Journal of Information Technology and Computer Science Volume 8, Number 2, August 2023, pp.60-71
Journal Homepage: www.jitecs.ub.ac.id
Development of Robotic Process Automation for Scraping Prospective Customer Data and
Implementation Haversine in PT. XYZ
Ninna Novila1, Andi Wahyu Rahardjo Emanuel *2, Stephanie Pamela Adithama3
1,2,3Atma Jaya University, Yogyakarta
1[email protected], 2[email protected], 3[email protected]
*Corresponding Author
Received 02 August 2022; accepted 17 April 2023
Abstract. PT. XYZ is a finance company that engages in lending. Its activities include branch offices, aiming to increase new customer acquisition. Prospective customer data is required to determine their potential and the location of the nearest branch office. PT. XYZ previously used a third party, but the result was not equal to what was required, and the company was forced to pay to obtain data equal to what was required. Google Maps is a data source that is utilized and freely accessible. This study scraped prospective customer data using robotic process automation. Robotic process automation was developed using UiPath with Google Maps and Plus Codes as data sources. The Haversine method is used to determine the location of the nearest branch office. 595 prospective customer records were obtained through robotic process automation using seven search keywords. The result for 579 prospective customers is correct, but the result for 16 is incorrect. Haversine Method applies to 582 prospective customer records;
however, the remaining records lack latitude and longitude, making Haversine Method inapplicable.
Keywords: robotic process automation, scraping, Haversine method, prospective customer data, UiPath
1 Introduction
PT. XYZ is one of Indonesia's largest banking companies. The company engages in lending and borrowing. This loan requires the borrower to make periodic payments in addition to the interest [1]. The lending and borrowing activities of the company are conducted through its branch offices. The branch office is directly responsible to the head office in this activity. When conducting lending and borrowing activities, a specific objective must be met: the addition of new customers. Customers are individuals or businesses that utilize banking services [2]. It is necessary to understand prospective customers to determine them. Collecting data on prospective customers enables the identification of potential customers. This data will aid in analyzing lending and borrowing activities and identifying potential customers for the company's branch offices.
Obtaining data has become easier due to the presence of a computer with internet access. This observation is demonstrated by data from We Are Social and Hootsuite, which indicate that most of the world's population has developed an interest in the internet and social media [3]. Ease of access to the internet will be used to obtain data on prospective customers. Previously, PT. XYZ collected data on prospective
Ninna Novila., et al. Development of Robotic Process... 61 customers through third-party tools, specifically Octoparse and Botsol. These tools' features are insufficiently practical, do not meet user requirements, and have many limitations. One of the limitations is that some data can't obtain by these tools, such as phone number data. To obtain this data, PT. XYZ must upgrade to the paid version.
With these constraints, businesses search for alternate, comprehensive, and cost-free data sources. Google Maps is one of the data sources which is freely accessible.
Additionally, these third-party tools cannot provide a feature that allows users to determine the location of the nearest branch office. As a result, obtaining prospective customer data without requiring payment is required.
This study will examine the methods implemented to address PT XYZ's difficulties acquiring prospective consumer data. Using Google Maps and Plus Code as free data sources, a system will be constructed. Google Maps is a Google product. Two hundred million businesses and places are registered on Google Maps [4]. Additionally, Plus Codes is used as a data source. This application displays numbers and letters by latitude and longitude [5]. Latitude and longitude are obtained using Plus Codes. We can get customer information using the data scraping idea by integrating these two techniques. Data scraping, or web scraping, is a technique for collecting irregular data from websites [6]. Scraping requires the website address to be accessed. The required website address can be obtained by utilizing one of the UiPath features, a robotic process automation development application. Robotic process automation (RPA) is a term that refers to software that has been developed to automate previously designed business processes. It is a collection of processes, activities, transactions, and tasks in one or more unrelated software applications that produce results without human intervention [7].
By combining these two techniques, we can gather consumer data using the notion of data scraping. RPA utilizing UiPath has been the subject of prior research.
Marcu attempted to acquire data on job openings from the LinkedIn website for his research. Using UiPath's data scraping feature, [10] data collecting was conducted.
Another study by K. C. Moffitt illustrated the application of RPA to the auditing procedure [7]. Additional research investigates the strategies and processes adopted by adopting BFSI sector organizations when implementing a touted technology like RPA [15]. Romao et al. investigate the benefits and hazards of employing RPA-enabled Business Process Management (BPM) systems in banking [16], Raza et al.
recommended RPA in financial services businesses in light of the security event [17], and. Williams et al. contend that the RPA system they built automates the annual compliance procedure and ad hoc client queries for Singaporean corporate service providers [18].
According to various studies that have deployed RPA in the finance sector, there has been no research utilizing RPA to collect prospective client data. This study is innovative in that it implements the concept of data scraping using RPA to aid business operations in a financial services organization in obtaining prospective client data for free. In this study, the authors will use RPA to collect data on prospective customers using the UiPath application. Google Maps and Plus Codes are used to collect data on prospective customers. The customer in question is a business entity, not an individual.
Because lending and borrowing require branch offices, having the branch office closest to the prospective customer's location is necessary. The author uses the Haversine Method to calculate the distance between the branch office and prospective customers to determine the nearest branch office. The final result of robotic process automation is an excel file containing information about prospective customers and the location of the nearest branch office.
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2 Research Methods
The experiment's findings will assist the agent's office to gather data on prospective customers. The data is obtained through data scraping, which is automated using RPA. Prospective customer data will be analyzed to determine potential customer value. Agent offices will communicate with sub-branch offices, main branch offices, and the nearest regional office based on scraping results for potential customers.
Identifying potential customers will aid in achieving the goal of adding new customers.
In automating the data scraping process, it is necessary to prepare a file containing the keywords to be scraped and a file containing information about the company's branch offices to determine the nearest office.
The author used various research methods in conducting this study, including the following: (1) The Literature Study Method: the author reads journals and books to gain knowledge about data scraping, robotic process automation, and the Haversine Method. Additionally, the author learns about UiPath, a tool for developing robotic process automation. (2) Analysis: the author analyzes to understand the UiPath tools used. The outcome is understanding the UiPath features, the work process, and the overall system flow that will be created. (3) Program Design: the author designs a previously analyzed program. At this point, it will generate an RPA flowchart that will be implemented later. (4) Implementation: the author will convert the previous design into a robotic process automation system using UiPath. This implementation results in robotic process automation used to scrape prospective customer data and implement the Haversine Method, with the output being an excel file. (5) Testing; during this stage, the author validates the robotic process automation used to scrape prospective customer data and implement the Haversine Method. (6) Evaluation: The author will analyze this stage's developed robotic process automation results. At this point, you should understand the advantages of robotic process automation.
Before developing RPA, the following tools must be prepared: (1) UiPath, the software used to develop RPA. UiPath includes drag-and-drop capabilities for developing RPA. (2) Operating System (Windows 10) is used to develop RPA. (3) Web Browsers (Google Chrome), the web browser that opened Google Maps. (4) Microsoft Office (Microsoft Excel) is the RPA's input and output. (5) Text (Notepad) as the RPA's input and output. (6) Command Prompt (cmd) executes Python scripts. (7) Visual Studio Code for Python script development. (8) Python 3.x, to execute Python scripts.
(9) ChromeDriver and Selenium WebDriver allow Python scripts to run while web scraping.
2.1 Data Scraping
Data is a collection of meaningless text, numbers, and symbols. A collection of data that has been processed and grouped can produce meaningful and understandable information. The information gathered can be used to establish facts or to solve problems. The data has been transformed into knowledge [8]. Knowledge is required to solve problems and make decisions. Data scraping, alternatively referred to as web scraping, is a technique for collecting irregular data from websites [6]. This irregular data will be transformed into more regular data. Web scraping is also called web harvesting, data extraction from the web, and data mining on the web.
2.2 Robotic Process Automation
According to a page on uipath.com, robotic process automation is an easy-to- build, develop, and maintain device technology for managing robot software that aims
Ninna Novila., et al. Development of Robotic Process... 63 to imitate human interaction with software [9][10]. Meanwhile, according to Automation Anywhere, RPA enables humans to create software robots that perform business processes. Additionally, you can think of this RPA as a digital worker; by informing the bot of the assigned task, the bot will complete it [11]. RPA's understanding of robots does not result in a physical form but in robot-based software (bots).
2.3 UiPath
UiPath is one of the tools for developing RPA. UiPath was founded in 2005 in Romania as DeskOver under the leadership of Daniel Dines. UiPath continues to expand each year through the addition of new offices. UiPath collaborates with consulting firms; until 2018, it was ranked first in RPA by Forrester WaveTM and 14th in the cloud by Forbes. UiPath's growth does not stop there; the company raised Series E funding in 2020. UiPath's RPA code is written in the VB.Net programming language.
UiPath as an RPA development tool consists of three primary components: UiPath Robot, UiPath Studio, and UiPath Orchestrator. UiPath Studio is used to convert business processes or human interactions into RPA.
2.4 Haversine Method
Haversine is a method that applies trigonometric shapes, which are applied to round shapes. A scientist created a Haversine table in 1805 to calculate the distance between two points [10]. This method utilizes latitude and longitude coordinates to determine the distance between two points. Latitude is a line that determines the distance between the earth's south and north poles and the equator. At the same time, longitude determines the distance between the earth's west and east poles relative to the meridian. The Haversine Method's formula is as follows [12]:
𝑑 = 2𝑟𝑠𝑖𝑛−1(√𝑠𝑖𝑛2(𝜙2−𝜙1
2 ) + cos(𝜙1) cos(𝜙2) 𝑠𝑖𝑛2(𝜓2−𝜓1
2 ) ) (1)
𝑑 = distance result 𝑟 = earth radius 𝜙1= first latitude point 𝜙2= second latitude point 𝜓1= first longitude point 𝜓2= second longitude point 2.5 Google Maps
Google Maps was officially launched on 8 February 2005, sixteen years ago.
Google Maps was initially launched to assist people in getting from point A to point B.
Google Maps became available for mobile devices in 2008. Google My Business, launched in 2014, enables business owners to publish information about their organization, photos, and reviews on Google Maps [5]. A few months later, the Local Guides Program was launched, allowing users to contribute reviews, photos, and information about a location. Google Maps can be accessed for free by entering
"https://www.google.com/maps" in the URL field of a web browser. Enter the keywords you're looking for in the search field, and Google Maps will show you the most closely related results to the keywords you entered. When a search is conducted, the URL field changes to include additional keywords.
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2.6 Plus Codes
Plus Codes is an address for an unknown location [4]. Compared to addresses composed of numbers and names, Plus Codes are composed of latitude and longitude, consisting of letters and numbers. Plus Codes were introduced in 2015, and their combination is shorter than latitude and longitude. Plus Codes has several characteristics, including the following: (1) It is free and open-source; (2) It can be accessed offline; (3) It is simple to use; (4) It is short and simple, and (5) It is easily identifiable due to the presence of a "+" symbol. Plus Codes are grid-based. Distinct alphanumeric characters identify the grids. There are two types of Plus Codes: those with four characters preceding the "+" symbol (short Plus Codes) and those with eight characters preceding the "+" symbol (long Plus Codes). On Google Maps, short Plus Codes are displayed. To obtain the long Plus Codes, navigate to
"https://plus.codes/map." Then, using short Plus Codes obtained from Google Maps, search. The search returns short Plus Codes, long Plus Codes, latitude, and longitude.
3. Results and Discussion
A flowchart is created to assist in the design of the RPA which can be seen in Figure 1. The flowchart will help in the creation of the RPA. This study has several flowcharts: (1) The Data Scraping and Haversine Method Implementation Flowchart is the RPA's main flowchart [11]. This flowchart has several flowcharts. This flowchart will verify the branch office and keyword data used. It also checks the status of each keyword to determine the next flow. (2) The URL for the flowchart is obtained from Google Maps. This flowchart will conduct a Google Maps keyword search and then scrape the URL for each result. When searching for keywords, no results may match the keywords; thus, URL scraping will not be found. If the URL is found, it will scrape it. The flowchart's result is the "url.txt" file containing the URL. (3) Flowcharts get data with Python. We will scrape data in this flowchart using an existing Python script [13]
[14]. Python scripts are executed via the command prompt by typing commands that the command prompt recognizes. The final output of this flowchart is the "result.txt"
file, which contains the Google Maps scraping results. (4) The flowchart converts text to excel. This flowchart is intended to organize prospective customer data obtained from Google Maps. Prospective customer data must be organized in an easily readable excel format. This flowchart takes as input "result.txt" and outputs "result.xlsx." (5) Flowcharts get data from Plus Codes. This flowchart will look for prospective customer data not obtained from Google Maps scraping. The missing data will be searched for in Plus Codes. The data search used the Plus Code extracted from Google Maps scraping.
The data is plus code complete, latitude, and longitude. The final result of this flowchart is in the "result.xlsx" file. (6) Flowchart of getting the nearest office. This flowchart will calculate the distance between the branch office and the prospective customer's location. The Haversine method was used to perform the calculations. The nearest branch office will be determined based on the calculation results and the prospective customer's location. The final result of this flowchart is contained in the "result.xlsx"
file.
Two parameters determine the success of this research: the data collected from prospective customers is appropriate and relevant to the assigned task, and the Haversine Method is implemented accurately. This method refers to the suitability of data from Google Maps and Plus Codes and the output in Excel format. The address
Ninna Novila., et al. Development of Robotic Process... 65 column contains the correct address obtained from Google Maps, Plus Codes, and other fields. The result's suitability determines the accuracy of the Haversine Method implementation for distance calculation using Google Maps features. The author uses seven keywords in running robotic process automation. These seven keywords are anticipated to generate more than 1,000 prospective customer records. Figure 2, the
"Keywords" column contains a list of searched keywords. There are five columns in Figure 2, labeled "No," "Start Time," "Finish Time," "Keywords," and "Status."
Figure 1. Flowchart for RPA Process
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Figure 2. Input for RPA Process
The "Start Time" and "Finish Time" columns indicate when robotic process automation will begin and scrap prospective customer data using these keywords. The following column contains the search terms, which are "PT Indofood CBP Makmur,"
"PT Nestle Indonesia Factory," "PT Unilever Indonesia,"
"asfdjhagslfjkhasIFHIOJHKDFA," "Pabrik di Bekasi," "Toko Kesehatan di Tangerang," and "Kantor di Sudirman."
The status column in Figure 3 has been filled, indicating that the robotic process automation process has been completed for scraping prospective customer data. As can be seen in the "Status" column, there are some distinctions between the statuses containing "SCRAPING COMPLETE," "NOT FOUND," and "ERROR." In Figure 3, the result of the seven-word search, five keywords have the status "SCRAPING COMPLETE," one keyword has the status "NOT FOUND," and one keyword has the status "ERROR."
Figure 3. Result of the RPA Process
Each status has a distinct meaning based on the developed robotic process automation flowchart. The status "SCRAPING COMPLETE" indicates that the flow is proceeding normally until an excel file is produced. The "NOT FOUND" status indicates no matching results were found during a Google Maps search. The "ERROR"
status indicates an error occurred during the data scraping process. Errors can be discovered during the implementation of robotic process automation. In Figure 4, an error occurs because the element that appears is different from the element previously indicated or the element is not found.
When the status "SCRAPING COMPLETE" is set, the flow produces several files, including "url.txt," "result.txt," and "result.xlsx." The "url.txt" file contains a URL pointing to the website address containing the prospective customer's information, and the URL is required in the Python script to scrape data from prospective customers.
Meanwhile, the "result.txt" file contains the raw output of the python script, which contains prospective customer data. Finally, the "result.xlsx" file contains the outcome of scraping prospective customer data and applying the Haversine Method. The final
Ninna Novila., et al. Development of Robotic Process... 67 file contains modified prospective customer data by the required prospective customer data. The following Table 1 summarizes the amount of data collected on prospective customers.
Figure 4. RPA is Unable to Locate an Element Table 1. Number of Prospective Customer Data Results
No Keywords File
"url.txt"
File
"result.txt"
File
"result.xlsx"
1 PT Indofood CBP Makmur 43 43 43
2 PT Nestle Indonesia Factory 4 4 4
3 PT Unilever Indonesia 4 4 4
4 asfdjhagslfjkhasIFHIOJHKDFA 0 0 0
5 Factory in Bekasi 254 254 254
6 Health Shop in Tangerang 290 290 290
7 Office in Sudirman 182 136 0
Total 777 731 595
Figure 5 displays the results of a search for the keyword "PT Nestle Indonesia Factory" in the "url.txt" file. The image shows that the "url.txt" file contains the URL used in the scraping Python script.
Figure 5. Result "url_PT Nestle Indonesia Factory.txt"
The results in the "result.txt" file for the keyword "PT Nestle Indonesia Factory"
are displayed in Figure 6. As seen in the image, the "result.txt" file contains raw data representing the output of the Python script. A "||" eases the process of modifying the final result.
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Figure 6. Result "result_PT Nestle Indonesia Factory.txt"
The results in the "result.xlsx" file for the keyword "PT Nestle Indonesia Factory" are displayed in Figure 7. The data in this final result has been modified to make it readable.
Figure 7. Result "result_PT Nestle Indonesia Factory.xlsx"
While scraping prospective customer data, several errors were discovered. As illustrated in Table 1, data was discovered to have inconsistent patterns. These different patterns result in inconsistencies between the collected data and the column headings.
This discrepancy is illustrated in Figure 8, one of the search results for the keyword "PT Indofood CBP Makmur."
Figure 8. Incorrect Results from "PT Indofood CBP Makmur"
The discrepancy in Figure 8 is in the column labeled "City / Regency," which contains "Kota Bukit Indah Raya No. Kav.2-5". The column should contain
"Karawang" because the "Address" column contains "Kabupaten Karawang." The discrepancy may occur because the data discovered has a new pattern, making the RPA method of segregating addresses incompatible with address patterns that contain multiple keywords (City, Regency, Regency).
Along with scraping prospective customer data, this study identifies the branch office closest to the Haversine Method implementation. The following is an implementation of the Haversine Method's formula.
dbDistance = 2 * Math.Asin(Math.Sqrt((Math.Sin(((Convert.ToDouble(dbLat2) - dbLat1) * (Math.PI / 180))/2))^2 + (Math.Cos(dbLat1*(Math.PI / 180)) * Math.Cos(Convert.ToDouble(dbLat2)*(Math.PI / 180)) *
(Math.Sin(((Convert.ToDouble(dbLong2) - dbLong1) * (Math.PI / 180))/2))^2))) * 6371
The author converts the formulas to UiPath's native language, C# (VB.Net). The variables used in the formula are as follows:
dbDistance = distance from both calculated places, dbLat1 = latitude of the place obtained from scraping, dbLong1 = longitude of the place obtained from scraping, dbLat2 = latitude of the branch office,
dbLong2 = longitude of the branch office.
The radius of the earth is 6371 kilometers.
Ninna Novila., et al. Development of Robotic Process... 69 The Haversine Method can be applied to 582 prospective customer records, while the remaining 13 lack latitude and longitude. The proof that the Haversine Method was implemented correctly was manually verified using Google Maps.
Under the business name "PT. Indofood CBP Sukses Makmur Seasonings Division" and at the address "Jalan Raya Tugurejo KM 10.2 No. 199, Tugurejo, Tugu, Kec. Tugu, Semarang City, Central Java 50151," the nearest KCP 1 is KCP XYZEF, and the nearest KCP 2 is KCP XYZEC. As illustrated in Figure 9, the distance to the nearest KCP 1 is 1.49 kilometers. As illustrated in Figure 10, the distance to the Nearest KCP 2 is 3.07 kilometers.
Figure 9. Distance to KCP XYZEF
Figure 10. Distance to KCP XYZEC
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These results indicate that the distance to the Nearest KCP 1 is closer than that of the Nearest KCP 2.Data in Table 2 is the number of prospective customer data that can be used to find the nearest branch office by implementing the Haversine Method.
This data can be seen in the "Information" column.
Table 2. Final Result Number of Prospective Customer Data
No Keywords A B C
1 PT Indofood CBP Makmur 43 0 0
2 PT Nestle Indonesia Factory 4 0 0
3 PT Unilever Indonesia 3 1 0
4 asfdjhagslfjkhasIFHIOJHKDFA 0 0 0
5 Factory in Bekasi 249 5 0
6 Health Shop in Tangerang 283 7 0
7 Office in Sudirman 0 0 0
Total 582 13 0
A = "-",
B = "Does not have Latitude and Longitude,"
C = "Nearest Branch Office Not Found."
Experiments conducted with 7 keywords resulted in 5 keywords with the status
"SCRAPING COMPLETE", 1 keyword with the status "NOT FOUND", and 1 keyword with the status "ERROR". This indicates that the RPA runs normally for 5 keywords while the other 2 are not. From these 5 keywords, the results obtained were 595 prospective customer data. In addition, out of 595 prospective customer data, 582 were used to find the nearest branch office with the implementation of the Haversine Method. 13 prospective customer data from 595 did not have latitude and longitude, so they could not implement the Haversine Method to find the nearest branch office.
4. Conclusions and Suggestions
According to the author's research, several conclusions have been drawn, including that robotic process automation has been successfully developed for scraping prospective customer data from Google Maps and Plus Codes data sources. There are 595 of the seven keywords gathered from prospective customers, and the Haversine method applies to 582 prospective customer data. 13 prospective customer data lack latitude and longitude information, so they could not implement the Haversine Method to find the nearest branch office. Along with the conclusions, several recommendations are made, including maintaining internet stability, optimizing the Python script used, and conducting continuous development when new data patterns for prospective customers are discovered.
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