Academic Journal
DETERMINING PROSPECTIVE RECIPIENTS OF THE INDONESIA SMART SCHOLARSHIP PROGRAM USING THE SUPPORT VECTOR MACHINE ALGORITHM AND SIMPLE ADDITIVE WEIGHTING.
| Τίτλος: | DETERMINING PROSPECTIVE RECIPIENTS OF THE INDONESIA SMART SCHOLARSHIP PROGRAM USING THE SUPPORT VECTOR MACHINE ALGORITHM AND SIMPLE ADDITIVE WEIGHTING. |
|---|---|
| Συγγραφείς: | Wibowo, Agus, Lusa, Sofian |
| Πηγή: | Journal of Syntax Transformation; Dec2023, Vol. 4 Issue 12, p62-78, 17p |
| Θεματικοί όροι: | Support vector machines, Scholarships, Disbursements, Accuracy, Smart cards |
| Περίληψη: | Between the period from 2018 to the first semester of 2020, a total of 2.4 million students who owned the Kartu Indonesia Pintar (Indonesia Smart Card) were at risk of not being able to access the Program Indonesia Pintar due to delays in entering recipient data, misdirected targeting, and fund disbursement obstacles. During its implementation, there were issues with inaccurate data of Kartu Indonesia Pintar recipients, resulting in the beneficiaries not being entirely targeted correctly. According to Article 12 of Minister of Education and Culture Regulation No. 10 of 2020, schools, as educational units, are assigned the responsibility of managing the Smart Indonesia Program at the school level. This includes tasks such as proposing eligible student candidates for the Program based on criteria, monitoring and assisting the smooth process of receiving the benefits, and accommodating students who hold the Kartu Indonesia Pintar (KIP). However, schools faced difficulties in obtaining the eligibility data of potential Indonesia Pintar Program recipients through the school proposal format. The objective of this research is to determine the eligibility of potential Indonesia Pintar Program recipients using a combination of Support Vector Machine (SVM) and Simple Additive Weighting (SAW) techniques. The SVM model was trained using kernel tricks and parameter selection (C and gamma) based on data sourced from the DAPODIK application of students in SMK Negeri 1 Kota Bekasi for the academic years 2020/2021 to 2022/2023, with a dataset containing 2499 rows . This dataset was used to determine the eligibility of potential Indonesia Pintar Program recipients. The SAW method was then employed to calculate scores based on the weighting of attributes possessed by the students, which were subsequently ranked from the highest score. The Linear Kernel with parameter values C=0.1 and gamma=0.1 achieved an accuracy of 88.8% during the classification of test data, using 10% of the total dataset, which amounted to 250 rows of data. The combination of SVM and SAW methods resulted in the classification of eligibility for potential Indonesia Pintar Program recipients, ranked based on their scores using the weighted assessment with the SAW method. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Syntax Transformation is the property of Ridwan Institute and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 174806905 RelevancyScore: 950 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 949.691833496094 |
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| Items | – Name: Title Label: Title Group: Ti Data: DETERMINING PROSPECTIVE RECIPIENTS OF THE INDONESIA SMART SCHOLARSHIP PROGRAM USING THE SUPPORT VECTOR MACHINE ALGORITHM AND SIMPLE ADDITIVE WEIGHTING. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wibowo%2C+Agus%22">Wibowo, Agus</searchLink><br /><searchLink fieldCode="AR" term="%22Lusa%2C+Sofian%22">Lusa, Sofian</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Syntax Transformation; Dec2023, Vol. 4 Issue 12, p62-78, 17p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Scholarships%22">Scholarships</searchLink><br /><searchLink fieldCode="DE" term="%22Disbursements%22">Disbursements</searchLink><br /><searchLink fieldCode="DE" term="%22Accuracy%22">Accuracy</searchLink><br /><searchLink fieldCode="DE" term="%22Smart+cards%22">Smart cards</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Between the period from 2018 to the first semester of 2020, a total of 2.4 million students who owned the Kartu Indonesia Pintar (Indonesia Smart Card) were at risk of not being able to access the Program Indonesia Pintar due to delays in entering recipient data, misdirected targeting, and fund disbursement obstacles. During its implementation, there were issues with inaccurate data of Kartu Indonesia Pintar recipients, resulting in the beneficiaries not being entirely targeted correctly. According to Article 12 of Minister of Education and Culture Regulation No. 10 of 2020, schools, as educational units, are assigned the responsibility of managing the Smart Indonesia Program at the school level. This includes tasks such as proposing eligible student candidates for the Program based on criteria, monitoring and assisting the smooth process of receiving the benefits, and accommodating students who hold the Kartu Indonesia Pintar (KIP). However, schools faced difficulties in obtaining the eligibility data of potential Indonesia Pintar Program recipients through the school proposal format. The objective of this research is to determine the eligibility of potential Indonesia Pintar Program recipients using a combination of Support Vector Machine (SVM) and Simple Additive Weighting (SAW) techniques. The SVM model was trained using kernel tricks and parameter selection (C and gamma) based on data sourced from the DAPODIK application of students in SMK Negeri 1 Kota Bekasi for the academic years 2020/2021 to 2022/2023, with a dataset containing 2499 rows . This dataset was used to determine the eligibility of potential Indonesia Pintar Program recipients. The SAW method was then employed to calculate scores based on the weighting of attributes possessed by the students, which were subsequently ranked from the highest score. The Linear Kernel with parameter values C=0.1 and gamma=0.1 achieved an accuracy of 88.8% during the classification of test data, using 10% of the total dataset, which amounted to 250 rows of data. The combination of SVM and SAW methods resulted in the classification of eligibility for potential Indonesia Pintar Program recipients, ranked based on their scores using the weighted assessment with the SAW method. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Syntax Transformation is the property of Ridwan Institute and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.46799/jst.v4i12.876 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 62 Subjects: – SubjectFull: Support vector machines Type: general – SubjectFull: Scholarships Type: general – SubjectFull: Disbursements Type: general – SubjectFull: Accuracy Type: general – SubjectFull: Smart cards Type: general Titles: – TitleFull: DETERMINING PROSPECTIVE RECIPIENTS OF THE INDONESIA SMART SCHOLARSHIP PROGRAM USING THE SUPPORT VECTOR MACHINE ALGORITHM AND SIMPLE ADDITIVE WEIGHTING. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wibowo, Agus – PersonEntity: Name: NameFull: Lusa, Sofian IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 27213854 Numbering: – Type: volume Value: 4 – Type: issue Value: 12 Titles: – TitleFull: Journal of Syntax Transformation Type: main |
| ResultId | 1 |