scholarly journals PENERAPAN ALGORITMA APRIORI DALAM MENENTUKAN POLA PEMBELIAN KONSUMEN DI KAFE HIDDEN TOAST AND FLOAT

2019 ◽  
Vol 4 (2) ◽  
pp. 83-88
Author(s):  
Ridwan Rismanto ◽  
Lucki Darmawan ◽  
Arief Prasetyo
Keyword(s):  
A Priori ◽  
Real Data ◽  
Test Results ◽  
Transaction Data ◽  
Online Purchases ◽  
Sample Data ◽  
Purchasing Patterns ◽  
The City ◽  
Computerized System ◽  
Confidence Value

Progress in Information Technology encourages culinary businesses to innovate, one of them is a computerized system, online-based sales and several interesting features that can increase consumer interest and increase sales to be the most frequently used innovation today. The cafe "Hidden Toast and Float" is a cafe in the City of Kediri. To increase sales from the cafe, a system is needed to facilitate the owner in recording sales and increasing the number of sales by providing automatic menu recommendations to customers. Based on the problem, in this thesis a website-based sales system and sales system will be created that is accompanied by the application of a priori algorithm to determine the purchasing patterns of customers and automatic menu recommendations from the system for customers. The test results of this thesis are two website-based systems with admin systems used to process existing data on the database and customer websites that are used for online purchases, as well as the application of a priori algorithms with the results of testing sample data and real data that produce menu combination recommendations. most often purchased based on all transaction data, namely Dark Choco Jam and Cappucino with a support value of 15% and a confidence value of 45%.

scholarly journals Implementasi Algoritma FG-Growth untuk Sistem Rekomendasi Penjualan Produk

2021 ◽  
Vol 5 (1) ◽  
pp. 41
Author(s):  
Siti Yuliyanti
Keyword(s):  
Association Rule ◽  
Frequent Itemset ◽  
Test Results ◽  
Rule Mining ◽  
Transaction Data ◽  
Data Object ◽  
Sample Data ◽  
Product Sales ◽  
Processing Techniques ◽  
Confidence Value

The variety of stationery marketed, makes business competition increasingly fierce in order to provide the best service to customers. Abundant sales transaction data, triggering piles of data so that it requires data mining processing techniques, namely association rule mining using the FP-Growth algorithm. Algorithm that generates frequent itemset used in the process of determining the rules that can produce an option by taking a product sales transaction data object. The test results show a rule that has the best confidence value and lift ratio of 100%, as well as 80% support with the rules that every purchase of a ballpoint product can be sure to buy a notebook from the dataset used as a sample data in the system trial (50 names). goods and 7 transaction data) with minimum support (5% = 0.05) and minimum confidence (30% = 0.3).

scholarly journals IMPLEMENTASI DATA MINING UNTUK MENGETAHUI POLA PEMBELIAN OBAT MENGGUNAKAN ALGORITMA APRIORI

2020 ◽  
Vol 17 (2) ◽  
pp. 396-402
Author(s):  
Nadya Febrianny Ulfha ◽  
Ruhul Amin
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
A Priori ◽  
Marketing Strategies ◽  
Business World ◽  
Association Pattern ◽  
Green Lake ◽  
Purchasing Patterns ◽  
The Relationship ◽  
Confidence Value

Competition in the business world requires entrepreneurs to think of finding a way or method to increase the transaction of goods sold. The purpose of this research is to provide drug stock data that is widely purchased by pharmacy customers at Kimia Farma, Green Lake branch in Jakarta. The algorithm used in this study is a priori to determine the relationship between the frequency of sales of drug brands most frequently purchased by customers. The association pattern formed with a minimum support of 40% and a minimum value of 70% confidence produces 17 association rules. The strong rules obtained are that if you buy a 500Mg Ponstan KPL @ 100, you will buy an Incidal OD 10Mg Cap with a support value of 59% and a confidence value of 84%. A priori algorithm can be used by companies to develop marketing strategies in marketing products by examining consumer purchasing patterns.

scholarly journals IMPLEMENTASI DATA MINING UNTUK MENGETAHUI POLA PEMBELIAN OBAT MENGGUNAKAN ALGORITMA APRIORI

2020 ◽  
Vol 17 (2) ◽  
pp. 396-402
Author(s):  
Nadya Febrianny Ulfha ◽  
Ruhul Amin
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
A Priori ◽  
Marketing Strategies ◽  
Business World ◽  
Association Pattern ◽  
Green Lake ◽  
Purchasing Patterns ◽  
The Relationship ◽  
Confidence Value

Competition in the business world requires entrepreneurs to think of finding a way or method to increase the transaction of goods sold. The purpose of this research is to provide drug stock data that is widely purchased by pharmacy customers at Kimia Farma, Green Lake branch in Jakarta. The algorithm used in this study is a priori to determine the relationship between the frequency of sales of drug brands most frequently purchased by customers. The association pattern formed with a minimum support of 40% and a minimum value of 70% confidence produces 17 association rules. The strong rules obtained are that if you buy a 500Mg Ponstan KPL @ 100, you will buy an Incidal OD 10Mg Cap with a support value of 59% and a confidence value of 84%. A priori algorithm can be used by companies to develop marketing strategies in marketing products by examining consumer purchasing patterns.  

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 AnANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions

2021 ◽  
Vol 11 (11) ◽  
pp. 4757
Author(s):  
Aleksandra Bączkiewicz ◽  
Jarosław Wątróbski ◽  
Wojciech Sałabun ◽  
Joanna Kołodziejczyk
Keyword(s):  
Atmospheric Pressure ◽  
Real Life ◽  
Real Data ◽  
Weather Conditions ◽  
Maximum Temperature ◽  
Air Masses ◽  
Weather Forecasts ◽  
Life Problems ◽  
The World ◽  
The City

Artificial Neural Networks (ANNs) have proven to be a powerful tool for solving a wide variety of real-life problems. The possibility of using them for forecasting phenomena occurring in nature, especially weather indicators, has been widely discussed. However, the various areas of the world differ in terms of their difficulty and ability in preparing accurate weather forecasts. Poland lies in a zone with a moderate transition climate, which is characterized by seasonality and the inflow of many types of air masses from different directions, which, combined with the compound terrain, causes climate variability and makes it difficult to accurately predict the weather. For this reason, it is necessary to adapt the model to the prediction of weather conditions and verify its effectiveness on real data. The principal aim of this study is to present the use of a regressive model based on a unidirectional multilayer neural network, also called a Multilayer Perceptron (MLP), to predict selected weather indicators for the city of Szczecin in Poland. The forecast of the model we implemented was effective in determining the daily parameters at 96% compliance with the actual measurements for the prediction of the minimum and maximum temperature for the next day and 83.27% for the prediction of atmospheric pressure.

scholarly journals Penalized partial least squares for pleiotropy

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Camilo Broc ◽  
Therese Truong ◽  
Benoit Liquet
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Association Studies ◽  
A Priori ◽  
Simulated Data ◽  
Real Data ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Multiple Traits ◽  
Application Fields

Abstract Background The increasing number of genome-wide association studies (GWAS) has revealed several loci that are associated to multiple distinct phenotypes, suggesting the existence of pleiotropic effects. Highlighting these cross-phenotype genetic associations could help to identify and understand common biological mechanisms underlying some diseases. Common approaches test the association between genetic variants and multiple traits at the SNP level. In this paper, we propose a novel gene- and a pathway-level approach in the case where several independent GWAS on independent traits are available. The method is based on a generalization of the sparse group Partial Least Squares (sgPLS) to take into account groups of variables, and a Lasso penalization that links all independent data sets. This method, called joint-sgPLS, is able to convincingly detect signal at the variable level and at the group level. Results Our method has the advantage to propose a global readable model while coping with the architecture of data. It can outperform traditional methods and provides a wider insight in terms of a priori information. We compared the performance of the proposed method to other benchmark methods on simulated data and gave an example of application on real data with the aim to highlight common susceptibility variants to breast and thyroid cancers. Conclusion The joint-sgPLS shows interesting properties for detecting a signal. As an extension of the PLS, the method is suited for data with a large number of variables. The choice of Lasso penalization copes with architectures of groups of variables and observations sets. Furthermore, although the method has been applied to a genetic study, its formulation is adapted to any data with high number of variables and an exposed a priori architecture in other application fields.

scholarly journals The crowd and citylife: Materiality, negotiation and inclusivity at Tokyo’s train stations

Urban Studies ◽  
2021 ◽  
pp. 004209802110078
Author(s):  
Romit Chowdhury ◽  
Colin McFarlane
Keyword(s):  
A Priori ◽  
Urban Density ◽  
History Of ◽  
Mode Of Being ◽  
The Relationship ◽  
The City ◽  
Human Embodiment

In the history of urban thought, density has been closely indexed to the idea of citylife. Drawing on commuters’ experiences and perceptions of crowds in and around Tokyo’s Shinjuku Station, this article offers an ethnographic perspective on the relationship between urban crowds and life in the city. We advance understandings of the relations between the crowd and citylife through three categories of ‘crowd relations’– materiality, negotiation and inclusivity – to argue that the multiplicity of meanings which accrue to people’s encounters with crowds refuses any a priori definitions of optimum levels of urban density. Rather, the crowd relations gathered here are evocations of citylife that take us beyond the tendency to represent the crowd as a particular kind of problem, be it alienation, exhaustion or a threshold for ‘good’ and ‘bad’ densities. The portraits of commuter crowds presented capture the various entanglements between human and non-human, embodiment and mobility, and multiculture and the civic, through which citylife emerges as a mode of being with oneself and others.

EXPRESS: How Physical Stores Enhance Customer Value: The Importance of Product Inspection Depth

2021 ◽  
pp. 002224292110121
Author(s):  
Jonathan Z. Zhang ◽  
Chun-Wei Chang ◽  
Scott A. Neslin
Keyword(s):  
Customer Value ◽  
Experiential Learning Theory ◽  
Transaction Data ◽  
Online Purchases ◽  
Serial Mediation ◽  
Multi Method Approach ◽  
Product Strategies ◽  
Method Approach ◽  
Customer Development

We investigate the role of the physical store in today’s multichannel environment. We posit that one benefit of the store to the retailer is to enhance customer value by providing the physical engagement needed to purchase deep products – products that require ample inspection in order for customers to make an informed decision. Using a multi-method approach involving a Hidden Markov Model (HMM) of transaction data and two experiments, we find that buying deep products in the physical store transitions customers to the high-value state more than other product/channel combinations. Findings confirm the hypotheses derived from experiential learning theory (ELT). A moderated serial mediation test supports the ELT-based mechanism for translating physical engagement into customer value: Customers purchase a deep product from the physical store. They reflect on this physical engagement experience, which, because it is tangible, concrete, and multi-sensory, enables them to develop strong learnings about the retailer. This experiential knowledge precipitates repatronage and generalizes to future online purchases online in the same category and in adjacent categories, thus contributing to higher customer value. This research suggests multichannel retailers use a combination of right-channel and right-product strategies for customer development and provides implications for experiential retail designs.

scholarly journals Analysis of Residual Dependencies of Independent Components Extracted from fMRI Data

2016 ◽  
Vol 2016 ◽  
pp. 1-15
Author(s):  
N. Vanello ◽  
E. Ricciardi ◽  
L. Landini
Keyword(s):  
Clustering Algorithm ◽  
A Priori ◽  
Similarity Measures ◽  
Real Data ◽  
Fmri Data ◽  
Order Selection ◽  
Functional Magnetic Resonance ◽  
Model Order Selection ◽  
Model Order ◽  
Independent Components

Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data can be employed as an exploratory method. The lack in the ICA model of strong a priori assumptions about the signal or about the noise leads to difficult interpretations of the results. Moreover, the statistical independence of the components is only approximated. Residual dependencies among the components can reveal informative structure in the data. A major problem is related to model order selection, that is, the number of components to be extracted. Specifically, overestimation may lead to component splitting. In this work, a method based on hierarchical clustering of ICA applied to fMRI datasets is investigated. The clustering algorithm uses a metric based on the mutual information between the ICs. To estimate the similarity measure, a histogram-based technique and one based on kernel density estimation are tested on simulated datasets. Simulations results indicate that the method could be used to cluster components related to the same task and resulting from a splitting process occurring at different model orders. Different performances of the similarity measures were found and discussed. Preliminary results on real data are reported and show that the method can group task related and transiently task related components.

scholarly journals Modeling the Process of Event Sequence Data Generated for Working Condition Diagnosis

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Jianwei Ding ◽  
Yingbo Liu ◽  
Li Zhang ◽  
Jianmin Wang
Keyword(s):  
Working Condition ◽  
Sequence Data ◽  
A Priori ◽  
Real Data ◽  
Data Sets ◽  
Main Task ◽  
Event Sequence ◽  
Telemetry Data ◽  
Condition Monitoring Systems ◽  
Condition Diagnosis

Condition monitoring systems are widely used to monitor the working condition of equipment, generating a vast amount and variety of telemetry data in the process. The main task of surveillance focuses on analyzing these routinely collected telemetry data to help analyze the working condition in the equipment. However, with the rapid increase in the volume of telemetry data, it is a nontrivial task to analyze all the telemetry data to understand the working condition of the equipment without any a priori knowledge. In this paper, we proposed a probabilistic generative model called working condition model (WCM), which is capable of simulating the process of event sequence data generated and depicting the working condition of equipment at runtime. With the help of WCM, we are able to analyze how the event sequence data behave in different working modes and meanwhile to detect the working mode of an event sequence (working condition diagnosis). Furthermore, we have applied WCM to illustrative applications like automated detection of an anomalous event sequence for the runtime of equipment. Our experimental results on the real data sets demonstrate the effectiveness of the model.

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