93 
 

 

IMPLEMENTATION ANALYTICAL HIERARCHY PROCESS AND 
WEIGHTED PRODUCT METHOD FOR LOVEBIRD SELECTION 

IDENTIFICATION APPLICATIONS  
 

Yolanda Nur Oktavia1, Nurhayati2, Iskandar Fitri3*) 
 

Sistem Informasi, Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional 
yolandanuroktavia2018@student.unas.ac.id1, nurhayati@civitas.unas.ac.id2, 

iskandar.fitri@civitas.unas.ac.id3*) 
 

(*)Corresponding Author 
 

Abstrak 
Diantara banyaknya jenis burung di Indonesia burung lovebird adalah hewan peliharaan yang paling banyak 
menarik perhatian dan menjadi favorit di kalangan masyarakat. Hal ini dibuktikan dengan sekian banyaknya 
komunitas pencinta lovebird di seluruh penjuru Indonesia. Permasalahan yang ada bagi orang awam yang 
kurang paham tentang dunia hewan dan memiliki pengetahuan yang sedikit mengenai bagaimana kualitas 
burung lovebird tersebut, tidak jarang kendalanya ialah dalam berbisnis bagi penjual sulit untuk memilih 
keputusan dalam mengatasi masalah pemilihan burung terbaik. Atas permasalahan tersebut penulis 
melakukan perbandingan kombinasi metode analytical hierarchy process dan weighted product dengan 
analytical hierarchy process dan TOPSIS dari perbandingan tersebut diperoleh nilai tertinggi sebesar 392.63 
pada analytical hierarchy process dan weighted product untuk aplikasi perancangan aplikasi rekomendasi 
seleksi lovebird berkualitas terbaik berbasis website. Dari hasil pengujian aplikasi maupun perhitungan 
manual dengan 64 data sampel disimpulkan 4 pengguna atau sekitar 6% masuk pada kategori ketepatan 
rendah dalam hasil perekomendasian lovebird terbaik, 12 pengguna atau sebesar 19% dari seluruh total 
pengujian dinyatakan pada dengan tingkat kategori ketepatan sedang dalam hasil perekomendasian lovebird 
terbaik dan 48 pengguna atau sebesar 75% dari seluruh total pengujian dinyatakan pada dengan tingkat 
kategori ketepatan tinggi dalam hasil perekomendasian lovebird terbaik dengan nilai ketepatan tertinggi 
sebesar 80.2% pada jenis lovebird Albino. 
 
Kata kunci: Sistem Pendukung Keputusan, Lovebird,, Berkualitas, Kombinasi, AHP, WP  
 

Abstract 
Among the many types of birds in Indonesia, lovebirds are pets that attract the most attention and become 
a favorite among the public. This is evidenced by the many communities of lovebird lovers throughout 
Indonesia. The problem that exists for ordinary people who do not understand the animal world and have 
little knowledge about the quality of the lovebirds, it is not uncommon for the problem to be in doing 
business for sellers it is difficult to make decisions in overcoming the problem of selecting the best birds. 
Based on this problem, the author compares the combination of the analytical hierarchy process and 
weighted product methods with the analytical hierarchy process and TOPSIS from this comparison, the 
highest score is 392.63 on the analytical hierarchy process and weighted product for the best quality web-
based lovebird selection recommendation application design application. From the results of application 
testing and manual calculations with 64 sample data, it was concluded that 4 users or about 6% were 
included in the low accuracy category in the best lovebird recommendation results, 12 users or 19% of the 
total tests were stated at a moderate level of accuracy in the lovebird recommendation results. The best 
and 48 users or 75% of the total tests were stated at the high accuracy category level in the best lovebird 
recommendation results with the highest accuracy value of 80.2% on the Albino lovebird type. 
 
Keywords: Decision Support System, Lovebird, Quality,Combination, AHP, WP 
 

INTRODUCTION 
 

Lovebirds are currently kept by many people 
because they have a melodious chirping sound and 
beautiful color gradations in the appearance of their 
feathers. The various types of lovebird names and 

their criteria are very unique and varied, so that 
they affect the price on the market every year 
depending on the season and the physical quality of 
the lovebird. Cases of problems in the system to be 
built are interrelated. 



 

 

94 
 

 

The type of poultry that is in demand on the 
market in the livestock category at this time is the 
lovebird. So that the interest from buyers who are 
looking for and buying the bird is very high. This of 
course requires the shop owner to understand the 
quality of good birds to sell to buyers. The problem 
is from the side of the seller who is still new to the 
business in the pet sector. The offer of a bird 
provider from a supplier is often rejected by the 
seller because it avoids the risk of loss due to not 
understanding the quality of a good lovebird for 
sale. The loss that occurred was that they had 
experienced fraud against the condition of the bird 
which was in fact unhealthy and imperfect, and in 
the end it affected income so that turnover 
decreased. As a result, the seller decided to stop 
selling lovebirds, to help the seller's decision 
problem in making it easier to choose a quality 
lovebird, a decision support system was made that 
can be used so as to increase buyer satisfaction. 

The research which in this case discusses the 
selection of pigeons that has been carried out 
previously in 2021 by A.Ramadhan et al., namely 
the application produced is very user friendly and 
the admin or user does not find it difficult to 
determine the best quality of pigeons with the 
conclusion of determining the highest ranking 
alternative of 0.327 in system(Ramadhan, 
Suprianto, Surmarno, & Dijaya, 2021).  

Other research is still on the same topic, 
namely the recommendation for selecting different 
types of chirping birds in 2019 by R.Rudiantoro et 
al by testing several different bird species to find 
out which sings best and the final result shows the 
highest preference value of 6.34 for the lovebird 
species.(Rudiantoro, Cholissodin, & Dewi, 2019). 

In a further study regarding similar research 
objects in 2019 conducted by S. Bahtiar et al, 
namely lovebirds, which aims to assist the judges in 
objectively assessing the best bird competition 
contest. The output generated by computerization 
and manual calculation is the same with the highest 
value reaching 1.85(Bahtiar, Gunawan, Safii, & 
Parlina, 2019).  

The next similar research conducted by E.L 
Amalia et al in 2019 was about determining in 
choosing the most superior lovebird in the 
competition. Application with a combination of AHP 
and TOPSIS methods (Amalia, RDA, & Pratama, 
2019). The system is made to determine the best 
quality in determining the assessment in decision 
making in the competition so that it can help the 
judges with an accuracy rate of 98% system with 
manual calculations. 

Then further research is still using the same 
method but with a different title for the discussion 
in 2021 conducted by S. Defit et al., discussing a 
system that identifies the quality of a wallet bird's 

nest with the application of the weighted product 
method. The results of this system show the ranking 
data well with an accuracy level reaching 100% 
according to manual calculations and 
applications(Defit, Nurcahyo, Studi, & Ilmu, 2021). 

However, research related to the selection of 
lovebirds and using a combination of analytical 
hierarchy process and weighted product on the 
same issue has not been found before. For this 
reason, the author proposes a combination of 
analytical hierarchy process and weighted product 
methods in this study regarding a recommendation 
system for selecting quality lovebirds that can be 
used by ordinary people, especially bird sellers to 
determine good quality for sale to buyers. The 
selection of the AHP and WP methods was based on 
the results of the comparison test of the 
combination of these methods with other methods, 
namely the analytical hierarchy process and the 
technique for others reference by similarity to ideal 
solution (TOPSIS)(Amalia et al., 2019) using the 
Mean Squared Error (MSE) forecasting model to 
find out which method is the best (Sugianto, 
Roslina, & Situmorang, 2021). The final result 
shows the highest value in the AHP-WP 
combination, which is 392.63, it can be concluded 
that the AHP-WP method is the best and most 
accurate method of research in determining the 
selection of quality lovebirds. It is hoped that with 
this decision support system in the application of 
recommendations for determining quality 
lovebirds, it can overcome decisions in choosing 
which types meet the good quality of lovebirds 
precisely and accurately. 

 
RESEARCH METHODS 

 
The stages of research are carried out so 

that the plan can be neatly arranged and get 
maximum results. 
 

 
Figure 1. Research Framework 

 
Figure 1 describes the framework of the 

stages in the preparation of this research, starting 
with collecting data on criteria and alternative 
lovebirds, then comparing the combination of 



 

 

95 
 

 

methods used with other combination methods, 
then making a system design after applying the AHP 
and WP methods into the system and finally doing 
testing. to determine the level of system accuracy. 

 
Collecting data 

This study relies on reference weighting 
criteria data derived from journals, interviewing 
interviewees of resource persons observation data 
from bird breeding places, in addition to 
strengthening other official sources as well as from 
the Indonesian lovebird breeding website.(DLHK 
Provinsi Banten, 2019) then processed manually as 
needed to design a decision support system(Defit et 
al., 2021). The data criteria are variables used in 
calculating the best lovebird quality 
recommendations 

 
Table.1 List of Criteria and Sub Criteria 

Code Criterions Sub Criteria 
Preference 

Value 

KC1 
Beak 

Shape 

Dry Short 1 
Thin Pointy 3 
Thick 
Curved 

5 

KC2 Foot 
Paralyzed 1 
Limp 3 
Gripping 5 

KC3 
Head 
Shape 

Oval Shape 1 
Round 
Shape 

3 

Protruding 
Forehead 

5 

KC4 Posture 

Disability 1 
No Defects 
(Standard) 

3 

Proportional 5 

KC5 
Fur 

Condition 

Loss 1 
Tidy 3 
Soft 5 
Shiny 7 

KC6 Behaviour 

Silent 
Snuggle 

1 

Very Agile 3 
Sounding 
Voice 

5 

 
Table 1 shows a list of types of criteria or 

physical characteristics of lovebirds  as many as 6 
criteria names and has each sub-criterion 
consisting of codes C1 to C6 that have been 
determined and processed preference values by 
related sources. Furthermore, alternative data are 
the names of lovebird species that belong to 9 
species of the genus Agapornis(Charli, Syaputra, 
Akbar, Sauda, & Panjaitan, 2020). And the 
assessment will be done is one of these types is the 
type of  Agapornis roseicollis. 

 
Table.2 List of Alternatives to Lovebird Options 

Alternative 
Code 

Alternative Name 

L1 Lovebird Albino 
L2 Lovebird Lutino 
L3 Lovebird Golden Cherry 
L4 Lovebird Pied 
L5 Lovebird Cinnamon 
L6 Lovebird Biru 
 
Table 2 contains data on lovebird names 

displayed as many as 6 tails of the same species, 
data obtained from the indonesian lovebird farming 
website (DLHK Provinsi Banten, 2019) It consists of 
each of the alternative codes from L1 to L6 applied 
to the study. 

 
Comparison of Method Combinations  
 

After collecting the data needed to find out 
the consistency of precision and accuracy of which 
combination of methods will be applied to this 
study, a comparison test is conducted. By 
comparing 2 other method combinations namely 
analitycal hierarchy process  and  weighted product  
with  analitycal hierarchy process  and technique for 
others reference by similarity to ideal solution. With 
the formula of the combination method ahp and WP 
(Krismadewi, 2021) 
 

Λmaks =   
Jumlah

n
 

CI = 
⋏𝑚𝑎𝑘𝑠−𝑛

𝑛
 …………………………………….……..(1) 

CR = 
𝐶𝐼

𝐼𝑅
   

CR = ratio consistency 
CI = index consistency 
n  = the number of elements 
IR  = random index 

 
The preference for Ai alternatives starts from 
looking for vector values S and vector V 

 

𝑆𝑖   = ∏ = 1
𝑋𝑖𝑗

𝑊𝑗
 𝑛𝑗 …………………………………………... (2)  

𝑉𝑖   = 
∏ =1

𝑋𝑖𝑗
𝑊𝑗

 
𝑛
𝑗

∏ =1
𝑋𝑖𝑗

𝑊𝑗
 𝑛𝑗 ∗𝑊𝑗

…………………………………………... (3) 

 
Stated, i  is the result of alternative 

preferences to – i and Π is the sum of the results of 
the multiplication of alternative rankings of each 
attribute. And the results showed the highest and 
lowest values(Perdana, Defit, & Sumijan, 2020). 

Furthermore, the formula combination of 
AHP and Topsis methods is used to determine the 
final data results (Amalia et al., 2019). For the 



 

 

96 
 

 

formula AHP is the same as the equation(1) and 
continued with the formula Topsis. 

The method comparison process in this 
study uses the Mean Squared Error  
(MSE)forecasting model to find out which method 
is best(Sugianto et al., 2021). The formula for 
calculating MSE is: 

 

MSE = 
∑𝑒𝑖

2

𝑛
=

∑(𝑋𝑖−𝐹𝑖)
2

𝑛
  ...................................................... (4) 

 
Xi : Preliminary Data 
Fi : Final Data 
n : Number of criteria 
 

The first step is done by determining the 
initial amount of data from each criterion and 
alternative. Then calculate the deviation value of 
each method.  
 

Table 3  Determination of Deviation of AHP-WP 
Preliminary  

Data 
Final Data (Deviation)

2 

20 0.185 392.63 
20 0.146 394.18 
18 0.249 315.09 
20 0.152 393.94 
16 0.137 251.63 
20 0.131 394.77 

Sum 2355.78 

  
Table 3 contains the initial data obtained 

from the sum of the comparison of criteria of the 
AHP method and the final data is obtained from the 
total calculation of the AHP and WP methods. Then 
count using the equation (4). 
 
MSE = 2355.78/6 = 392.63 
 

Table.4  Determination of Deviation of AHP-
TOPSIS 

Preliminary  
Data 

Final Data (Deviation)
2 

20 0.494 380.48 
20 0.429 383.02 
18 0.678 300.05 
20 0.442 382.51 
16 0.437 242.20 
20 0.372 385.25 

Sum 2073.51 

 
Table 4 contains the initial data obtained 

from the sum of the comparison of criteria of the 
AHP method and the final data is obtained from the 
total calculation of the AHP and Topsis methods. 
Then calculate using the equation (1). 
MSE = 2073.51/6 = 345.58 
 

Table.5 Final Results of Method Comparison 

No Combination of Methods MSE 

1 analytical hierarchy process 
dan weighted product  

392.63 

2 analytical hierarchy rocess dan 
TOPSIS 

345.58 

Max Deviasi 392.63 

 

 
Figure2. End Results of Method Comparison 

 
Based on Table 5 it can be determined that 

the visualization of the graph calculation of the bar 
diagram shows the AHP-WP method gets a 
deviation value of 2355.78 and a total of MSE 
392.63 and the AHP-TOPSIS method gets a 
deviation value of 2073.51 and a total of MSE 
345.58, so the researcher decided to use the 
combination of AHP-WP methods as the basic 
reason for producing the highest value in 
determining the selection of quality lovebirds 
(Imam, 2020).  
System Creation 

Creation starts from research on criteria 
data and criterion values. The creation of a flow 
overview of the implementation of the analitycal 
hierarchy process  and  weighted product method 
can be seen in the flowchart image(Novira, 
Mubarok, & Shofa, 2020): 
 

 
Figure 3. Flowchart System 

Figure 3 can be explained that the flow of 
the system design begins by using the analytical 
hierarchy process method. The first process is to 
input the criteria data, then continue by inputting 

2355,78
2073,51

392,63 345,58

0

500

1000

1500

2000

2500

AHP-WP AHP-TOPSIS

Deviasi Mean Squared Error (MSE)



 

 

97 
 

 

the weights for each criterion, then the system will 
calculate the eigenvalues and CR values to find out 
the logical consistency of the criteria. calculate the 
value of vector S then vector V and arrive at the 
ranking of vector values so that it can display 
alternative recommendations based on the highest 
rank. 

 

 
Figure 4. Data Flow Diagram 

 
In Figure 4 of the system's data flow 

diagram for admins and users. The administrator's 
role is to manage system work starting from login 
and then inputting each criterion and preference 
weight value on alternative data and criteria. Then 
the role of the user is to input some alternative data 
that will later be processed by the system and 
produce a calculation output  in the form of a list of 
the best quality lovebirds (Ramadhan et al., 2021). 

 

 
Figure 5. Database View 

 
The next stage is to create a database 

containing 12 tables with results in the database 
view in Figure 5 which is used to accommodate 
table data both input and output in the application 
of quality lovebird selection decision support 
system. 

 

 
Figure 6. Program Code View 

 
After creating the database, in Figure 6 is 

designed in the form of a coding structure using the 
PHP Native programming language with visual 
studio code editor software, which begins to create 
a framework with Bootstrap, to produce a website 
application support system for choosing the best 
lovebird  decision from the input process to output 
that can be run. 
 
System Implementation 

The application of this system is website-
based  using php native, HTML, and mySQL 
languages as databases used. The interface results 
of this SPK system consist of login pages, home, 
criteria data, alternative data, analysis, and 
calculations. 

 

 
Figure 7. Home View 

 
In figure 7 is the main page and there are several 
menus, namely criteria data, alternative data, 
analysis, and calculations. 
 

 
Figure 8. Criteria Data View 



 

 

98 
 

 

In figure 8 is a menu that contains criteria 
information consisting of the criteria id, the name of 
the  criteria, the weight of  the criteria,and then the 
option to edit. 
 

 
Figure 9. Alternative Data View 

 
Figure 9 is an alternative data menu that features 
add alternative data and options for editing and 
deleting. In addition, there is a search feature to 
search for the required data using keywords 
quickly. 
 
 

 
Figure 10. Analysis View 

 
In figure 10 is an analysis menu whose function 
provides a graph of the results of calculations done 
to choose a quality lovebird 
 

RESULTS AND DISCUSSION 
 

Determining Criteria and Weighting 
Based on the information that has been 

collected by direct observation in the field, some 
quality lovebird criteria that have the following 
characteristics that will be applied to the AHP 
method are: 
 

Table 6. Physical Characteristics 
Kode Kriteria Jenis 
KC1 Beak shape Benefits 
KC2 Foot Benefits 
KC3 Head Shape Benefits 
KC4 Posture Benefits 
KC5 Fur Condition Benefits 
KC6 Behavior Benefits 

 Table 6 classifies criteria based on the 
physical characteristics of a lovebird. Where 
because all criteria have a value weight that the 
higher the value means the better, then it belongs to 
the category of benefits. 

 
Table 7.  Criteria Comparison Matrix 

 KC1 KC2 KC3 KC4 KC5 KC6 
KC1 1.00 4.00 2.00 3.00 7.00 5.00 
KC2 0.25 1.00 0.33 0.50 5.00 2.00 
KC3 0.50 3.00 1.00 2.00 4.00 5.00 
KC4 0.33 2.00 0.50 1.00 5.00 3.00 
KC5 0.14 0.20 0.25 0.20 1.00 0.33 
KC6 0.20 0.50 0.20 0.33 3.00 1.00 
Sum 2.43 10.7 4.28 7.03 25.00 16.33 

 
In Table 7 to determine the assessment, a 

comparison of the criteria comparison matrix 
(Andriyani & Yuma, 2020). The description is based 
on the level of importance of the criteria compared 
to other criteria. The weighting of each criterion is 
based on determining the ahp formula that has been 
determined according to the priority interests 
obtained from the table(Andriyani & Yuma, 2020)  

 
Table 8.  Saaty Table 

Value Definition Information 

1 
Equally - equally 
important 

Both have the same 
influence. 

3 
A Little 
Important 

The ratio of one is 
slightly higher than 
the second. 

5 More Important 
The ratio of one is 
higher than the 
second. 

7 Very Important 
The ratio of one is 
very higher than the 
second. 

9 
Absolutely 
Essential 

The ratio of one is 
absolutely very 
strong from the 
second. 

2, 4, 6, 8 
Value among 
them 

Both have an 
adjacent 
assessment. 

 
Table 8 is a saaty table that has a relative 

importance level value between two criteria based 
on the decision maker's assessment and will form a 
paired comparison matrix(Nurajizah, Ambarwati, & 
Muryani, 2020). 

 
Table 9.  Synthesis of Criteria Comparison 

Number of Each Element 
Su
m 

Averag
e 

0.4
1 

0.3
7 

0.4
7 

0.4
3 

0.2
8 

0.3
1 

2.2
7 

0.38 

0.1
0 

0.0
9 

0.0
8 

0.0
7 

0.2
0 

0.1
2 

0.6
7 

0.11 

0.2
1 

0.2
8 

0.2
3 

0.2
8 

0.1
6 

0.3
1 

1.4
7 

0.25 



 

 

99 
 

 

0.1
4 

0.1
9 

0.1
2 

0.1
4 

0.2
0 

0.1
8 

0.9
7 

0.16 

0.0
6 

0.0
2 

0.0
6 

0.0
3 

0.0
4 

0.0
2 

0.2
2 

0.04 

0.0
8 

0.0
5 

0.0
5 

0.0
5 

0.1
2 

0.0
6 

0.4
0 

0.07 

 
Table 9 is the result of the calculation of 

each element and the number and average of the 
elements form a comprehensive comparison table 
of standards that will be used as the basis for 
ranking criteria. The next step is to calculate the 
consistency ratio (CR). By calculating the first 
contingency index (CI) using the equation (1). 
 

CI  = 
⋏𝑚𝑎𝑘𝑠−𝑛

𝑛
  

Λ maks  = 
(2.43*0.38)+(10.7*0.12)+(4.28*0.25)+(
7.03*0.16)+(25*0.04)+(16.33*0.07) = 
6,533 

n  = 6  
CI  = (6,533-6)/(6-1) = 0,106  
CR  = CI/IR = 0,106/1,24 = 0,085 

 
Referring to the above point of 0.085 it can meet the 
provisions of CR<0.1 so that the process of 
analyzing quality lovebird selection criteria is said 
to be consistent. 

The result of calculating the above average 
value is the main weight or component of each 
criterion, and certainly becomes the preferred 
weight of the WP method. Then continued with the 
calculation of the WP method that determines some 
alternatives to lovebird and previous criteria to get 
a quality type ofovebird  l.   
 

Table 10.  Alternative Value Criteria 

Alternatif 
Criterions 

KC1 KC2 KC3 KC4 KC5 KC6 
L1 3 5 1 5 3 3 
L2 1 3 3 5 7 1 
L3 5 1 5 3 1 3 
L4 3 5 1 1 5 5 
L5 3 1 1 3 7 1 
L6 1 5 3 1 5 5 

 
Table 10 is a data input by the user that 

displays the preference value based on the type of 
lovebird  with its sub criteria referring to Table 1 
and then is the initial data in the determination of 
the deviation of AHP-WP in Table 3. 

 
Table11. Preference Weights 

KC1 KC2 KC3 KC4 KC5 KC6 

0.38 0.11 0.25 0.16 0.04 0.07 

 

Table 11 is the result of calculating the 
average value of each criterion in table 12 and will 
be the power value in the next calculation step. 

 
Determining Vector Value S 

The next step is to determine the vector S, 
i.e. by adjusting the weight of the criteria and 
multiplying by the weight of each preference using 
the equation (2).  

 
Tabel 12. Vector Class S 

Vector Value S Result 
VS1 2,645 
VS2 2,076 
VS3 3,548 
VS4 2,163 
VS5 1,956 
VS6 1,875 
Sum 14,265 

 
Table 12 is the result of the weight of the data from 
each calculation of the value of the vector S of each 
alternative. 
 
Determining value V 

The next determination is the process of 
playing by calculating vector classes. That is, the 
result of dividing the value of weight S with the 
number of alternative rating multiplication results 
per attribute using the equation (3).  
 

Table 13. Ranking Results 
VECTOR RESULT RANKING 

V1 0.185 2 
V2 0.146 4 
V3 0.249 1 
V4 0.152 3 
V5 0.137 5 
V6 0.131 6 

 
In Table 13 is a settlement in the 

assessment process in the form of a role. Based on 
the results of preference calculations in table 13. So 
in order of the best quality lovebird  is the first rank 
of golden cherry lovebird  with code L3, second rank 
albino lovebird  with code L1, third rank  lovebird 
pied  with code L4, fourth rank  lovebird lutino  with 
code L2, fifth rank  lovebird cinnamon with code L5, 
and last rank blue lovebird  with code L6. 

 
Testing Results 
From the results of manual calculations that have 
been done by entering 6 different criteria obtained 
the final results of the assessment that has been 
included in reference to Table 13 has 
recommended the best quality lovebird  that 



 

 

100 
 

 

ranked first, namely  the golden cherry lovebird  
with a value of 0.249. 
 

 
 

Table 14. Alternative Sample Data Input Scenario Testing Results 

No Alternative Code & Preference Value 
System 
Values 

Manual 
Value 

Highest 
Rank 

Desc 

1 
L1: [KC1.5], [KC2.5], [KC3.5], [KC4.5], [KC5.7], [KC6.5] ; L2: 
[KC1.3], [KC2.1],[KC3.3], [KC4.1], [KC5.1], [KC6.3] 

0.704 0.704 
Lovebird 
Albino 

28 

2 

L1: [KC1.3], [KC2[5], [KC3.1], [KC4.5], KC5.3], [KC6.3] ; L2: 
[KC1.1], [KC2.3], [KC3.3], [KC4.5], [KC5.7], [KC6.1] ; L3: [KC1.5], 
[KC2.1], [KC3.5], [KC4.3], [KC5.1], [KC6.3] ; L4: [KC1.3], [KC2.5], 
[KC3.1], [KC4.1], [KC5.5], [KC6.5] ; L5: [KC1.3], [KC2.1], [KC3.1], 
[KC4.53, [KC5.7], [KC6.1] ; L6 : [KC1.1], [KC2.5], [KC3.3], 
[KC4.1], [KC5.5], [KC6.5] 

0.2487 0.249 
Lovebird 
Golden 
Cherry 

63 

3 
L4: [KC1.1], [KC2.5], [KC3.1], [KC4.1], [KC5.3], [KC6.5] ; L5: 
[KC1.5], [KC2.5], [KC3.5], [KC4.5], [KC5.7], [KC6.5] 

0.786 0.786 
Lovebird 
Cinnamon 

12 

4 
L4: [KC1.3], [KC2.5], [KC3.1], [KC4.1], [KC5.5], [KC6.5] ; L2: 
[KC1.3], [KC2.5], [KC3.3], [KC4.3], [KC5.3], [KC6.3] 

0.5973 0.597 
Lovebird 
Lutino 

49 

5 
L1: [KC1.5], [KC2.5], [KC3.5], [KC4.3], [KC5.7], [KC6.5] ; L3: 
[KC1.1], [KC2.3], [KC3.1], [KC4.1], [KC5.1], [KC6.5] 

0.789 0.790 
Lovebird 
Albino 

9 

6 
L1: [KC1.1], [KC2.5], [KC3.5], [KC4.3], [KC5.1], [KC6.3] ; L4: 
[KC1.3], [KC2.3], [KC3.3], [KC4.1], [KC5.5], [KC6.5] ; L5: [KC1.1], 
[KC2.1], [KC3.1], [KC4.3], [KC5.3], [KC6.1] 

0.4316 0.432 
Lovebird 
Pied 

59 

7 
L2: [KC1.5], [KC2.5], [KC3.3], [KC4.3], [KC5.7], [KC6.5] ; L4: 
[KC1.1], [KC2.1], [KC3.1], [KC4.1], [KC5.1], [KC.5] 

0.799 0.799 
Lovebird 
Lutino 

4 

8 
L2: [KC1.5], [KC2.1], [KC3.5], [KC4.3], [KC5.3], [KC6.3] ; L5: 
[KC1.1], [KC2.3], [KC3.3], [KC4.5], [KC5.7], [KC6.1] ; L6: [KC1.3], 
[KC2.3], [KC3.3], [KC4.1], [KC5.5], [KC6.1] 

0.4528 0.453 
Lovebird 
Lutino 

57 

9 
L3: [KC1.5], [KC2.5], [KC3.5], [KC4.5], [KC5.7], [KC6.1] ; L4: 
[KC1.3], [KC2.1], [KC3.1], [KC4.1], [KC5.3], [KC6.1] 

0.743 0.743 
Lovebird 
Golden 
Cherry 

20 

10 
L3: [KC1.3], [KC2.3], [KC3.3], [KC4.1], [KC5.5], [KC6.1] ; L5: 
[KC1.5], [KC2.3], [KC3.3], [KC4.5], [KC5.5], K[C6.5] 

0.6176 0.618 
Lovebird 
Cinnamon 

46 

11 
L4: [KC1.5], [KC2.5], [KC3.5], [KC4.5], [KC5.7], [KC6.5] ; L1: 
[KC1.1], [KC2.1], [KC3.1], [KC4.1], [KC5.1], [KC6.1] 

0.837 0.837 
Lovebird 
Pied 

1 

12 
L2: [KC1.1], [KC2.1], [KC3.5], [KC4.1], [KC5.1], [KC6.3] ; L4: 
[KC1.5], [KC2.5], [KC3.1], [KC4.5], [KC5.5], [KC6.5] 

0.623 0.623 
Lovebird 
Lutino 

44 

13 
L6: [KC1.5], [KC2.5], [KC3.5], [KC4.3], [KC5.7], [KC6.3] ; L1: 
[KC1.1], [KC2.1], [KC3.1], [KC4.3], [KC5.1], [KC6.1] 

0.793 0.793 
Lovebird 
Albino 

6 

14 
L5: [KC1.3], [KC2.5], [KC3.5], [KC4.3], [KC5.5], [KC6.5] ; L3: 
[KC1.3], [KC2.1], [KC3.1], [KC4.1], [KC5.1], K[C6.3] 

0.701 0.702 
Lovebird 
Cinnamon 

29 

15 
L5: [KC1.1], [KC2.3], [KC3.3], [KC4.3], [KC5.3], [KC6.1] ; L6: 
[KC1.5], [KC2.1], [KC3.1], [KC4.3], [KC5.7], [KC6.5] 

0.610 0.600 
Lovebird 
Biru 

48 

16 
L3: [KC1.5], [KC2.3], [KC3.5], [KC4.5], [KC5.7], [KC6.3] ; L2: 
[KC1.1], [KC2.3], [KC3.3], [KC4.1], [KC5.1], [KC6.1] 

0.759 0.760 
Lovebird 
Golden 
Cherry 

16 

17 

L3: [KC1.1], [KC2.5], [KC3.3], [KC4.5], [KC5.5], [KC6.5] ; L4: 
[KC1.3], [KC2.3], [KC3.1], [KC4.3], [KC5.7], [KC6.3] ; L5: [KC1.5], 
[KC2.1], [KC3.5], [KC4.3], [KC5.7], [KC6.3] ; L6: [KC1.1], [KC2.5], 
[KC3.3], [KC4.5], [KC5.7], [KC6.1] 

0.353 0.353 
Lovebird 
Golden 
Cherry 

62 

18 
L5: [KC1.5], [KC2.5], [KC3.5], [KC4.3], [KC5.5], [KC6.5] ; L6: 
[KC1.3], [KC2.1], [KC3.1], [KC4.3], [KC5.1], [KC6.1] 

0.733 0.733 
Lovebird 
Cinnamon 

22 

19 
L4: [KC1.3], [KC2.5], [KC3.3], [KC4.1], [KC5.3], [KC6.5] ; L2: 
[KC1.3], [KC2.5], [KC3.3], [KC4.3], [KC5.5], [KC6.5] 

0.556 0.556 
Lovebird 
Lutino 

51 

20 
L1: [KC1.5], [KC2.5], [KC3.5], [KC4.1], [KC5.7], [KC6.5] ; L3: 
[KC1.5], [KC2.1], [KC3.1], [KC4.1], [KC5.1], [KC6.3] 

0.666 0.666 
Lovebird 
Albino 

36 

21 
L3: [KC1.5], [KC2.5], [KC3.3], [KC4.3], [KC5.3], [KC6.5] ; L5: 
[KC1.3], [KC2.3], [KC3.3], [KC4.1], [KC5.1], [KC6.1] 

0.660 0.660 
Lovebird 
Golden 
Cherry 

37 

22 
L2: [KC1.5], [KC2.5], [KC3.5], [KC4.5], [KC5.7], [KC6.5] ; L4: 
[KC1.1], [KC2.3], [KC3.1], [KC4.3], [KC5.1], [KC6.1] 

0.771 0.771 
Lovebird 
Lutino 

14 



 

 

101 
 

 

No Alternative Code & Preference Value 
System 
Values 

Manual 
Value 

Highest 
Rank 

Desc 

23 
L2: [KC1.1], [KC2.3], [KC3.1], [KC4.3], [KC5.1], [KC6.1] ; L5: 
[KC1.5], [KC2.5], [KC3.3], [KC4.5], [KC5.1], [KC6.5] 

0.757 0.757 
Lovebird 
Lutino 

17 

24 
L3: [KC1.5], [KC2.5], [KC3.5], [KC4.5], [KC5.7], [KC6.5] ; L4: 
[KC1.1], [KC2.3], [KC3.1], [KC4.3], [KC5.3], [KC6.1] 

0.791 0.792 
Lovebird 
Golden 
Cherry 

7 

25 
L3: [KC1.3], [KC2.5], [KC3.3], [KC4.1], [KC5.5], [KC6.1] ; L5: 
[KC1.5], [KC2.3], [KC3.3], [KC4.5], [KC5.5], [KC6.5] 

0.624 0.624 
Lovebird 
Cinnamon 

43 

26 
L4: [KC1.1], [KC2.1], [KC3.5], [KC4.1], [KC5.3], [KC6.1] ; L1: 
[KC1.5], [KC2.5], [KC3.3], [KC4.5], [KC5.1], [KC6.3] 

0.721 0.721 
Lovebird 
Albino 

25 

27 
L2: [KC1.5], [KC2.5], [KC3.3], [KC4.5], [KC5.7], [KC6.5] ; L4: 
[KC1.5], [KC2.1], [KC3.1], [KC4.1], [KC5.5], [KC6.1] 

0.697 0.697 
Lovebird 
Lutino 

30 

28 
L6: [KC1.5], [KC2.3], [KC3.5], [KC4.3], [KC5.7], [KC6.5] ; L5: 
[KC1.3], [KC2.1], [KC3.5], [KC4.1], [KC5.1], [KC6.3] 

0.646 0.647 
Lovebird 
Pied 

40 

29 
L3: [KC1.5], [KC2.3], [KC3.5], [KC4.7], [KC5.3], [KC6.5] ; L5: 
[KC1.1], [KC2.3], [KC3.1], [KC4.1], [KC5.1], [KC6.3] 

0.794 0.794 
Lovebird 
Golden 
Cherry 

5 

30 
L5: [KC1.1], [KC2.1], [KC3.5], [KC4.3], [KC5.7], [KC6.5] ; L6: 
[KC1.5], [KC2.1], [KC3.1], [KC4.3], [KC5.7], [KC6.3] 

0.543 0.543 
Lovebird 
Biru 

52 

31 
L1: [KC1.5], [KC2.5], [KC3.5], [KC4.3], [KC5.7], [KC6.5] ; L2: 
[KC1.1], [KC2.1], [KC3.1], [KC4.1], [KC5.1], [KC6.3] 

0.800 0.800 
Lovebird 
Albino 

3 

32 
L1: [KC1.3], [KC2.3], [KC3.1], [KC4.1], [KC5.7], [KC6.3]; L3: 
[KC1.1], [KC2.5], [KC3.1], [KC4.1], [KC5.1], [KC6.3] ; L4: [KC1.1], 
[KC2.3], [KC3.5], [KC4.1], [KC5.1], [KC6.5] 

0.394 0.394 
Lovebird 
Albino 

61 

33 
L4: [KC1.5], [KC2.5], [KC3.3], [KC4.5], [KC5.5], [KC6.3] ; L5: 
[KC1.3], [KC2.1], [KC3.1], [KC4.1], [KC5.1], [KC6.3] 

0.724 0.725 
Lovebird 
Pied 

24 

34 
L4: [KC1.3], [KC2.5], [KC3.1], K[C4.1], [KC5.5], [KC6.5] ; L2: 
[KC1.3], [KC2.5], [KC3.3], [KC4.3], [KC5.5], [KC6.3] 

0.622 0.622 
Lovebird 
Lutino 

45 

35 
L1: [KC1.1], [KC2.5], [KC3.3], [KC4.1], [KC5.3], [KC6.5] ; L3: 
[KC1.5], [KC2.1], [KC3.5], [KC4.3], [KC5.7], [KC6.5] 

0.6839 0.684 
Lovebird 
Golden 
Cherry 

33 

36 
L1: [KC1.5], [KC2.5], [KC3.5], [KC4.5], [KC5.7], [KC6.5] ; L4: 
[KC1.3], [KC2.3], [KC3.3], [KC4.1], [KC5.5], [KC6.5] 

0.802 0.802 
Lovebird 
Albino 

2 

37 
L2: [KC1.5], [KC2.5], [KC3.5], [KC4.5], [KC5.7], [KC6.1] ; L4: 
[KC1.3], [KC2.1], [KC3.1], [KC4.1], [KC5.1], [KC6.5] 

0.730 0.730 
Lovebird 
Lutino 

23 

38 
L2: [KC1.5], [KC2.5], [KC3.5], [KC4.3], [KC5.3], [KC6.3] ; L5: 
[KC1.1], [KC2.3], [KC3.3], [KC4.3], [KC5.7], [KC6.1] ; L6: [KC1.3], 
[KC2.3], [KC3.3], [KC4.1], [KC5.5], [KC6.1] 

0.484 0.484 
Lovebird 
Lutino 

56 

39 
L3: [KC1.5], [KC2.5], [KC3.3], [KC4.5], [KC5.5], [KC6.5] ; L4: 
[KC1.3], [KC2.3], [KC3.1], [KC4.1], [KC5.1], [KC6.3] 

0.707 0.707 
Lovebird 
Golden 
Cherry 

27 

40 
L3: [KC1.3], [KC2.3], [KC3.3], [KC4.1], [KC5.5], [KC6.1] ; L5: 
[KC1.5], [KC2.5], [KC3.3], [KC4.5], [KC5.5], [KC6.5] 

0.650 0.650 
Lovebird 
Cinnamon 

39 

41 
L4: [KC1.5], [KC2.5], [KC3.3], [KC4.5], [KC5.5], [KC6.1] ; L1: 
[KC1.3], [KC2.3], [KC3.3], [KC4.1], [KC5.1], [KC6.1] 

0.639 0.639 
Lovebird 
Pied 

42 

42 
L2: [KC1.3], [KC2.1], [KC3.1], [KC4.1], [KC5.1], [KC6.3] ; L4: 
[KC1.5], [KC2.3], [KC3.5], [KC4.5], [KC5.7], [KC6.5] 

0.748 0.748 
Lovebird 
Pied 

18 

43 
L6: [KC1.5], [KC2.5], [KC3.5], [KC4.5], [KC5.7], [KC6.1] ; L1: 
[KC1.3], [KC2.3], [KC3.3], [KC4.1], [KC5.1], [KC6.1] 

0.671 0.671 
Lovebird 
Biru 

34 

44 
L3: [KC1.5], [KC2.5], [KC3.3], [KC4.5], [KC5.7], [KC6.3] ; L5: 
[KC1.1], [KC2.1], [KC3.3], [KC4.1], [KC5.1], [KC6.1] 

0.768 0.768 
Lovebird 
Golden 
Cherry 

15 

45 
L5: [KC1.1], [KC2.3], [KC3.3], [KC4.3], [KC5.3], [KC6.5] ; L6: 
[KC1.5], [KC2.1], [KC3.5], [KC4.3], [KC5.7], [KC6.5] 

0.6575 0.657 
Lovebird 
Biru 

38 

46 
L1: [KC1.3], [KC2.5], [KC3.1], [KC4.5], [KC5.3], [KC6.3] ; L2: 
[KC1.1], [KC2.3], [KC3.3], [KC4.5], [KC5.7], [KC6.1] 

0.560 0.560 
Lovebird 
Albino 

50 

47 

L1: [KC1.1], [KC2.5], [KC3.1], [KC4.5], [KC5.3], [KC6.3] ; L2: 
[KC1.1], [KC2.3], [KC3.3], [KC4.5], [KC5.7], [KC6.1] ; L3: [KC1.5], 
[KC2.1], [KC3.5], [KC4.3], [KC5.1], [KC6.3] ; L4: [KC1.3], [KC2.5], 
[KC3.5], [KC4.1], [KC5.5], [KC6.5] ; L5: [KC1.3], [KC2.5], [KC3.1], 
[KC4.3], [KC5.7], [KC6.1] ; L6 : [KC1.1], [KC2.5], [KC3.3], [KC4.1], 
[KC5.7], [KC6.5] 

0.239 0.239 
Lovebird 
Golden 
Cherry 

64 



 

 

102 
 

 

No Alternative Code & Preference Value 
System 
Values 

Manual 
Value 

Highest 
Rank 

Desc 

48 
L4: [KC1.5], [KC2.5], [KC3.3], [KC4.5], [KC5.5], [KC6.5] ; L5: 
[KC1.1], [KC2.5], [KC3.1], [KC4.1], [KC5.1], [KC6.1] ; 

0.789 0.789 
Lovebird 
Pied 

10 

49 
L4: [KC1.3], [KC2.5], [KC3.1], [KC4.1], [KC5.5], [KC6.5] ; L2: 
[KC1.3], [KC2.5], [KC3.3], [KC4.3], [KC5.5], [KC6.3] 

0.612 0.612 
Lovebird 
Lutino 

47 

50 
L1: [KC1.5], [KC2.5], [KC3.3], [KC4.1], [KC5.5], [KC6.5] ; L3: 
[KC1.5], [KC2.1], [KC3.5], [KC4.3], [KC5.1], [KC6.5] 

0.515 0.515 
Lovebird 
Golden 
Cherry 

54 

51 
L1: [KC1.1], [KC2.5], [KC3.5], [KC4.3], [KC5.1], [KC6.1] ; L4: 
[KC1.3], [KC2.3], [KC3.3], [KC4.1], [KC5.5], [KC6.5] ; L5: [KC1.1], 
[KC2.5], [KC3.1], [KC4.3], [KC5.3], [KC6.1] 

0.4267 0.427 
Lovebird 
Pied 

60 

52 
L2: [KC1.1], [KC2.3], [KC3.1], [KC4.1], [KC5.5], [KC6.1] ; L4: 
[KC1.5], [KC2.5], [KC3.5], [KC4.5], [KC5.7], [KC6.1] 

0.792 0.791 
Lovebird 
Pied 

8 

53 
L2: [KC1.1], [KC2.3], [KC3.1], [KC4.1], [KC5.1], [KC6.1] ; L5: 
[KC1.5], [KC2.5], [KC3.3], [KC4.5], [KC5.1], [KC6.5] 

0.787 0.787 
Lovebird 
Lutino 

11 

54 
L3: [KC1.3], [KC2.1], [KC3.3], [KC4.1], [KC5.1], [KC6.1] ; L1: 
[KC1.3], [KC2.5], [KC3.5], [KC4.5], [KC5.5], [KC6.3] 

0.668 0.668 
Lovebird 
Albino 

35 

55 
L3: [KC1.3], [KC2.3], [KC3.3], [KC4.1], [KC5.5], [KC6.1] ; L5: 
[KC1.5], [KC2.3], [KC3.3], [KC4.5], [KC5.7], [KC6.5] 

0.640 0.640 
Lovebird 
Cinnamon 

41 

56 
L4: [KC1.5], [KC2.5], [KC3.5], [KC4.5], [KC5.3], [KC6.1] ; L1: 
[KC1.3], [KC2.1], [KC3.3], [KC4.1], [KC5.1], [KC6.1] 

0.690 0.690 
Lovebird 
Pied 

31 

57 
L2: [KC1.3], [KC2.1], [KC3.1], [KC4.1], [KC5.5], [KC6.1] ; L4: 
[KC1.5], [KC2.5], [KC3.5], [KC4.5], [KC5.1], [KC6.5] 

0.746 0.746 
Lovebird 
Pied 

19 

58 
L6: [KC1.5], [KC2.5], [KC3.3], [KC4.5], [KC5.5], [KC6.3] ; L1: 
[KC1.1], [KC2.3], [KC3.1], [KC4.3], [KC5.1], [KC6.5] 

0.741 0.741 
Lovebird 
Biru 

21 

59 
L3: [KC1.1], [KC2.5], [KC3.1], [KC4.3], [KC5.3], [KC6.5] ; L5: 
[KC1.1], [KC2.3], [KC3.3], [KC4.5], [KC5.7], [KC6.1] ; L6: [KC1.3], 
[KC2.3], [KC3.5], [KC4.1], [KC5.1], [KC6.5] 

0.4338 0.434 
Lovebird 
Biru 

58 

60 
L5: [KC1.5], [KC2.3], [KC3.3], [KC4.3], [KC5.3], [KC6.1] ; L6: 
[KC1.5], [KC2.5], [KC3.1], [KC4.3], [KC5.7], [KC6.5] 

0.5179 0.518 
Lovebird 
Cinnamon 

53 

61 
L4: [KC1.3], [KC2.5], [KC3.5], [KC4.1], [KC5.5], [KC6.5] ; L2: 
[KC1.3], [KC2.5], [KC3.3], [KC4.3], [KC5.7], [KC6.3] 

0.506 0.506 
Lovebird 
Lutino 

55 

62 
L1: [KC1.1], [KC2.5], [KC3.3], [KC4.1], [KC5.5], [KC6.3] ; L3: 
[KC1.5], [KC2.1], [KC3.5], [KC4.3], [KC5.7], [KC6.5] 

0.687 0.687 
Lovebird 
Golden 
Cherry 

32 

63 
L1: [KC1.1], [KC2.5], [KC3.1], [KC4.1], [KC5.3], [KC6.1] ; L4: 
[KC1.5], [KC2.5], [KC3.3], [KC4.5], [KC5.7], [KC6.5] 

0.784 0.784 
Lovebird 
Albino 

13 

64 
L2: [KC1.5], [KC2.1], [KC3.5], [KC4.5], [KC5.1], [KC6.5] ; L4: 
[KC1.3], [KC2.1], [KC3.1], [KC4.1], [KC5.1], [KC6.3] 

0.708 0.708 
Lovebird 
Lutino 

26 

 
 
Referring to Table 14 contains the results 

of 64 data testing scenarios input alternative 
sample data based on lovebird type and sub-criteria 
obtained an accuracy rate of 100% with true test 
data as much as 64 out of 64 total tests by manual 
calculation and system calculation. Good quality  
lovebird determination tests were conducted as 
many as 64 tests of evidence of different input  
samples. Of the 64 best rankings, the first rank 
lovebird is Lovebird Pied with a value 0.837. From 
the experiment, the table was created containing 
alternative codes for the bird's name and weight 
preference criteria, the results of manual 
calculation values, the results of system calculation 
values, the highest ranking of the bird's name, and 
description of the rating of each lovebird. 

    

Table 15. Basic Accuracy Percentage Assessment 

Category Level Percentage Value 
Very Low 0% - 20% 

Low 21% - 40% 
Moderate 41% - 60% 

High 61% - 80% 
Very High 81% - 100% 

 
In Table 15(Kurniawan, 2017) shows the accuracy 
of the system accuracy which refers to the test 
results from table 14 it can be concluded that there 
are 3 levels of categories where as many as 4 users 
or about 6% of 64 tests with  Low accuracy category 
levels in the results of lovebird    immersion  best 
with a percentage value of 21%-40%. Furthermore, 
as many as 12 users or about 19% of 64 tests with a 
Moderate accuracy category level in the best 
lovebird communication results with a percentage 
value of 41%-60% Then as many as 48 users or 



 

 

103 
 

 

about 75% of 64 tests with a High accuracy 
category level in the results of lovebird 
communication best with a percentage value of 
61%-80%. And there are no users whose 
percentage value is below 20% or very low and 
above 81% or very high that has been 
recommended by the system or manually. 

 
CONCLUSION 

 
Based on the results of research and design 
obtained a website-based application system on 
identifying the selection of quality lovebirds  
designed with Native PHP programming and 
MySQL as databases. This application contains 6 
criteria data consisting of each of 3 physical sub-
criteria  of lovebird  and 6 alternative data of selected 
lovebird names in the same species. In addition, this 
application also combines 2 methods namely  
analytical hierarchy process  and  weighted product 
with test conclusions on 64 alternative sample 
evidence based on lovebird  type  and sub-criteria 
both in system calculations and manual calculations 
that result in low accuracy category  levels.   with a 
percentage value of 21%-40%. Furthermore, as 
many as 12 users or about 19% of 64 tests with  a 
moderate accuracy category level with a percentage 
value of 41%-60% Then as many as 48 users or 
about 75% of 64 tests with a high accuracy category 
level in the best lovebird  communication results 
with a percentage value of 61%-80%. This decision 
support system as an application that produces the 
highest ranking in helping recommend the decision 
of determining the best quality lovebird.   This 
designed application can still be developed even 
better and is recommended for its development to 
be used with other methods and implementations 
 

REFERENCE 
 
Amalia, E. L., RDA, R. A., & Pratama, A. N. (2019). 

Sistem Pendukung Keputusan Menentukan 
Lovebird Unggul dalam Perlombaan 
Menggunakan Metode AHP-Topsis. Matics, 
11(1), 21. 
https://doi.org/10.18860/mat.v11i1.7690 

Andriyani, S., & Yuma, F. M. (2020). Kombinasi 
Metode Analitical Hierarchy Process Dan 
Weighted Product Dalam Penentuan Benih 
Cabai Unggul. 6(2), 117–124. 

Bahtiar, S., Gunawan, I., Safii, M., & Parlina, I. (2019). 
Implementasi Dengan Metode Promethee 
Dalam Merekomendasikan Keputusan Juri 
Pada Kompetisi Burung Lovebird. 1, 658–663. 

Charli, F., Syaputra, H., Akbar, M., Sauda, S., & 
Panjaitan, F. (2020). Implementasi Metode 
Faster Region Convolutional Neural Network 
(Faster R-CNN) Untuk Pengenalan Jenis 

Burung Lovebird. Journal of Information 
Technology Ampera, 1(3), 185–197. 
https://doi.org/10.51519/journalita.volume
1.isssue3.year2020.page185-197 

Defit, S., Nurcahyo, G. W., Studi, P., & Ilmu, M. (2021). 
Decision Support System in Identification of 
Swallow ’ s Nest Quality with Weighted Product 
Method. 3(1), 181–192. 

DLHK Provinsi Banten. (2019). Mengenal Satwa 
Tidak Dilindungi (Simungil Burung Cinta/ 
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