Tri Astuti
Amikom University Purwokerto, Indonesia

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Analysis of Customer Transaction Data Associations Based on The Apriori Algorithm Tri Astuti; Bella Puspita
International Journal of Informatics and Information Systems Vol 3, No 1: March 2020
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v3i1.4

Abstract

UD Dian Pertiwi is one of the small and medium enterprises engaged in materials with the main product is building materials. This business experiences large amounts of transactions every day, the data obtained becomes increasingly large, and it will only be limited to a pile of useless data or commonly called junk. By utilizing a data mining approach apriori algorithm technique, the data can be utilized to support the sales process and achieve a target of UD Dian Pertiwi. Based on research and data mining that has been done using association analysis and apriori algorithms by applying a minimum of support = 1% and a minimum of confidence = 70% resulted in the ten strongest association rules can be used by UD Dian Pertiwi in the process of applying a sales strategy including determining interrelationships, in short, the product has the potential to be purchased at the same time, increasing the amount of product stock and conducting promotions.
Analysis of Sequential Book Loan Data Pattern Using Generalized Sequential Pattern (GSP) Algorithm Tri Astuti; Lisdya Anggraini
International Journal of Informatics and Information Systems Vol 2, No 1: March 2019
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v2i1.10

Abstract

As a center for learning and information services, STMIK Amikom Purwokerto Library is a source of learning and a source of intellectual activity that is very important for the entire academic community in supporting the achievement of the college Tridharma program. Book lending transaction data, can produce information that is important as supporting decision making when further analyzed. One useful information is that it can provide information in the form of user behavior patterns in borrowing books that are used to maintain the availability of related book stocks to be balanced. This study uses the Generalized Sequential Pattern (GSP) algorithm, which can be used to determine the behavior patterns of users in each transaction and can show relationships or associations between books, both requested simultaneously and sequentially. From the calculations that have been done, 295 frequent sequences are consisting of 3 sequence patterns that are formed from the minimum support of 0.53% or the minimum number of books borrowed, namely 2 books. Three book items have very strong linkages in book lending transactions, namely book code 6690, 2026, and 8131.
Product Review Sentiment Analysis by Artificial Neural Network Algorithm Tri Astuti; Irnawati Pratika
International Journal of Informatics and Information Systems Vol 2, No 2: September 2019
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v2i2.15

Abstract

Buying and selling and marketing goods and services are now done online. The online store provides facilities that enable its customers to provide review related products offered. The number of reviews received by the store, online sometimes does not allow the store online to analyze one by one. Thus, it takes the help of machines to assist in the analysis of such sentiments. Analysis of the sentiments of the review the product is done to help the shop get a general overview related to the level of consumer satisfaction. In this study, the ANN algorithm will be used to analyze sentiment for review. A product ANN algorithm used because it can provide high accuracy performance. This research resulted in a reasonably high accuracy performance is 88.2%.
Effect of Selection of Classification Features C4.5 Algorithm in Student Alcohol Consumption Dataset Tri Astuti; Pungky Dwi Putra Handoko
International Journal of Informatics and Information Systems Vol 1, No 1: September 2018
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v1i1.20

Abstract

Alcoholic beverages are psychoactive substances that are addictive. Psychoactive substances are a class of substances that work selectively, especially in the brain, which can cause changes in behavior, emotion, cognition, perception and awareness of one's and others. Police survey results in 2014 showed that users of narcotics and liquor. Most of the group of students, both junior, and senior student, which amounts to 70%, while only 20% of primary school graduates. In the modern era, especially in information technology, the need for information and the latest knowledge is multiplying. One of them is the user information of alcohol among teenagers is more accurate. Data mining is the process for extracting and identifying information useful and relevant knowledge from a variety of big data. In the data mining, there is a classification technique that assesses the data objects to include it in a particular class of several classes available, can be applied in the case - the case in the health sector, for example, in the case of alcohol addiction in adolescents. The algorithm that can be used in the classification is the C4.5 decision tree. The use of the decision tree algorithm to determine the level of alcohol use in teenagers using two methods, namely, the selection of attributes and without attributes.