E. (2021). Decision tree learning through a Predictive Model for Student Academic Performance in Intelligent M-Learning environments. Computers and Education: Artificial Intelligence, 2. https://doi.org/10.1016/j.caeai.2021.100035
Nailil Amani, N., & Hayati, U. (2024). Penggunaan algoritma decision tree untuk prediksi prestasi siswa di Sekolah Dasar Negeri 3 Bayalangu Kidul. Jurnal Mahasiswa Teknik Informatika, 8(1). https://doi.org/https://doi.org/10.36040/jati.v8i1.8355
Njeri, N. R. (2022). Data preparation for machine learning modelling. International Journal of Computer Applications Technology and Research, 11(06). https://doi.org/10.7753/ijcatr1106.1008
Patel, H. H., & Prajapati, P. (2018). Study and analysis of decision tree based classification algorithms. JCSE: International Journal of Computer Sciences and Engineering, 6(10), 74–78. https://doi.org/10.26438/ijcse/v6i10.7478
Potdar, K., S., T., & D., C. (2017). A comparative study of categorical variable encoding techniques for neural network classifiers. International Journal of Computer Applications, 175(4). https://doi.org/10.5120/ijca2017915495
Puspita, E. D. (t.t.). Pengaruh dukungan keluarga terhadap prestasi belajar siswa pada mata pelajaran ekonomi di sekolah menengah atas negeri olahraga Provinsi Riau.
Qisthiano, M. R., Prayesy, P. A., & Ruswita, I. (2023). Penerapan algoritma decision tree dalam klasifikasi data prediksi kelulusan mahasiswa. G-Tech: Jurnal Teknologi Terapan, 7(1), 21–28. https://doi.org/10.33379/gtech.v7i1.1850
Rajagukguk, S. A. (2021). Tinjauan pustaka sistematis: Prediksi prestasi belajar peserta didik dengan algoritma pembelajaran mesin. Jurnal Sains, Nalar, dan Aplikasi Teknologi Informasi, 1(1). https://doi.org/10.20885/snati.v1i1.4
Retnowati, P., & Khotimah, T. (2020). Aplikasi forecasting kehadiran siswa di SMP 2 Jekulo menggunakan metode regresi linear. Jurnal SIMETRIS, 11(2). https://doi.org/https://doi.org/10.24176/simet.v11i2.4886
Sanjaya, R. (2020). Pengaruh kegiatan ekstrakurikuler palang merah remaja terhadap prestasi siswa di SMPN 20 Kota Bengkulu. Jurnal Pendidikan Agama Islam Indonesia (JPAII), 1(1), 9–22. https://doi.org/10.37251/jpaii.v1i1.61
Saragih, R., Gunawan, I., Parlina, I., Tunas Bangsa, S., & Artikel, G. (2023). Implementasi metode regresi linier berganda untuk prediksi siswa berprestasi berdasarkan status sosial dan kedisiplinan implementation of the multiple linear regression method to predict student achievement based on social status and discipline. JOMLAI: Journal of Machine Learning and Artificial Intelligence, 2(2), 2828–9099. https://doi.org/10.55123/jomlai.v2i2.3128
Setiawan, A. Y. (2015). Pengaruh tingkat pendidikan orang tua dan disiplin belajar siswa terhadap prestasi belajar akuntansi siswa Kelas XI.
Sihombing, P. R., Suryadiningrat, S., Sunarjo, D. A., & Yuda, Y. P. A. C. (2023). Identifikasi data outlier (pencilan) dan kenormalan data pada data univariat serta alternatif penyelesaiannya. Jurnal Ekonomi Dan Statistik Indonesia, 2(3), 307–316. https://doi.org/10.11594/jesi.02.03.07
Suriani, U. (2023). Penerapan data mining untuk memprediksi tingkat kelulusan mahasiswa menggunakan algoritma decision tree C4.5. Journal of Computer and Information Systems Ampera, 3(2). https://doi.org/10.51519/journalcisa.v4i2.393
Susanto, H., & Sudiyatno. (2014). Data mining untuk memprediksi prestasi siswa berdasarkan sosial ekonomi, motivasi, kedisiplinan dan prestasi masa lalu. Jurnal Pendidikan Vokasi, 4(2). https://doi.org/http://dx.doi.org/10.21831/jpv.v4i2.2547
Tulus, T. (2004). Peran disiplin pada perilaku dan prestasi belajar siswa. Grasindo : Mataram., 2004.
Vujović, Ž. (2021). Classification Model Evaluation Metrics. International Journal of Advanced Computer Science and Applications, 12(6). https://doi.org/10.14569/IJACSA.2021.0120670
Wu, J., Chen, X. Y., Zhang, H., Xiong, L. D., Lei, H., & Deng, S. H. (2019). Hyperparameter optimization for machine learning models based on Bayesian optimization. Journal of Electronic Science and Technology, 17(1). https://doi.org/10.11989/JEST.1674-862X.80904120
Yulianto, A., & Firmansyah, F. (2022). Prediksi hasil belajar peserta didik menggunakan model multiple linier regression. REMIK: Riset dan E-Jurnal Manajemen Informatika Komputer, 6(4), 654–663. https://doi.org/10.33395/remik.v6i4.11763
Downloads
View: 59
Download: 31
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Monica Tiara Gunawan, Jeane Yosefa Tine, Chatarina Enny Murwaningtyas
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal. Please also carefully read Jurnal Pendidikan Informatika dan Sains Posting Your Article Policy at http://journal.ikippgriptk.ac.id/index.php/saintek/about/submissions#onlineSubmissions
- That it is not under consideration for publication elsewhere,
- That its publication has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
Copyright
Authors who publish with Jurnal Pendidikan Informatika dan Sains agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
