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Journal : Journal of Applied Data Sciences

Market Basket Analysis Using FP-Growth Algorithm to Design Marketing Strategy by Determining Consumer Purchasing Patterns Saputra, Jeffri Prayitno Bangkit; Rahayu, Silvia Anggun; Hariguna, Taqwa
Journal of Applied Data Sciences Vol 4, No 1: JANUARY 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i1.83

Abstract

The development and competition that exists in the business world today leads every manager or company to be more dexterous in making marketing strategies to increase sales. Various things are done to keep up with existing market competition, such as analyzing customer purchase transaction data to serve as a policy determination and decision-making system in making marketing strategies. In determining marketing strategies, it can be done by taking transaction data to see existing purchase or transaction patterns. Market Basket Analysis is part of a data mining method that uses the FP-Growth algorithm technique to find out associated products. This research uses data taken from sales transaction data archives as much as 150 sales transaction data and 26 product data. In this study, it is determined that the minimum support value is 50% and the minimum confidence is ≥ 0.75 From the test results, 9 products have superior support values and meet the minimum value. From the test results, a rule with a confidence value of 0.870 was obtained: D → W (if consumers buy Wardah Lightening Gentle Wash, then buy Azarine Sunscreen SPF50), and 0.808: A → E → O (if consumers buy Emina Face Wash, then buy Azarine Night Moisturizer and Himalaya Neem Mask).
Data Mining Implementation with Algorithm C4.5 for Predicting Graduation Rate College Student Saputra, Jeffri Prayitno Bangkit; Waluyo, Retno
Journal of Applied Data Sciences Vol 2, No 3: SEPTEMBER 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i3.37

Abstract

Academic evaluation and graduation of students are critical components of an academic information system's (AIS) effectiveness since they allow for the measurement of student learning progress. Additionally, the assessment stating whether the student passed or failed would benefit both the student and teacher by acting as a reference point for future performance suggestions and evaluations. Using Decision Tree C4.5, a comprehensive analysis of the student academic evaluation approach was conducted. Age, gender, public or private high school status, high school department, organization activity, age at high school admission, progress GPA (pGPA), and total GPA (tGPA) were all documented and evaluated from semester 1–4 utilizing three times the graduation criterion periods. The article's scope is confined to undergraduate programs. An accuracy algorithm (AC) with a performance accuracy of 79.60 percent, a true positive rate (TP) of 77.70 percent, and 91 percent quality training data achieved the highest performance accuracy value.
Development of Gamification-Based Learning Management System (LMS) with Agile Approach and Personalization of FSLSM Learning Style to Improve Learning Effectiveness Saputra, Jeffri Prayitno Bangkit; Prabowo, Harjanto; Gaol, Ford Lumban; Hertono, Gatot Fatwanto
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.486

Abstract

This research focuses on designing a Learning Management System (LMS) that incorporates gamification elements while addressing student learning styles based on the Felder-Silverman Learning Style Model (FSLSM). Using Agile methodology in the development process, the LMS is designed to deliver a more personalized learning experience, with features tailored to students' unique learning style preferences. The research process began with a comprehensive user needs analysis, followed by iterative design and development in accordance with Agile principles. System evaluation involved user feedback and performance analysis, revealing that the developed LMS increased student engagement by 25% and improved learning motivation by 30% compared to the previous system. Furthermore, 88% of users reported a positive experience with the personalized features, and the system achieved an overall satisfaction score of 85% in usability testing. These results demonstrate that the LMS effectively enhances student motivation and engagement in the learning process while providing a more individualized learning experience. This research contributes to the advancement of adaptive and responsive learning systems that better meet the diverse needs of students.