View of Machine Learning-Based Classification for Scholarship Selection

(1)

https://doi.org/10.33558/piksel.v11i2.7393

Machine Learning-Based Classification for Scholarship Selection

Asriyanik ^1,*, Agung Pambudi ¹

* Corespondence Author: e-mail: [email protected]

1 Computer Engineering; Universitas Muhammadiyah Sukabumi; Jl. R. Syamsudin, S.H. No. 50 Sukabumi;

(0266) 218345; e-mail:

[email protected] [email protected] Submitted : 10/08/2023 Revised : 31/08/2023 Accepted : 07/09/2023 Published : 30/09/2023

Abstract

Universitas Muhammadiyah Sukabumi (UMMI) is a private university that accepts KIP (Indonesian Smart Indonesia Card) scholarship recipients every year.

However, the number of KIP scholarship applicants always exceeds the available quota, necessitating a reselection process to determine the scholarship recipients. Currently, the UMMI scholarship management does not have a clear method to support decision-making in the KIP scholarship selection process. To address this issue, a classification modeling process will be conducted using machine learning algorithms, namely Decision Tree (DT) and Support Vector Machine (SVM), based on data from previous scholarship recipients. The model development approach follows the SEMMA (Sample, Explore, Modify, Model, Assess) method. It begins with the collection of KIP scholarship recipient datasets at UMMI from 2021-2022, totaling 519 data points with 16 attributes. Through exploration, the primary attributes that serve as features for modeling are identified, including Status DTKS, Status P3KE, combined parental income, and achievements. These attributes are transformed into numerical data to facilitate modeling. The K-Fold Cross- Validation results for the DT model in the case of classifying KIP scholarship recipients from the entire test dataset show an accuracy of 78.44%, a precision indicating correct model predictions of 73.11%, a recall (sensitivity rate) of 78.45%, and an F1 score of 73.20%.

The modeling and validation results with SVM yield an accuracy of 80.17%, precision of 84.44%, and recall of 80.17%. The SVM model demonstrates slightly better results in terms of accuracy and precision. However, both models exhibit competitive performance in classifying KIP scholarship recipients at UMMI.

Keywords: KIP Scholarship Awardee, Classification, Decision Tree, Suppport Vector Machine

1. Introduction

Kartu Indonesia Pintar (KIP) Kuliah is one of the Indonesia Pintar Programs listed in the Ministry of Education and Culture Regulation No. 10 of 2020, aimed at students accepted in higher education institutions (Kemendikbud, 2020). KIP Kuliah Merdeka aims to increase the economic potential and social mobility of students from to pursue higher education (Puslapdik, 2023). Based on the KIP Kuliah registration guidelines in 2023, the eligibility criteria for KIP recipients include graduates of high school, vocational

(2)

school, or equivalent institutions who graduated in the current year or a maximum of two (2) years prior; have passed the selection for new students at state or private universities in officially accredited study programs, have good academic potential but facing economic limitations with special considerations supported by valid documents.

Furthermore, the economic requirements for KIP Kuliah Merdeka recipients are students coming from low-income/vulnerable families, as evidenced by: a) students who own the Kartu Indonesia Pintar (KIP) Pendidikan Menengah; b) being listed in the Data Terpadu Kesejahteraan Sosial (DTKS) or receiving social assistance programs determined by the Ministry of Social Affairs such as PKH, Bansos Penerima Bantuan Iuran Jaminan Kesehatan (PBI JK), Bansos Bantuan Pangan Non-Tunai (BPNT); c) being listed in the maximum range of low-income/vulnerable families, up to the 3^rd decile of the Data Pensasaran Percepatan Penghapusan Kemiskinan Ekstrem (P3KE) and d) students from social welfare/orphanages.

Every higher education institution has a quota for accepting KIP Kuliah students, including Universitas Muhamamdiyah Sukabumi (UMMI). The number of KIP Kuliah recipients is adjusted according to the accreditation ranking, consequently, when the number of KIP Kuliah applicants exceeds the quota, not all applicants can be accommodated, necessitating a reselection process. To ensure that those receiving KIP Kuliah are truly deserving, it is essential to conduct verification and eligibility selection for KIP Kuliah recipients (Iskandar, 2022).Currently, the selection and verification process for KIP Kuliah has been carried out through various methods such as interviews, direct visits to the prospective recipients' residences, and specialization selection for the intended study programs. However, the determination of eligibility for final acceptance has not yet been processed with a clear and transparent method (Khotimah, 2022). One method that can be employed to assist decision-makers in determining potential KIP Kuliah recipients is using machine learning, which involves classifying existing KIP Kuliah selection data from previous years (Firmansyah et al., 2019).The classification process can be performed using various algorithms, including Naive Bayes, Decision Tree, K- Nearest Neighbor, and Support Vector Machine (Elejla et al., 2019).

Several similar studies have been conducted by other researchers, such as the classification of KIP Kuliah recipients using logistic regression (Susetyoko et al., 2022) studied by Ronny Susetyoko et al., where in the result, the classification process using regression could only be used to generate numeric outcomes with input variables consisting solely of numerical values. On the other hand, Gagan Suganda conducted research on the classification of KIP Kuliah recipients using Naïve Bayes, which is a

(3)

simple mathematical formula for conditional probability (Suganda et al., 2022). However, there is a drawback to this method; if the conditional probability becomes zero, the prediction probability will also be zero, leading to suboptimal prediction results (Astuti et al., 2020). Furthermore, Arfyanti conducted research using the decision tree algorithm, where the determining variables for KIP Kuliah recipients were hierarchically structured in the form of a tree. This approach allows for identifying which variables take priority in KIP Kuliah acceptance. Some of the variables used to determine the eligibility of KIP Kuliah recipients include parents’ income, the number of dependents, academic achievement, home ownership, the number of vehicles, electricity payments, land area, building area, water source, test scores, etc (Arfyanti et al., 2022).Another study utilizing the decision tree algorithm in a different case was conducted by Sathiyanarayanan regarding breast cancer identification. It was mentioned that the decision tree algorithm is simpler and can compare each attribute by assigning values to each node, resulting in an accuracy rate of 99% (Sathiyanarayanan et al., 2019). Additionally, there is also the Support Vector Machine (SVM) algorithm, which is a reliable classification and regression algorithm capable of recognizing subtle patterns in complex data sets and achieving high accuracy (Zhao et al., 2020).

From those studies, researchers utilized a single algorithm. Therefore, this study aims to compare two different algorithms by adjusting specific hyperparameters appropriately to achieve more accurate and precise modeling for the classification of KIP Kuliah recipients. Hence, this research will conduct an analysis comparing the use of the decision tree and support vector machine algorithms in classifying KIP Kuliah recipients at Universitas Muhammadiyah Sukabumi. The machine learning development method employed will follow the SEMMA framework (Sample, Explore, Modify, Model, Assess), beginning with data collection, data understanding, data preprocessing, modeling, and the evaluation of accuracy and precision levels of the model.

This research aims to explore and compare the performance of Decision Tree and SVM in classifying prospective KIP Kuliah recipients eligibility. The findings of this study can serve as a guideline for Universitas Muhammadiyah Sukabumi to enhance the selection process of KIP Kuliah recipients, making it more targeted and efficiently.

Additionally, this research can contribute to the advancement of science and technology in the context of data analysis and decision-making.

2. Research Method

This research will employ the SEMMA (Sample, Explore, Modify, Model, and Assess) methodology. The research process begins with dataset selection, exploration

(4)

and visualization of the dataset, also dataset modification to prepare it for modeling, followed by modeling using machine learning algorithms, and model accuracy evaluation (Suwitono & Kaunang, 2022). Model validation will be performed using K-fold cross- validation to measure the model's performance more reliably and reduce the risk of overfitting on the training data. Model performance evaluation will be measured using a confusion matrix table, which can calculate model evaluation metrics such as accuracy, precision, recall, and F1 score. Accuracy represents the ratio of correctly classified instances to all classified instances (Hozairi et al., 2021). Recall measures how successfully the algorithm recognizes a particular class, while precision measures the accuracy of the classification results from the entire dataset. The F1 score combines recall and precision, representing the overall performance of the method (Latifah et al., 2019). Figure 1 shows the research stages that will be conducted.

Source: Research Result (2023)

Figure 1. Steps in this Research

The research was conducted beginning with 1) the collection of the KIP Kuliah recipients' dataset for the past 5 years for modeling, 2) Dataset exploration through visualization and description, which is the process of understanding the dataset to select data suitable for modeling, 3) Data modification: variable selection, data cleaning, and data transformation. This process was carried out to ensure that the dataset to be modeled has been verified, with actions such as feature selection, data cleaning, and data transformation, 4) Modeling the dataset using machine learning algorithms, namely decision tree and SVM. In classifying data using the SVM method, the kernel function K(xi, xd) is utilized. The kernel function to be employed is as described in equation (1) below (Pradnyana et al., 2021):

𝐾(𝑥_𝑖, 𝑥_𝑑) = (𝑥_𝑗^𝑇𝑋_𝑗+ 𝐶))^𝑑, 𝑦 > 0 (1) A decision tree learns from a set of independent data, depicted in a tree diagram, using the "divide and conquer" approach (Wahyuningsih & Utari, 2018). Equations (2) and (3) represent the data equations within the tuple D.

Data collection

Data exploration:

description and visualization

Data modification:

Data cleaning and Data transformation

Data modeling using machine learning algorithms (Decision

tree and SVM)

Model evaluation and testing (K-Fold CV and Confusion matrix)

(5)

𝐼𝑛𝑓𝑜(𝐷) = ∑ −𝑝_𝑖log₂𝑝_𝑖

𝑁

𝑖=1

(2)

𝐼𝑛𝑓𝑜(𝐷) = ∑−𝑝𝑖 𝑛 𝑖=1 𝑙𝑜𝑔2 (𝑝𝑖)

(3)

𝑝_𝑖 represents the probability of a tuple in D belonging to class 𝐶_𝑖, also known as the entropy of D, which is the average information. The accuracy of the machine learning model is measured using the confusion matrix. The confusion matrix equation (Apriyani

& Kurniati, 2020) for calculating accuracy, precision, recall, and F1 score involves collaborating the classification results of the system with the actual observations grouped into True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN). The respective equations for calculating confusion matrix is as follows:

𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = (𝑇𝑃 + 𝑇𝑁) / (𝑇𝑃 + 𝑇𝑁 + 𝐹𝑃 + 𝐹𝑁) (4)

𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑇𝑃 / (𝑇𝑃 + 𝐹𝑃) (5)

𝑅𝑒𝑐𝑎𝑙𝑙 = 𝑇𝑃 / (𝑇𝑃 + 𝐹𝑁) (6)

𝐹1 𝑆𝑐𝑜𝑟𝑒 = 2 ∗ 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 ∗ 𝑅𝑒𝑐𝑎𝑙𝑙 / (𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑅𝑒𝑐𝑎𝑙𝑙) (7) 3. Results and Analysis

3.1. Data Collection

The dataset is sourced from prospective KIP Kuliah applicants registered through the KIP website for the years 2021-2022, including the list of names nominated for KIP Kuliah scholarships at UMMI, as indicated in the Rector's Decree for the same period.

The dataset of prospective KIP Kuliah scholarship recipients consists of 519 rows of data with 16 columns, namely Student Name, DTKS Status, P3KE Status, Father's Occupation, Father's Income, Father's Status, Mother's Occupation, Mother's Income, Mother's Status, Number of Dependents, Home Ownership, Electricity Source, Land Area, Building Area, Achievement, and Scholarship Status.

Figure 2. Dataset of KIP Kuliah Scholarship Recipients at UMMI for the year 2021-2022

(6)

3.2. Data Exploration

The data exploration stage aims to describe the KIP Kuliah dataset through data description and visualization. The raw dataset consists of 16 attributes containing non- numeric data objects.

Table 1. Dataset Attribute

Index Attribute Name Condition Data Type

0 Nama Siswa Non-Null Object

1 Status DTKS Non-Null Object

2 Status P3KE Non-Null Object

3 Pekerjaan Ayah Non-Null Object

4 Penghasilan Ayah Non-Null Object

5 Status Ayah Non-Null Object

6 Pekerjaan Ibu Non-Null Object

7 Penghasilan Ibu Non-Null Object

8 Status Ibu Non-Null Object

9 Jumlah Tanggungan Non-Null Object

10 Kepemilikan rumah Non-Null Object

11 Sumber listrik Non-Null Object

12 Luas Tanah Non-Null Object

13 Luas Bangunan Non-Null Object

14 Prestasi Non-Null Object

15 Status Beasiswa Non-Null Object

In the implementation of the decision tree and SVM algorithms, numerical data is required, so data transformation is necessary. Additionally, not every attribute will be used in the research. The attributes that will form the dataset are DTKS Status, P3KE Status, the combined income of the father and mother, and the label will be Scholarship Status.

3.3. Data Modification

The initial dataset obtained still contains raw data as per the input from the KIP Kuliah registration application, so adjustments are needed to process it using the decision tree and SVM algorithms. Some of the attribute modifications made include:

1. The attributes "Status DTKS" and "Status P3KE" which contain the value "Belum Terdata" are changed to "0" and for "Terdata" they are changed to "1". The attribute modification process is illustrated in Figure 3.

(7)

Figure 3. DTKS and P3KE Status Attribute Modification

2. The attributes "Penghasilan Ayah" and "Penghasilan Ibu" which contain values such as "No Income", "-", and various income ranges, will be merged to create a new attribute called "Penghasilan Orang Tua". The attribute modification process is illustrated in Figure 4.

Figure 4. Parents’ Income Attribute Modification

3. The "Penghasilan Orang Tua" attribute, as mentioned in point number two, will be divided into three categories: 1) "Low" for incomes below Rp 2,000,000, 2)

"Medium" for incomes between Rp 2,000,000 and Rp 4,000,000, and 3) "High"

for incomes above Rp 4,000,000. All values are in monthly income. The attribute modification process is illustrated in Figure 5.

Figure 5. Parents Income Attribute Grouping

(8)

4. The "Prestasi" attribute, which contains descriptions of achievements, will be grouped into "Berprestasi" which will be changed to "1" and "Tidak Berprestasi"

which will be changed to "0". The attribute modification process is illustrated in Figure 6.

Figure 6. Student Achievement Atribut Modification

From the modified dataset, a clearer dataset distribution can be obtained, as illustrated in Figure 7.

Figure 7. Distribution of the Dataset for each Attribute

Hence, the KIP Kuliah dataset used consists of DTKS Status, P3KE Status, Achievement, Scholarship Status, Parental Income, Low, Medium, and High.

(9)

Figure 8. Final Attributes Used in Research 3.4. Data Modeling, Evaluation, and Validation

In this research, data modeling techniques, specifically Decision Trees and Support Vector Machines (SVM), were used for classifying recipients of the KIP Kuliah scholarship program. Decision Tree were employed to construct a tree-like structure that systematically divides the dataset based on attributes such as DTKS status, P3KE status, academic achievement, and parental income, ultimately predicting whether a student qualifies for the scholarship. SVM, on the other hand, aimed to find the optimal hyperplane that effectively separates the two classes which are KIP Kuliah recipients and non-recipients, with the goal of maximizing the margin between them. Furthermore, model evaluation is also conducted to assess the performance and capabilities of the decision tree and SVM models in making predictions or classifications of KIP Kuliah recipients. Model evaluation will utilize the confusion matrix approach, followed by validation using the K-Fold cross-validation technique.

Figure 9 presents the modeling and validation results of the decision tree. The K- Fold Cross-Validation results for the Decision Tree model in the case of classifying KIP Scholarship recipients can be explained as follows:

(10)

Figure 9. Decision Tree Data Modeling, Evaluation, and Validation

1) Accuracy: The accuracy value of 0.78439 indicates that the Decision Tree model can correctly predict approximately 78.44% of all cases in the test dataset. Accuracy measures the extent to which the model can classify KIP Scholarship recipients correctly.

2) Precision: A precision of 0.73107 reflects the model's ability to accurately identify KIP Scholarship recipients among all those predicted as positive (True Positives + False Positives). This indicates that around 73.11% of the model's predictions are correct. 3) Recall (Sensitivity): The recall value of 0.78439, approximately 78.45%, indicates that the model can identify a significant number of KIP Scholarship recipients out of the total that should have been predicted as positive (True Positives + False Negatives). 4) F1 Score: The F1 Score of 0.73201 is the harmonic mean of precision and recall. It provides an overall overview of the model's performance at 73.20%.

(11)

Figure 10. SVM Data Modeling, Evaluation, and Validation

Figure 11. Comparison of Confusion Matrix Results Between Decision Tree and SVM The Confusion Matrix results in Figure 11 above serve as a visual representation of the evaluation outcomes for the classification or prediction models of both the decision tree and SVM. The "Actual" labels (horizontal) refer to the actual classes or categories of the tested data. Meanwhile, the "Predicted" labels (vertical) pertain to the classes or categories predicted by the models based on the provided data. From the confusion matrix results for the decision tree model, among all samples that truly belong to category 0 (Actual 0), the model correctly predicted 397 data points as category 0 (True Negative) but also incorrectly predicted 19 data points as category 1 (False Positive). Conversely,

(12)

among all samples that truly belong to category 1 (Actual 1), the model only accurately predicted 10 data points as category 1 (True Positive) and erroneously predicted 93 data points as category 0 (False Negative). On the other hand, the confusion matrix results for the SVM model indicate that, among all samples that truly belong to category 0 (Actual 0), the model correctly predicted all 416 data points as category 0 (True Negative).

However, among all samples that truly belong to category 1 (Actual 1), the model did not correctly predict any data points as category 1 (True Positive), instead incorrectly predicting 103 data points as category 0 (False Negative).

4. Conclusion

In conclusion, the performance of the Support Vector Machine (SVM) model in classifying recipients of KIP Kuliah scholarships appears to be slightly superior to that of the decision tree model. As shown in Table 2, the SVM model achieved a higher accuracy of 80.17% compared to the decision tree's accuracy of 78.44%. Similarly, SVM outperformed in terms of precision with a score of 84.44%, whereas the decision tree had a precision score of 73.11%. Additionally, both models showed similar recall values to their respective accuracies, indicating their ability to correctly identify KIP Kuliah recipients from the dataset. Overall, when considering the F1 score, which combines precision and recall, the SVM model achieved a score of 71.46%, while the decision tree model closely followed with an F1 score of 73.20%. Thus, while the SVM model showcased slightly better accuracy and precision, both models demonstrated competitive performance in this classification task.

Acknowledgements

The authors would like to express their gratitude to the Ministry of Education, Culture, Research, and Technology of Indonesia for funding this research under the PDP 2023 program.

Author Contributions

Asriyanik proposed the research topic; Asriyanik explores and modifies KIP’s Dataset; Asriyanik and Agung Pambudi build the model; Agung Pambudi evaluates and validates the model.

Conflicts of Interest

The author declares no conflict of interest.

References

Apriyani, H., & Kurniati, K. (2020). Perbandingan Metode Naïve Bayes Dan Support Vector Machine Dalam Klasifikasi Penyakit Diabetes Melitus. Journal of Information Technology

(13)

Ampera, 1(3), 133–143.

https://doi.org/10.51519/journalita.volume1.isssue3.year2020.page133-143

Arfyanti, I., Fahmi, M., & Adytia, P. (2022). Penerapan Algoritma Decision Tree Untuk Penentuan Pola Penerima Beasiswa KIP Kuliah. Building of Informatics, Technology and Science (BITS), 4(3), 1196–1201. https://doi.org/10.47065/bits.v4i3.2275

Astuti, L. W., Saluza, I., & Alie, M. F. (2020). Optimalisasi Klasifikasi Kanker Payudara Menggunakan Forward Selection Pada Naive Bayes. Jurnal Ilmiah Informatika Global, 11(2), 63–67. https://archive.ics.uci.edu/ml/machine-learning-

Elejla, O. E., Belaton, B., Anbar, M., Alabsi, B., & Al-Ani, A. K. (2019). Comparison of classification algorithms on ICMPv6-based DDoS attacks detection. Lecture Notes in Electrical Engineering, 481, 347–357. https://doi.org/10.1007/978-981-13-2622-6_34

Firmansyah, S., Gaol, J., & Susilo, S. B. (2019). Perbandingan Klasifikasi SVM dan Decision Tree untuk Pemetaan Mangrove Berbasis Objek Menggunakan Citra Satelit Sentinel-2B di Gili Sulat. Journal of Natural Resources and Environmental Management, 9(3), 746–757.

https://doi.org/10.29244/jpsl.9.3.746-757

Hozairi, H., Anwari, A., & Alim, S. (2021). Implementasi Orange Data Mining Untuk Klasifikasi Kelulusan Mahasiswa Dengan Model K-Nearest Neighbor, Decision Tree Serta Naive Bayes. Network Engineering Research Operation, 6(2), 133.

https://doi.org/10.21107/nero.v6i2.237

Iskandar, A. (2022). Sistem Pendukung Keputusan Kelayakan Penerima Bantuan Dana KIP Kuliah Menggunakan Metode ROC-EDAS. Building of Informatics, Technology and Science (BITS), 4(2), 856–864. https://doi.org/10.47065/bits.v4i2.2265

Kemendikbud. (2020). Peraturan Menteri Pendidikan Dan Kebudayaan Republik Indonesia Nomor 10 Tahun 2020 Tentang Program Indonesia Pintar. jdih.kemdikbud.go.id

Khotimah, K. (2022). Teknik Data Mining menggunakan Algoritma Decision Tree (C4.5) untuk Prediksi Seleksi Beasiswa Jalur KIP pada Universitas Muhammadiyah Kotabumi. Jurnal SIMADA (Sistem Informasi Dan Manajemen Basis Data), 4(2), 145–152.

https://doi.org/10.30873/simada.v4i2.3064

Latifah, R., Wulandari, E. S., & Kreshna, P. E. (2019). Model Decision Tree Untuk Prediksi Jadwal Kerja Menggunakan Scikit-Learn. Jurnal Universitas Muhammadiyah Jakarta, 1–6.

https://jurnal.umj.ac.id/index.php/semnastek/article/download/5239/3517

Pradnyana, G. A., Darmawiguna, I. G. M., Suditresna Jaya, D. K. S., & Sasmita, A. (2021).

Performance analysis of support vector machines with polynomial kernel for sentiment polarity identification: A case study in lecturer’s performance questionnaire. Journal of Physics: Conference Series, 1810(1), 1–9. https://doi.org/10.1088/1742-

(14)

6596/1810/1/012033

Puslapdik. (2023). Pedoman Pendaftaran Kartu Indonesia Pintar Kuliah Kip Kuliah Merdeka

2023. https://kip-

kuliah.kemdikbud.go.id/uploads/BsImnu09yFOxop5dfJAwkaRleMTUqP_tgl202004122054 59.pdf

Sathiyanarayanan, P., Pavithra, S., Sai Saranya, M., & Makeswari, M. (2019). Identification of breast cancer using the decision tree algorithm. 2019 IEEE International Conference on System, Computation, Automation and Networking, ICSCAN 2019, 1–6.

https://doi.org/10.1109/ICSCAN.2019.8878757

Suganda, G., Asfi, M., Subagio, R. T., & Kusuma, R. P. (2022). Penentuan Penerima Bantuan Beasiswa Kartu Indonesia Pintar (Kip) Kuliah Menggunakan Naïve Bayes Classifier. JSiI (Jurnal Sistem Informasi), 9(2), 193–199. https://doi.org/10.30656/jsii.v9i2.4376

Susetyoko, R., Wiratmoko Yuwono, & Elly Purwantini. (2022). Model Klasifikasi Pada Seleksi Mahasiswa Baru Penerima KIP Kuliah Menggunakan Regresi Logistik Biner. Jurnal Informatika Polinema, 8(4), 31–40. https://doi.org/10.33795/jip.v8i4.914

Suwitono, Y. A., & Kaunang, F. J. (2022). Implementasi Algoritma Convolutional Neural Network (CNN) Untuk Klasifikasi Daun Dengan Metode Data Mining SEMMA Menggunakan Keras.

Jurnal Komtika (Komputasi Dan Informatika), 6(2), 109–121.

https://doi.org/10.31603/komtika.v6i2.8054

Wahyuningsih, S., & Utari, D. R. (2018). Perbandingan Metode K-Nearest Neighbor , Naive Bayes dan Decision Tree untuk Prediksi Kelayakan Pemberian Kredit. Konferensi Nasional Sistem Informasi 2018 STMIK Atma Luhur Pangkalpinang, 8 – 9 Maret 2018, 619–623.

Zhao, W., Lai, X., Liu, D., Zhang, Z., Ma, P., Wang, Q., Zhang, Z., & Pan, Y. (2020). Applications of Support Vector Machine in Genomic Prediction in Pig and Maize Populations. Frontiers in Genetics, 11(December), 1–7. https://doi.org/10.3389/fgene.2020.598318