Revealing the Popularity of Indonesian Local Government Mobile

Apps in Google Play Store

I Wayan Budi Sentana

, I Made Ari Dwi Suta Atmaja

and I Nyoman Gede Arya Astawa

Bali State Polytechnic, Bukit Jimbaran, Badung, Indonesia

Keywords: Mobile Apps, Popularity, Province Government, Number of Install, Number of Review, Score Rating.

Abstract: In this research, we revealed the popularity of mobile apps affiliated with the provincial governments in

Indonesia. To the best of our knowledge, this is the first study to conduct an empirical analysis related to this

area. In total, we found 283 mobile apps available on the Google Play Store and correlated to 32 province

governments in Indonesia. In this research, we scraped metadata of each provincial government-affiliated

apps available at the Google Play Store and gathered the information based on three indicators, including the

number of installs, number of reviews and score ratings. As a result, we found that 69.9% of mobile apps had

a number of installs smaller than 1000, 89% of mobile apps had reviews less than 100 and 50.2% of mobile

apps had scored less than 3. In addition, based on the popularity index that we defined, we found high

disparities in the popularity index among provinces in Indonesia. There is only 1 province that has a Popularity

index above 90, indicating that the popularity of mobile apps affiliated to the provincial government in

Indonesia is considered to be low. Hence, these results can be tailored as a reference for the provincial

government in determining the level of effectiveness and impact of an app when developing mobile-based

software in the future.

1 INTRODUCTION

Android consider being the most successful mobile

Operating System globally. This is based on the

number of mobile apps developed in this platform as

well as the market share of devices supporting this

platform around the world. Based on Statista, in July

2021, there were 2.8 million Android apps available

in Google Play Store (Statista.2021). This number

considers being the lower bound of total Android

apps available globally, because there are a number

of marketplaces other than the Play Store available,

including Xiaomi market, Tencent, LG and some

other marketplace related to the device vendors. In the

number of market share, Android recorded to have

around 86% of the total mobile platform in 2020, as

mentioned in (Samhi, Allix et al. 2021). The open

source model and the freedom to customize the

Android platform have made it easier for many

vendors to adopt this platform, thereby increasing the

https://orcid.org/0000-0003-3559-5123

https://orcid.org/0000-0002-1103-528X

https://orcid.org/0000-0003-1472-896X

compatibility of mobile apps on devices with a very

diverse range of technologies and prices.

This phenomenon attempting a lot of institution,

including the Indonesian province government, to

leveraging Android-based applications (apps) in

order to support their daily operations. In total, we

found 283 Android apps affiliated to 32 provinces

(except Maluku and North Maluku) available in

Google Play Store. These apps were developed for

several purposes including presentation recording,

policy dissemination, community aspiration

submission, and other citizenship services. However,

to the best of our knowledge, there has never been a

study showing the effectiveness of using these

government-owned applications.

A parameter that can be used to measure the

effectiveness of mobile apps is the level of popularity

of these apps for their users. Therefore, we conducted

an empirical study to review how popular mobile

apps belong to the provincial government in

Indonesia. In this study, we use the indicators

844

Sentana, I., Atmaja, I. and Astawa, I.

Revealing the Popularity of Indonesian Local Government Mobile Apps in Google Play Store.

DOI: 10.5220/0010955200003260

In Proceedings of the 4th International Conference on Applied Science and Technology on Engineering Science (iCAST-ES 2021), pages 844-850

ISBN: 978-989-758-615-6; ISSN: 2975-8246

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

available on the Google Play Store which include the

number of installs, the number of reviews and the

score rating. We create tools to scrape the metadata

and analyze the information contained in these

indicators. We also rank the level of popularity of

mobile apps among all provinces in Indonesia and

introduce a measurement method that we call the

Popularity Index.

We believed that this is the first empirical study to

measure the popularity of mobile apps affiliated to the

Indonesian province government. We also expect that

the result of this study can be used as a reference for

the province to make a policy in case of mobile apps

development in the future.

2 RESEARCH METHODS

To reveal the popularity of Indonesian province

government mobile apps, we followed six stage of

research as shown in Figure. 1. More detail of these

stages is explained in the following sub sections.

Figure 1: The six-stages of research methods to reveal the

popularity of Indonesian province government mobile apps

in Google Play Store.

2.1 Apps Fingerprinting

This stage is used to find mobile apps affiliated with

the provincial government in Indonesia in Google

Play Store app market. This stage is considered to be

challenging because we have to fingerprint apps from

the corpus of 2.8 million apps available on that app

market (NPM.2021). At this stage, we collecting the

apps candidate by identifying a certain keyword

(province’s name) appears in the Apps’ name or

Apps’ description as a conducted by (Sentana, Ikram

et al. 2021). For this purpose, we created Python

script and take advantage of the google-play-scraper

library developed in Java script by (NPM.2021). We

made a list of 34 provinces in Indonesia and used

them as keywords to be sent to the Google Play Store

Search Page. After the Google Play Store returns

search results for each keyword, we then scrap the

content page and collect information which includes

Apps ID, Apps name, and Apps Description.

We managed to collect 854 candidate apps that

corresponding to 34 keywords which are the name of

the province. This result is considered to be a coarse-

grained form of list, because any snippet word

contained in the Apps’ identifier that corresponded to

the keyword will be considered as candidates. For

example, when we search based on the keyword

"provinsi Maluku", then any Apps that have the

"Maluku" snippet in its name or description will be

considered as candidate Apps. Moreover, the search

result based on a province name often returns a

massive number of irrelevant data and overlap data

from one province to another. Hence, we did some

manual searching to get valid data in the process of

forming candidate apps list. This candidate then

filtered in the next stage.

2.2 Apps Filtering

This stage is used to manually filter the candidate lists

generated in the previous stage. For this purpose, we

analyse the name and description of each app on the

candidate list and determine the app's affiliation with

each provincial government. In a certain case, we

have to do cross validation to Play Store website to

get a comprehensive understanding on each candidate

apps in the list. This stage cannot be done

automatically, considering that determining app

affiliation to each province requires context

understanding based on apps description in the

metadata.

As this measurement intended for apps affiliated

to province government, we then exclude apps owned

by non-province governments, police departments,

courts, attorneys, and apps owned by representatives

of the central government located in each province.

The result of this stage is a list of App IDs that

correspond to each province in Indonesia. In total we

found 283 apps affiliated to 32 provincial

governments. Jakarta is listed as the province with the

highest number of Mobile Apps with 36 Apps,

followed by West Sumatra with 28 Apps. We were

unable to identify Mobile apps from Maluku and

North Maluku provinces. More detailed about the

number of apps per province can be seen in Table I.

2.3 Metadata Scraping

After the App ID list is obtained, the next step is to

scrape the metadata of each app. The Play Store

provides information related to apps which includes

the name, description, developer, number of installs,

score rating, number of reviews, and even the date of

the last update. For this purpose, we have modified

Apps

Fingerprinting

Apps Filtering

Metadata

Scraping

Metadata

Analysis

Popularity

Measurement

Result

Visualization

Revealing the Popularity of Indonesian Local Government Mobile Apps in Google Play Store

845

Python and Java Script at the Apps Fingerprinting

stage to collect information that matches the filtered

list of App Ids. To assess the popularity of an app, we

use three indicators including the number of installs

(I), the number of reviews (R) and the score rating

(S). The number of installs is an indicator of how

many times the app has been downloaded and

installed on the user's device. While the review is the

number of reviews from users of apps. The Play Store

allows users to provide reviews and provide a rating

in the form of a star rating which is worth 1 to 5, after

installing apps on their device. The aggregation

results from this user review used by Play Store to

create a rating score for each app.

Based on our observations, these three indicators are

appropriate parameters to measure the level of

popularity of Android apps, which is in this research

are Android apps that affiliated with provincial

governments in Indonesia.

2.4 Metadata Analysis

The next stage is to analyse the metadata that has been

collected. At the early stage of analysis, we sorted the

value of selected indicator metadata for each apps. By

the number of installs, we found "SAMBARA"

(id.go.bapenda.sambara) to be the most downloaded

and installed app of 3,862,879 times, followed by

"PIKOBAR Jawa Barat" (id.go.jabarprov.pikobar)

that installed in 957,898 devices. Both apps are

regulated by the West Java province government.

While the least installed app was recorded by

"Boyang Aspirasi Prov. Sulbar"

(com.thp.boyangaspirasi) that was installed by 32

users and owned by West Sulawesi province. By this

indicator, we found 198 (69.9%) province

government mobile apps in Indonesia were installed

less than 1000 times.

While by Review indicator, "SAMBARA" and

"PIKOBAR Jawa Barat" again showed their

domination by 10.539 and 4.130 number of reviews,

respectively. In opposite, we found 252 (89%)

province government apps that recorded only have

less than 100 reviews and 136 (48%) of the apps

recorded 0 reviews.

In addition to both indicators, we found 110

(38.8%) apps recorded Score ratings more than or

equal to 4, 31 (10.9%) apps recorded Score in the

range of 3 to less than 4, and 142 (50.2%) province

government apps recorded the Score rating less than

3. The range of those scores indicates that the apps

obtained positive, neutral and negative sentiment

respectively, as explained in the user-review analysis

conducted by (Tangari, Ikram et al. 2021).

The distribution of data on the ECDF score rating

looks more encouraging than the other two indicators.

The proportion of the number of ratings above 3

having a greater proportion than those below 3. There

are about 53% of mobile apps owned by the

provincial government get a positive score and the

rest are neutral or negative. A score rating of 3 is

considered to be neutral value, while values above are

considered to be positive and below are considered to

be negative (Tangari, Ikram et al. 2021).

To have an insight into the data distribution per

province, we then aggregate the value in each

indicator and group them by province. As a result, we

found West Java dominated the number of installs by

an average of 374,967.4 installs per app, followed by

Banten by 160,349.7 of average installed per app.

West Java recorded the highest value in the average

number of reviews by 1,304.3 and West Papua

recorded the highest average score ratings of 4.3,

even though West Papua only has 1 mobile app.

While the aggregation process also reveals the

fact that 15 (44%) of provinces recorded average

install rates less than 1000, 28 (82.3%) provinces

recorded average reviews less than 100, and 26

(76.4%) provinces recorded average score rating less

than 3. More detail about the aggregation result per

province can be seen in Table 1.

2.5 Popularity Measurement

As we use three indicators to measure the app's

popularity per province, then the next stage of this

research is to combine the value contained in all

indicators to form an index of average value per

province. Since the Install rate shows having a high

value among other indicators, then we normalized the

value of each indicator to avoid dominance by a

certain indicator.

For that purpose, we then introduce Popularity

index (Pi) by ranked the province apps popularity

based on the indicators explained previously. The Pi

then denoted as followed:

𝑃𝑖 = (𝐼 ̃ +𝑅 ̃ +𝑆 ̃)/𝑑 ∗ 100 (1)

𝐼

represent the normalize form of average Install

rate for each province that resulted from min-max

normalization denoted as followed:

𝐼 ̃ = (𝐼_(𝑛 ) − 𝐼_𝑚𝑖𝑛)/(𝐼_𝑚𝑎𝑥 − 𝐼_𝑚𝑖𝑛 ) (2)

represent the value of install number for

corresponding apps, while I

max

and I

min

respectively

represent the maximum and minimum value in install

vector which is the highest and the lowest install rate

among all Apps. The similar operation is also

iCAST-ES 2021 - International Conference on Applied Science and Technology on Engineering Science

846

conducted to define 𝑅



dan 𝑆



that represent the

normalized form of average review and average score

respectively. Since we prefer to display the index in

range of 0 to 100, then we multiply the equation to

100 and divided it by the number of indicator (d).

Based on the calculation using Formula (1), we

found that West Java obtained the highest Pi of 97

from 14 apps affiliated to that province, followed by

North Sumatera with Pi of 38 obtained from 3 mobile

apps. Ironically, by using this index we found 33

provinces obtained Pi less than 50, indicating the high

disparity of apps popularity among provincial

governments in Indonesia. More detail about the Pi

obtained by each province can be found in Figure 2.

The Pi result was plotted in each province

Geolocation in the notation of x(y), where x represents

the Pi score and y represent the number of apps

regulated by each province.

2.6 Result Visualization

The last stage in this research is to visualize the results

of data analysis and popularity measurement into

geolocation image. The most challenging part of this

stage is to find the geolocation data of 34 province in

Indonesia. Most of the currently data available in the

Internet only consist of 33 provinces without North

Kalimantan. Fortunately, we found a link provide by

Kompas (Purba.2021) that directing us to the Shape

file of the newest Indonesian province geolocation.

Shape (SHP) file itself is a collection of files

containing geometry and index feature that represent

multiple dot based on longitude and latitude

coordinate of a certain area.

We then leveraging Geopandas library in Python

to convert the Shape file into GeoJson data so we can

merge it with the Popularity Index (Pi) and the result

from data aggregation per province. Then we

leveraging Matplotlib library to plot the index and

data on the top of geolocation data as shown in Figure

2. We also adding heatmap indicator (Blue color) to

represent the value of Popularity index on each

province.

As a takeaway for this stage, we provide the

Shape files and Geo Json files of Indonesian province

in our online repository and can be found in

https://github.com/budisentana/indonesian_mobile_a

pps.

3 RESULT AND DISCUSSION

Since the Apps Filtering stage, we have found

disparities in terms of mobile apps adoption among

all provinces in Indonesia. At this stage, we found that

the number of applications owned by the provinces in

the western and central parts of Indonesia was higher

than the provinces in the eastern part of Indonesia.

Surprisingly, we found West Sumatra have the

second largest number of mobile apps (28 apps) after

Jakarta. This number far exceeds the average

ownership of mobile apps by provinces on the island

of Sumatra.

On the other hand, the number of mobile apps

ownership by provinces in eastern Indonesia is

consider to be very low. Papua and West Papua have

4 and 1 apps respectively, East Nusa Tenggara only

has 2 apps and even Maluku and North Maluku have

0 mobile apps. This provides an overview of the

existing IT maturity levels in each province. This

phenomenon can be related to many factors including

the readiness of infrastructure and human resources

who manage these facilities.

During the metadata analysis stage, we found a high

gap of data in each indicator. In general, the

applications owned by the provincial governments in

Java and Bali relatively have a higher number of

installs and the number of reviews compared to

provinces in other islands. For example,

“SAMBARA” owned by West Java province has the

number of installs and the number of reviews,

respectively 3,862,879 and 10.539. If we compare

this apps to “RIC DPMPTSP”

(com.dalakriau.ricdpmptspriau) owned by province

of Riau, which only has 66 number of installs and 9

number of reviews, then the difference is very

significant. This presents its own challenges in the

data analysis stage, where we cannot directly present

the data and compare it between indicators in the

process of determining the popularity of mobile apps.

In this stage, we also reveal discouraging facts

related to data distribution on each indicator. We

found 69.9% of mobile apps were installed less than

1000 times, 48 % does not have any reviews, and 49%

have a negative score rating. This fact shows the low

willingness of citizens to use the facilities that have

been prepared by the government. The factors that

cause this are beyond the scope of this research.

However, from our observations, most of the existing

reviews show low maintenance of existing apps,

causing many problems and affecting the willingness

of citizens to use these apps. This can be very ironic

considering that we have found several applications

that are intended for public hearings to absorb

Revealing the Popularity of Indonesian Local Government Mobile Apps in Google Play Store

847

aspirations and accommodate public complaints.

With this condition, the purpose of developing these

mobile apps will not be achieved due to the low desire

of the people to use the applications provided by the

government.

Table 1: Summary of Metadata Scraping of Indonesian

province government mobile apps, order by apps number

per province.

Province

Name

App

Average Average Average

Install Review Score

1 Jakarta 36 93107 400.4 3.3

2 West Sumatera 28 2225.7 9.1 1.9

South

Sulawesi

23 1625.3 12.7 1

4 Yogyakarta 19 27623.4 150.6 2.5

5 West Java 14 374967.4 1304.3 3.9

6 Riau 13 138.7 6.2 2.9

West Nusa

Tenggara

12 268.1 2.4 1.9

West

Kalimantan

12 9546.9 53.8 1

9 Bali 11 2944.9 7.1 1.9

10 Gorontalo 9 3072.2 27.8 1.3

11 West Sulawesi 9 293.8 6.9 1.6

12 Lampung 9 127.7 4.9 2.1

North

Kalimantan

8 1994.1 6.8 2.9

South

Kalimantan

6 5780.7 30 3.7

15 Riau Islands 6 24307 59.8 2.8

16 Aceh 6 3297.7 23.2 2.8

Central

Kalimantan

6 94.7 0.8 0.8

North

Sulawesi

6 32436 113.2 3.1

19 Bengkulu 5 844.8 5.8 1.5

20 East Java 5 436.6 2.4 2.7

South

Sumatera

5 329 1 0.9

22 Central Java 5 2731.6 17.6 3.4

East

Kalimantan

4 153.5 1.3 1.1

24 Jambi 4 166 0 0

25 Papua 4 3100.5 16.8 2.4

South East

Sulawesi

4 943.5 6.3 2.4

Central

Sulawesi

3 4604.3 31.3 3

28 Banten 3 160349.7 1050.7 0.9

North

Sumatera

3 13397.3 202.3 4

East Nusa

Tenggara

2 98 0 0

Bangka

Belitung

2 377.5 4 2.4

32 West Papua 1 7047 62 4.3

33 Maluku 0 0 0 0

34 North Maluku 0 0 0 0

In the Popularity Measurement phase, we are

tempted to include popular mobile apps, such as

Facebook or Instagram, as the upper limit of our

proposed popularity index calculation. However,

considering the low value of each indicator vector, we

are afraid that the results given will make the index

disparity even higher. Therefore, we only use the

highest value in each vector as the upper limit of the

calculation of this popularity index. For example, in

the install number vector, we use the highest number

of installs among all the applications in our research

corpus. Likewise, for vector indicators for the number

of reviews and score ratings.

The results of the popularity index calculation

presented in Figure 2 still show a high data disparity,

although only using the data in our corpus to

determine the upper limit of normalization. In the

picture, West Java has the highest heatmap level and

a popularity index of 97 obtained from 14

applications. However, 33 other provinces have a

popularity index below 50. This shows that the level

of popularity of mobile apps owned by West Java is

far above other provinces. This can be a benchmark

for other provinces when developing mobile-based

applications in the future.

iCAST-ES 2021 - International Conference on Applied Science and Technology on Engineering Science

848

Figure 2: Popularity index (Pi) of Indonesian Local Government Mobile Apps per province. West Java has the Mobile Apps

with the most popular in Indonesia with 97 point obtained from 14 Apps. The data present in x(y) format where x represent

Pi and y represent the number of apps per province.

4 LIMITATIONS

We were attempted to analyse the Mobile Apps

affiliated with all District and City governments,

which is the second level of local government in

Indonesia. However, due to the massive number of

mobile apps that we found during the Fingerprinting

stage, we keep our focus on Province government

apps and preserve the result for our future works. We

are also considering analysing the sentiment from

each user review as conduct by (Tangari, Ikram,.et

all.2021), to determine the user's opinion related to

local government apps. For this purpose, we believe

the adoption of Natural Language Processing in

Bahasa Indonesia, such as in (Iswanto, Poerwoto.

2018) and (Manik, et all. 2017), would be useful for

our future works because most of the user review are

written in Bahasa Indonesia.

5 CONCLUSIONS

This is the first empirical study that reveals the

popularity of Indonesian province government

mobile apps. We are using three indicators including

the number of installs, number of reviews and score

ratings available at the Google Play Store. The

analysis result shows 69,9% of mobile apps affiliated

with the province government installed less than 1000

devices. While 89% of those apps have less than 100

reviews and 50.2% of those apps have a negative

score rating. In this study, we introduced a popularity

index to rank the popularity of mobile apps among

provinces in Indonesia. As a result, we found 33

provinces have a score less than 50. This result shows

that the Popularity of the Indonesian province

government is considered to be low.

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