scholarly journals IMPLEMENTASI ALGORITMA K-MEANS UNTUK PEMILIHAN KERAMIK DAN PELANGGAN POTENSIAL PADA CV. JAYA TUNGGAL KERAMIK

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Yuliana Yuliana ◽  
Mario Richie ◽  
Halim Agung
Keyword(s):  
Cluster Center ◽  
Test Results ◽  
Inventory Data ◽  
Clustering Method ◽  
Regular Customer ◽  
Using Data ◽  
Iterative Partitioning ◽  
A Company ◽  
Potential Customers

CV. Jaya Tunggal Keramik is a company that sale of ceramics. CV. Jaya Tunggal Keramik experienced some problems regarding ceramics and customers such as difficulties in sale ceramics to customers so that some ceramic products accumulate in the warehouse, such as being damaged and ceramic display becomes less good because it is stored too long and difficulty retaining customers because some customers do not want to order ceramic products. Lack of precise decision taken by the management CV. Jaya Tunggal Keramik in determining the strategy to supply ceramic and how to make it CV. Jaya Tunggal Keramik is difficult to estimate the stock of ceramic products to be provided and it is difficult to determine which potential customers can be maintained as a regular customer. This research uses K-Means algorithm. K-Means algorithm is a partitioning clustering method that separates data into different groups with iterative partitioning. By using this application, users can find out the estimated stock and price of ceramics as well as information about potential customers. Testing in this research using data of November 2017 that compared with data of December 2017. Based on ceramic data test results, there are some ceramics that are not in accordance with the predicted results so it can be concluded that the K-Means algorithm on the test inventory data inventory in this study is not fully can provide accurate estimates, this is because the use of the K-Means algorithm is strongly influenced by the cluster center results and the attributes used.

scholarly journals Goods Stock Management using the K-Means Algorithm Method

2020 ◽  
Vol 10 (1) ◽  
pp. 22-45
Author(s):  
Dhio Saputra
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Test Results ◽  
Stock Management ◽  
Clustering Method ◽  
Sales Data ◽  
Using Data ◽  
Cluster 2

The grouping of Mazaya products at PT. Bougenville Anugrah can still do manuals in calculating purchases, sales and product inventories. Requires time and data. For this reason, a research is needed to optimize the inventory of Mazaya goods by computerization. The method used in this research is K-Means Clustering on sales data of Mazaya products. The data processed is the purchase, sales and remaining inventory of Mazaya products in March to July 2019 totaling 40 pieces. Data is grouped into 3 clusters, namely cluster 0 for non-selling criteria, cluster 1 for best-selling criteria and cluster 2 for very best-selling criteria. The test results obtained are cluster 0 with 13 data, cluster 1 with 25 data and cluster 2 with 2 data. So to optimize inventory is to multiply goods in cluster 2, so as to save costs for management of Mazayaproducts that are not available. K-Means clustering method can be used for data processing using data mining in grouping data according to criteria.

scholarly journals Simulasi Pengadaan Barang menggunakan Metode Monte Carlo

2019 ◽  
Vol 1 (3) ◽  
pp. 7-11
Author(s):  
Kiki Hariani Manurung ◽  
Julius Santony
Keyword(s):  
Monte Carlo ◽  
Data Processing ◽  
Inventory Management ◽  
Monte Carlo Algorithm ◽  
Market Demand ◽  
Test Results ◽  
Inventory Data ◽  
Web Based ◽  
The Right ◽  
A Company

Inventory is a very important aspect for the development of a company. Inventory management is needed to determine the inventory of goods needed within a certain period so that market demand can be fulfilled. The data used in this study are inventory data from 2016 to 2018. Data processing in this study uses the Monte Carlo algorithm to predict procurement data. In accelerating data processing, this research applies a Web-based program with the PHP (Hypertext Processor) programming language. The results of testing this method are to obtain predictions of the supply of goods in a certain period of time with the right level of accuracy. From the test results obtained the level of accuracy in predicting inventory stock by 93% so that it can help companies in making decisions in the future.

scholarly journals Designing Customer Target Recommendation System Using K-Means Clustering Method

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Evasaria M. Sipayung ◽  
Herastia Maharani ◽  
Benny A. Paskhadira
Keyword(s):  
Information System ◽  
Recommendation System ◽  
Product Category ◽  
Clustering Method ◽  
Marketing Department ◽  
Customer Group ◽  
Product Sales ◽  
Net Framework ◽  
A Company ◽  
Potential Customers

UD Swiss is a company engaged in the field of goods distribution located in Cirebon. In achieving sales targets, customer marketing department sets customer targets to be visited based on the type and location of outlets. However, the method of targeting customers does not achieve the sales target yet due to the differences in the characteristics of purchases per product category for each type of outlet. The research in this paper focuses on the analysis and implementation of management information system to help the company gain knowledge in targeting customers based on the profile and characteristics of each customer group in doing transactions. The information system is made to load each of the knowledge generated by the analysis of customers’ characteristic using the k-means clustering. The system is designed to use the programming language “Groovy and Grails” and is built using the .NET Framework that can run on the Java platform with support of PostgreSQL as a database. Grouping customers using k-means clustering method generates groups of potential customers who are considered to be the target in the process of product sales. Customers who have an average purchase at least Rp 2,028,813.00 per transaction with the minimum purchase frequency of 25 transactions a year is a potential customer.

scholarly journals MATCHING THE CONTROLLER ROLE: INDIVIDUALS VS COMPANIES

2020 ◽  
Vol 21 (5) ◽  
pp. 1411-1431
Author(s):  
Ariel La Paz ◽  
Daniela Gracia ◽  
Jonathan Vásquez
Keyword(s):  
Data Mining ◽  
Clustering Analysis ◽  
Business Value ◽  
Competitive Advantages ◽  
Clustering Method ◽  
Good Match ◽  
Using Data ◽  
A Company ◽  
Extended Review ◽  
Mining Tools

Finding a good match in the C-Suite is critical for maximizing business value and capitalizing on competitive advantages. This study analyzes the roles of controllers in organizational descriptions and executives’ profiles, as they both define the position and craft the profession. This paper proposes a framework to describe and classify the existing controller types based on an extended review and description of the available literature. Then, using a clustering method based on the semisupervised k-means, we mapped a sample of 45 controllers to their dominant profile and contrasted individuals’ characteristics against the companies’ definitions of the role. This study reveals that the understanding of the types of controllers, which is usually presented as a dichotomy, may lead to mismatches between what is needed and what is performed. The clustering analysis reveals a significant mismatch between individuals and the organizations defining the role. The framework and clustering method could be used for multiple purposes, such as evaluating what type of controller an individual is or a company needs, which would improve recruitment processes to achieve better matches. The framework expands the identification of controllers from a dichotomy to a gradual classification and describes four controller profiles. This perspective further describes the role and validates the framework using data mining tools.

Simulasi Pengadaan Barang Menggunakan Metode Monte Carlo

2019 ◽  
pp. 7-11
Author(s):  
Kiki Hariani Manurung ◽  
Julius Santony
Keyword(s):  
Monte Carlo ◽  
Data Processing ◽  
Inventory Management ◽  
Monte Carlo Algorithm ◽  
Market Demand ◽  
Test Results ◽  
Inventory Data ◽  
Web Based ◽  
The Right ◽  
A Company

Inventory is a very important aspect for the development of a company. Inventory management is needed to determine the inventory of goods needed within a certain period so that market demand can be fulfilled. The data used in this study are inventory data from 2016 to 2018. Data processing in this study uses the Monte Carlo algorithm to predict procurement data. In accelerating data processing, this research applies a Web-based program with the PHP (Hypertext Processor) programming language. The results of testing this method are to obtain predictions of the supply of goods in a certain period of time with the right level of accuracy. From the test results obtained the level of accuracy in predicting inventory stock by 93% so that it can help companies in making decisions in the future.

scholarly journals Pengaruh Brand Image dan Sikap Konsumen Terhadap Ekuitas Merek Pada Produk Ija Kroeng di Kota Banda Aceh

2018 ◽  
Vol 2 (2) ◽  
pp. 99-109
Author(s):  
Rahmi Rahmi ◽  
Nelly Nelly
Keyword(s):  
Brand Equity ◽  
Brand Image ◽  
Consumer Attitudes ◽  
Test Results ◽  
Brand Associations ◽  
Brand Quality ◽  
A Company ◽  
Potential Customers ◽  
Dominant Influence ◽  
Banda Aceh

Brand image and consumer attitudes  are important factors in influencing brand equity. Brand equity is the value of a brand, depend the extent to which the brand has high loyalty, the quality that is accepted by consumers and able to add more value to consumers. A strong brand equity can help a company in an effort to attract potential customers as well as efforts to establish good relationships with consumers and can eliminate consumer doubts about brand quality. This study was aimed to determine The Influence Of Brand Image And Consumer Attitudes Toward Brand Equity In Ija Kroeng Products In Banda Aceh. The sample of this study was 100 respondents. The data used in this study was quantitative data. The results showed that brand image and consumer attitudes had a significant effect on brand equity in Ija Kroeng products in Banda Aceh, it was proven by the brand image and attitude of consumers as factors that influence brand equity, which was proven by 84.9% of research results and the remaining 15.1 % explained by other variables outside of this research, for instance product quality, brand awareness, brand associations, etc. Statistical test results show  that simultan brand image and consumer attitudes have a significan effect on brand equity in Ija Kroeng products in Banda Aceh, Fcount>Ftable (272.719>3,090). While partially brand image and consumer attitudes have a significant effect on brand equity in Ija Kroeng products in Banda Aceh, with the value of tcount>ttable (11,871 and 2,051> 1,984). Based on the results of multiple linear regression  it can be seen that of the two variables that the brand image (X1) had the most dominant influence on brand equity in Ija Kroeng products in Banda Aceh, with a coefficient value of 0,805, and the followed by the variable attitudes of consumers (X2) with a coefficient value of 0,139.

Perancangan Aplikasi Prediksi Kelulusan Tepat Waktu Bagi Mahasiswa Baru Dengan Teknik Data Mining (Studi Kasus: Data Akademik Mahasiswa STMIK Dipanegara Makassar)

2015 ◽  
Vol 1 (4) ◽  
pp. 270
Author(s):  
Muhammad Syukri Mustafa ◽  
I. Wayan Simpen
Keyword(s):  
Data Mining ◽  
Nearest Neighbor ◽  
Test Results ◽  
K Nearest Neighbor ◽  
Accuracy Rate ◽  
Sample Data ◽  
New Students ◽  
K Nearest Neighbor Algorithm ◽  
Using Data ◽  
Existing Data

Penelitian ini dimaksudkan untuk melakukan prediksi terhadap kemungkian mahasiswa baru dapat menyelesaikan studi tepat waktu dengan menggunakan analisis data mining untuk menggali tumpukan histori data dengan menggunakan algoritma K-Nearest Neighbor (KNN). Aplikasi yang dihasilkan pada penelitian ini akan menggunakan berbagai atribut yang klasifikasikan dalam suatu data mining antara lain nilai ujian nasional (UN), asal sekolah/ daerah, jenis kelamin, pekerjaan dan penghasilan orang tua, jumlah bersaudara, dan lain-lain sehingga dengan menerapkan analysis KNN dapat dilakukan suatu prediksi berdasarkan kedekatan histori data yang ada dengan data yang baru, apakah mahasiswa tersebut berpeluang untuk menyelesaikan studi tepat waktu atau tidak. Dari hasil pengujian dengan menerapkan algoritma KNN dan menggunakan data sampel alumni tahun wisuda 2004 s.d. 2010 untuk kasus lama dan data alumni tahun wisuda 2011 untuk kasus baru diperoleh tingkat akurasi sebesar 83,36%.This research is intended to predict the possibility of new students time to complete studies using data mining analysis to explore the history stack data using K-Nearest Neighbor algorithm (KNN). Applications generated in this study will use a variety of attributes in a data mining classified among other Ujian Nasional scores (UN), the origin of the school / area, gender, occupation and income of parents, number of siblings, and others that by applying the analysis KNN can do a prediction based on historical proximity of existing data with new data, whether the student is likely to complete the study on time or not. From the test results by applying the KNN algorithm and uses sample data alumnus graduation year 2004 s.d 2010 for the case of a long and alumni data graduation year 2011 for new cases obtained accuracy rate of 83.36%.

scholarly journals Derivation and Performance of Standardized Enhanced Liver Fibrosis (ELF) Test Thresholds for the Detection and Prognosis of Liver Fibrosis

2019 ◽  
Vol 3 (5) ◽  
pp. 815-826 ◽  
Author(s):  
James Day ◽  
Preya Patel ◽  
Julie Parkes ◽  
William Rosenberg
Keyword(s):  
Liver Fibrosis ◽  
Delphi Panel ◽  
Test Results ◽  
Hazard Ratios ◽  
Test Thresholds ◽  
And Performance ◽  
Using Data ◽  
Relationship Of ◽  
The Relationship ◽  
Selection Of

Abstract Introduction Noninvasive tests are increasingly used to assess liver fibrosis and determine prognosis but suggested test thresholds vary. We describe the selection of standardized thresholds for the Enhanced Liver Fibrosis (ELF) test for the detection of liver fibrosis and for prognostication in chronic liver disease. Methods A Delphi method was used to identify thresholds for the ELF test to predict histological liver fibrosis stages, including cirrhosis, using data derived from 921 patients in the EUROGOLF cohort. These thresholds were then used to determine the prognostic performance of ELF in a subset of 457 patients followed for a mean of 5 years. Results The Delphi panel selected sensitivity of 85% for the detection of fibrosis and >95% specificity for cirrhosis. The corresponding thresholds were 7.7, 9.8, and 11.3. Eighty-five percent of patients with mild or worse fibrosis had an ELF score ≥7.7. The sensitivity for cirrhosis of ELF ≥9.8 was 76%. ELF ≥11.3 was 97% specific for cirrhosis. ELF scores show a near-linear relationship with Ishak fibrosis stages. Relative to the <7.7 group, the hazard ratios for a liver-related outcome at 5 years were 21.00 (95% CI, 2.68–164.65) and 71.04 (95% CI, 9.4–536.7) in the 9.8 to <11.3 and ≥11.3 subgroups, respectively. Conclusion The selection of standard thresholds for detection and prognosis of liver fibrosis is described and their performance reported. These thresholds should prove useful in both interpreting and explaining test results and when considering the relationship of ELF score to Ishak stage in the context of monitoring.

scholarly journals Strategies of SMM Management, Comparison of Instagram and Facebook Social Networks

2017 ◽  
Vol 3 (5) ◽  
pp. 51
Author(s):  
Ramis Akhmedov
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Customer Relationship ◽  
Social Media Marketing ◽  
Crm System ◽  
Research Questions ◽  
Short Time ◽  
A Company ◽  
Potential Customers ◽  
The Ideal

<p class="Default">SMM occupies an important role in the lives of people and so many people are represented in social networks, it provides the ideal platform for companies so they can communicate with their current and potential customers. This study continues to explore how companies can use social media marketing to build and maintain relationships with customers. This investigates through conducted research questions. How SMM is effective in terms of CRM? Can Facebook replace CRM system? Why do people choose to follow a company on Instagram? To analyze more clearly the focus will be on Instagram and Facebook applications, which in a short time acquired great popularity among private users as well as among the companies. The purpose of this study is to indicate the integration of customer relationship management (CRM) with social media marketing (SMM) strategies, and defines its benefits for business.</p><p class="Default"> </p>

scholarly journals KOMPARASI METODE CLUSTERING K-MEANS DAN K-MEDOIDS DENGAN MODEL FUZZY RFM UNTUK PENGELOMPOKAN PELANGGAN

2018 ◽  
Vol 6 (2) ◽  
Author(s):  
Elly Muningsih - AMIK BSI Yogyakarta
Keyword(s):  
Data Clustering ◽  
Small Data ◽  
Clustering Methods ◽  
Monetary Model ◽  
Clustering Method ◽  
Online Sales ◽  
Rfm Model ◽  
Potential Customers ◽  
Cluster 2 ◽  
Better Than

Abstract ~ The K-Means method is one of the clustering methods that is widely used in data clustering research. While the K-Medoids method is an efficient method used for processing small data. This study aims to compare two clustering methods by grouping customers into 3 clusters according to their characteristics, namely very potential (loyal) customers, potential customers and non potential customers. The method used in this study is the K-Means clustering method and the K-Medoids method. The data used is online sales transaction. The clustering method testing is done by using a Fuzzy RFM (Recency, Frequenty and Monetary) model where the average (mean) of the third value is taken. From the data testing is known that the K-Means method is better than the K-Medoids method with an accuracy value of 90.47%. Whereas from the data processing carried out is known that cluster 1 has 16 members (customers), cluster 2 has 11 members and cluster 3 has 15 members. Keywords : clustering, K-Means method, K-Medoids method, customer, Fuzzy RFM model. Abstrak ~ Metode K-Means merupakan salah satu metode clustering yang banyak digunakan dalam penelitian pengelompokan data. Sedangkan metode K-Medoids merupakan metode yang efisien digunakan untuk pengolahan data yang kecil. Penelitian ini bertujuan untuk membandingkan atau mengkomparasi dua metode clustering dengan cara mengelompokkan pelanggan menjadi 3 cluster sesuai dengan karakteristiknya, yaitu pelanggan sangat potensial (loyal), pelanggan potensial dan pelanggan kurang (tidak) potensial. Metode yang digunakan dalam penelitian ini adalah metode clustering K-Means dan metode K-Medoids. Data yang digunakan adalah data transaksi penjualan online. Pengujian metode clustering yang dilakukan adalah dengan menggunakan model Fuzzy RFM (Recency, Frequenty dan Monetary) dimana diambil rata-rata (mean) dari nilai ketiga tersebut. Dari pengujian data diketahui bahwa metode K-Means lebih baik dari metode K-Medoids dengan nilai akurasi 90,47%. Sedangkan dari pengolahan data yang dilakukan diketahui bahwa cluster 1 memiliki 16 anggota (pelanggan), cluster 2 memiliki 11 anggota dan cluster 3 memiliki 15 anggota. Kata kunci : clustering, metode K-Means, metode K-Medoids, pelanggan, model Fuzzy RFM.

Sign in / Sign up

Export Citation Format

Share Document