Comparative SVM and Decision Tree Algorithm in Identifying the Eligibility of KIP Scholarship Awardee
DOI:
https://doi.org/10.34306/conferenceseries.v4i1.625Keywords:
Classification, Awardee, Decision Tree, Support Vector MachineAbstract
Scholarship selection process has specific rules, but if the number of applicants exceeds the quota, a selection process is needed. Based on the observation of a university in Sukabumi, the selection for KIP scholarship has not yet had a standard method. Several methods can be used to assist the selection process, such as classification based on historical data of applicants. The algorithms used for classification include Decision Tree (DT) and Support Vector Machine (SVM). The research process uses SEMMA (Sample, Explore, Modify, Model, Assess) method. Dataset for KIP scholarship awardee from 2021-2022 consist of 519 samples with 16 attributes. From the exploration results, the most important features for model modeling are Status DTKS, Status P3KE, Father's income, mother's income, combined income, and performance. These attributes are converted into numerical data to facilitate model fitting. The K-Fold Cross-Validation results for the Decision Tree model in the case of KIP Scholarship classification yield an accuracy of 78.44% for the entire test dataset, a precision of 0.73107, indicating that 73.11% of the predictions are true, a recall (sensitivity) of 78.45%, and an F1 score of 73.20%. The results for the SVM model are an accuracy of 80.17%, a precision of 84.44%, and a recall of 80.17%.
References
Kemendikbud. PERATURAN MENTERI PENDIDIKAN DAN KEBUDAYAAN REPUBLIK INDONESIA NOMOR 10 TAHUN 2020 TENTANG PROGRAM INDONESIA PINTAR [Internet]. Jakarta; 2020. Available from: jdih.kemdikbud.go.id
U. Usanto, L. Nurlaela, A. Sopian, and F. Alfiah, “Umrah Registration System Using Extreme Programming Method Towards Worship Tourism,” Int. J. Cyber IT Serv. Manag., vol. 3, no. 1, pp. 22–31, 2023.
Iskandar A. Sistem Pendukung Keputusan Kelayakan Penerima Bantuan Dana KIP university Menggunakan Metode ROC-EDAS. Build Informatics, Technol Sci. 2022;4(2):856–64.
D. Jonas, E. Maria, I. R. Widiasari, U. Rahardja, and T. Wellem, “Design of a TAM Framework with Emotional Variables in the Acceptance of Health-based IoT in Indonesia,” ADI J. Recent Innov., vol. 5, no. 2, pp. 146–154, 2024.
Firmansyah S, Gaol J, Susilo SB. Perbandingan Klasifikasi SVM dan Decision Tree untuk Pemetaan Mangrove Berbasis Objek Menggunakan Citra Satelit Sentinel-2B di Gili Sulat, Lombok Timur. J Pengelolaan Sumberd Alam dan Lingkung (Journal Nat Resour Environ Manag. 2019;9(3):746–57.
I. Khong, N. A. Yusuf, A. Nuriman, and A. B. Yadila, “Exploring the Impact of Data Quality on Decision-Making Processes in Information Intensive Organizations,” APTISI Trans. Manag., vol. 7, no. 3, pp. 253–260, 2023.
Susetyoko R, Wiratmoko Yuwono, Elly Purwantini. Model Klasifikasi Pada Seleksi Mahasiswa Baru Penerima KIP university Menggunakan Regresi Logistik Biner. J Inform Polinema. 2022;8(4):31–40.
K. Diantoro, D. Supriyanti, Y. P. A. Sanjaya, and S. Watini, “Implications of Distributed Energy Development in Blockchain-Based Institutional Environment,” Aptisi Trans. Technopreneursh., vol. 5, no. 2sp, pp. 209–220, 2023.
Astuti LW, Saluza I, Alie MF. Optimalisasi Klasifikasi Kanker Payudara Menggunakan Forward Selection Pada Naive Bayes. J Ilm Inform Glob [Internet]. 2020;11(2):63–7. Available from: https://archive.ics.uci.edu/ml/machine-learning-
T. Handra and V. P. K. Sundram, “The Effect of Human Resource Information Systems (HRIS) and Artificial Intelligence on Defense Industry Performance,” IAIC Trans. Sustain. Digit. Innov., vol. 4, no. 2, pp. 155–163, 2023.
Zhao W, Lai X, Liu D, Zhang Z, Ma P, Wang Q, et al. Applications of Support Vector Machine in Genomic Prediction in Pig and Maize Populations. Front Genet. 2020;11(December):1–7.
W. Sejati and V. Melinda, “Education on the Use of Iot Technology for Energy Audit and Management Within the Context of Conservation and Efficiency,” Int. Trans. Educ. Technol., vol. 1, no. 2, pp. 138–143, 2023.
Hozairi H, Anwari A, Alim S. Implementasi Orange Data Mining Untuk Klasifikasi Kelulusan Mahasiswa Dengan Model K-Nearest Neighbor, Decision Tree Serta Naive Bayes. Netw Eng Res Oper. 2021;6(2):133.
R. Widayanti, M. H. R. Chakim, C. Lukita, U. Rahardja, and N. Lutfiani, “Improving Recommender Systems using Hybrid Techniques of Collaborative Filtering and Content-Based Filtering,” J. Appl. Data Sci., vol. 4, no. 3, pp. 289–302, 2023.
Pradnyana GA, Darmawiguna IGM, Suditresna Jaya DKS, Sasmita A. Performance analysis of support vector machines with polynomial kernel for sentiment polarity identification: A case study in lecturer’s performance questionnaire. J Phys Conf Ser. 2021;1810(1):1–9.
Wahyuningsih S, Utari DR. Perbandingan Metode K-Nearest Neighbor , Naive Bayes dan Decision Tree untuk Prediksi Kelayakan Pemberian Kredit. Konf Nas Sist Inf 2018 STMIK Atma Luhur Pangkalpinang, 8 – 9 Maret 2018. 2018;619–23.
Apriyani H, Kurniati K. Perbandingan Metode Naïve Bayes Dan Support Vector Machine Dalam Klasifikasi Penyakit Diabetes Melitus. J Inf Technol Ampera. 2020;1(3):133–43.
U. Rahardja, Q. Aini, P. A. Sunarya, D. Manongga, and D. Julianingsih, “The Use of TensorFlow in Analyzing Air Quality Artificial Intelligence Predictions PM2. 5,” Aptisi Trans. Technopreneursh., vol. 4, no. 3, pp. 313–324, 2022.
Khotimah K. Teknik Data Mining menggunakan Algoritma Decision Tree (C4.5) untuk Prediksi Seleksi Beasiswa Jalur KIP pada Universitas Muhammadiyah Kotabumi. J SIMADA (Sistem Inf dan Manaj Basis Data). 2022;4(2):145–52.
Puslapdik. PEDOMAN PENDAFTARAN KARTU INDONESIA PINTAR university KIP university MERDEKA 2023 [Internet]. Jakarta; 2023. Available from: https://kip-university.kemdikbud.go.id/uploads/BsImnu09yFOxop5dfJAwkaRleMTUqP_tgl20200412205459.pdf
Suganda G, Asfi M, Subagio RT, Kusuma RP. Penentuan Penerima Bantuan Beasiswa Kartu Indonesia Pintar (Kip) university Menggunakan Naïve Bayes Classifier. JSiI (Jurnal Sist Informasi). 2022;9(2):193–9.
Sathiyanarayanan P, Pavithra S, Sai Saranya M, Makeswari M. Identification of breast cancer using the decision tree algorithm. In: 2019 IEEE International Conference on System, Computation, Automation and Networking, ICSCAN 2019. IEEE; 2019. p. 1–6.
Suwitono YA, Kaunang FJ. Implementasi Algoritma Convolutional Neural Network (CNN) Untuk Klasifikasi Daun Dengan Metode Data Mining SEMMA Menggunakan Keras. J Komtika (Komputasi dan Inform. 2022;6(2):109–21.
Latifah R, Wulandari ES, Kreshna PE. Model Decision Tree Untuk Prediksi Jadwal Kerja Menggunakan Scikit-Learn. J Univ Muhammadiyah Jakarta [Internet]. 2019;1–6. Available from: https://jurnal.umj.ac.id/index.php/semnastek/article/download/5239/3517
Puslapdik. PEDOMAN PENDAFTARAN KARTU INDONESIA PINTAR university KIP university MERDEKA 2023 [Internet]. Jakarta; 2023. Available from: https://kip-university.kemdikbud.go.id/uploads/BsImnu09yFOxop5dfJAwkaRleMTUqP_tgl20200412205459.pdf
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