Indoor Lighting Needs Optimization Using Simple Additive

Weighting Method

Tita Karlita, Ardiansyah Dwi Saputra, Ira Prasetyaningrum and Fitri Setyorini

Electronic Engineering Polytechnic Institute of Surabaya, Indonesia

Keywords: Decision Support System, Indoor, Lights, Simple Additive Weighting.

Abstract: Lack or excess of lighting in the room, causing disrupted activities where the cause is the wrong choice of

lamp type, lamp brand, number of lamps, and required wattage. The development of computers has caused

changes in various areas of life, one of which is in the decision-making process. Sometimes decisions made

by a person or group manually are less accurate in their judgment, such as deciding to buy a suitable lamp for

the desired room. The Simple Additive Weighting (SAW) method can be used to help determine the type and

the brand of lamp that will be used. Some parameters used as calculations are the base shape of the room with

that size, the type of room, and the ranking of attributes that have been provided. The result of this research

is the creation of a Decision Support System with the SAW method for optimizing room lighting needs. Based

on the experiments, it proves that if the parameter value is greater, the lumen requirement will also be greater

and the method used also works correctly. The final score will decide on the best alternative from the available

alternatives. So it can be concluded that the SAW method is a decision support system in solving various

multi-criteria decision-making problems. It can also be used as a decision support system for optimizing

lighting needs in the room.

1 INTRODUCTION

The development of an increasingly advanced era like

today increases the community’s needs, especially

with the lighting factor of the room. Lighting is one

of the essential factors in designing a space to support

user comfort. A room with a good lighting system can

support the activities carried out in it (Ponamon et al.,

2017). The use of sunlight as the primary light source

can reduce the use of electricity. However, the

availability of natural light sources that are not

constant due to weather changes and problems related

to the depth of space cause the distribution of light

entering the room to be uneven because not all parts

of the room are exposed to sunlight. The lighting

conditions in these two conditions can be said not to

meet the lighting standards, so the role of artificial

light is needed in synergy with natural light (N.

Azizah, 2017)(P. Satwiko, 2008).

A good lighting system must meet three main

criteria: quality, quantity, and lighting rules. The lack

of lighting support in-room results in disrupted

activities in the room. For example, when lighting is

too excessive, it will interfere with vision. Thus, the

light intensity needs to be regulated to produce the

appropriate vision needs in the room based on the

type of activity (Ponamon et al., 2017). A decision

support system is a computer-based system that can

solve problems by producing the best alternative to

support decisions taken by decision-makers (Kusrini,

2007).

Therefore, this final project will discuss a decision

support system that is expected to assist in selecting

the type of lamp desired. The Simple Additive

Weighting method because this method can make a

more precise assessment based on the predetermined

criteria and preference weights. In addition, the SAW

method can select the best alternative from several

existing choices.

At a time when the times are more advanced, the

lighting factor of the room is still an important thing.

The problems faced by the community in choosing

the best lamps are common in choosing these lamps.

They must pay attention to the brand, quality, and

lighting (Siburian, B., Octiviani, M., and Milawati,

2018). In addition, we must know how much lux is

needed from each type of room and the lumen

requirement of the space we choose. Another

challenge is choosing the type of lamp based on our

needs, starting from the aspect of price, lamp

798

Karlita, T., Saputra, A., Prasetyaningrum, I. and Setyorini, F.

Indoor Lighting Needs Optimization Using Simple Additive Weighting Method.

DOI: 10.5220/0011887700003575

In Proceedings of the 5th International Conference on Applied Science and Technology on Engineering Science (iCAST-ES 2022), pages 798-805

ISBN: 978-989-758-619-4; ISSN: 2975-8246

resistance, lumens per watt for each kind of lamp,

wattage requirements, etc.

This final project aims to design a Web/Mobile-

based decision support system with the Simple

Additive Weighting method to decide on optimizing

lighting needs in the room. By implementing the

physics method and SAW into the system and

evaluating the performance of the SAW method as a

decision support system for optimizing the needs for

lighting in the room.

The benefits of the final project and the writing of

this final project are expected to be an alternative

solution for the community in helping make decisions

in choosing the lighting needs in the desired room.

2 RELATED WORKS

In this study, the author was inspired by various

related references. In this study, the author was

inspired by Ponamon, et al. study (Ponamon et al.,

2017). From the results of this study, a decision

support system for selecting lamp types for room

lighting has been produced using the Analytical

Hierarchy Process (AHP) method to decide the type

or brand of lamps to be used in the room. Based on

the AHP calculation example results by weighting the

criteria and alternatives for choosing the type or brand

of lamp used, the best lamp brand was selected,

namely Philips. This decision support system can

help people buy the kind of lamp that will be used

more quickly and easily in making choices.

In (Hadikurniawati & Ami, 2016),

Hadikurniawati et al. proposed multi-attribute

decision-making for selecting lamp types that can

provide the best-ranking order of the criteria used to

determine the type of lamp. The results of the

calculation of the AHP method are based on technical

and economic calculations, as well as an assessment

of the technical specifications of the lamp. In

addition, it also uses the assistance of the super

decision program to generate alternative priorities,

namely alternative lamps of the Osram HQI 400W/D

type, which occupy the highest priority and can be

used as a consideration for decision-makers to be

used in sports arena lighting systems, especially

badminton courts. Furthermore, Seema et al. built a

decision support system for selecting the best lamps

using the Vikor method (Seema & Kumar, 2015).

Table 1 shows the comparison of our proposed

method with other previous related works.

Table 1: Comparison study with other previous methods.

Title Method A B C

Decision

support system

for selection of

lighting types

for room

lighting using

AHP method

Analytic

Hierarch

y Process

(AHP)

No No

Applicat

ion

Impleme

ntation

A decision

support system

for IMS

selection based

on fuzzy

VIKOR method

VIKOR No No

Concept

Theory

Multi attribute

decision in light

selection in

badminton field

lighting system

Analytic

Hierarch

y Process

(AHP)

No No

Applicat

ion

Impleme

ntation

Our proposed

method

Simple

Additive

Weightin

g (SAW)

Yes

Applicat

ion

Impleme

ntation

A: Choosing a Room Type

B: Generating Wattage Requirements for The Selected Room

C: Application Implementation / Concept Theory

3 METHODOLOGY

3.1 System Design

The system design can bee seen in Figure 1. The first

step is when the user inputs data, it includes things

like Room Type (Bedroom, Bathroom, etc.), then

Room Size (in centimeters), and will rank the

attributes that will be provided. For the physics

formula itself, we will use the formula of Lux to

Lumen formula, which can be calculated as

𝛷𝑉

()

= 𝐸𝑣

()

× 𝐴

(



)



()



()

(1)

( 𝛷𝑉

()

) is the total lumen requirement of a room.

To get it, it must be known beforehand ( 𝐸𝑣

()

)

which is the lux requirement of the room. Based on

Equation 1, ( 𝐴

(



)

) is the area of the room and (



()



()

) is the height of the room.

Indoor Lighting Needs Optimization Using Simple Additive Weighting Method

799

Figure 1: System Design.

and Lumen to Watts Formula, which can be

calculated as

𝑃

()



()



(







)

(2)

( 𝑃

()

) is the total wattage for the lamp that will

be used. To get it, it must be known beforehand (

𝛷𝑉

()

) which is the total lumen requirement of a

room and ( 𝜂

(







)

) is the coefficient of lumens per

watt of the lamp.

Table 2: The need for lux for each room type (SNI 03-6575-

2021, 2001).

Room Function

Illumination

Level (lux)

Residential Home :

Terrace 60

Living room 120 ~ 250

Dining room 120 ~ 250

Workspace 120 ~ 250

Bedroom 120 ~ 250

Bathroom 250

Kitchen 250

Garage 60

Office :

Director’s Room 350

Workspace 350

Computer room 350

Meeting room 300

Drawing Room 750

Archives 150

Active Archive Space 300

Educational institutions :

Classroom 250

Library 300

Laboratory 500

Drawing Room 750

Canteen 200

Hotels and Restaurants

Lobby, Corridor 100

Ballroom/courtroom 200

Dining room 250

Cafeteria 250

Bedroom 150

Kitchen 300

Hospital / treatment centre:

Inpatient Room 250

Operating Room, Delivery Room 300

Laboratory 500

Recreation and Rehabilitation Room 250

Shops/showrooms :

Showrooms with large objects (e.g.,

cars)

500

Cake and food shop 250

Book and stationery/drawing shop 300

Jewellery shop, watch 500

Leather goods and shoe shop 500

Clothing store 500

Supermarkets 500

Electrical appliance shop (TV,

Radio/tape, washing machine, etc.)

250

General :

Parking Space 50

Warehouse 100

Rough work 100 ~ 200

Medium job 200 ~ 500

Smooth work 500 ~ 1000

Very smooth work 1000 ~ 2000

Color check 750

Praying room :

Mosque 200

Church 200

Monastery 200

Table 2 will be used in the Physics Calculation

method. Churchman and Ackoff first used the SAW

method to solve the selection problem. The basic

concept of the SAW method is to find the sum of the

weights of the performance rating for each candidate

on all attributes. The SAW method requires the

process of normalizing the decision matrix (X) to a

scale that can be compared with all existing candidate

ratings (Eka P et al., 2016).

In this method, it is required for the decision-

maker to determine the weight of each attribute. The

total score of an alternative is obtained from the sum

of all the multiplication results between the rating and

iCAST-ES 2022 - International Conference on Applied Science and Technology on Engineering Science

800

Figure 2: Our proposed decision diagram.

the weight of each attribute itself (Firnando, I. and

Joni, W. 2020).

Compared to other methods, the Simple Additive

Weighting method lies in the assessment more

precisely because it is based on the value of the

criteria and preference weights that have been

determined. Besides, the SAW can select the best

alternative from several alternatives because of the

ranking process after determining the weight value

for each attribute (Firnando, I. and Joni, W.

2020).The problem hierarchy is structured to assist

the decision-making process by considering all

decision elements involved in the system.

Figure 2 is a decision diagram or decision

hierarchy based on this research and will be used until

the next stage. Then, these attributes have attribute

data for each type of lamp (Table 3). These attributes

will be used as input in Simple Additive Weighting.

4 RESULTS

In the experiments, we try a test scenario with the

following input options:

- Base shape: square

- Size: side length → 200 cm

room height → 300 cm

- Room type: residential house

- Sub room: bedroom

- Attribute rank: see Table 4

Table 3: Data attribute (Lim et al., 2013) (Spesifikasi

Teknis, 2021).

Attribute Data

Score

LED CFL

Usage Period 40000 9000

CRI (Color Rendering Index) 80 78

Contains mercury TOXIC -4.8 -83.7

Average weight (grams) -172 -58

Carbon Dioxide Emissions (30

lamps per year)

-451 -4500

Electricity Bill Fee/Year

(based on the use of 1 lamp for

8 hours/day at 900VA power)

-14454 -36144

Purchase Price (Cheaper) 0.01 1

TPI (Toxicity Probability

Interval)

-42027 -80454

Lamp Replacement for 40,000

hours

-0.000001 -4

Warm-up time up to 60% light -1 -40

Number of cycles on/off 20000 4000

Table 4: Attribute Ranking in scenario-1.

Attribute Data Ranking

Usage Period Ranking 1

CRI (Color Rendering Index) Ranking 2

Contains mercury TOXIC Ranking 3

Average weight (grams) Ranking 4

Carbon Dioxide Emissions (30 lamps per year) Ranking 5

Electricity Bill Fee/Year (based on the use of 1

lamp for 8 hours/day at 900VA power)

Ranking 6

Purchase Price (Cheaper) Ranking 7

TPI (Toxicity Probability Interval) Ranking 8

Lamp Replacement for 40,000 hours Ranking 9

Warm-up time up to 60% light Ranking 10

Number of cycles on/off Ranking 11

Indoor Lighting Needs Optimization Using Simple Additive Weighting Method

801

The ranking above is used in the Simple Additive

Weighting method, where rank 1 has the highest

weight. Then, we will describe the calculations

performed by the application system. Figure 3 is a

Data Entry Page Display, the earliest display to enter

data that will be processed by the system later. The

Data is following input options from the test scenario.

Then Figure 4 is a display of the Attribute Rank Page

which can still be scrolled down again. Rank with

number 1 has the highest value until number 11 has

the lowest value.

Figure 3: Data Entry Page Display.

Figure 4: Rank Attribute-1 Page Display.

1) Physics Calculation Method

Using the Equation (1) and Equation (2). We

calculate data from user input in the application

according to scenario-1. In Table II. The bedroom has

about 120 to 250, and we take the amount of 250 lux.

The room is a square with a side length of 200 cm.

Then the area of the room is

Area of Room = Length of Side x Length of Side

= 200 cm x 200 cm

= 40000 cm

= 4 m

Then the lumen requirement in the room is

Room Lumen = Room Lux Requirement x

Room Area x

(Room Height / 3 m)

= 250lx x 4m

x (3m / 3m )

= 1000 lm

Therefore, the requirement of scenario-1 is 1000

lumens. Next will go to the second method.

2) Simple Additive Weighting Method

This method calculates the best type of lamp for the

user after the user has ranked the attributes provided

by the system. The attribute ranks for scenario-1 are

based on Table 4.

2.1) The First Phase

The first stage is to get the weight normalization value

from the ranking input. To find the weight value,

subtract the largest rank value of all attributes with

the rank value. The normalized value of the weights

is by dividing weight by total weight.

Table 5: Table for First Phase.

Attribute

Data

Rank Weight

Weight

Normalization

Value

1. Usage Period 1 11.0 0.167

CRI (Color

Rendering

Index)

2 10.0 0.152

Contains

mercury

TOXIC

3 9.0 0.136

Average

weight (grams)

4 8.0 0.121

Carbon

Dioxide

Emissions (30

lamps per

year)

5 7.0 0.106

Electricity Bill

Fee/Year

(based on the

use of 1 lamp

for 8 hours/day

at 900VA

power)

6 6.0

0.091

iCAST-ES 2022 - International Conference on Applied Science and Technology on Engineering Science

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Table 5: Table for First Phase. (cont.)

Purchase Price

(Cheaper)

7 5.0 0.076

TPI (Toxicity

Probability

Interval)

8 4.0 0.061

Lamp

Replacement

for 40,000

hours

9 3.0 0.046

10.

Warm-up time

up to 60%

ligh

10 2.0 0.031

11.

Number of

cycles on/off

11 1.0 0.015

Total 66 1

2.2) The Second Phase

Next is the second stage, calculating the

normalization score from Table 3. The normalization

value divides the attribute value by the largest among

the available attribute values. The normalization

score can be seen in Table 6.

Table 6: Attributes for Second Phase Normalization

Results.

Attribute Data

Normalization

Score

LED CFL

Usage Period 1.000 0.225

CRI (Color Rendering Index) 1.000 0.975

Contains mercury TOXIC 1.000 0.057

Average weight (grams) 0.337 1.000

Carbon Dioxide Emissions (30

lamps per year)

1.000 0.101

Electricity Bill Fee/Year (based

on the use of 1 lamp for 8

hours/day at 900VA power)

1.000 0.399

Purchase Price (Cheaper) 0.010 1.000

TPI (Toxicity Probability

Interval)

1.000 0.522

Lamp Replacement for 40,000

hours

1.000 0.001

Warm-up time up to 60% light 1.000 0.025

Number of cycles on/off 1.000 0.200

2.3) The Final Stage

After normalizing, the next step is calculating the

final score of the normalized attribute values by

multiplying the weight normalization value and

attribute normalization value. The final score stage

attributes can be seen in Table 7.

Table 7 shows the highest final score is the Type

of LED Lamp. Then the system results, the best type

of lamp for the user is an LED (Light Emitting

Diode). We try to do a manual calculation to validate

the result, and the results are the same output as our

system. Therefore, the system is declared to have no

errors. There is a display of the system calculation

results to produce the best choice for the user. There

are how many lux needed from the selected room to

the best type of lamp. The number of watts the user

needs is shown in Figure 5.

2.4) The Final Experiment Results

From experiments conducted based on test scenarios

using input options, among others, Base Shape

(Square) with Size (Side Length is 200 cm and Room

Height is 300 cm), Room Type is (Residential House

with Sub Room (Bedroom)), attribute ranking in

Table 4 which then uses the Physics Formulas (Lux

to Lumen Conversion Formula and Lumen to Watts

Conversion Formula) and Simple Additive

Weighting method, the results of the optimization

decision are obtained, the room requires an LED type

of lamp that produces a lumen size of 1000 lm or

about 10 watts.

If the parameters of the Base Shape and Room

Size of the room are getting bigger, the greater the

Lumen requirement will be. In addition, the Lux

Requirement of the Room Type also affects the

amount of Lumen required for the room. For

example, if we changed the shape of the base into a

(circle with a diameter of 200cm) and a room height

of 300cm, and the type of room is a drawing room,

the lumen requirement of the room is 2357 lm. The

experiment required 2357 lm, while the first

experiment required 1000 lm. This proves that if the

parameter value is greater, the lumen requirement

will also be greater, and the method used works

correctly.

Figure 5: System Calculation Result Display.

Then, the Attribute Rank will generate the weight that

is used to get the final score. Here it is found that the

final total score will result in a decision on the best

alternative from the available alternatives. This system

is dynamic toward weights in decision-making so that

the weight of each attribute can change at any time by

following the rank of the attribute itself.

Indoor Lighting Needs Optimization Using Simple Additive Weighting Method

803

Table 7: Final Score Stage.

Attribute Data

Final Score

LED CFL

Usage Period 0.166 0.037

CRI (Color Rendering Index) 0.152 0.147

Contains mercury TOXIC 0.136 0.007

Average weight (grams) 0.041 0.121

Carbon Dioxide Emissions (30

lamps per year)

0.106 0.011

Electricity Bill Fee/Year (based

on the use of 1 lamp for 8

hours/day at 900VA power)

0.091 0.036

Purchase Price (Cheaper) 0.001 0.075

TPI (Toxicity Probability

Interval)

0.061 0.031

Lamp Replacement for 40,000

hours

0.045 0.001

Warm-up time up to 60% light 0.031 0.001

Number of cycles on/off 0.0152 0.003

Total 0.844 0.472

5 CONCLUSIONS

Based on the experiments and analyzes that have been

carried out, the following conclusions can be drawn.

From experiments conducted based on test scenarios

using input options, among others, Base Shape

(Square) with Size (Side Length is 200 cm and Room

Height is 300 cm), Room Type is (Residential House

with Sub Room (Bedroom)), attribute ranking in table

4 which then uses the Physics Formulas (Lux to

Lumen Conversion Formula and Lumen to Watts

Conversion Formula) and Simple Additive

Weighting method, the results of the optimization

decision are obtained, the room requires an LED type

of lamp that produces a lumen size of 1000 lm or

about 10 watts. This proves that if the parameter value

is greater, the lumen requirement will also be greater

and the method used also works correctly. The final

total score will result in a decision on the best

alternative from the available alternatives. So it can

be concluded that the SAW method is a decision

support system in solving various multi-criteria

decision-making problems. It can also be used as a

decision support system for optimizing lighting needs

in the room. With this decision support system, it is

hoped that it can help the community choose the

lighting needs in the desired room.

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