Paper Title (use style: paper title)


 

ISSN : 2715-2448 | E-ISSN : 2715-7199 

Vol.2 No.1  January 2021 

Buana Information Technology and Computer Sciences (BIT and CS) 

 

1 | Vol.2 No.1, January 2021 

Buana Information Technology and Computer Sciences (BIT and CS) 

Implementation of Simple Additive Weighting (SAW) Method 

To Determine Exemplary PKH Social Worker 

(Case Study: PPKH Garut Regency) 

 

Topan Setiawan 

Information Systems Study Program 

Faculty of Computer Science 

Universitas Ma’soem  
Bandung, Indonesia 

topansetiawan@masoemuniversity.ac.id  

Bayu Priyatna 

Information System, Faculty of 

Engineering and Computer Science 

Universitas Buana Perjuangan 
Karawang, Indonesia 

bayu.priyatna@ubpkarawang.ac.id 

 

‹β› 

Abstract—One measure of the success of an employee in 

carrying out his job is his election as an exemplary employee in 

the agency where he works. The selection must of course be 

based on standard measurement and objective assessment, the 

goal is that the predetermined results can be justified. A 

decision support system using the Simple Additive Weighting 

(SAW) method can help PPKH Garut regency in determining 

exemplary PKH social worker in each sub-district PPKH unit, 

this method will look for weight values for each predetermined 

criterion consisting of quantity, integrity, dedication, 

reliability, initiative, diligence, attitude, motivation and 

presence. The data sample taken in this research was PPKH X 

Sub-distric, where the results of the research are in the form of 

a ranking that can support the decision to choose an exemplary 

PKH social worker in PPKH Garut Regency. 

Keywords: Decision Support System, PKH Social Worker, 

PPKH Garut Regency. 

 

Abstrak —Salah satu ukuran keberhasilan seorang pegawai 

dalam menjalankan pekerjaannya adalah terpilihnya sebagai 

pegawai teladan di instansi tempatnya bekerja. Pemilihan 

tersebut tentunya harus didasarkan pada standar ukuran dan 

penilaian yang objektif, tujuannya agar hasil yang telah 

ditentukan dapat dipertanggung jawabkan. Sistem pendukung 

keputusan dengan menggunakan metode Simple Additive 

Weighting (SAW) dapat membantu PPKH Kab. Garut dalam 

menentukan pendamping sosial PKH teladan di setiap unit 

PPKH kecamatan, metode ini akan mencari nilai bobot untuk 

setiap kriteria yang telah ditentukan yang terdiri dari kuantitas, 

integritas, dedikasi, kehandalan, inisiatif, kerajinan, sikap, 

motivasi dan kehadiran. Sampel data yang diambil pada 

penelitian ini adalah PPKH Kec. X, dimana hasil penelitian 

berupa pemeringkatan yang dapat mendukung keputusan 

pemilihan pendamping sosial PKH teladan di PPKH Kab. Garut. 

 

Kata Kunci: Sistem Pendukung Keputusan, Pendamping Sosial 

PKH, PPKH Kab.  

 

I. INTRODUCTION  

Program Keluarga Harapan (PKH) is a program that 
provides conditional social assistance to Keluarga Penerima 
Manfaat (KPM) that has been established by the Ministry of 
Social Affairs or Kementerian Sosial (Kemensos), as an 

effort to accelerate poverty reduction and has been launched 
by the Government of Indonesia since 2007 [1]. 

PKH implementers or Pelaksana PKH (PPKH) are 
agencies scattered in every region starting from the central 
level (PPKH Pusat), provincial level (PPKH Provinsi), 
regency or city level (PPKH Kabupaten / Kota) and sub-
district level (PPKH Kecamatan). PKH social worker at the 
sub-district level are employees or people who have direct 
contact with KPMs to ensure that KPMs get their rights and 
carry out their obligations in accordance with the terms and 
conditions. 

In an uncertain period of time, PPKH Garut regency in 
collaboration with the Garut regency Social Service has 
twice selected exemplary PKH social workers to give 
appreciation to workers who have shown achievements in 
carrying out their work. The selection was carried out on a 
bottom-up basis starting at the sub-district level where 
workers selected from the sub-district level were submitted 
to the regency level to take further tests. In its 
implementation the standards for measuring and assessing 
work performance are less clear and not transparent, so that 
dissatisfaction for those who feel that their performance has 
been maximized but are not selected. This in turn creates a 
negative stigma that the selection is subjective and the 
reward received is not based on work performance, but on 
other factors outside of work assignments. To overcome this 
problem, it is necessary to implement a decision support 
system with standardized measures so that the results can be 
accounted for [1]. 

Decision support system (DSS) is a system that is used to 
assist and determine decisions to information users to be 
more precise in solving problems that exist within a 
company, agency, or organization by data and certain 
methods [2]. One method that can be applied is the Simple 
Additive Weighting (SAW) method where the result is a 
ranking of exemplary employees [3]. By using the DSS in 
decision making, the standard for measuring and evaluating 
the performance of each worker will be determined properly, 
so that all parties involved can accept the decision [2]. 

 

 

mailto:topansetiawan@masoemuniversity.ac.id
mailto:bayu.priyatna@ubpkarawang.ac.id


2 | Vol.2 No.1, January 2021 

 

 

II. METHOD 

 

A. Research Framework 

Input Output

Study of Literature

Data Collection

Data Processing

Reporting

Understanding 

Theories & Concepts

Data and Information 

Required

List of Worker Ratings

Research Report

 
Fig. 1. Research Framework 

 

B. SAW Method 

SAW is a simple multi-criteria decision-making method 

[4]. The steps in this method include: 

1. Assessment criteria (Cj, j = 1, 2, 3,…, m), which are 

used as a reference in making this decision are shown in 

Table I. 

 

TABLE I. ASSESSMENT CRITERIA 
Kode  Criteria (Cj) 

C1 Quantity 

C2 Integrity 

C3 Dedication 

C4 Realibility 

C5 Initiative 

C6 Diligence 

C7 Attitude 

C8 Motivation 

C9 Presence 

 

Information: 

Quantity : How quickly the worker gets the job done 

Integrity : How committed the worker is to the job 

Dedication : How much dedication is devoted by worker to 

realize the ideals and success of the PKH 

program 

Realibility : Relates to whether or not worker can be relied 

on on certain issues 

Initiative : How often worker take corrective action, 

make suggestions for job improvement and 

accept responsibility for completing work 

Diligence : Willingness to carry out tasks without 

coercion and also of a routine nature 

Attitude : Worker's behavior towards the organization or 

boss or coworkers 

Motivation : How successful are worker in motivating 

KPM to leave PKH program participation. 

Presence : How often are worker present at the 

workplace to work or attend internal 

organization meetings 

 

2. Determine the weight of each criterion with (Wj, j = 1, 2, 

3, …, m) where ∑Wj = 1. 

 

3. Determine a decision matrix using Equation (1). 

 

if j is benefit criteria 

(1) 

if j is cost criteria 

 

 

Information: 

rij : normalized performance rating value 

xij : the attribute value that each criterion has 

Max xij : the largest value of each criterion 

Min xij : the smallest value of each criterion 

Benefit : if the largest value is the best 

Cost : if the smallest value is the best 

 

4. Calculating the preference value for each alternative 

using Equation (2). 

 

(2) 

Information: 

Vi : ranking for each alternative 

Wj : the weighted value of each criterion 

rij : normalized performance rating value 

 

The biggest Vi value indicates that the alternative Ai is an 

alternative choice. 

 

III. RESULTS AND DISCUSSION 

A. Determination of the Scale and Weight of the Criteria 

Determination of alternative values for each criterion 
using a Likert scale of 9-1 [5], with the following 
formulations: 

TABLE II. CRITERIA LIKERT SCALE 

Value Quality 

9 A 

8 A- 

7 B+ 

6 B 

5 B- 

4 C+ 

3 C 

2 C- 

1 D 

 
The weight for each criterion based on the value of 

importance is addressed as in Table III. 

TABEL III. CRITERIA WEIGHT 



3 | Vol.2 No.1, January 2021 

 

Kode  Criteria (Cj) Weight (Wj) 

C1 Quantity 0.05 

C2 Integrity 0.20 

C3 Dedication 0.10 

C4 Realibility 0.03 

C5 Initiative 0.09 

C6 Diligence 0.15 

C7 Attitude 0.17 

C8 Motivation 0.08 

C9 Presence 0.13 

 Summary 1.00 

 

B. Implementation 

This study used a sample of 21 PKH Social Workers in 

X Sub-district, with the following steps: 

1. Value tabulation for each of the alternative criteria 

obtained from interviews with informants and supporting 

data is shown in Table IV. 

 

 

 

 

TABEL IV. WORKER DATA AND VALUE TABULATION 

Alternative C1 C2 C3 C4 C5 C6 C7 C8 C9 

Worker 1 A A A A A B+ A B+ A 

Worker 2 A C+ B B+ B B+ B+ B B 

Worker 3 B+ B B B B+ C+ C B C 

Worker 4 B B+ B+ B B+ B B+ B B+ 

Worker 5 B B+ C C B C+ B+ B C+ 

Worker 6 B C+ B C+ B B B B B 

Worker 7 A C+ B B A A B+ A B+ 

Worker 8 A B+ A A B A B+ A A 

Worker 9 A B+ B+ B+ B B A B B 

Worker 10 B A C A D B B B B 

Worker 11 A B A B+ B A B B+ A 

Worker 12 A B B+ B+ B B+ B A B 

Worker 13 A B A A B B B+ B+ B+ 

Worker 14 B D D C D D C D D 

Worker 15 C+ B+ B C+ B C B C+ C+ 

Worker 16 B A A B B B B B A 

Worker 17 A A A A A A A B+ A 

Worker 18 A B B B B B B B B 

Worker 19 C+ B+ B B C+ B B C+ C+ 

Worker 20 B C B B B B+ C+ B B+ 

Worker 21 B B C+ B B+ C+ B+ B C+ 

 

2. The data value for each of the alternative criteria is then 

converted according to the Likert scale of 9-1. So that 

you get the following results: 

TABEL V. CRITERIA VALUE CONVERSION TABLE 

Alternative C1 C2 C3 C4 C5 C6 C7 C8 C9 

Worker 1 9 9 9 9 9 7 9 7 9 

Worker 2 9 4 6 7 6 7 7 6 6 

Worker 3 7 6 6 6 7 4 3 6 3 

Worker 4 6 7 7 6 7 6 7 6 7 

Worker 5 6 7 3 3 6 4 7 6 4 

Worker 6 6 4 6 4 6 6 6 6 6 

Worker 7 9 4 6 6 9 9 7 9 7 

Worker 8 9 7 9 9 6 9 7 9 9 

Worker 9 9 7 7 7 6 6 9 6 6 

Worker 10 6 9 3 9 1 6 6 6 6 

Worker 11 9 6 9 7 6 9 6 7 9 

Worker 12 9 6 7 7 6 7 6 9 6 

Worker 13 9 6 9 9 6 6 7 7 7 

Worker 14 6 1 1 3 1 1 3 1 1 

Worker 15 4 7 6 4 6 3 6 4 4 

Worker 16 6 9 9 6 6 6 6 6 9 

Worker 17 9 9 9 9 9 9 9 7 9 

Worker 18 9 6 6 6 6 6 6 6 6 

Worker 19 4 7 6 6 4 6 6 4 4 

Worker 20 6 3 6 6 6 7 4 6 7 

Worker 21 6 6 4 6 7 4 7 6 4 

 

3. Normalizing the decision matrix using Equation (1). If a 

criterion is included in the profit criteria type, the greater 

the value the better. Meanwhile, if the criteria are 

included in the type of cost criteria, the smaller the value 

the better. 

 

 

TABLE VI. TYPES OF CRITERIA 

Kode  Criteria (Cj) Type 

C1 Quantity Benefit 

C2 Integrity Benefit 

C3 Dedication Benefit 

C4 Realibility Benefit 

C5 Initiative Benefit 

C6 Diligence Benefit 

C7 Attitude Benefit 

C8 Motivation Benefit 

C9 Presence Benefit 

 

1) Quantity 

r11 =  =  

r21 =  =  1 

… 

r211 =  =  

 

2) Integrity 

r12 =  =  

r22 =  =  

… 

r212 =  =  

 

3) Dedication 

r13 =  =  

r23 =  =  

… 

r213 =  =  

 
… 

 



4 | Vol.2 No.1, January 2021 

 

 

 

9) Presence 

r19 =  =  

r29 =  =  

… 

r219 =  =  

 
TABLE VII. NORMALIZATION RESULTS 

Alternative R1 R2 R3 … R9 

Worker 1 1.00 1.00 1.00 … 1.00 

Worker 2 1.00 0.44 0.67 … 0.67 

Worker 3 0.78 0.67 0.67 … 0.33 

Worker 4 0.67 0.78 0.78 … 0.78 

Worker 5 0.67 0.78 0.33 … 0.44 

Worker 6 0.67 0.44 0.67 … 0.67 

Worker 7 1.00 0.44 0.67 … 0.78 

Worker 8 1.00 0.78 1.00 … 1.00 

Worker 9 1.00 0.78 0.78 … 0.67 

Worker 10 0.67 1.00 0.33 … 0.67 

Worker 11 1.00 0.67 1.00 … 1.00 

Worker 12 1.00 0.67 0.78 … 0.67 

Worker 13 1.00 0.67 1.00 … 0.78 

Worker 14 0.67 0.11 0.11 … 0.11 

Worker 15 0.44 0.78 0.67 … 0.44 

Worker 16 0.67 1.00 1.00 … 1.00 

Worker 17 1.00 1.00 1.00 … 1.00 

Worker 18 1.00 0.67 0.67 … 0.67 

Worker 19 0.44 0.78 0.67 … 0.44 

Worker 20 0.67 0.33 0.67 … 0.78 

Worker 21 0.67 0.67 0.44 … 0.44 

 

4. Calculating the preference value of each worker using 

Equation (2). 

VWorker1 = (0.05*1.00) + (0.20*1.00) + (0.10*1.00) + 

(0.03*1.00) + (0.09*1.00) + (0.15*0.78) + 

(0.17*1.00) + (0.08*0.78) + (0.13*1.00) 

 = 0.95 

VWorker2 = (0.05*1.00) + (0.20*0.44) + (0.10*0.67) + 

(0.03*0.78) + (0.09*0.67) + (0.15*0.78) + 

(0.17*0.78) + (0.08*0.67) + (0.13*0.67) 

 = 0.68 

VWorker3 = (0.05*0.78) + (0.20*0.67) + (0.10*0.67) + 

(0.03*0.67) + (0.09*0.78) + (0.15*0.44) + 

(0.17*0.33) + (0.08*0.67) + (0.13*0.33) 

 = 0.55 

VWorker4 = (0.05*0.67) + (0.20*0.78) + (0.10*0.78) + 

(0.03*0.67) + (0.09*0.78) + (0.15*0.67) + 

(0.17*0.78) + (0.08*0.67) + (0.13*0.78) 

 = 0.74 

VWorker5 = (0.05*0.67) + (0.20*0.78) + (0.10*0.33) + 

(0.03*0.33) + (0.09*0.67) + (0.15*0.44) + 

(0.17*0.78) + (0.08*0.67) + (0.13*0.44) 

 = 0.60 

VWorker6 = (0.05*0.67) + (0.20*0.44) + (0.10*0.67) + 

(0.03*0.44) + (0.09*0.67) + (0.15*0.67) + 

(0.17*0.67) + (0.08*0.67) + (0.13*0.67) 

 = 0.61 

VWorker7 = (0.05*1.00) + (0.20*0.44) + (0.10*0.67) + 

(0.03*0.67) + (0.09*1.00) + (0.15*1.00) + 

(0.17*0.78) + (0.08*1.00) + (0.13*0.78) 

 = 0.78 

VWorker8 = (0.05*1.00) + (0.20*0.78) + (0.10*1.00) + 

(0.03*1.00) + (0.09*0.67) + (0.15*1.00) + 

(0.17*0.78) + (0.08*1.00) + (0.13*1.00) 

 = 0.89 

VWorker9 = (0.05*1.00) + (0.20*0.78) + (0.10*0.78) + 

(0.03*0.78) + (0.09*0.67) + (0.15*0.67) + 

(0.17*1.00) + (0.08*0.67) + (0.13*0.67) 

 = 0.78 

VWorker10 = (0.05*0.67) + (0.20*1.00) + (0.10*0.33) + 

(0.03*1.00) + (0.09*0.11) + (0.15*0.67) + 

(0.17*0.67) + (0.08*0.67) + (0.13*0.67) 

 = 0.65 

VWorker11 = (0.05*1.00) + (0.20*0.67) + (0.10*1.00) + 

(0.03*0.78) + (0.09*0.67) + (0.15*1.00) + 

(0.17*0.67) + (0.08*0.78) + (0.13*1.00) 

 = 0.81 

VWorker12 = (0.05*1.00) + (0.20*0.67) + (0.10*0.78) + 

(0.03*0.78) + (0.09*0.67) + (0.15*0.78) + 

(0.17*0.67) + (0.08*1.00) + (0.13*0.67) 

 = 0.74 

VWorker13 = (0.05*1.00) + (0.20*0.67) + (0.10*1.00) + 

(0.03*1.00) + (0.09*0.67) + (0.15*0.67) + 

(0.17*0.78) + (0.08*0.78) + (0.13*0.78) 

 = 0.76 

VWorker14 = (0.05*0.67) + (0.20*0.11) + (0.10*0.11) + 

(0.03*0.33) + (0.09*0.11) + (0.15*0.11) + 

(0.17*0.33) + (0.08*0.11) + (0.13*0.11) 

 = 0.18 

VWorker15 = (0.05*0.44) + (0.20*0.78) + (0.10*0.67) + 

(0.03*0.44) + (0.09*0.67) + (0.15*0.33) + 

(0.17*0.67) + (0.08*0.44) + (0.13*0.44) 

 = 0.58 

VWorker16 = (0.05*0.67) + (0.20*1.00) + (0.10*1.00) + 

(0.03*0.67) + (0.09*0.67) + (0.15*0.67) + 

(0.17*0.67) + (0.08*0.67) + (0.13*1.00) 

 = 0.80 

VWorker17 = (0.05*1.00) + (0.20*1.00) + (0.10*1.00) + 

(0.03*1.00) + (0.09*1.00) + (0.15*1.00) + 

(0.17*1.00) + (0.08*0.78) + (0.13*1.00) 

 = 0.98 

VWorker18 = (0.05*1.00) + (0.20*0.67) + (0.10*0.67) + 

(0.03*0.67) + (0.09*0.67) + (0.15*0.67) + 

(0.17*0.67) + (0.08*0.67) + (0.13*0.67) 

 = 0.68 

VWorker19 = (0.05*0.44) + (0.20*0.78) + (0.10*0.67) + 

(0.03*0.67) + (0.09*0.44) + (0.15*0.67) + 

(0.17*0.67) + (0.08*0.44) + (0.13*0.44) 

 = 0.62 

VWorker20 = (0.05*0.67) + (0.20*0.33) + (0.10*0.67) + 

(0.03*0.67) + (0.09*0.67) + (0.15*0.78) + 

(0.17*0.44) + (0.08*0.67) + (0.13*0.78) 

 = 0.59 



5 | Vol.2 No.1, January 2021 

 

VWorker21 = (0.05*0.67) + (0.20*0.67) + (0.10*0.44) + 

(0.03*0.67) + (0.09*0.78) + (0.15*0.44) + 

(0.17*0.78) + (0.08*0.67) + (0.13*0.44) 

 = 0.60 

TABLE VIII. PREFERENCE VALUE OF EACH WORKER 

Alternative V1 V2 V3 … V9 Vi 

Worker 1 0.05 0.20 0.10 … 0.13 0.95 

Worker 2 0.05 0.09 0.07 … 0.09 0.68 

Worker 3 0.04 0.13 0.07 … 0.04 0.55 

Worker 4 0.03 0.16 0.08 … 0.10 0.74 

Worker 5 0.03 0.16 0.03 … 0.06 0.60 

Worker 6 0.03 0.09 0.07 … 0.09 0.61 

Worker 7 0.05 0.09 0.07 … 0.10 0.78 

Worker 8 0.05 0.16 0.10 … 0.13 0.89 

Worker 9 0.05 0.16 0.08 … 0.09 0.78 

Worker 10 0.03 0.20 0.03 … 0.09 0.65 

Worker 11 0.05 0.13 0.10 … 0.13 0.81 

Worker 12 0.05 0.13 0.08 … 0.09 0.74 

Worker 13 0.05 0.13 0.10 … 0.10 0.76 

Worker 14 0.03 0.02 0.01 … 0.01 0.18 

Worker 15 0.02 0.16 0.07 … 0.06 0.58 

Worker 16 0.03 0.20 0.10 … 0.13 0.80 

Worker 17 0.05 0.20 0.10 … 0.13 0.98 

Worker 18 0.05 0.13 0.07 … 0.09 0.68 

Worker 19 0.02 0.16 0.07 … 0.06 0.62 

Worker 20 0.03 0.07 0.07 … 0.10 0.59 

Worker 21 0.03 0.13 0.04 … 0.06 0.60 

 

The Vi with the largest preference value is the chosen 

worker, so that worker 17 is the recommended worker to 

become an exemplary PKH Social Worker in PPKH X Sub-

district. 

CONCLUSION 

Based on the results described, it is concluded that the 

implementation of the SAW method is effective as a 

decision support system in determining exemplary PKH 

social workers in PPKH Garut Regency. The results of 

research conducted on 21 PKH Social Workers in X Sub-

district showed that exemplary PKH social worker in the 

region received a preference value of 0.98. 

 

REFERENCES 

 

[1] A. M. Siregar, S. Faisal, Y. Cahyana, and B. Priyatna, 

“Perbandingan Algoritme Klasifikasi Untuk Prediksi 

Cuaca,” J. Account. Inf. Syst., vol. 3, no. 1, pp. 15–24, 

2020. 

[2] F. I. Manek, S. Faisal, and B. Priyatna, “Penerapan K-

Means Clustering untuk Mengelompokkan Pelanggan 

Berdasarkan Data Penjualan Ayam,” Techno Xplore J. 

Ilmu Komput. dan Teknol. Inf., vol. 3, no. 2, pp. 88–93, 

2018. 
[3]  Kementerian Sosial RI. 2019. Pedoman Pelaksanaan 

Program Keluarga Harapan Tahun 2019. Direktorat 
Jaminan Sosial Keluarga. Direktorat Jenderal 
Perlindungan dan Jaminan Sosial. 

[4] Penta, Mega Fidia, dkk. 2019. Sistem Pendukung 
Keputusan Pemilihan Karyawan Terbaik 

Menggunakan Metode SAW Pada PT. Kujang Sakti 

Anugrah. JSAI (Journal Scientific and Applied 

Informatics), 2(3), 185-192. 

[5] Malau, Yesni, dkk. 2018. Sistem Pendukung 
Keputusan Pemilihan Pegawai Berprestasi di Komisi 

Pemilihan Umum Kabupaten Bogor. Jurnal Teknik 
Komputer, 4(1), 66-73. 

[6] Diana. 2018. Metode & Aplikasi Sistem Pendukung 
Keputusan. Yogyakarta: Deepublish. 

[7] Budiaji, Weksi. 2013. Skala Pengukuran dan Jumlah 
Respon Skala Likert. JIPP (Jurnal Ilmu Pertanian dan 

Perikanan), 2(2), 127-133.