International Journal of Interactive Mobile Technologies (iJIM) – eISSN: 1865-7923 – Vol. 14, No. 16, 2020
Paper—Enhanced of Attendance Records Technology used Geospatial Retrieval based on Crossing …
Enhanced of Attendance Records Technology used
Geospatial Retrieval based on Crossing Number
https://doi.org/10.3991/ijim.v14i16.13911
Bayu Utomo, Achmad Teguh Wibowo (), Mujib Ridwan,
Muhammad Andik Izzuddin
UIN Sunan Ampel, Surabaya, Indonesia
atw@uinsby.ac.id
Agustinus Bimo Gumelar
Universitas Narotama, Surabaya, Indonesia
Sirajul Arifin
UIN Sunan Ampel, Surabaya, Indonesia
Abstract—Nowadays, the fingerprint scanner widely used to records attend-
ance. However, this technology has a weakness. Much research has done to im-
prove the attendance system by utilizing mobile technology, like usage a finger-
print smartphone and location by GPS sensor to validate user location manually.
In this research, we developed an application to enhance the records attendance
system with a smartphone by crossing numbers to verify user position automati-
cally, which implemented in a mobile app. This application using the PNPOLY
method for detecting the location of the user inside of the polygon area predeter-
mined. This method is part of the crossing number algorithm for increasing x and
fixed y from point P, which x is latitude, and y is a longitude. The result of the
experiment demonstrated that the percentage of successful validate user coordi-
nate inside edges of the polygon boundary is 83%, depending on the GPS sensor
embedded into a mobile device.
Keywords—Attendance system, crossing number, geospatial retrieval, mobile
fingerprint, PNPOLY.
1 Introduction
The fingerprint scanner is one of the attendance records technology that has widely
used. This technology uses an employee's fingerprint identity to authenticate the attend-
ance process [1]. So that the technology could be a reduced wastage of paper resources,
improved efficiency, security, and enhanced trusted by data, thus human error and pos-
sibly manipulated data by a human could be avoided [2].
At present, a new method for records attendance still developed. Much research has
extended by an attendance system with mobile technology based on Global Positioning
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mailto:atw@uinsby.ac.id
Paper—Enhanced of Attendance Records Technology used Geospatial Retrieval based on Crossing …
System (GPS). The result of the study says that the new method could improve accuracy
from the attendance system [1].
Geographic Information System (GIS) contains much spatial data that can only be
retrieved by geospatial retrieval methods [3]. GPS is one of the technologies that can
retrieves data from GIS for a showing of user's locations with digital cartographic. So
GPS can be tracking a user location in real-time [4]. However, GPS may not be used in
some environments, such as tunnels, forests, and tall buildings [5].
Smartphone devices have become a part of every person in recent years, and almost
everyone uses this device to help daily activity [6]. Now, many smartphones have a
fingerprint sensor for biometric authentication, such as unlocking the smartphone and
activating device security-critical functionality [7]. The combination of mobile technol-
ogy, GPS, and geospatial retrieval could be utilized enhanced recording the attendance
system. Mobility needed in this system to avoid queues in the fingerprint machine [1].
This system supported the trends of Bring Your Own Device (BYOD) to help work,
also as an attendance device system for employees [8].
This research introduces a new method of attendance system using smartphone fin-
gerprint and geospatial retrieval. User coordinates will be validated by spatial data from
a polygon area predetermine. To determine the coordinate inside a polygon uses a Point
Inclusion in Polygon Test (PNPOLY) method [9]. This method is a part of the crossing
number algorithm that uses ray lines in determining the point value for the detection of
location a user [10].
If coordinates of the user inside a polygon location, then the user can do scan finger-
print to the attendance system process else the user can not trigger to fingerprint au-
thentication. Furthermore, sending data to the server use the application in the form of
data of device id and location coordinates of the users.
Restriction of the developed application used the concept of Security Information
and Event Management (SIEM) [11] that can be used only inside on polygon predeter-
mined and could not use a mock location app. So that application is security guaranteed
to a fake location by a user.
Back to the paper: in section 2, we show the research that is related, section 3, shows
the method of attendance system using geospatial retrieval based on the PNPOLY, sec-
tion 4 shows the results and section 5 shows the evaluation of this experiment, the dis-
cussion and the conclusion given in section 6 and 7 respectively.
2 Related Work
Their much research had been done to improve the smartphone-based attendance
data collection system [1,9,10]. Many of them discuss the attendance system by mobile
fingerprint used location GPS. User coordinates sent to the server as attendance proofed
[14]. However, this system needed an administrator to check the location that sent are
inside an area predetermined. The research could send the result of attendance through
SMS to the smartphone device of a user application [15].
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Other studies used a local connection to validate the user [2]. Attendance system by
smartphone fingerprint activated when the user connected to the local wi-fi. The attend-
ance system area covers coverages radius of Wi-Fi connection. Other than that, the re-
search about the attendance system using local Wi-Fi could be used to vigilance attend-
ance system by fusion of wireless network and biometric fingerprint authentication
[16]. The result of the study conducted monitoring performance employees with vali-
dated real-time position when connected to the local Wi-Fi.
Furthermore, research about mobile-based attendance system using QR code [17].
That study developed an application for lecturer and student. The lecturer could gener-
ate QR code for records attendance students with a mobile app in the classroom, this
research using Android technology for front-end and PHP to the back-end.
The next study developed an application to records attendance systems using Blue-
tooth Low Energy (BLE) technology [18]. The application analyzed every device that
attached each other with an infrared motion analyzer to count the number of students
that entering into the classroom [19].
Based on this research of the smartphone-based attendance system, this study to be
inspired to develop the application with point inclusion method to validate the user is
inside a polygon area that predetermined. So that developed application has been made
could be enhanced attendance system and advantage usage of a mobile device such as
run in everywhere and anytime.
3 Inclusion of a Point in a Polygon
3.1 Geospatial retrieval
This research used polygon coordinates of Islamic State University of Sunan Ampel
Surabaya taken from OpenStreetMap service [20]. The mapping used a react-native-
map library [11,12], and the process used Google API Service [23] to develop an ap-
plication. That map displayed the polygon area of an object region to enhanced of at-
tendance system based on geospatial retrieval [24].
3.2 Crossing number
A crossing number is a method to determine a point inside a polygon. This method
was counting ray lines crossing the polygon of boundary edges from point P. If the
crossing number is even, then the point is outside a polygon area else, the crossing
number is odd, then the value is inside a polygon area [10].
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Fig. 1. Crossing number algoritm [10]
The movement of the inside or outside in the crossing number method is base on the
Jordan Curve Theorem [10]. That theorem says a line repetition that does not intersect
an object, separated into two components [25]. Equation this theorem showed in below.
𝐶 = {(𝑥,𝑦);𝑥2 + 𝑦2 = 1} (1)
Where:
C = Curve Theorem
x, y = the points of coordinated tested
3.3 PNPOLY
PNPOLY is a development method from a ray casting algorithm. This algorithm is
a part of the crossing number method. The PNPOLY runs a semi-infinite ray horizontal
(increasing x, fixed y) from point P, and counting many boundary edges it crosses. An
equation a PNPOLY method explained below.
𝑛 ∑ 𝑦(𝑥𝑛 − 𝑥𝑚) − 𝑚 ∑ 𝑥(𝑦𝑛 − 𝑦𝑚) > 𝑛𝑚(𝑥𝑛𝑦𝑚 + 𝑥𝑚𝑦𝑛)
𝑛
𝑦=0
𝑚
𝑦=0 (2)
Where n is a point of ray crossed polygon boundary, and m is a point of the edge of
a polygon. Then, symbol x and y tested point. The pseudocode of this method shows
below.
Started by defining nvert, vertx, verty, testx, testy
nvert = many edges of the polygon area
vertx and verty = array of coordinates (x, y) from pol-
ygon boundary edges
testx and testy = coordinates (x, y) of point P
Set i, j, c
i = 0
j = nvert - 1
c = false
If i < nvert = false, then exit loop
If (verty[i] > testy != verty[j] > testy) and
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If testx < (vertx[j] - vertx[i]) * ((testy - verty[i]))
/ ((verty[j] - verty[i])) + vertx[i] = true, then
c != c
Set j = i while
i + 1 and
Back to step 9
Finish
4 Design and Implementation
4.1 Design system
We implemented the PNPOLY method that integrated with the mobile app has
developed. This application could run in a smartphone device that has a fingerprint
sensor with Android or IOS. Fig. 2 illustrates the design architecture of the proposed
application consists of a recommender server, client, and validated method. The
recommender server divided into three-layer. First, the API layer to perform a convert
of raw data to a JSON array. The next is the application layer that executes a query to
databases from each function, and the last layer database used to store data of the
attendance system.
Fig. 2. Design architecture of a proposed application
The mobile layer in the client area used to perform records attendance on the client-
side by a fingerprint scanner, retrieved data sent to the database via the API and
application layer, respectively. Before the fingerprint function is activated, the user
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real-time location validated with the PNPOLY method from a validated layer. Fig. 3
shows workflows of the application that proposed.
Fig. 3. Workflows of a proposed application
The design of detailed workflow used Undefined Modeling Language (UML) se-
quence and class diagram. Sequence diagrams divided into three sections. The regis-
tered device diagram inserts arrival time diagram and update the return time diagram.
That flow of the first sequence diagram starts from the process checking device id on
the splash screen. If resulted false, then the screen would redirect to register device
form. Furthermore, the user could access a home screen. The sequence diagram of the
registered device shown in Fig. 4.
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Fig. 4. Sequence diagram of the register device
The validation method would determine position users when the home screen of the
application is open. Flows of the second sequence diagram start after these result true
and fingerprint scanner used by a user. Then, user attendance data sent to the database
server. Fig. 5 illustrated the sequence diagram of insert arrival time. The third sequence
used the same flows but used different queries that show in Fig. 6.
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Fig. 5. Sequence diagram of the insert arrival time
Fig. 6. Sequence diagram of the update return time
This system has nine class diagram that divides into two sections. Four class (User,
NIP, FetchAPI, DeviceID) in frontend section and five class (QueryEmployee,
ControllerNIP, QueryAttendance, ControllerUser, ControllerAttendance) in backend
section. FetchAPI is the main class of this application witch control all function in
fronted and makes request API to the backend. Fig. 7 sown class diagram for the
proposed application.
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Fig. 7. Class diagram of the proposed application
4.2 Coordinates validation
The proposed application needed an active GPS sensor to retrieve coordinates posi-
tion (latitude and longitude) when it runs by a user. These coordinates used to validate
user position from a predetermined polygon area with the PNPOLY method. The vali-
dation processed every time the user position changed. Fig. 8 illustrated screens of an
application when the user position is different.
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a) Screenshot of
a mobile app
with the condi-
tion inside a
polygon area
b) Screenshot of a
mobile app with a
mock location from
Fake GPS.
c) Screenshot of
a mobile app
with the condi-
tion outside of
the polygon area
Fig. 8.
The records attendance feature activated when the PNPOLY method indicates user
coordinates are on inside a polygon. This application used fake location detection to
restricted user usage. So the user can not use a mock location app and only can usage
this application inside of the area that predetermined. Users could record attendance
used fingerprint id in the device.
5 Testing
In this section, we tested a developed application by evaluating the PNPOLY imple-
mentation method and tested the effect of the cellular network. The first testing used 48
data coordinates inside edges of the polygon boundary, which has specified. The next
experiment used four operating systems and three network cellular categories that tested
100 times by each network.
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5.1 Evaluate PNPOLY implementation
The percentage of successful PNPOLY methods implemented in this application
showed in Table 1. We expected 48 data successfully tested, but in fact, 8 data failed to
check. That resulted in a 17% margin error in the test because of the different accuracy
of the GPS sensor embedded into a mobile device. So, lowers margin error affected by
higher GPS accuracy.
Table 1. The percentage of successful PNPOLY method
Expectation Fact
Success 48 40
Failed 0 8
Total 48 48
Percentage of success 100% 83%
Percentage of failed 0% 17%
5.2 Effect of cellular network
In the next of experiments, we showed the effect of the cellular network on the
application. The operating systems used for the testing divide to Android versions (8
and 9) and IOS versions (11 and 12). The Android operating system tested by three
category networks (2G, 3G, and 4G), but on IOS, only two network categories (3G and
4G) because of IOS architecture restrict applications that run on 2G network. The
experiment resulted in network latency that shows in Fig. 9.
Fig. 9. (a) The network latency when using an application on Android 8. (b) The network la-
tency on Android 9. (c) The network latency on IOS 12. (d) The network latency on
IOS 13
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Furthermore, we calculated the average network latency in each operating system
then compared. The result of the comparison shown in Fig 10.
Table 2. The average of network latency
Android 8 Android 9 IOS 12 IOS 13
2G 2.776 6.459
3G 0.732 0.657 0.875 0.761
4G 0.198 0.533 0.461 0.254
Fig. 10. The average of network latency while using the application
Fig 10 explained the average network latency while using the application. The result
of the 4G connection on Android 8 gets the smallest latency network compared to oth-
ers. The operating system that tested shows network latency in 3G connection, a slight
difference from each other, and the 2G network resulted in the large latencies. The re-
sult of the latency Android 8 (Oreo) recorded average amounts of 2.776s, and Android
9 (Pie) amounts to 6.459s. However, IOS 12 and IOS 13 could not run in this network.
6 Discussion
Enhance of attendance records technology used geospatial retrieval based on
crossing number is not only restricted to usage fingerprint ID but also can use other
biometric ID on mobile, such as face ID, voice recognition or iris ID [26].
The implement of crossing numbers not only applied to the attendance system but
also applied for surveillance technology using Unmanned Aerial Vehicles (UAVs),
based on the polygon area predetermined. This technology combined with IoT for
various services such as military, smart city, agriculture, and many more [27]. Other
than that, the advantage of this combined technology could implement for agriculture
technology to extends the product of agriculture based on geospatial retrieval [28].
0.000
1.000
2.000
3.000
4.000
5.000
6.000
7.000
ANDROID 8 ANDROID 9 IOS 12 IOS 13
Average of Network Latency
2G 3G 4G
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Furthermore, PNPOLY can implement use a winding number algorithm [29]. This
algorithm does it possible to enhance for validated the location of the user on each
building inside of the main polygon. However, this method suitable for usage in a large
area that has many buildings inside of the polygon. The combining of winding number
algorithm and IoT can implement to detect an object that enters dangerous polygon area
predetermined, such as mine suspect area [30].
The security development of data in the application can integrate with blockchain
technology. Because this technology emphasize security with cryptography and smart
contracts for all transaction depend on the method to the consensus that used. Other
than that, blockchain technology supported a decentralized database, so challenging to
change the data into the ledger.
7 Conclusion
The records attendance system using a smartphone fingerprint is a solution to reduce
the weakness of the conventional fingerprint machine. A combination of the fingerprint
with a GPS sensor of the smartphone could track to the attended user location like
previous research.
The goal in this study to enhance attendance records technology using geospatial
retrieval based on the crossing number algorithm for automatically detected the live
location of the user inside or outside the polygon area predetermined. This research
developed applications using the mobile app because this technology could be used
anywhere and anytime. The experiment results from the evaluation of around the
polygon boundary are work, and margin of errors of 17% depending on the accuracy of
the GPS sensor embedded to a mobile device.
We suggested to use an application with a 4G connection on Android 8 because the
result of the experiment has done, showed this operating system has the smallest
average latency of all network. Furthermore, we not recommended used this application
on the 2G network, especially IOS; see Table 2 and edges of the polygon boundary
predetermined because the determine of user location utilizes the accuracy of the GPS
sensor.
8 References
[1] B. Soewito, F. L. Gaol, E. Simanjuntak, and F. E. Gunawan, "Attendance system on Android
smartphone," ICCEREC 2015 - Int. Conf. Control. Electron. Renew. Energy Commun., pp.
208-211, 2015. https://doi.org/10.1109/ICCEREC.2015.7337046
[2] S. S., V. D., and S. Waghmare, "Remote Biometric Authentication System using Android
Phone," Int. J. Comput. Appl., vol. 180, no. 33, pp. 6-12, 2018. https://doi.org/10.5120
/ijca2018916811
[3] R. S. Purves, P. Clough, C. B. Jones, M. H. Hall, and V. Murdock, "Geographic information
retrieval: Progress and challenges in spatial search of text," Found. Trends Inf. Retr., vol.
12, no. 2-3, pp. 164-318, 2018. https://doi.org/10.1561/1500000034
iJIM ‒ Vol. 14, No. 16, 2020 113
https://doi.org/10.1109/ICCEREC.2015.7337046
https://doi.org/10.5120%0b/ijca2018916811
https://doi.org/10.5120%0b/ijca2018916811
https://doi.org/10.1561/1500000034
Paper—Enhanced of Attendance Records Technology used Geospatial Retrieval based on Crossing …
[4] L. E. Wood, "Locating People, Places, and Things: Situating GIS in the Intelligent Network
Landscape," Glob. Media J., vol. 10, no. 19, pp. 1-11, 2011.
[5] Y. Zhang, "A Fusion Methodology to Bridge GPS Outages for INS/GPS Integrated Naviga-
tion System," IEEE Access, vol. 7, pp. 61296-61306, 2019. https://doi.org/10.1109
/ACCESS.2019.2911025
[6] H. M. azer Al Fawareh and S. Jusoh, "The use and effects of smartphones in higher educa-
tion," Int. J. Interact. Mob. Technol., vol. 11, no. 6, pp. 103-111, 2017.
https://doi.org/10.3991/ijim.v11i6.7453
[7] Y. H. Jo, S. Y. Jeon, J. H. Im, and M. K. Lee, "Security analysis and improvement of fin-
gerprint authentication for smartphones," Mob. Inf. Syst., vol. 2016, no. Krait 400, 2016.
https://doi.org/10.1155/2016/8973828
[8] S. B. Utomo and B. Hendradjaya, "Multifactor Authentication on Mobile Secure Attendance
System," Proceeding - 2018 Int. Conf. ICT Smart Soc. Innov. Towar. Smart Soc. Soc. 5.0,
ICISS 2018, 2018. https://doi.org/10.1109/ICTSS.2018.8550017
[9] W. R. Franklin, "PNPOLY - Point Inclusion in Polygon Test - WR Franklin (WRF)," 2006.
[Online]. Available: http://www.ecse.rpi.edu/Homepages/wrf/Re-
search/Short_Notes/pnpoly.html.
[10] D. Sunday, "Inclusion of a Point in a Polygon," http://geomalgorithms.com, 2012. [Online].
Available: http://geomalgorithms.com/a03-_inclusion.html.
[11] M. Schölzel, E. Eren, K. O. Detken, and L. Schwenke, "Monitoring android devices by using
events and metadata," Int. J. Comput., vol. 15, no. 4, pp. 248-258, 2016.
[12] P. Wadhwa, "Attendance System Using Android integrated Biometric Fingerprint Recogni-
tion," Int. Res. J. Eng. Technol., vol. 4, no. 6, pp. 1069-1073, 2017.
[13] S. Sultana, A. Enayet, and I. J. Mouri, "A Smart, Location Based Time and Attendance
Tracking System using Android Application," Int. J. Comput. Sci. Eng. Inf. Technol., vol.
5, no. 1, pp. 01-05, 2015. https://doi.org/10.5121/ijcseit.2015.5101
[14] M. Y. Khan, "GPS Enabled Employee Registration and Attendance Tr acking System," pp.
62-65, 2015.
[15] L. Kamelia, E. A. D. Hamidi, W. Darmalaksana, and A. Nugraha, "Real-Time Online At-
tendance System Based on Fingerprint and GPS in the Smartphone," Proceeding 2018 4th
Int. Conf. Wirel. Telemat. ICWT 2018, 2018. https://doi.org/10.1109/ICWT.2018.8527837
[16] H. Adal, N. Promy, S. Srabanti, and M. Rahman, "Android based advanced attendance vig-
ilance system using wireless network with fusion of bio-metric fingerprint authentication,"
Int. Conf. Adv. Commun. Technol. ICACT, vol. 2018-Febru, pp. 217-222, 2018.
https://doi.org/10.23919/ICACT.2018.8323701
[17] H. Abdelhafez, M. Alamri, R. Alomari, B. Alzoman, R. Binsheeha, and A. Albawardi, "Mo-
bile Based Attendance System Using QR Code," vol. 9, no. 4, pp. 17-21, 2019.
[18] S. Noguchi, M. Niibori, E. Zhou, and M. Kamada, "Student attendance management system
with bluetooth low energy beacon and android devices," Proc. - 2015 18th Int. Conf. Net-
work-Based Inf. Syst. NBiS 2015, pp. 710-713, 2015. https://doi.org/10.1109
/NBiS.2015.109
[19] R. Apoorv and P. Mathur, "Smart attendance management using Bluetooth Low Energy and
Android," IEEE Reg. 10 Annu. Int. Conf. Proceedings/TENCON, pp. 1048-1052, 2017.
https://doi.org/10.1109/TENCON.2016.7848166
[20] OpenStreetMap Foundation, "OpenStreetMap," 2019. [Online]. Available: https://www.
openstreetmap.org/way/470103964.
[21] Facebook Inc, "React Native," 2019. [Online]. Available: https://facebook.github.io/react-
native/docs/getting-started
114 http://www.i-jim.org
https://doi.org/10.1109%0b/ACCESS.2019.2911025
https://doi.org/10.1109%0b/ACCESS.2019.2911025
https://doi.org/10.3991/ijim.v11i6.7453
https://doi.org/10.1155/2016/8973828
https://doi.org/10.1109/ICTSS.2018.8550017
http://www.ecse.rpi.edu/Homepages/wrf/Research/Short_Notes/pnpoly.html
http://www.ecse.rpi.edu/Homepages/wrf/Research/Short_Notes/pnpoly.html
http://geomalgorithms.com/a03-_inclusion.html
https://doi.org/10.5121/ijcseit.2015.5101
https://doi.org/10.1109/ICWT.2018.8527837
https://doi.org/10.23919/ICACT.2018.8323701
https://doi.org/10.1109%0b/NBiS.2015.109
https://doi.org/10.1109%0b/NBiS.2015.109
https://doi.org/10.1109/TENCON.2016.7848166
https://facebook.github.io/react-native/docs/getting-started
https://facebook.github.io/react-native/docs/getting-started
Paper—Enhanced of Attendance Records Technology used Geospatial Retrieval based on Crossing …
[22] Airbnb, "React Native Map," 2017. [Online]. Available: https://github.com/react-native-
community/react-native-maps
[23] Google Inc, "Google Map API," 2019. [Online]. Available: https://develop-
ers.google.com/maps/documentation.
[24] C. B. Jones and R. S. Purves, "Geographical information retrieval," Int. J. Geogr. Inf. Sci.,
vol. 22, no. 3, pp. 219-228, 2008. https://doi.org/10.1080/13658810701626343
[25] F. Ross and W. T. Ross, "The Jordan curve theorem is non-trivial," J. Math. Arts, vol. 5, no.
4, pp. 213-219, 2011. https://doi.org/10.1080/17513472.2011.634320
[26] B. Soewito, F. E. Gunawan, and M. Hapsara, "Smartphone for next generation attendance
system and human resources payroll system," Int. Conf. Electr. Eng. Comput. Sci. Informat-
ics, vol. 4, no. September, pp. 313-318, 2017. https://doi.org/10.1109/EECSI.2017.8239130
[27] J. H. Park, S. C. Choi, I. Y. Ahn, and J. Kim, "Multiple UAVs-based surveillance and re-
connaissance system utilizing IoT platform," ICEIC 2019 - Int. Conf. Electron. Information,
Commun., pp. 1-3, 2019. https://doi.org/10.23919/ELINFOCOM.2019.8706406
[28] W. Han, Z. Yang, L. Di, B. Zhang, and C. Peng, "Enhancing agricultural geospatial data
dissemination and applications using geospatial web services," IEEE J. Sel. Top. Appl. Earth
Obs. Remote Sens., vol. 7, no. 11, pp. 4539-4547, 2014. https://doi.org/10.1109/
JSTARS.2014.2315593
[29] G. N. Kumar and M. Bangi, "An Extension to Winding Number and Point-in-Polygon Al-
gorithm," IFAC-PapersOnLine, 2018. https://doi.org/10.1016/j.ifacol.2018.05.092
[30] T. Baroš and T. Stojanović, "Geographic Information System (GIS) in Mapping of Mine
Suspected Area in the Republic of Serpska," Glob. J. Sci. Front. Res. H Environ. Earth Sci.,
vol. 15, no. 3, pp. 0-4, 2015.
9 Authors
Bayu Utomo is a bachelor's degree from the Islamic State University of Sunan Am-
pel Surabaya, Indonesia. His current interests in Computer Science and Technology
with a concentrate on Mobile Computing.
Achmad Teguh Wibowo received a bachelor's degree in Information systems from
STIKOM, Surabaya, Indonesia, in 2010. He received his Master of Electrical Engineer-
ing from Brawijaya University, Malang, Indonesia in 2013. He joined the Information
System Department as a lecturer at the Islamic State University of Sunan Ampel, Sura-
baya. Indonesia since 2014. His current interests are Blockchain, and Intelligent Sys-
tem. He is currently pursuing a Ph.D. degree at Institut Teknologi Sepuluh Nopember
(ITS), Surabaya, Indonesia, since 2019.
Mujib Ridwan received a bachelor's degree in Information Technology from Is-
lamic State University of Maulana Malik Ibrahim, Malang, Indonesia, in 2009. He re-
ceived his Master of Electrical Engineering from Brawijaya University, Malang, Indo-
nesia in 2013. He joined the Information System Department as a lecturer at the Islamic
State University of Sunan Ampel, Surabaya. Indonesia since 2014. His current interest
in Data Mining and Deep Learning.
Muhammad Andik Izzuddin received a bachelor's degree in Information and Tech-
nology Education from Universitas Negeri Malang (UM), Malang, Indonesia in 2011.
He received his Master of Electrical Engineering from Institute of Technology Bandung
(ITC), Bandung, Indonesia in 2013. He joined the Information System Department as
iJIM ‒ Vol. 14, No. 16, 2020 115
https://github.com/react-native-community/react-native-maps
https://github.com/react-native-community/react-native-maps
https://developers.google.com/maps/documentation
https://developers.google.com/maps/documentation
https://doi.org/10.1080/13658810701626343
https://doi.org/10.1080/17513472.2011.634320
https://doi.org/10.1109/EECSI.2017.8239130
https://doi.org/10.23919/ELINFOCOM.2019.8706406
https://doi.org/10.1109/%0bJSTARS.2014.2315593
https://doi.org/10.1109/%0bJSTARS.2014.2315593
https://doi.org/10.1016/j.ifacol.2018.05.092
Paper—Enhanced of Attendance Records Technology used Geospatial Retrieval based on Crossing …
a lecturer at the Islamic State University of Sunan Ampel, Surabaya. Indonesia since
2014. His current interests are Computer Networks, and Internet of Things.
Agustinus Bimo Gumelar received the Bachelor's degree in Industrial Engineering
from Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia, in 2004 and the
Master Degree in Electrical Engineering from Institut Teknologi Sepuluh Nopember
(ITS), Surabaya, Indonesia in 2010 with Honour. In 2019, He is currently working to-
ward the Ph.D. degree in Electrical Engineering at Institut Teknologi Sepuluh Nopem-
ber (ITS), Surabaya, Indonesia. He is a lecture in the Faculty of Computer Science at
the Narotama University, Surabaya, Indonesia. His research interests include auditory-
based neuroscience and affective computing, signal processing, and computational in-
telligence.
Sirajul Arifin is an Associate Professor in the Faculty of Islamic Economics and
Business UIN Sunan Ampel Surabaya. He completed both bachelor's degrees at IAI
Ibrahimy Situbondo and the University of Indonesia Jakarta. The first degree is in Is-
lamic Economic Law, whereas the second is Information Sciences. In 2002 he obtained
a Master of Islamic Economics from IAIN Sunan Ampel Surabaya. Six years later he
pursued a Ph.D. degree in Islamic Economics at UIN Sunan Kalijaga Yogyakarta and
completed in 2014. Now he serves as Vice Dean for Academic and Institutional Affairs
at the Faculty of Islamic Economics and Business UIN Sunan Ampel Surabaya.
Article submitted 2020-02-24. Resubmitted 2020-07-11. Final acceptance 2020-07-12. Final version pub-
lished as submitted by the authors.
116 http://www.i-jim.org