Visi Institut : Menjadi Perguruan Tinggi yang unggul dibidang sains dan teknologi dengan reputasi internasional.
Visi Fakultas : Fakultas yang unggul dibidang aplikasi teknologi dengan reputasi internasional.
YAYASAN PEMBINA POTENSI PEMBANGUNAN INSTITUT SAINS & TEKNOLOGI AKPRIND
FAKULTAS TEKNOLOGI INDUSTRI
Jl. Kalisahak 28 Komplek Balapan Yogyakarta 55222 Telp (0274) 563029 ext 113 Fax (0274) 563846 email: [email protected]
Petikan
SURAT KEPUTUSAN DEKAN FAKULTAS TEKNOLOGI INDUSTRI
INSTITUT SAINS & TEKNOLOGI AKPRIND YOGYAKARTA Nomor : 003 /SK/Dek/FTI/IX/2021
Tentang:
PENUGASAN DOSEN DALAM PENYUSUNAN LUARAN PENELITIAN ATAU PENGABDIAN KEPADA MASYARAKAT PADA SEMESTER GANJIL
TAHUN AKADEMIK 2021/2022
DEKAN FAKULTAS TEKNOLOGI INDUSTRI
MENIMBANG : 1. Bahwa Dosen di Fakultas Teknologi Industri Institut Sains & Teknologi AKPRIND Yogyakarta diwajibkan menyusun luaran penelitian maupun pengabdian kepada masyarakat, yang dapat berupa Publikasi Ilmiah Jurnal atau Seminar serta Kekayaan Intelektual.
2. Bahwa untuk pelaksanaan tugas tersebut perlu diberi penugasan melalui Surat Keputusan Dekan.
MENGINGAT : 1. Undang- undang nomor 20 tahun 2003 tentang Sistem Pendidikan Nasional pasal 20.
2. Undang-undang nomor 12 tahun 2012 tentang Pendidikan Tinggi pasal 45 3. Peraturan Menteri Riset, Teknologi dan Pendidikan Tinggi
(Permenristekdikti) Republik Indonesia nomor 44 tahun 2015 tentang Standar Nasional Pendidikan Tinggi pasal 1.
4. Buku Panduan Dosen Institut Sains & Teknologi AKPRIND Yogyakarta Tahun 2017.
MEMPERHATIKAN : Tugas dan Kewajiban Dosen dalam Pelaksanaan Tri Dharma Perguruan Tinggi.
MEMUTUSKAN
MENETAPKAN : Surat Keputusan Dekan tentang "Penugasan Dosen dalam Penyusunan Luaran Penelitian atau Pengabdian Kepada Masyarakat pada Semester Ganjil Tahun Akademik 2021/2022
Surat Keputusan ini berlaku sejak tanggal ditetapkan dan apabila dikemudian hari ternyata terdapat kekeliruan dalam Surat Keputusan ini, akan dibetulkan sebagaimana mestinya.
Dikeluarkan di : Yogyakarta Pada Tanggal : 6 September 2021 Dekan
Ir. Murni Yuniwati, M.T.
NIK. 88.0661.344.E Petikan disampaikan :
Kepada Yth.
Argaditia Mawadati, S.T., M.Sc.
Dosen FTI-IST AKPRIND Yogyakarta
I am happy to inform you that your paper [14]
“Sales Pattern Identification for Marketing Strategies Using Data Mining”
has been accepted for presentation at the 2
ndInternational Conference on Engineering Science and Technology (virtual conference). Please confirm receipt of this message, and follow instruction for camera ready and payment as mention in icest.id.
I have attached the reviewers' comments, which I hope you will find helpful in revising your paper. Instructions for producing the camera-ready copy, and for preparing a talk for the workshop, appear below. Much of this information, plus the preliminary technical program and registration information, is available via the http://icest.id/
I look forward to seeing you in 15 September for the virtual international conference. Thank you for submitting to ICEST 2021, and please accept our congratulations on your paper's acceptance.
Sincerely,
Ellyawan Arbintarso
Chairman ICEST 2021
FORMATTING YOUR CAMERA READY PAPER
The papers of ICEST 2021 will be electronically submitted in the Journal of Physics:
Conference Series (tbc). The accepted papers must exactly fulfill the Journal of Physics: Conference Series requirements regarding the final submitted version.
The maximum length is ten (10) pages. Please follow the following steps strictly:
All papers submitted to the ICEST 2021 must be written in English and formatted in the standard Journal of Physics: Conference Series (A4 size).
Please download the template in Word as a guideline. Failing to conform to the standard format will result in rejection.
Generate a PDF version of your final paper by using PDF.
Click on the “New Submissions” menu item at the top of the page; it will produce a list of all your papers on the easychair system.
Click on the title of the paper that you want to submit. It will take you to the individual paper page that contains all details of the paper.
Ensure that all author information is stored correctly on Author
Information and matches the information on the PDF file, and click on the
“Submit” button.
Use the links at the top-right corner for updating information, click on the button to change the information or add files.
More information, please visit http://icest.id/
Sales Pattern Identification for Marketing Strategies Using Data Mining
Argaditia Mawadati
1, a)Andrean Emaputra
1, b)and Kartinasari Ayuhikmatin Sekarjati
1, c)1Departement of Industrial Engineering, Institut Sains & Teknologi AKPRIND Yogyakarta
a)
Corresponding author: [email protected]
b)
[email protected]
c)
[email protected]
Abstract. The development of the culinary business, such as food stalls, is increasing rapidly. This causes the competition to become even tighter. Food stalls must have targeted and precise strategies to survive and compete. Identifying the customer’s preference is one of the strategies that can increase sales and retain customers. The customer’s preference can be observed through purchase transaction data. Observation of sales transaction data in a simple way does not always get effective results because of the large volume of data processed and the difficulty in finding the association between one menu sales and another. With data mining, this problem can be overcome. In this study, the data mining method used is the Apriori Algorithm. The application of the apriori algorithm helps in forming candidate item combinations that may occur. Thus, it can be seen the relationship between the combination of menus that are sold, whether including the most frequently sold or not. This information can be taken into consideration to determine the next marketing strategies.
INTRODUCTION
The culinary business is growing every year. Data from the Indonesian Creative Economy Agency or BEKRAF (Badan Ekonomi Kreatif Indonesia) states that the culinary business provides one of the largest contributions to the creative economy sector. In fact, in 2018 the Ministry of Industry (Kemenperin) said that the food and beverage sector succeeded in contributing to the national gross domestic product (GDP) to 6.34%, which made the food industry one of the top five largest contributors to GDP in Indonesia (1). So that the competition among culinary businesses becomes more and more competitive over time. To be able to compete, the owner must carry out various strategies to increase sales and retain customers.
To develop strategies that can increase sales and retain customers, the owner needs to be able to identify the consumer’s preferences. To find out consumer preferences, it can be done by checking past sales data. From the sales data, it can be seen which menus are selling the best, the menus that are most often bought together, the menus that are not very popular, and so on. However, merely observing sales transaction data does not always get effective results because of the large volume of data processed and the difficulty in seeing the association between one menu sale and another. With data mining, this problem can be overcome. Data mining is the discovery of the structure of interesting, unexpected, or valuable big data sets (2).
This research was conducted on sales data of Friendzone Cafe which is one of the food stalls located in Kemusuk,
Argomulyo, Sedayu, Bantul. This stall previously sold a penyet menu such as Ayam Penyet, but later changed the
concept to a cafe to expand its target market. The menus sold at this cafe also vary, ranging from fried rice menus,
rice bowls, noodles, to steaks. The owner's decision to change this concept has succeeded in increasing the number
of visitors to this cafe. However, since the Covid-19 pandemic, cafe sales have started to decline. Sales data processing with an apriori algorithm is expected to be able to find frequent patterns of the menus that are most frequently sold. So that later a marketing strategy can be made from the results of these outputs.
In this study, the data mining method used is the Apriori Algorithm. The application of the apriori algorithm helps in forming candidate combinations of items that may occur. So that it can be seen the relationship between the combinations of menus sold, whether they are the most frequently sold or not. This information can be taken into consideration to determine the next marketing strategy.
The use of data mining on consumer buying patterns is most commonly applied to shoppers in supermarkets (3)(4)(5). One of them is research to detect products that consumers often buy simultaneously using the apriori algorithm as a basis for decisions in determining the placement of goods in areas that are close to each other according to consumer behavior in buying goods simultaneously (6). Other studies use this consumer buying pattern as support for managers' decisions in managing company activities (7). In addition to supermarkets, data mining can also be applied to the sale of other products, such as medical field (8)(9)(10)(11), determining the most desirable clothing brands in fashion models (12)(13)(14), agriculture (15)(16), etc.
METHOD
The method used in this research is data mining with an apriori algorithm. Where the apriori algorithm is included in the type of association rules in data mining. Association analysis or association rule mining is a data mining technique to find associative rules between a combination of items (17). In particular, one of the stages of association analysis that has attracted the attention of many researchers to produce efficient algorithms is high-frequency pattern analysis (frequent pattern mining). The importance of an associative rule can be determined by two parameters, namely support and confidence. Support (support value) is the percentage of the combination of these items in the database. Meanwhile, confidence (certainty value) is the strength of the relationship between items in the association rules. The basic methodology of association analysis is divided into two stages, namely determining high-frequency pattern analysis and forming association rules.
The first stage is high-frequency pattern analysis. At this stage, look for a combination of items that meet the minimum requirements of the support value in the database. The results obtained from this stage will be called Support. Support can be an item set, it can also be a combination of several itemsets. The support value of an item can be found with the following formula:
𝑆𝑢𝑝𝑝𝑜𝑟𝑡 (𝐴) =𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛 𝑐𝑜𝑛𝑡𝑎𝑖𝑛 𝑖𝑡𝑒𝑚 𝐴 𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛
Meanwhile, to find support for two items, it is obtained from the following formula:
𝑆𝑢𝑝𝑝𝑜𝑟𝑡 (𝐴 → 𝐵) =𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛 𝑐𝑜𝑛𝑡𝑎𝑖𝑛 𝑖𝑡𝑒𝑚 𝐴 𝑎𝑛𝑑 𝐵 𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛
After all the high-frequency patterns are found, the next step is to look for association rules which we then call Confidence. The confidence value of P (B|A) has a rule meaning. If A then B, it can be obtained by the formula:
𝐶𝑜𝑛𝑓𝑖𝑑𝑒𝑛𝑐𝑒 = 𝑃(𝐵|𝐴) =𝑆𝑢𝑝𝑝𝑜𝑟𝑡 (𝐴 → 𝐵) 𝑆𝑢𝑝𝑝𝑜𝑟𝑡 𝐴
The first step in this research is to collect sales data from the Friendzone Mania Cafe. The data in the form of
receipts is selected and inputted into an excel file. The attributes used from the receipt data are the date of the
transaction, the name of the menu purchased, and the number of the menu purchased. After obtaining the dataset
from the sales transaction data, then look for the support value for one item set and a combination of 2 itemsets. The
search is carried out only for up to 2 itemsets because it will be used to make recommendations for package menu
combinations. Consists of one food menu and one drink menu. The last stage is to find the association rules. From
the confidence value obtained, then selected which has a value > 50%. This combination later became the package
menu recommendation.
RESULTS AND DISCUSSION
The data used in this study is the sales data of the Friendzone Mania Cafe from January to March 2020. In this cafe, there are 62 food and beverage menus. However, for research purposes, there is one menu that is not used, namely the water drink menu. This is done with consideration because the purpose of this research is to plan a menu that can be used as the basis for a marketing strategy. While the water menu is not suitable to be included in the marketing strategy.
Itemset Formation
At this stage of forming the itemset, the aim is to capture the best-selling menus. The condition for selecting the best-selling menu is a menu that meets the minimum amount of 5% support. How to calculate the minimum support is to divide the number of transactions containing menu A divided by the total transactions. For example, the calculation is as follows:
𝑆𝑢𝑝𝑝𝑜𝑟𝑡 (𝐶ℎ𝑖𝑐𝑘𝑒𝑛 𝐹𝑟𝑖𝑒𝑑 𝑅𝑖𝑐𝑒) =𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛𝑠 𝑐𝑜𝑛𝑡𝑎𝑖𝑛𝑖𝑛𝑔 𝑐ℎ𝑖𝑐𝑘𝑒𝑛 𝑓𝑟𝑖𝑒𝑑 𝑟𝑖𝑐𝑒
𝑡𝑜𝑡𝑎𝑙 𝑡𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛𝑠 × 100%
𝑆𝑢𝑝𝑝𝑜𝑟𝑡 (𝐶ℎ𝑖𝑐𝑘𝑒𝑛 𝐹𝑟𝑖𝑒𝑑 𝑅𝑖𝑐𝑒) = 36
207× 100% = 17%
The calculation is carried out on the entire menu, then the results of the calculation of the support value for the entire menu are obtained, as can be seen in table 1.
TABLE 1
.
Recap of Support Value Calculation ResultsNumber Menu Qty Support (%)
1 Chicken Fried Rice 36 17%
2 Jumbo chicken fried rice 3 1%
3 Shrimp fried rice 6 3%
…
207 Blueberry squash 2 1%
From the results of the calculation of the support value, it can be concluded that not all menus meet the minimum support value. For example, as can be seen in table 1, the jumbo chicken fried rice menu only has a support value of 1% and so on. A recap of the menu that meets the minimum support value can be seen in Table 2
TABLE 2. Recap Menu 1-itemsets Meets Support
No Menu Name S upport
(%)
No Menu Name Support
(%)
1 Chicken fried rice 17% 17 chicken cordon bleu 8%
2 seafood fried rice 23% 18 Crispy Mushroom Steak 6%
3 jumbo seafood fried rice 5% 19 Crispy Beef Steak 7%
4 Jumbo Sausage Fried Rice 6% 20 french fries 9%
5 white rice 13% 21 ur crispy jam 9%
6 plain fried noodles 6% 22 pray 10%
7 seafood fried noodles 5% 23 fried chicken 6%
8 boiled noodles bia sa 5% 24 ice tea 17%
9 ordinary kwetiau 17% 25 t e h hot 16%
10 seafood kwetiau 12% 26 hot orange 12%
11 fried chapcay 10% 27 Orange juice 14%
12 chapcay gravy 5% 28 lemon tea ice 6%
13 crispy prawns 9% 29 guava juice 6%
14 crispy squid 8% 30 Mango juice 8%
15 sweet and sour squid 12% 31 water 8%
16 Crispy Chicken Steak 24%
Combination of 2 Itemset
From the menu that meets the minimum support as shown in table 2, a list of the combinations of the two menus in the sales transaction is made. This result is used to calculate the frequent 2 itemsets, by dividing the transactions containing menus A and B divided by the total transactions. Just like the previous stage, at this stage, the menu combinations that meet the minimum 30% support are also selected. The calculation results can be seen in Table 3.
TABLE 3. Recap Menu 2 -Itemset Meets S upport
Number Menu Name Support t (%)
1 seafood fried rice, crispy chicken steak 5%
2 N ation fried seafood , Ice oranges 4%
3 Crispy Chicken Steak , white rice 7%
5 Crispy Chicken Steak , Iced tea 5%
6 Crispy Chicken Steak , hot orange 5%
7 Crispy Chicken Steak , orange ice 5%
8 Crispy Chick en Steak , water 5%
9 Crispy beef steak , iced tea 4%
\In the next stage, the L2 cross item process is again carried out to form a candidate itemset containing 3 menus, then the support is calculated again. However, none of the itemset combinations of these 3 menus meets the minimum support requirements. So that the formation of 4-itemset cannot be done. Then the iteration process of the a priori algorithm stops here
Formation of Association Rules
After all the high-frequency patterns are found, from all the itemsets formed, then the separation is carried out into antecedent and consequent. It aims to determine all possible association rules that can be formed. The results of the recap of the results of the calculation of confidence can be seen in table 4.
TABLE 4. Confidence Value Calculation Recap
Number Menu Name Confidence
1 seafood fried rice => crispy chicken steak 22%
2 crispy chicken steak => seafood fried rice 21%
3 Seafood fried rice => orange ice 17%
5 orange juice => seafood fried rice 29%
6 white rice => crispy chicken steak 29%
7 crispy chicken steak => white rice 54%
8 crispy chicken steak => iced tea 21%
9 es the => crispy chicken steak 29%
10 crispy chicken steak => hot orange 21%
11 hot orange => crispy chicken steak 42%
12 crispy chicken steak => orange ice 21%
13 ice orange => crispy chicken steak 36%
14 crispy chicken steak => water 63%
15 water => crispy chicken steak 21%
16 crispy beef steak => iced tea 57%
The specified minimum confidence is 50%, so items that have a confidence value of less than 50% are eliminated.
So that the applicable association rules can be seen in table 5.
TABLE 5. Association Rules Apply
Number Menu Name Confidence
1 crispy chicken steak => white rice 54%
2 crispy chicken steak => water 63%
3 crispy beef steak => iced tea 57%
So from the association rules data shown in table 5, it can be concluded that if a buyer buys crispy chicken steak, 54% of buyers will also buy white rice. If buyers buy crispy chicken steak, 63% of buyers will buy water, and 57%
will choose iced tea as their drink menu. From this data, the owner can make package menus as a marketing strategy to increase sales of menus that are selling well to customers. Package menus can consist of a combination of two menus that are proven to be purchased together with crispy chicken steak with white rice, crispy chicken steak with iced tea, and chicken steak with water.
Another strategy that can be done besides increasing sales of menus that are already selling well is to create special packages consisting of a combination of menus that are selling well and additional menus that are not selling well.
This aims to boost sales of menus that are not selling well. The determination of menu combinations is made by considering the suitability of the combination between one menu and another. For example, combining crispy chicken steak with iced tea and french fries, or combining crispy chicken steak with iced tea and sausage, and so on.
Another strategy that can be done is to change the menus that are not in demand with the menus that are currently trending. Because the Friendzone Mania Cafe itself carries the theme of a modern food stall with a market share of families and young people, the drink menu can be changed or added to the drink menu that is currently trending.
Like the contemporary coffee menu that combines coffee with milk and additional flavors. Or you can also make a menu of variations of Thai Tea and Boba. This can be considered by the owner because the location of the Friendzone Cafe is quite close to Mercu Buana University Yogyakarta and several junior high schools, high schools, and vocational schools. So that these contemporary menus can attract student buyers or students who are in junior high and high school.
CONCLUSION
From the results of the previous description of eating, it can be concluded that the use of the Apriori Algorithm in purchase transaction data can show the pattern of menu combinations that are most in-demand by food stall buyers. The results of the menu combination pattern can then be used as the basis for making package menus to further increase sales of the best-selling menus and boost sales of menus that are less in demand. Another marketing strategy that can be done to attract more visitors or buyers at food stalls is to add menus that are trending among young people. Because the location of the food stalls is quite close to campus and several schools.
In further research, it can be developed by making applications that can show patterns of association rules. So that when the owner inputs transaction data, recommendations for menu combinations can immediately appear that are potential to be used as marketing strategies. This application can be used as an evaluation material to detect menus that are still selling and those that are not selling so that the next latest strategy can be made
ACKNOWLEDGMENTS
The authors gratefully express their gratitude for the financial support provided by the Institute for Education and Community Development (LPPM) IST AKPRIND Yogyakarta.
REFERENCES
1. Agung, Firsto. Industri Kuliner Jadi Penopang Terbesar Perekonomian Kreatif Indonesia [Internet].
Www.Ugm.Ac.Id. 2019. p. 1. Available from: https://www.ugm.ac.id/id/berita/18389-industri-kuliner-jadi- penopang-terbesar-perekonomian-kreatif-indonesia
2. Hand DJ. Principles of Data Mining. Drug Saf [Internet]. 2007;30(7):621–2. Available from:
http://link.springer.com/10.2165/00002018-200730070-00010
3. Liao S, Chu P, Chen Y, Chang C-C. Mining customer knowledge for exploring online group buying behavior. Expert Syst Appl [Internet]. 2012 Feb;39(3):3708–16. Available from:
https://linkinghub.elsevier.com/retrieve/pii/S0957417411013753
4. Ahmed SR. Applications of data mining in retail business. In: International Conference on Information Technology: Coding and Computing, 2004 Proceedings ITCC 2004 [Internet]. IEEE; 2004. p. 455-459 Vol.2. Available from: http://ieeexplore.ieee.org/document/1286695/
5. Gupta S, Mamtora R. A Survey on Association Rule Mining in Market Basket Analysis. Int J Inf Comput Technol [Internet]. 2014;4(4):409–14. Available from: http://www.irphouse.com/ijict.htm
6. Santoso H, Hariyadi IP, Prayitno. Data Mining Analisa Pola Pembelian Produk. Tek Inform [Internet].
2016;(1):19–24. Available from:
http://ojs.amikom.ac.id/index.php/semnasteknomedia/article/download/1267/1200
7. Rodiyansyah S. Algoritma Apriori untuk Analisis Keranjang Belanja pada Data Transaksi Penjualan.
Infotech J. 2015;1(2):236599.
8. Tampubolon K, Saragih H, Reza B, Epicentrum K, Asosiasi A, Apriori A. Implementasi Data Mining Algoritma Apriori Pada Sistem Persediaan Alat-Alat Kesehatan. 2013;93–106.
9. Ilayaraja M, Meyyappan T. Mining medical data to identify frequent diseases using Apriori algorithm. In:
2013 International Conference on Pattern Recognition, Informatics and Mobile Engineering [Internet].
IEEE; 2013. p. 194–9. Available from: http://ieeexplore.ieee.org/document/6496471/
10. Pandey SC. Data mining techniques for medical data: A review. In: 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES) [Internet]. IEEE; 2016. p. 972–82.
Available from: http://ieeexplore.ieee.org/document/7955586/
11. Xing Y, Wang J, Zhao Z, Gao A. Combination Data Mining Methods with New Medical Data to Predicting Outcome of Coronary Heart Disease. In: 2007 International Conference on Convergence Information Technology (ICCIT 2007) [Internet]. IEEE; 2007. p. 868–72. Available from:
http://ieeexplore.ieee.org/document/4420369/
12. Zhao L, Min C. The Rise of Fashion Informatics: A Case of Data-Mining-Based Social Network Analysis in Fashion. Cloth Text Res J [Internet]. 2019 Apr 30;37(2):87–102. Available from:
http://journals.sagepub.com/doi/10.1177/0887302X18821187
13. Blasi S, Brigato L, Sedita SR. Eco-friendliness and fashion perceptual attributes of fashion brands: An analysis of consumers’ perceptions based on twitter data mining. J Clean Prod [Internet]. 2020 Jan;244:118701. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0959652619335711 14. An H, Park M. Approaching fashion design trend applications using text mining and semantic network
analysis. Fash Text [Internet]. 2020 Dec 5;7(1):34. Available from:
http://link.springer.com/10.1186/s40691-020-00221-w
15. Kaur M, Gulati H, Kundra H. Data Mining in Agriculture on Crop Price Prediction: Techniques and Applications. Int J Comput Appl. 2014;99(12):1–3.
16. ATAY CE, Burdur Z. Reporting pesticide abuse through data mining in agriculture: The case of Nazilli - Turkey. Anadolu Univ J Sci Technol Appl Sci Eng [Internet]. 2018 Sep 28;1–1. Available from:
https://dergipark.org.tr/en/doi/10.18038/aubtda.426946
17. Larose DT, Larose CD. DISCOVERING KNOWLEDGE IN DATA An Introduction to Data Mining Second Edition Wiley Series on Methods and Applications in Data Mining. IEEE Computer Society. 2014.
1 p.
Institut Sains & Teknologi Akprind Yogyakarta
Universitas Negeri Semarang
Universitas Lampung Universitas Malahayati
Universitas Batanghari
Universitas Abulyatama
https://www.teahub.io/viewwp/iixRbib_illustration-of-green-and-red-floral-art-cdr/
Developing Research to Create Prime Resources in the Industrial Revolution Era 4.0
The 2 International Conference on Engineering Science and Technology (ICEST 2021)
nd
BOOK PROGRAMME
Yogyakarta-Indonesia, September 15-16, 2021
Developing Research to Create Prime Resources in the Industrial Revolution Era 4.0
The 2nd International Conference on Engineering Science and Technology (ICEST) 2021
BOOK PROGRAMME
September 15 & 16 - IST AKPRIND Yogyakarta, Indonesia
2
Preface
Distinguished Guests and Participants,
Ladies and Gentlemen, Brothers and Sisters, and Colleagues from Universities Assalamu’alaikum Warahmatullahi Wabarakatuh
It gives me pleasure to welcome you to The 2nd International Conference on Engineering Science and Technology virtually, where academicians, professionals, and governments in the engineering fields present the latest research finding and describe science and technology in related areas.
ICEST is organized by a consortium of seven universities, namely Malahayati University (Lampung), Batanghari University (Jambi), IST AKPRIND (Yogyakarta), UNNES (Semarang), Lampung University (Lampung), Abulyatama University (Aceh) and Universiti Malaysia Pahang (Malaysia). Today, IST AKPRIND will host ICEST 2021 virtually.
This conference will focus on the theme of “Developing Research to Create Prime Resources in the Industrial Revolution Era 4.0” and is dedicated to bringing together researchers and practitioners from all over the world. The multidisciplinary theme of the conference provides an excellent forum for engineers, scientists, specifiers, researchers, academics, practitioners, and professionals to connect and share knowledge and learn about collaboration in research to create prime resources in the industrial revolution era 4.0.
Ladies and Gentlemen,
Conferences such as this provide a valuable opportunity for research scientists, industry specialists, and decision- makers to share experiences. This conference was attended by 39 (thirty-nine) presenters from Indonesia, Taiwan, Timor Leste, and of course, Malaysia, also attended by more than 100 participants. Thank you for joining us today.
3
I am grateful to many experts who have joined to share their knowledge this day. I also welcome the many representatives of governments, industry associations, and organizations who have joined us.
I am sure you will have fruitful and rewarding exchanges today. I wish you every success with this important conference, and I look forward to learning about the outcome.
Thank you.
Wassalamu’alaikum warahmatullahi wabarakatuh
Dr. Ellyawan S Arbintarso General Chair
4
Rector Speech
Your Excellency,
Professor Ron Harris, Brigham Young University, USA
Professor Rizalman Mamat, Univeristi Malaysia Pahang, Malaysia
Professor Chuen-Jinn Tsai, National Yang Ming Chiao-Tung University, Taiwan Professor Andika Widya Pramono, Indonesian’s Institute of Science, Indonesia Honourable Guests,
Ladies and Gentlemen, Bapak-ibu
Assalamu’alaikum Warahmatullahi Wabarakatuh Good morning Selamat Pagi and Sugeng Enjang
On behalf of the Institut Sains & Teknologi AKPRIND, I am delighted to join this Virtual Conference, despite the challenge posed by the Covid 19 pandemic. I wish to convey my sincere appreciation to ICEST 2021 committee for making this event happen today. I would also like to thank all the distinguished panelists and moderators for taking the time to participate in today's conference from near and far.
5
This year's conference focused on Developing Research to Create Prime Resources in the Industrial Revolution Era 4.0 will address subjects in the physical sciences, including physics, math, chemistry, material science, and engineering.
Globalization of education and the 4.0 Industrial Revolution presence are inevitable and must be faced by higher education institutions. This event challenges us to deal with global change and heightened competition in many aspects, including disruptive phenomena in the higher education model.
Research activities are focused on developing knowledge and research that can make a direct contribution to society. Scientific research will produce results published in international/national journals and can be disseminated in international discussion forums. From the other side, the research results are also expected to assist in determining government/regulator policies and improve sound educating practices.
Ladies and Gentlemen,
The digitalization of society creates new professions and new competency areas for established professions, which increases demands on educational development to manage these. At the same time that the academic environment is being digitalized and offers new opportunities, new digital tools also place new demands on educational development. The new generation of students entering higher education has a different approach to digital solutions and likely also another expectation that educational environments have a high degree of digital sophistication.
These are questions that require activity and development and where educational development helps manage the pressures for change from digitalization, lifelong learning, distance education, and other factors. Academic growth is often dealt with concerning other issues, and I am sure that each higher education institution has its way of dealing with these.
6
Research universities are core components of any country's economic, political and social development systems.
If knowledge for augmenting productive power is necessary, research is the fundamental process, and its institutionalization will produce researchers and further research.
I note that a workshop preceded this conference to train and mentor young researchers in ICEST. The workshop is really commendable, and I want to encourage the consortium ICEST to continue with this annual conference. I hope that this series of annual conferences will help create a solid continental network of researchers that will begin to have a life of its own.
I wish you an engaging and successful conference. Let's get started, and thank you again for joining us. Terima kasih.
Wassalamu’alaikum warahmatullahi wabarakatuh
Dr. Edhy Sutanta Rector
Institut Sains & Teknologi AKPRIND Yogyakarta
7
Committee
Advisor
Prof. Dr. Rizalman Mamat (UMP, Malaysia)
Prof. Dr. Ir. Sudarsono, MT (IST AKPRIND, Indonesia)
Assoc. Prof. Dr. Edhy Sutanta, MKom (IST AKPRIND, Indonesia) Assoc. Prof. Dr. Ir. Amir Hamzah, MT (IST AKPRIND, Indonesia) Assoc. Prof. Dr. Ir. Toto Rusianto, MT (IST AKPRIND, Indonesia) Ir. R. Agung Efriyo Hadi, M.Sc., Ph.D (UNAYA, Indonesia) General Chair
Assoc. Prof. Dr. Ellyawan Setyo Arbintarso (IST AKPRIND, Indonesia) Vice General Chair
Dr. Muchlis (IST AKPRIND, Indonesia) Ir. Yan Juansyah, DEA (UNMALA, Indonesia) Treasure & Finance
Ir. Murni Yuniwati, M.T. (IST AKPRIND, Indonesia) Secretariat
M Andang Novianta, S.T., M.T. (IST AKPRIND, Indonesia)
8
PublicationAssoc. Prof. Dr. Mohd Shahrir bin Mohd Sani (UMP, Malaysia) Dr. Suwanto Raharjo, S.Si, M.Kom. (IST AKPRIND, Indonesia)
Assoc. Prof. Dr. Anak Agung Putu Susastriawan (IST AKPRIND, Indonesia) Assoc. Prof. Dr. Sri Mulyaningsih (IST AKPRIND, Indonesia)
Assoc. Prof. Dr. Emy Setyaningsih (IST AKPRIND, Indonesia) Dr. Januar Parlaungan Siregar (UMP, Malaysia)
Dr. Samuel Kristiyana, M.T. (IST AKPRIND, Indonesia) Promotion
Catur Iswahyudi, S.Kom., S.E, M.Cs. (IST AKPRIND, Indonesia) Dr. Eng. Rina Febriana, S.T., M.T. (UNMALA, Indonesia)
Assoc. Prof. Dr. Ir. Fakhrul Rozi Yamali, ME (UNBARI, Indonesia) Assoc. Prof. Dr. Lusmeilia Afriani, DEA (UNILA, Indonesia) Sita Nurmasitah, M.Hum (UNNES, Indonesia)
Cut Rahmawati, M.T. (UNAYA, Indonesia) Website
Erma Susanti, S.Kom., M.Cs. (IST AKPRIND, Indonesia) Ruzan Fikra, S.Kom., MBA. (IST AKPRIND, Indonesia) Event
Erna Kumalasari Nurnawati, S.T., M.T. (IST AKPRIND, Indonesia) Eska Almuntaha, S.E., M.Sc., Ak.CA. (IST AKPRIND, Indonesia) Beny Firman, S.T., M.Eng. (IST AKPRIND, Indonesia)
9
International Programme CommitteeAgustinus Purna Irawan (Universitas Tarumanagara, Jakarta, Indonesia) Andika Widya Pramono (Indonesian’s Institute of Sciences, Jakarta, Indonesia) Arbi Haza Nasution (Universitas Islam Riau, Pekanbaru, Indonesia)
Benny Hidayat (Universitas Andalas, Padang, Indonesia)
Cahyo Mustiko Okta Muvianto (Universitas Maratam, Mataram, Indonesia)
Cahyorini Kusumawardani (Universitas Negeri Yogyakarta, Yogyakarta, Indonesia) Chuen-Jinn Tsai (National Yang Ming Chiao Tung University, Taipe, Taiwan) Cut Rahmawati (Universitas Abulyatama, Banda Aceh, Indonesia)
Denny Vitasari (Universitas Muhammadiyah Surakarta, Surakarta, Indonesia) Dewi Fitria (Universitas Riau, Pekanbaru, Indonesia)
Eko Setiawan (Universitas Muhammadiyah Surakarta, Surakarta, Indonesia) Evizal Abdul Kadir (Universitas Islam Riau, Pekanbaru, Indonesia)
Ezri Hayat (Teesside University, Middlesbrough, UK)
Hari Agung Yuniarto (Universitas Gadjah Mada, Yogyakarta, Indonesia) Ihwan Ghazali (Univeristi Teknikal Malaysia Melaka, Melaka, Malaysia) Januar P Siregar (Universiti Malaysia Pahang, Pekan, Malaysia)
Mohammad Sapari Dwi Hadian (Universitas Padjajaran, Bandung, Indonesia) Mohd Shahrir Mohd Sani (Universiti Malaysia Pahang, Pekan, Malaysia)
Muhammad Norhisham Abdul Rani (Universiti Teknologi Mara, Shah Alam, Malaysia) Nurgiyatna (Universitas Muhammadiyah Surakarta, Surakarta, Indonesia)
Nuryono (Universitas Gadjah Mada, Yogyakarta, Indonesia) Purwanto (Universitas Diponegoro, Semarang, Indonesia) Rizalman Mamat (Universiti Malaysia Pahang, Pekan, Malaysia) Ron Harris (Birgham Young University, Provo, USA)
Taufika Ophiyandri (Universitas Andalas, Padang, Indonesia)
10
Tri Widodo Besar Riyadi (Universitas Muhammadiyah Surakarta, Surakarta, Indonesia) Yuli Panca Asmara (INTI International University, Nilai, Malaysia)
Zahrul Mufrodi (Universitas Ahmad Dahlan, Yogyakarta, Indonesia) Zufialdi Zakaria (Universitas Padjajaran, Bandung, Indonesia)
11
Keynote Material
Nu Keynote Speaker Title
1. Prof. Dr. Ron A Harris
2. Prof. Dr. Rizalman Mamat Renewable Fuel for Future Transportation Engines 3. Prof. Dr. Andika Widya Pramono Some Updates In Materials Science And Engineering To
Create Prime Resources In The Industrial Revolution Era 4.0 4. Prof. Dr. Chuen-Jinn Tsai Micro-Pollution Control
12
Zoom Access 2
ndICEST 2021
Zoom id for 2
ndICEST 2021 Pleno Session:
Meeting id : 922 6953 6333 Passcode: 2ndiceeest
Parallel session will use breakout room. The presenter can enter the breakout room according to the specified room. Please check the author code and your room in the list
provided in this Book Programme. Participants can choose the room according to your
interest.
13
Presenters Guidelines
✓ Please Rename your profile name by your Author Code_your name. Ex: R1_004 Ajeng Lestari.
✓ Sessions will be opened by the host 10 minutes prior to the starting time, to allow every participant to join on time. No sessions will start before the host opens the session. All presenters are expected to join the zoom meeting conference 10 minutes before the event starts.
✓ The presenters prepares a presentation script in PPT format a maximum of 15 pages. You are allocated a presentation for 10 minutes. If you are presenting live, you will “Share” your screen or document.Otherwise, the room PIC can set this up on your behalf. Please ensure that your webcam is on so that attendees can view you during your presentation. Discussion sessions will be held per two presenters for 10 minutes. Please prepare your presentation documents, so that the presentation session runs smoothly.
✓ Please follow the rules given by the moderator during the meeting.
14
Participants Guidlines
✓ Please Rename your profile name by P_your name. Ex: P_Vera Nurul.
✓ All participants will enter the session in Mute mode (to avoid interruptions of presentations that may be occurring). The hosts will enable the sound as soon as it is fit. The hosts will be monitoring the sessions, so if you have any technical question during the session, you can send chat messages to them, so they can assist you.
✓ This event consist two session, name Pleno Session (plenary) and parallel session. Parallel session contain research presentation based on field are. You can choose the room according to your interest.
✓ Each session will have a chair that will manage times and moderate questions and answers.
✓ Please raise your hand if you want to pose a question to the presenter (if you prefer, you can pose a question by sending a chat message directly to the presenter).
15
Conference Programs
Date : 15th September 2021
Time (GMT+7) Activities Official
07.30 - 08.30 Preparation and Attendance Registration Authors and Participants entering to Virtual Conference 2nd ICEST 2021
Committee
08.30 - 08.45 Opening Session Master of Ceremony
08.45 - 09.00 Chairman Committee Greeting Chairman Committe 09.00 - 09.15 Rector of Institut Sains & Teknologi AKPRIND
Greeting
Rector 09.15 – 09.45 Keynote Speech 1:
Prof. Dr. Ron A Harris
Neotectonics Department of Geological Sciences, Brigham Young University, USA.
Moderator:
Dr. Mohd Shahrir Mohd Sani PIC:
Erna Kumalasari Nurnawati 09.50 – 10.20 Keynote Speech 2:
Prof. Dr. Rizalman Mamat
Mechanical and Manufacture Engineering College, Universiti Malaysia Pahang, Malaysia.
10.20 – 10.50 Discussion session 1
16
11.00 – 11.30 Keynote Speech 3:
Prof. Dr. Andika Widya Pramono Superconductor Materials Lembaga Ilmu
Pengetahuan Indonesia, Indonesia. Moderator:
Rahayu Khasanah, S.T., M.Eng PIC:
Erna Kumalasari Nurnawati 11.30 – 12.00 Keynote Speech 4:
Prof. Dr. Chuen-Jinn Tsai
Micro-pollution Control Institute of Environmental Engineering, National Yang Ming Chiao Tung University, Taiwan.
12.00 – 12.30 Discussion session 2
12.30 - 13.00 Preparation for Parallel Session Committee
13.00 – 16.00 Parallel Session Committee
16.00 – 16.15 Closing Ceremony MC
17
Breaking Room 1PIC : Bayu Hendra Permana, S.E Moderator : Dewi Wahyuningtyas, S.T., M.Eng
Author
Code Authors Title
R1_004 Ajeng Lestari, Zainus Salimin, Sukirman Sukirman, Anjanetta Pasha, Miqdam Humami, Muhammad Putra and Ikrom Amar
Pb (II) and Cu (II) Adsorption Performance Using Magnetic Modified Watermelon Rind as a Low Cost Adsorbent
R1_013 Muhammad Ansori, Wahyuningsih Wahyuningsih, Siti Fathonah, Rosidah Rosidah and Nur Aini Halimah Yulianti
Characteristics of Parijoto Fruit as a Preservative Of Dodol Food
R1_021 Octavianti Paramita and Fitriyani Irfanti
Nutritional Characteristics Of Sagon Cake With Breadnut Seed (Artocarpus Communis G.Forst) Flour By Variations Of Flouring Process
R1_022 Atika Atika, Endah Rinasti, Wulansari Prasetyaningtyas, Musdalifah and Siti Nurrohmah
Utilization of Lorotis Weed (Aeschynomene Indica) as Natural Textile Dyes: Soil Pollution Prevention in the Use of Pesticides
18
R1_023 Wulansari Prasetyaningtyas and Fenny Fitria Kartika
Colorfastness of Batik Dyed with Dyes from Banana Hump
R1_028 Roudlotus Sholikhah, Widowati Widowati and Sita Nurmasitah
The Impact Of The Use Of Different Mordan Types On The Ecoprint Dyeing Using Secang Dye On Primisima Fabric
Breaking Room 2
PIC : Eska Almuntaha, S.E., M.Sc.,Ak.CA.
Moderator : Dr. Benny Hidayat Author
Code Authors Title
R2_016 Rina Febrina, Lusmeilia Afriani and Mira Wisman
Integration of Geospatial Technology with Evacuation Pathway Planning for Tsunami Mitigation
R2_001 T Listyani Ra Groundwater Aggressiveness in Jonggrangan Karst, West Progo Area
R2_007 Mesias Citra Dewi Geomorphological Quantitative Analysis for Interpretation of Geological Structures as the Basis for Decision Making on Coal Exploration Activities in East Kutai Regency, East Borneo Province
19
R2_031 Elvira Handayani, Kiki Rizky Amalia, Fakhrul Rozi Yamali, Anisa Ramadona
Analysis Of The Causes Of The Difference In Financing Prices On Road Improvement In Tanjung Jabung Timur Regency
R2_032 Kiki Rizky Amalia, Elvira Handayani, Annisaa Dwiretnani, Rudi Harianto
Pricing Strategy For Project Tender By Contractor
R2_033 Amsori Muhammad Das, Ari Setiawan, Wari Dony
Assessment of How Satisfaction with Service Quality and Service Value Affect Passenger’s Loyalty to the MRT-SBK Service In Greater Kuala Lumpur
R2_34 Ari Setiawan, Azwarman Azwarman, Rioni Rizki Aldiansyah, Riki Saputra
Analysis of The Degree of Saturation Due to Influence Vehicle Speed in Office Area in New Normal Condition (Case Study:
Jalan H. Agus Salim Jambi City)
20
Breaking Room 3PIC : Ruzan Fikra, S.Kom.,M.BA Moderator : Dr. Suwanto Raharjo,S.Si,M.Kom
Author
Code Authors Title
R3_015 Budhi Anto, Amir Hamzah, Dahliyusmanto, Yonni Safadro and Fadly Arif
Sliding Gate Opener System with Smartphone Control Using Bluetooth Connection
R3_025 Samuel Kristiyana and Gatot Santosa
Design Of Heart Rate Monitoring System Using Mobile Telecardiac And Showing The Patient’s Position R3_037 Venny Yusiana, Hendi Matalata,
Safriyudin Safriyudin and Amrih Apike
Gate Driver Design with Phase Shift SPWM technique on H- Bridge Multilevel Inverter topology
R3_038 Fadli Eka Yandra, Venny Yusiana and Amrih Apike
Use of the OriginPro Application for Lightning Wave Spectrum Analysis
R3_012 Noeryanti Noeryanti, Noviana Pratiwi, Sinum Fariasi and Erna Kumalasari Nurnawati
Comparison of Fuzzy Mamdani and Backpropagation Methods to predict Yogyakarta Human Development Index
21
R3_018 Erna Kumalasari Nurnawati, Juniana Husna,Yusra Yusra and Ismail Setiawan
Implementation of Analytical Hierarchy Process Method with Eigenvectors Efficiency for Culinary Object Selection in Aceh
R3_024 Uning Lestari, Catur Iswahyudi and Beny Firman
Internet of Things on Smart Parking Systems Based on Vehicle Loop Detector
R3_003 Muhammad Mukhlisin, Azizah Wismaningsih, Hany Windri Astuti, Amin Suharjono and Sri Anggraini Kadiran
Ground Water Flow Monitoring Systems Using Permeability Tank Experiment
Breaking Room 4
PIC : Ir. Muhammad Yusuf, M.T.
Moderator : Angge Dhevi Warisaura, S.T. M.Eng Author
Code Authors Title
R4_011 Muchlis Muchlis, Sri Hastutiningrum and William Sutrisna
Optimization Waste Disposal Route Using GIS in Yogyakarta Municipality
22
R4_020 Rani Ergantara, Dewi Fadilasari and Marcelly Wardana
Analysis Of Green Open Space Adequacy Malahayati University Area In Absorbing Carbon Dioxide Emissions R4_027 Rani Ergantara, Destrangga
Maulana, Dewi Fadilasari and Marcelly Wardana
The Study Of Carbon Dioxide Emissions From Motor Vehicles At Malahayati University
R4_035 Anggrika Riyanti, Hadrah Hadrah, Agung Gusti Khairi Azwar
Investigation of Carbon Footprint from School Activities: A Case Study in Jambi, Indonesia
R4_036 Monik Kasman, Peppy Herawati, Lucya Handayani
Adsorption of Hg(II) from Aqueous Solution by Water Treatment Plant Sludge
R4_041 Sepridawati Siregar, Edo Riyandani and Nora Idiawati
Association of Particulate Matter Exposure and Lung Cancer Mortality in the Workers. A Systematic Review and Meta- Analysis.
23
Breaking Room 5PIC : Argaditia Mawadati, S.T., M.Sc
Moderator : Dr. Anak Agung Putu Susastriawan, S.T., M.Tech Author
Code Authors Title
R5_002 Fungky Dyan Pertiwi, Arif Wahjudi, Wawan Aries Widodo and Setyo Hariyadi SP
The Effect of Slat Clearance and Flap on the Aerodynamic Performance of the NACA 43018 Wing in the Landing Process
R5_009 Hendrix Noviayanto Firmansayah and Samsudin Anis
Preliminary Design of Composite Safety Shoes Toe Cap Using Finite Element Method
R5_019 Adhi Kusumastuti, Samsudin Anis and Nur Qudus
Fluid Flow Visualization and Parameter Evaluation of Vegetable Oil/ Water Mixture in Taylor-Couette Column R5_008 Agus Hindarto Wibowo,
Windyaning Ustyannie, Argaditia Mawadati and Syauqi Rahman Wibowo
Quality Level Measurement Analysis Using Six Sigma DMAIC Method
R5_014 Argaditia Mawadati, Andrean Emaputra and Kartinasari Ayuhikmatin Sekarjati
Sales Pattern Identification for Marketing Strategies Using Data Mining
24
R5_026 Marcelly Wardana, Ahmad Sidiq and Nanda Ari Setiawan
Working Posture Analysis On Press Tile Using Rapid Upper Limb Assessment
Breaking Room 6
PIC : Erma Susanti,S.Kom., M.Cs Moderator : Ir. Saiful Huda, M.T., M.E.
Author
Code Authors Title
R6_029 Ria Zulfiati, Fakhrul Rozi Yamali Mechanical Properties of Geopolymer Mortar Based on Fly Ash with Polypropylene Fiber
R6_030 Wari Doni, Ria Zulfiati, Amsori Muhammad Das
Compressive Strength Geopolymer Mortar with Fine Aggregate Soaked in NaOH Solution
R6_005 Rusnaldy Rusnaldy, Susilo Adi Widyanto, Natalino Fonseca D. S.
Guterres and Achmad Widodo
The Effect Of Chill Used In The Ductile Iron Casting Process To Gear Manufacture Application On The Wear Resistance
R6_039 Lilis Kistriyani, Muhammad Khafidh, Donny Suryawan and Rifky Ismail
Physical Characteristic of Polymer Formulations For Phrosthetic Foot Materials : Comparison of Natural Rubber and Ethylene Vinyl Acetate
32
Paper ID : 8
Title : Quality Level Measurement Analysis Using Six Sigma DMAIC Method
Author(s) : Agus Hindarto Wibowo, Windyaning Ustyannie, Argaditia Mawadati, Syauqi Rahman Wibowo
Abstract :
CV Sinar Albasia Utama is an industry where wood is used as the raw material, and their product is barecore which can reach ± 426,970 production units per year. CV Sinar Albisia Utama always does the best to satisfy the customers’ needs and strives to produce high quality products, since the products are exported to China and Taiwan, and they conduct quality improvements continually. In this study, the researchers used Six Sigma method in measuring and processing data, by implementing DMAIC methodological stages, namely Define, Measure, Analyze, Improve, and Control. The Defect Per Million Opportunities (DPMO) value of the products in the last 1 year was 2065.133 with 4 Critical-to-Quality (CTQ), including the unsuitable size which causes the wood to be not tight (60.16%), unsuitable thickness (26.11%), untidy wood cutting (9.92%), and wood is not completely dry so it is getting rotten (3.79%). The results revealed that that the company sigma level is 4.4, or in other words, the company is in a very good and productive condition, and it is able to compete globally.
37
Paper ID : 14
Title : Sales Pattern Identification for Marketing Strategies Using Data Mining Author(s) : Argaditia Mawadati, Andrean Emaputra, Kartinasari Ayuhikmatin Sekarjati Abstract :
The development of the culinary business, such as food stalls, is increasing rapidly. This causes the competition to become even tighter. Food stalls must have targeted and precise strategies to survive and compete. Identifying the customer’s preference is one of the strategies that can increase sales and retain customers. The customer’s preference can be observed through purchase transaction data.
Observation of sales transaction data in a simple way does not always get effective results because of the large volume of data processed and the difficulty in finding the association between one menu sales and another. With data mining, this problem can be overcome. In this study, the data mining method used is the Apriori Algorithm. The application of the apriori algorithm helps in forming candidate item combinations that may occur. Thus, it can be seen the relationship between the combination of menus that are sold, whether including the most frequently sold or not. This information can be taken into consideration to determine the next marketing strategies.
