Jurnal SISFOKOM (Sistem Informasi dan Kompute.
Volume 15.
Nomor 01.
PP 151-156 Teacher Selection Analysis at Pangkalpinang Baptist School Using SAW Method Fitriyani.
Devi Irawan.
Ari Amir Alkodri.
Yuyi Andrika.
Melati Suci Mayasari.
Chandra Kirana.
Information Systems.
Faculty of Information Technology.
, .
, .
Informatics Engineering.
Faculty of Information Technology.
, .
, .
ISB Atma Luhur.
, .
, .
, .
, .
Pangkalpinang.
Indonesia.
, .
, .
, .
, .
, fitriyani@atmaluhur.
id deviirawan@atmaluhur.
, arie_a3@atmaluhur.
yuyiandrika@atmaluhur.
, melati_imeal@atmaluhur.
, chandra.
kirana@atmaluhur.
AbstractAi Selecting competent teachers plays a vital role in enhancing the quality of education at Pangkalpinang Baptist School.
However, the use of subjective judgment in the recruitment process often leads to less effective decision-making.
Therefore, this research aims to examine the teacher selection mechanism by applying the Simple Additive Weighting (SAW) method to strengthen the objectivity and precision of the selection The SAW approach assesses applicants using three main criteria: interview performance .
%), academic test results .
%), and micro teaching skills .
%).
Each criterion receives specific weights according to importance levels, followed by calculations to determine candidates with the highest scores.
Research results demonstrate that SAW method implementation provides more systematic and transparent decisions in teacher The study evaluated 10 teacher candidates with Eka Sitompul achieving the highest score of 0.
85, followed by Fandi Saputra and Vitta Natalia with 0.
825 each.
This method enables schools to conduct data-based selection, reducing subjectivity in recruitment processes and ensuring selected teaching staff possess competencies aligned with school requirements.
KeywordsAi Decision Making.
Education Quality.
SAW Method.
Simple Additive Weighting.
Teacher Selection.
INTRODUCTION
Selecting qualified teachers represents one of the key factors in enhancing educational quality in schools.
Pangkalpinang Baptist School, as a faith-based educational institution, maintains specific standards for teacher recruitment to ensure candidates possess not only academic competencies but also character traits aligned with school values .
Teacher selection processes require consideration of various criteria including educational qualifications, teaching experience, pedagogical competencies, and personality and moral aspects.
However, selection processes are often conducted subjectively without structured methodologies, risking suboptimal decision outcomes.
Therefore, systematic and data-driven methods are needed to enhance objectivity in teacher selection .
Simple Additive Weighting (SAW) represents one of the Multi-Criteria Decision Making (MCDM) methods applicable for teacher selection processes.
The SAW method operates by assigning weights to predetermined criteria, then performing calculations to determine optimal candidates based on obtained scores.
This method aims to make teacher selection decisions more transparent, accurate, and accountable .
This research analyzes teacher selection processes at Pangkalpinang Baptist School by implementing the SAW The study serves as a reference for schools to improve teacher recruitment process effectiveness and ensure selected teaching staff truly meet established standards .
One of the contributions of this study is the use of the SAW approach specifically tailored to the context of a religiously affiliated school, .
A focus on objectivity using specific criteria that are truly implemented in the real teacher selection process at the school, .
Transparency of the selection results by openly presenting the weights and final scores of each candidate, .
Supporting data-driven decision-making, and .
Providing the best alternative recommendations based on SAW scores.
Previous research on teacher selection using SAW method and Waterfall method included 4 criteria, all representing benefit attributes, with 25 alternatives presented .
Another study utilized the ARAS (Additive Ratio Assessmen.
method with 5 criteria and 15 alternatives .
II.
LITERATURE REVIEW
AU Decision Support Systems Decision support systems are computer-based interactive tools designed to help decision-makers leverage data and models in addressing unstructured issues .
These systems leverage individual intellectual resources combined with computer capabilities to enhance decision quality.
Decision-making stages include .
AU Intelligence Stage: Environmental observation to identify problems requiring resolution.
This stage represents thinking development phases requiring information systems for decision-makers to understand internal and external conditions for appropriate final p-ISSN 2301-7988, e-ISSN 2581-0588 DOI : 10.
32736/sisfokom.
Copyright A2026 Submitted : November 11, 2025.
Revised : November 25, 2025.
Accepted : December 1, 2025.
Published : December 15, 2025 Jurnal SISFOKOM (Sistem Informasi dan Kompute.
Volume 15.
Nomor 01.
PP 151-156 AU Design Stage: Activities for discovering, developing, and analyzing various possible alternative actions.
This stage involves analyzing and evaluating various AU Choice and Examine Stage: Decision-makers select and examine action sequences from several alternatives while evaluating chosen actions.
utilization of organizational resources are vital, especially as competitive pressures make the decision-making process increasingly complex.
AU Simple Additive Weighting Method Simple Additive Weighting (SAW) method is a decision support system approach used to find optimal solutions by summing weights from normalized criteria.
The SAW method is commonly referred to as a scoring technique, as it essentially assigns scores to alternatives based on the predefined weights of each criterion.
SAW Method Stages The SAW method consists of several main steps:
Determining criteria and alternatives AU Define criteria used in decision-making AU Determine alternatives for evaluation AU Fig.
1 SPK Components.
Decision Support System (DSS) components include three main elements: data management, model management, and dialog management .
AU Each criterion receives weights based on importance levels AU Weights are subjective and typically determined by decision-makers AU AU Communication (Dialog Subsyste.
: This subsystem enables users to interact with and issue instructions to the DSS, providing an interface composed of three components: action language, display and presentation language, and a knowledge base.
Objectives of Decision Support Systems .
To help managers make decisions when dealing with semi-structured problems.
To enhance managerial judgment by offering support, without aiming to replace the managerAos role.
To boost productivity.
Creating a decision-making team, particularly one that includes experts, can be expensive.
A computerized support system can decrease the group size and enable members to collaborate remotely.
To improve decision quality.
Computer-based systems allow access to more comprehensive data, which makes it possible to examine more alternatives and make better-informed decisions.
To enhance competitiveness.
Efficient management and Creating decision matrix AU Arrange tables based on alternative values AU AU Data Management: Refers to the use of databases that store context-specific information, which are organized and controlled through Database Management Systems (DBMS).
AU Model Management: Involves the use of financial, statistical, management science, and other quantitative models that offer analytical functionality and supporting software management.
Determining weights for each criterion Decision matrix normalization AU Values in decision matrices must be normalized for uniform scales AU Normalization based on criteria types: benefit or AU Normalization formulas:
For benefit criteria: r_ij = x_ij/max.
_i.
For cost criteria: r_ij = min.
_i.
/x_ij AU Computing the overall value of each option.
AU The final scores are obtained by adding the results of multiplying normalized values with their respective criteria weights.
: V_i = .
_j y r_i.
AU Determining optimal alternatives AU Alternatives with highest scores are most optimal and selected as best decisions SAW Advantages and Disadvantages Advantages:
AU Simple and easy to understand AU Suitable for various multi-criteria decision-making AU Provides objective results if weights are properly Disadvantages:
AU Sensitive to assigned weights, requiring careful AU Does not consider inter-criteria relationships AU Unsuitable for uncertain criteria values p-ISSN 2301-7988, e-ISSN 2581-0588 DOI : 10.
32736/sisfokom.
Copyright A2026 Submitted : November 11, 2025.
Revised : November 25, 2025.
Accepted : December 1, 2025.
Published : December 15, 2025 Jurnal SISFOKOM (Sistem Informasi dan Kompute.
Volume 15.
Nomor 01.
PP 151-156 Robustness AU Strong in operational terms .
table, deterministi.
, but not robust to changes in weights Ai even small changes in weights can alter the ranking.
AU Does not consider correlations or dependencies between criteria.
Handling Uncertainty Assumes deterministic numerical values.
not suitable for uncertainty or linguistic assessments without modification.
Real-World Validation Easy to implement and explain the results to stakeholders .
However, validation requires:
AU Sensitivity analysis on the weights.
AU Cross-checking with actual recruitment outcomes .
, how the selected teachers perform after several months/year.
Fig 3.
Research Flow Scheme AU Data Processing Stages The steps in the SAW method are:
The research flow carried out by the researcher is the first to collect data by conducting interviews with sources, in this case the principal.
Then, observation or direct observation of the teacher recruitment process at Kasih Baptist Kindergarten was conducted, then conducted a literature study, namely by reading references in the form of journals related to the topic being worked on.
The next step is to formulate the problem, namely by identifying the problems that occur in the current business process, after that looking for solutions or alternative solutions to the problems that occurred.
Following the determination of alternative options, data processing was performed using the SAW approach.
Upon completion, the processed results were implemented.
RESEARCH METHODOLOGY
This research employs quantitative approaches using the Simple Additive Weighting (SAW) method.
Data collection involved interviews with school principals, direct observation of teacher recruitment processes, and literature studies from relevant journals.
Fig 2.
SAW Method Flowchart The flowchart above represents the SAW method.
The steps for data processing using the SAW method include selecting criteria data and several alternatives, subsequently, a decision matrix is developed based on the data from the criteria and the set of alternatives.
The next step is to calculate the normalized Once the normalized value is obtained, the normalized matrix is AUmultiplied.
The subsequent step involves computing the preference value of each alternative to identify which one obtains the highest and lowest weight.
AU Research Flow The research flow begins with data collection, problem formulation, solution alternative identification, data processing using SAW method, and result implementation.
Data includes 10 teacher candidates evaluated using 3 assessment criteria:
%), academic tests .
%), and micro teaching .
%).
Research stages follow systematic approaches: data collection through interviews and observations, literature review, problem identification, solution alternative analysis.
SAW method data processing, and result implementation.
IV.
RESULTS AND DISCUSSION
AU Criteria Definition Based on school principal interviews, three main teacher selection criteria were established:
Table 1.
Selection Criteria p-ISSN 2301-7988, e-ISSN 2581-0588 DOI : 10.
32736/sisfokom.
Copyright A2026 Submitted : November 11, 2025.
Revised : November 25, 2025.
Accepted : December 1, 2025.
Published : December 15, 2025 Jurnal SISFOKOM (Sistem Informasi dan Kompute.
Volume 15.
Nomor 01.
PP 151-156 Code Criteria Interview Academic Test Micro Teaching Weight Attribute Benefit Benefit Benefit The data for the criteria above were obtained from interviews with the resource person, namely the school From the interview results, three criteria were identified: interview, academic test, and microteaching.
Each criterionAos weight represents its level of importance within the overall decision-making framework.
Therefore, the resource person assigned the following weights: interview 30%, academic test 35%, and microteaching 35%.
Criteria Interview (C.
1 = Very Fluent 5 = Moderate 0 = Not Fluent Academic Test (C.
1 = Highly Competent 5 = Moderate 0 = Not Competent Micro Teaching (C.
1 = Highly Skilled 5 = Moderate 0 = Not Skilled Table 2.
Normalization Value Interview (C.
Fandi Saputra Eka Sitompul Andreas Gunarto Vitta Natalia Theodorus Suranto Ribka Novita Bangun Rita Astuti Prengki Pintubatu Aditya Salindeho Academic Test (C.
Micro Teaching (C.
AU Matrix Values Table 3.
Matrix Values Table .
1 0, 5 0, 5 1 1 1 0 1 1 0, 5 1 0 1 0, 5 0, 5 0, 5 0, 5 1 1 0 0 AU Weighting for Interview Criteria Table 4.
Table of Weighting for Interview Criteria Participant Name Weight Fandi Saputra Eka Sitompul Andreas Gunarto Vitta Natalia Theodorus Suranto Ribka Novita Bangun Rita Astuti Prengki Pintubatu Aditya Salindeho The interview criteria are the benefit attributes so:
R11=1/1=1
R21=0.
5/1=0.
R31=1/1=1
R41=1/1=1
R51=0.
5/1=0.
R61=0.
5/1=0.
R71=1/1=1
R81=0.
5/1=0.
R91=0.
5/1=0.
R101=1/1=1
AU Weighting for Academic Test Criteria Table 5.
Table of Weighting for Academic Test Criteria Participant Name Weight Fandi Saputra Eka Sitompul Andreas Gunarto Vitta Natalia Theodorus Suranto Ribka Novita Bangun Rita Astuti Prengki Pintubatu Aditya Salindeho The academic test criteria are the benefit attributes so:
R12=1/1=1
R22=1/1=1
R32=0/1=0
R42=0.
5/1=0.
R52=1/1=1
R62=0.
5/1=0.
R72=1/1=1
R82=0.
5/1=0.
R92=0/1=0
R102=0.
5/1=0.
AU Weighting for Micro Teaching Criteria Table 6.
Table of Weighting for Micro Teaching Criteria Participant Name Weight Fandi Saputra Eka Sitompul p-ISSN 2301-7988, e-ISSN 2581-0588 DOI : 10.
32736/sisfokom.
Copyright A2026 Submitted : November 11, 2025.
Revised : November 25, 2025.
Accepted : December 1, 2025.
Published : December 15, 2025 Jurnal SISFOKOM (Sistem Informasi dan Kompute.
Volume 15.
Nomor 01.
PP 151-156 Andreas Gunarto Vitta Natalia Theodorus Suranto Ribka Novita Bangun Rita Astuti Prengki Pintubatu Aditya Salindeho The Micro Teaching criteria are benefit attributes so :
R13=0.
5/1=0.
R23=1/1=1
R33=1/1=1
R43=1/1=1
R53=0/1=0
R63=0.
5/1=0.
R73=0/1=0
R83=1/1=1
R93=1/1=1
R103=0.
5/1=0.
The evaluation of micro teaching and teaching activities is carried out by the school principal, as they are responsible for the recruitment process and understand the schoolAos values and The principal also has the authority to ensure that the selected teachers truly meet the academic, pedagogical, and ethical standards of the institution and conducts direct monitoring of teacher performance over a certain period .
uch as during the probation or initial contract phas.
UO1 1 0.
5 1 1 1 0 1 1 0.
5 1 0.
5 1 0 0.
5 1 1 0 0.
W = .
Preference Value V1 = .
= 0.
175 = 0.
V2 = .
= 0.
35 = 0.
V3 = .
= 0.
30 0 0.
35 = 0.
V4 = .
35*0.
= 0.
V5 = .
3*0.
= 0.
35 0=0.
V6 = .
3*0.
35*0.
35*0.
= 0.
175=0.
V7 = .
= 0.
35 0=0.
V8 = .
3*0.
35*0.
= 0.
35=0.
V9 = .
3*0.
= 0.
175=0.
V10 = .
35*0.
35*0.
= 0.
175=0.
DISCUSSION
This research shows that applying the Simple Additive Weighting (SAW) method in the teacher recruitment process at Pangkalpinang Baptist School effectively improves the decision-making.
The evaluation results show that the method provides clear and systematic scoring based on the three actual criteria used in the recruitment process: interview performance, academic test, and micro teaching.
The findings indicate that SAW effectively supports data-driven decision-making by identifying the most suitable candidate, with Eka Sitompul achieving the highest score .
followed by Fandi Saputra and Vitta Natalia .
However, this study has several limitations.
First, the scope of evaluation is limited to only 10 candidates, which may reduce the generalizability of the results.
Second, no sensitivity analysis was conducted to observe the impact of changes in weight distribution on ranking stability, even though SAW is known to be sensitive to weight assignment.
Third, the study did not validate the SAW results with real-world teaching performance after hiring, which is important to confirm the accuracy and reliability of the model in practice.
From these limitations, several lessons can be drawn.
The use of SAW is effective for transparent and straightforward decision-making, but its reliability in educational contexts may improve if combined with techniques capable of addressing weight sensitivity and uncertainty in assessments.
Additionally, validation using post-selection teacher performance would provide stronger evidence of the modelAos practical effectiveness.
Future research could explore the integration of Machine Learning to dynamically adjust criterion weights based on historical recruitment data or incorporate uncertainty modeling using fuzzy logic.
TOPSIS, or probabilistic MCDM approaches to improve robustness.
Longitudinal studies are also recommended to compare SAW-based selection outcomes with actual teaching performance in the school environment.
REFERENCES