Penerapan Algoritma Apriori Untuk Menentukan Persediaan Peralatan Dan Mesin

Abstract: The DKI Jakarta Regional Asset Management Agency (BPAD) is a State Agency that regulates budget costs for many types of assets in the DKI Jakarta area. One type of budget is for equipment and machinery that are needed annually. The local budget so far has not focused on the equipment and machinery needed. This is needed in order to be able to minimize the budget and focus on the equipment and machinery needed each year. In this case Data Mining can be applied to find information from the dataset. In order to know the Equipment and Machines that are most needed each year, searching for information on the dataset can be done by one of the Data Mining methods, namely the A priori Algorithm by looking for patterns of relationships in a dataset. If you know the equipment and machinery that are most needed each year, BPAD can focus the budget on the goods most needed and can find out which SKPD need more equipment and machinery each year.Keywords: BPAD, Data Mining, Algoritma Apriori.Abstraksi: Badan Pengelolaan Aset Daerah (BPAD) DKI Jakarta adalah Instansi Negara yang mengatur biaya anggaran untuk banyak jenis aset di daerah DKI Jakarta. Salah satu jenis anggaran yang ada ialah untuk Peralatan dan Mesin yang tiap tahunnya dibutuhkan. Anggaran daerah sejauh ini belum berfokus pada Peralatan dan Mesin yang dibutuhkan. Hal ini diperlukan agar dapat lebih meminimalisir anggaran dan berfokus kepada Peralatan dan Mesin yang dibutuhkan tiap tahunnya. Dalam hal ini Data Mining dapat diterapkan untuk mencari informasi dari dataset. Agar dapat mengetahui Peralatan dan Mesin yang paling dibutuhkan tiap tahunnya, pencarian informasi pada dataset dapat dilakukan dengan salah satu metode Data Mining yaitu Algoritma Apriori dengan mencari pola hubungan dalam sebuah dataset. Jika mengetahui Peralatan dan Mesin yang paling dibutuhkan tiap tahunnya maka BPAD dapat memfokuskan anggaran pada barang yang paling dibutuhkan dan dapat mengetahui SKPD mana yang lebih membutuhkan Peralatan dan Mesin setiap tahunnya..Kata Kunci: BPAD, Data Mining, Algoritma Apriori.

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Redesign Prototype of Fintech Application

E3S Web of Conferences ◽

10.1051/e3sconf/202132804002 ◽

2021 ◽

Vol 328 ◽

pp. 04002

Author(s):

A F Doni ◽

Y D P Negara ◽

Eka Retno Wulandari ◽

Muhamad Zainul Mustofa

Keyword(s):

Data Mining ◽

Asset Management ◽

Interactive Systems ◽

Test Results ◽

User Centered Design ◽

Payment Methods ◽

Use Of Technology ◽

User Expectations ◽

Mining Methods ◽

User Centered

The development of Fintech's business in Indonesia helps maximize the use of technology to change, sharpen, or improve various financial aspects ranging from payment methods, transferring funds, obtaining funds, loans to asset management can begin to be accelerated and shortened using technology. However, considering technology that is not well prepared will have an impact on a poor user experience. The DANA application still requires UI / UX difficulties on transactions so it does not meet user expectations. Assessing user-centered design can help in user-related issues. Supporting user-centered design has been supported by various techniques, methods, tools, procedures and processes that help design interactive systems that are more user-centered. Data mining methods used are interviews with end users and literature studies. New design and prototype recommendations according to usability test results. A total of seven participants tested the usability of the new transaction prototype. The expected results of this design help overcome/control the user in conducting transactions.

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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 MODEL FOR THE ANALYSIS OF PHARMACOEPIDEMIOLOGICAL DATA BASED ON DATA MINING METHODS

The Journal of scientific articles Health and Education millennium ◽

10.26787/nydha-2226-7425-2017-19-10-289-298 ◽

2017 ◽

Vol 19 (10) ◽

pp. 289-298

Author(s):

I.M. Burykin ◽

◽

G.N. Aleeva ◽

R.Kh. Khafizianova ◽

◽

...

Keyword(s):

Data Mining ◽

Mining Methods

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Recognizing Job Apathy Patterns of Iraqi Higher Education Employees Using Data Mining Techniques

Journal of Southwest Jiaotong University ◽

10.35741/issn.0258-2724.54.4.30 ◽

2019 ◽

Vol 54 (4) ◽

Author(s):

Mustafa S. Abd ◽

Suhad Faisal Behadili

Keyword(s):

Higher Education ◽

Data Mining ◽

Psychiatric Patients ◽

Human Life ◽

A Priori ◽

Psychological Research ◽

Attribute Selection ◽

Specific Class ◽

Data Mining Techniques ◽

Using Data

Psychological research centers help indirectly contact professionals from the fields of human life, job environment, family life, and psychological infrastructure for psychiatric patients. This research aims to detect job apathy patterns from the behavior of employee groups in the University of Baghdad and the Iraqi Ministry of Higher Education and Scientific Research. This investigation presents an approach using data mining techniques to acquire new knowledge and differs from statistical studies in terms of supporting the researchers’ evolving needs. These techniques manipulate redundant or irrelevant attributes to discover interesting patterns. The principal issue identifies several important and affective questions taken from a questionnaire, and the psychiatric researchers recommend these questions. Useless questions are pruned using the attribute selection method. Moreover, pieces of information gained through these questions are measured according to a specific class and ranked accordingly. Association and a priori algorithms are used to detect the most influential and interrelated questions in the questionnaire. Consequently, the decisive parameters that may lead to job apathy are determined.

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Using Unstated Cases to Correct for COVID-19 Pandemic Outbreak and Its Impact on Easing the Intervention for Qatar

Biology ◽

10.3390/biology10060463 ◽

2021 ◽

Vol 10 (6) ◽

pp. 463

Author(s):

Narjiss Sallahi ◽

Heesoo Park ◽

Fedwa El Mellouhi ◽

Mustapha Rachdi ◽

Idir Ouassou ◽

...

Keyword(s):

Public Health ◽

A Priori ◽

Epidemiological Models ◽

Sir Epidemic Model ◽

Gulf Region ◽

Epidemiological Modeling ◽

Public Health Policies ◽

Sir Epidemic ◽

The Impact ◽

Case Data

Epidemiological Modeling supports the evaluation of various disease management activities. The value of epidemiological models lies in their ability to study various scenarios and to provide governments with a priori knowledge of the consequence of disease incursions and the impact of preventive strategies. A prevalent method of modeling the spread of pandemics is to categorize individuals in the population as belonging to one of several distinct compartments, which represents their health status with regard to the pandemic. In this work, a modified SIR epidemic model is proposed and analyzed with respect to the identification of its parameters and initial values based on stated or recorded case data from public health sources to estimate the unreported cases and the effectiveness of public health policies such as social distancing in slowing the spread of the epidemic. The analysis aims to highlight the importance of unreported cases for correcting the underestimated basic reproduction number. In many epidemic outbreaks, the number of reported infections is likely much lower than the actual number of infections which can be calculated from the model’s parameters derived from reported case data. The analysis is applied to the COVID-19 pandemic for several countries in the Gulf region and Europe.

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