Facilitators of and Barriers to Sustainable Development in Small and Medium-Sized Enterprises: A Descriptive Exploratory Study in Romania

In the context of growing concerns regarding the deterioration of the environment and the increase in social inequalities, the concept of sustainability emerged as a response of companies, in order to contribute to community goodwill. The drivers and obstacles for the businesses engaging in sustainable policies have been explored at large by scientific literature. However, research gaps were observed, namely regarding SMEs, that tend to have a less formal and more fragmented approach to sustainability. The goal of our study is to determine the main barriers and facilitators for sustainability that Romanian SMEs face, and the connections between them and with the firms’ characteristics. Our study identifies the main barriers and facilitators for adopting sustainable practices in SMEs located in Romania, based on a study on a panel of 71 firms. The association rule method of data mining and quantitative statistic methods were used to explore barrier–facilitator relationships and how they vary depending on the firms’ or managers’ characteristics. The results show a wide range of drivers and obstacles for implementing sustainability in SMEs, while identifying connections between different barriers and facilitators and correlations with the firms’ characteristics. Our findings contribute to a better understanding of factors that impact sustainable behavior in SMEs and identify further aspects to be explored.

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Research on Teaching Quality Evaluation Using Data Mining Technique

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.926-930.4582 ◽

2014 ◽

Vol 926-930 ◽

pp. 4582-4585

Author(s):

Ai Feng Li ◽

Ying Hu ◽

Wen Jing Zhao

Keyword(s):

Data Mining ◽

Decision Tree ◽

Association Rule ◽

Quality Evaluation ◽

Teaching Quality ◽

Data Mining Technique ◽

Higher Education System ◽

Potential Factors ◽

Rule Method ◽

Using Data

—In this paper, we employ data mining (DM) technique to analyze various potential factors which impact the in-class teaching quality evaluation. Based on an effective dataset, we first exploit association rule method to mine the relationship between the teacher’s attributions, such as title, degree, age, seniority, and load, and the in-class teaching quality evaluation results. Then, we construct the decision tree of course’s attributions to reveal how the course’s attributions, such as property, credit, week hour, and number of students, impact the in-class teaching quality evaluation results. Our mined rules can provide effective guidance to talent development, teaching management, and input of talent in higher education system. Index Terms—data mining, decision tree, association rule, teaching quality evaluation

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Implementasi Algoritma Apriori Pada Penyusunan Menu Makanan Rumah Makan Prasmanan

Jurnal Teknik Informatika dan Sistem Informasi ◽

10.28932/jutisi.v6i2.2742 ◽

2020 ◽

Vol 6 (2) ◽

Author(s):

Asep Budiman Kusdinar ◽

Daris Riyadi ◽

Asriyanik Asriyanik

Keyword(s):

Data Mining ◽

Association Rules ◽

Association Rule ◽

High Frequency ◽

A Priori ◽

Apriori Algorithm ◽

Frequency Pattern ◽

Data Mining Techniques ◽

Rule Method

A buffet restaurant is a restaurant that provides buffet food that is served directly at the dining table so that customers can order more food according to their needs. This study uses the association rule method which is one of the methods of data mining and a priori algorithms. Data mining is the process of discovering patterns or rules in data, in which the process must be automatic or semi-automatic. Association rules are one of the techniques of data mining that is used to look for relationships between items in a dataset. While the apriori algorithm is a very well-known algorithm for finding high-frequency patterns, this a priori algorithm is a type of association rule in data mining. High- frequency patterns are patterns of items in the database that have frequencies or support. This high-frequency pattern is used to develop rules and also some other data mining techniques. The composition of the food menu in the Asgar restaurant is now arranged randomly without being prepared on the food menu between one another. The result of this research is to support the composition of the food menu at the Asgar restaurant so that it is easier to take food menu with one another.

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Penggunaan Association Rule Data Mining Untuk Menentukan Pola Lama Studi Mahasiswa F-MIPA UNSRAT

d'CARTESIAN ◽

10.35799/dc.3.1.2014.3777 ◽

2014 ◽

Vol 3 (1) ◽

pp. 1

Author(s):

M. Zainal Mahmudin ◽

Altien Rindengan ◽

Winsy Weku

Keyword(s):

Data Mining ◽

Association Rule ◽

Large Data ◽

Apriori Algorithm ◽

Data Mining Algorithms ◽

Adequate Information ◽

Processing Data ◽

Rule Method ◽

Mining Algorithms ◽

Confidence Value

Abstract The requirement of highest information sometimes is not balance with the provision of adequate information, so that the information must be re-excavated in large data. By using the technique of association rule we can obtain information from large data such as the college data. The purposes of this research is to determine the patterns of study from student in F-MIPA UNSRAT by using association rule method of data mining algorithms and to compare in the apriori method and a hash-based algorithms. The major’s student data of F-MIPA UNSRAT as a data were processed by association rule method of data mining with the apriori algorithm and a hash-based algorithm by using support and confidance at least 1 %. The results of processing data with apriori algorithms was same with the processing results of hash-based algorithms is as much as 49 combinations of 2-itemset. The pattern that formed between 7,5% of graduates from mathematics major that studied for more 5 years with confidence value is 38,5%. Keywords: Apriori algorithm, hash-based algorithm, association rule, data mining. Abstrak Kebutuhan informasi yang sangat tinggi terkadang tidak diimbangi dengan pemberian informasi yang memadai, sehingga informasi tersebut harus kembali digali dalam data yang besar. Dengan menggunakan teknik association rule kita dapat memperoleh informasi dari data yang besar seperti data yang ada di perguruan tinggi. Tujuan penelitian ini adalah menentukan pola lama studi mahasiswa F-MIPA UNSRAT dengan menggunakan metode association rule data mining serta membandingkan algoritma apriori dan algoritma hash-based. Data yang digunakan adalah data induk mahasiswa F-MIPA UNSRAT yang diolah menggunakan teknik association rule data mining dengan algoritma apriori dan algoritma hash-based dengan minimum support 1% dan minimum confidance 1%. Hasil pengolahan data dengan algoritma apriori sama dengan hasil pengolahan data dengan algoritma hash-based yaitu sebanyak 49 kombinasi 2-itemset. Pola yang terbentuk antara lain 7,5% lulusan yang berasal dari jurusan matematika menempuh studi selama lebih dari 5 tahun dengan nilai confidence 38,5%. Kata kunci : Association rule data mining, algoritma apriori, algoritma hash-based

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Aturan Asosiasi Antar Item Terjual pada Data Penjualan Minimarket Milik Komunitas di Hari Besar Tertentu Menggunakan Algoritma Apriori

JURNAL AKUNTANSI EKONOMI dan MANAJEMEN BISNIS ◽

10.30871/jaemb.v9i2.3621 ◽

2021 ◽

Vol 9 (2) ◽

pp. 208-215

Author(s):

Luky Fabrianto ◽

Novianti Madhona Faizah ◽

Johan Hendri Prasetyo ◽

Bobby Suryo Prakoso ◽

Gani Wiharso

Keyword(s):

Data Mining ◽

Association Rule ◽

A Priori ◽

Threshold Value ◽

Frequent Items ◽

Mining Methods ◽

Rule Method ◽

One Year ◽

The Relationship

The popular data mining methods to find the relationship between an item and another item is the association rule method using A Priori algorithm, this method is precise to generate a pattern of relationship rules between the types of items sold based on sales data. Support values on frequent items and confidence in the rules obtained can be an actionable insight that can be follow up by minimarket managers, cooperatives and etc. The categorization of product types in minimarkets is much while the total number of transactions in one year is also very large, but the number of types of items sold in a transaction is very few, thus the threshold value cannot be high. In this study, the association rule method was carried out per event or certain period related to Muslim holidays, the highest rule was obtained is Makanan ringan => Sembako with 46% confidence and 16% support which occurred in the month of Ramadan.

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THE OPTIMIZE OF ASSOCIATION RULE METHOD FOR THE BEST BOOK PLACEMENT PATTERNS IN LIBRARY: A MONTHLY TRIAL

TEKNOKOM ◽

10.31943/teknokom.v4i2.63 ◽

2021 ◽

Vol 4 (2) ◽

pp. 53-59

Author(s):

T. Husain ◽

Nuzulul Hidayati

Keyword(s):

Data Mining ◽

Association Rule ◽

Research Method ◽

Information Service ◽

User Needs ◽

Research Approach ◽

Sources Of Information ◽

Rule Method ◽

Confidence Value

Data mining is the process of finding interesting patterns and knowledge from large amounts of data. Sources of information service, especially in the library, include books, reference books, serials, scientific gray literature (newsletters, reports, proceedings, dissertations, theses, and others). The importance of this research being carried out in the library in this study aims to implement data mining with the association rule method to solve problems, especially in the placement of shelves based on the category of the printed version of the book collection. This research method uses a qualitative research approach. Data was collected using documentation techniques and deep analysis of existing weaknesses to identify user needs whose information was obtained through observation and interviews with key informants (admin, user, etc.). For example, the determination of the best book placement patterns can be done by looking at the results of the tendency of visitors to borrow books based on a combination of 2 item sets with 60 percent of confidence value every month or week and must be evaluated or take a calculate again.

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Rancang Bangun Aplikasi Data Mining pada Penjualan Distro Bloods Berbasis Web menggunakan Algoritma Apriori

Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) ◽

10.35870/jtik.v5i1.197 ◽

2021 ◽

Vol 4 (2) ◽

pp. 26

Author(s):

Muhammad Muttaqin Muchlis ◽

Iskandar Fitri ◽

Rini Nuraini

Keyword(s):

Data Mining ◽

Data Processing ◽

Association Rule ◽

Web Applications ◽

A Priori ◽

Sales Data ◽

Development Life Cycle ◽

Technological Developments ◽

Rule Method ◽

Purchasing Patterns

The design of this data mining application is a computerized system in the field of technology, this proves that technological developments in data processing are increasingly advanced, this can be the basis for the development of data processing systems for sales of bloods based web applications using a priori algorithms, problems in this bloods distribution cannot Minimizing the decline in sales at the Jakarta clothing event in 2019, it is necessary to evaluate the sales data, with market basket analysis or consumer shopping baskets to find out consumer shopping patterns as a reference for the sale strategy of event Jakarta clothing at the end of the year. This analysis uses a priori algorithm with the association rule method, while the SDLC (Software Development Life Cycle) method is used as the basis for developing expert systems. From the results of the study, it was found that sales data for 5 days and 7 items got the highest 100% confidence value from the itemset calculation 1,2,3 which passed the selection so that they became aware of consumer purchasing patterns and rearranged product layouts for promotion and improving the correct sales strategy.Keywords:Applications, Data Mining, Apriori Algorithms, Association Rule Method, SDLC.

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Application of Association Rule Method Using Apriori Algorithm to Find Sales Patterns Case Study of Indomaret Tanjung Anom

Brilliance: Research of Artificial Intelligence ◽

10.47709/brilliance.v1i2.1228 ◽

2021 ◽

Vol 1 (2) ◽

pp. 54-66

Author(s):

M. Hamdani Santoso

Keyword(s):

Data Mining ◽

Association Rule ◽

A Priori ◽

Apriori Algorithm ◽

Rule Discovery ◽

Association Rule Discovery ◽

Rule Method ◽

Confidence Value ◽

Method Show

Data mining can generally be defined as a technique for finding patterns (extraction) or interesting information in large amounts of data that have meaning for decision support. One of the well-known and commonly used association rule discovery data mining methods is the Apriori algorithm. The Association Rule and the Apriori Algorithm are two very prominent algorithms for finding a number of frequently occurring sets of items from transaction data stored in databases. The calculation is done to determine the minimum value of support and minimum confidence that will produce the association rule. The association rule is used to produce the percentage of purchasing activity for an itemset within a certain period of time using the RapidMiner software. The results of the test using the priori algorithm method show that the association rule, that customers often buy toothpaste and detergents that have met the minimum confidence value. By searching for patterns using this a priori algorithm, it is hoped that the resulting information can improve further sales strategies.

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PEMANFAATAN DATA MINING UNTUK PENEMPATAN BUKU DI PERPUSTAKAAN MENGGUNAKAN METODE ASSOCIATION RULE

Jurnal Ilmu Komputer ◽

10.33060/jik/2019/vol8.iss2.127 ◽

2019 ◽

Vol 8 (2) ◽

pp. 59-63

Author(s):

Eka Sabna

Keyword(s):

Data Mining ◽

Data Collection ◽

Data Processing ◽

Association Rule ◽

Research Data ◽

Rule Method ◽

Hidden Data ◽

The Relationship

Abstract Data mining is the process of analyzing data to find a pattern of hidden data collection by utilizing library visit data, can dig up information about what books are often borrowed by visitors and the relationship between each borrower to be able to compile and layout books. To be able to place books in accordance with the needs of members, it is necessary to process a data processing of book borrowing using the Associtation Rule method so that support and confidence can be found between books that are often borrowed so that patterns can be identified. The research data were obtained from book lending data at the Hang Tuah Pekanbaru STIKes Library. In processing the data using the Rapidminer software with the Association Rule method with support = 0.6 and confidence = 0.9. The resulting rule to be used as a book placement pattern consists of 7 patterns / rules. Keywords : Data Mining, Library, Association Rule. Abstrak Data mining merupakan proses analisa data untuk menemukan suatu pola dari kumpulan data yang tersembunyi dengan memanfaatkan data kunjungan perpustakaan, dapat menggali informasi tentang buku-buku apa yang sering dipinjam oleh pengunjung dan keterkaitan antar masing-masing peminjam hingga dapat melakukan penyusunan dan tata letak buku. Untuk dapat melakukan penempatan buku sesuai dengan kebutuhan anggota perlu dilakukan suatu proses pengolahan data peminjaman buku dengan menggunakan metode Associtation Rule agar dapat diketahui support dan confidence antara buku-buku yang sering di pinjam sehingga dapat diketahui pola penempatan buku. Data penelitian diperoleh dari data peminjaman buku di Perpustakaan STIKes Hang Tuah Pekanbaru. Dalam pengolahan data nya menggunakan software Rapidminer dengan metode Association Rule dengan nilai support = 0.6 dan confidence = 0,9. Rule yang dihasilkan untuk dijadikan pola penempatan buku terdiri dari 7 pola/rule. Kata Kunci : Data Mining, perpustakaan, Assocition Rule

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Data Mining Technology in Book Copyright Information Management Decision System

Mobile Information Systems ◽

10.1155/2021/4388235 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Song Lifang

Keyword(s):

Data Mining ◽

Decision Making ◽

Information Management ◽

Association Rule ◽

Information Management System ◽

Management Decision ◽

Apriori Algorithm ◽

Mining Technology ◽

Rule Method

Today is an era of data “big bang”; Internet information technology is widely used in various fields of society. As an indispensable spiritual food in people’s daily life, books are increasing in number and scale. In order to better manage book information, people have introduced data mining technology. Based on this, this article takes the research and application of data mining technology in book copyright information management decision-making system as the theme, explores the role of data mining technology in book copyright information management, and aims to provide reference for our country’s book copyright information management and decision-making. This article first introduces the common algorithms of data mining technology and then elaborates on the advantages and effectiveness of the association rule method in data mining. Aiming at some defects of the original Apriori algorithm of the association rule method, an improved Apriori algorithm is proposed. After taking the library book information management system and database of a university in our province as the experimental research object, the performance gap between the two algorithms is compared through experiments, and it is concluded that when the number of transaction set item records is less than 1400, the Apriori algorithm performs better, and when the number of records in the transaction set is greater than 1400, the improved Apriori algorithm is obviously more advantageous. The research results show that the introduction and application of data mining technology make the information management of books more efficient and convenient, and it is more convenient for the management and decision-making of book copyright information.

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PENERAPAN DATA MINING MENGGUNAKAN METODE ASSICIATION RULE DENGAN ALGORITMA APRIORI UNTUK ANALISA POLA PENJUALAN BARANG

JURTEKSI ◽

10.33330/jurteksi.v5i2.362 ◽

2019 ◽

Vol 5 (2) ◽

pp. 193-198

Author(s):

Yori Apridonal M ◽

Wirdah Choiriah ◽

Akmal Akmal

Keyword(s):

Data Mining ◽

Association Rule ◽

Frequent Itemset ◽

Apriori Algorithm ◽

Data Set ◽

Processing Data ◽

New Information ◽

Alternative Algorithm ◽

Rule Method ◽

Events Data

Abstract: Fantasy Kids is a children's clothing distribution in the Bangkinang area, Kampar Regency, Riau. In its operations, distros sell their products to the general public, including the sale of children's shirts, children's shirts, jackets or children's sweaters which are usually sold in other distros. These distributions carry out product updates at certain events. Data Mining is the development or discovery of new information by looking for certain patterns or rules of a large amount of data expected to overcome these conditions. The method that will be used in the construction of this application is the Association Rule method with the Apriori Algorithm. Association Rule method is a procedure to find relationships between items in a specified data set. In determining a Association Rule, there is a measure of trust obtained from the results of processing data with certain calculations. Apriori Algorithm is an alternative Algorithm that can be used to determine the frequent itemset in a data set. Keywords : Data Mining, Algoritma, Apriori, Association Rule, Sales, Distro Abstrak: Fantasy Kids merupakan sebuah distro baju anak-anak di kawasan Bangkinang, Kabupaten Kampar, Riau. Dalam operasionalnya, distro menjual produknya kepada masyarakat umum meliputi penjualan kaos anak, kemeja anak, bag, jaket atau sweater anak yang biasa dijual di distro-distro lainnya. Distro ini melakukan pembaruan produk pada event tertentu. Data Mining merupakan pegembangan atau penemuan informasi baru dengan mencari pola atau aturan tertentu dari sejumlah data dalam jumlah besar diharapkan dapat mengatasi kondisi tersebut. Metode yang akan digunakan dalam pembangunan aplikasi ini adalah metode Association Rule dengan Algoritma Apriori. Metode Association Rule adalah suatu prosedur untuk mencari hubungan antara item dalam suatu kumpulan data yang ditentukan. Dalam menentukan suatu Association Rule, terdapat suatu ukuran kepercayaan yang di dapatkan dari hasil pengolahan data dengan perhitungan tertentu. Algoritma Apriori merupakan salah satu alternatif Algoritma yang dapat digunakan untuk menentukan himpunan data yang paling sering muncul (frequent itemset) dalam suatu kumpulan data. Kata kunci: Data Mining, Algoritma, Apriori, Association Rule, Penjuaan, Distro

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