Decision Support System for Department Selection Using the Analytical Hierarchy Process (AHP) and Profile Matching Algorithms at Vocational High School - A Case Study at SMK Pembangunan Jaya Yakapi

Sep 2, 2026

Annual new student admissions at SMK Pembangunan Jaya Yakapi rely on a rigorous process that becomes increasingly difficult as enrollment grows. Students often choose majors that do not match their interests and competencies, leading to weaker academic performance and frequent department changes after the first semester. The study develops a Decision Support System combining Analytical Hierarchy Process (AHP) and Profile Matching to increase major selection accuracy. AHP computes criterion priority weights via pairwise comparisons, while Profile Matching compares student profiles to department requirements to find the best fit.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Introduction

Research Problem and Objective

Decision Support System Method

AHP Criteria Weighting

Profile Matching to Evaluate Fit

Results and Discussion

Criterion Weights and Consistency

Profile Matching Final Department Recommendations

| Decision Support System for Department Selection Using the Analytical Hierarchy Process (AHP) and Profile Matching Algorithms at Vocational High School: A Case Study at SMK
Pembangunan Jaya Yakapi
Sri Rahayu1, Goenawan Brotosaputro2
1,2 Magister Ilmu Komputer, Universitas Budi Luhur, Jakarta Selatan | | | --- | --- | | Article Info
Article history:
Received 20/02/2026 Revised 14/06/2026 Accepted 25/06/2026 Keywords:
DSSAHP
Profile Matching Department SMK.
Corresponding Author:
Sri Rahayu,
Magister Ilmu Komputer, Universitas Budi Luhur, Jakarta Selatan, Indonesia. | ABSTRACT
The annual new student admissions (PPDB) at SMK Pembangunan Jaya Yakapi involve a rigorous selection process that consistently faces challenges due to growing enrollment. Students frequently select majors that do not align with their interests and competencies, which negatively affects academic achievement and often leads to department changes after the first semester. To address these issues, this study implements a Decision Support System (DSS) combining the Analytical Hierarchy Process (AHP) and Profile Matching methods to improve the accuracy of major selection. AHP determines the priority weights of assessment criteria, including students' interests and academic achievement, through a pairwise comparison matrix. Meanwhile, Profile dual student profiles against department requirements to evaluate the optimal fit. This integrated approach establishes an objective selection workflow that better matches students’ capabilities with major requirements.

This work is licensed under a CC-BY-NC | | Email: [2111600322@student.budiluhur.ac.id](2111600322@student.budiluhur.ac.id) | | | Intensity of Importance | Description | | --- | --- | | 1 | Both elements are equally important. | | 3 | One element is slightly more important than the other. | | 5 | One element is moderately more important than the other. | | 7 | One element is strongly more important than the other. | | 9 | One element is absolutely more important than the other. | | 2,4,6,8 | Intermediate values between two adjacent judgment values. | | Reciprocal | If activity i gets one number compared to activity j, then j has the reciprocal value compared to i. | | No | Criterion Code | Criterion | CF (%) | SF (%) | | --- | --- | --- | --- | --- | | 1 | K1 | Nilai Mata Pelajaran | 40 | 60 | | 2 | K2 | Nilai Wawancara | 30 | 70 | | 3 | K3 | Minat | 50 | 50 | | 4 | K4 | Nilai Prestasi Akademik | 60 | 40 | | No | Department Code | Department Name | | --- | --- | --- | | 1 | RPL | Software Engineering (Rekayasa Perangkat Lunak) | | 2 | TKJ | Computer and Network Engineering (TeknikKomputer dan Jaringan) | | 3 | OTKP | Automation and Office Governance (Otomatisasi Tata Kelola Perkantoran) | | 4 | AKL | Accounting and Institutional Finance (Akuntansi Keuangan dan Lembaga) | | Department Code | Department Name | K1 | K2 | K3 | K4 | | --- | --- | --- | --- | --- | --- | | RPL | Software Engineering | 85 | 80 | 90 | 88 | | TKJ | Computer and Network Engineering | 82 | 85 | 85 | 87 | | OTKP | Automation and Office Governance | 80 | 78 | 88 | 85 | | AKL | Accounting and Institutional Finance | 88 | 83 | 87 | 90 | | No Criterion | School Subjects
Score | Interview
Score | Interest | Academic
Achievement
Score | | --- | --- | --- | --- | --- | | 1 School Subjects Score 1,000 0,500 0,200 0,333
2 Interview Score 2,000 1,000 0,500 0,250
3 Interest 5,000 2,000 1,000 0,333
4 Academic Achievement Score 3,000 4,000 3,000 1,000 | | | | | | b. The result of the matrix multiplication row x column
Table 5. Matrix Multiplication Results | | | | | | No Criterion | School Subjects
Score | Interview
Score | Interest | Academic
Achievement
Score | | 1 School Subjects Score 3,999 2,732 1,649 0,858
2 Interview Score 7,250 4,000 2,150 1,333
3 Interest 14,999 7,832 3,999 2,831
4 Academic Achievement Score 29,000 15,500 8,600 3,998 | |

FAQs

Why are major selections during PPDB challenging at SMK Pembangunan Jaya Yakapi?

Growing enrollment makes the selection process more difficult, and many students choose majors that do not match their interests and competencies, which harms academic performance and triggers department changes after the first semester.

How does AHP contribute to the decision support system?

AHP determines the priority weights of assessment criteria using a pairwise comparison matrix, including students’ interests and academic achievement.

How does Profile Matching evaluate student fit to departments?

Profile Matching compares students’ dual profiles against each department’s requirements and assesses which major provides the optimal match based on those profiles.

Sep 2, 2026

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