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All Journal Jurnal Sains Matematika dan Statistika AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat Martabe : Jurnal Pengabdian Kepada Masyarakat Jurnal Ilmiah Matematika Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi International Journal of Research in Community Services MATHunesa: Jurnal Ilmiah Matematika Journal of Data Mining and Information Systems JAR'S (Journal of Advanced Research in Informatics) Journal of Statistics and Data Science Limits: Journal of Mathematics and Its Applications
Sunandi, Etis
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Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Mathematics Social Sciences Other

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Implementasi Market Basket Analysis Dengan Algoritma Frequent Pattern Growth Pada Data Transaksional di Electronic Commerce Fairuzindah, Athaya; Islami, Istiqomah Rabithah Alam; Rexa, Nafa; Anggraini, Silvia; Sunandi, Etis
JDMIS: Journal of Data Mining and Information Systems Vol. 3 No. 2 (2025): August 2025
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/jdmis.v3i2.4593

Abstract

The Growth of the e-commerce industry has resulted in a massive volume of transaction data, necessitating effective data analysis techniques to extract customer purchasing patterns. The Frequent Pattern Growth (FP-Growth) algorithm is one of the data mining methods that can be used to identify frequently occurring purchase patterns without explicitly generating candidate itemsets. This study aims to implement and evaluate the performance of the FP-Growth algorithm in analyzing e-commerce transaction data to identify recurring shopping patterns. The research methodology includes transaction data collection, data preprocessing, FP-Growth algorithm implementation, and result analysis. This study utilizes an e-commerce transaction dataset from an online retail store based in the United Kingdom, comprising 541,909 transaction records. The research findings indicate that the FP-Growth algorithm is efficient in identifying frequently occurring transaction patterns. Using a support threshold of 1% and a confidence level of 80%, 13 association rules were discovered, demonstrating relationships between frequently co-purchased products. Further analysis shows that these findings can be leveraged by e-commerce businesses to develop marketing strategies based on product recommendations. In conclusion, the FP-Growth algorithm is an effective approach for extracting purchasing patterns from large-scale e-commerce transaction data.
Perbandingan Metode Regresi Ridge dan Jackknife Ridge Regression pada Data Tingkat Pengangguran Terbuka Andini, Agita; Sunandi, Etis; Novianti, Pepi; Sriliana, Idhia; Agwil, Winalia
Limits: Journal of Mathematics and Its Applications Vol. 22 No. 1 (2025): Limits: Journal of Mathematics and Its Applications Volume 22 Nomor 1 Edisi Ma
Publisher : Pusat Publikasi Ilmiah LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/limits.v22i1.3374

Abstract

Regression analysis is a statistical technique used to analyze the relationship between predictor and response variables. One of the parameter estimation methods commonly used for regression analysis is Ordinary Least Squares. This method produces unbiased and efficient estimates, known as BLUE (Best Linear Unbiased Estimator). In multiple linear regression analysis involving more than one predictor variable, it is essential to meet model assumptions such as the absence of multicollinearity. Multicollinearity is a condition where predictor variables have a high correlation, which can disrupt the stability of parameter estimates. Therefore, Ridge Regression and Jackknife Ridge Regression methods were used to address this issue. Both methods modify the least squares method by adding a bias constant value. This research uses the Open Unemployment Rate (OUR) data in Sumatra in 2022, and 3 predictor variables exhibit multicollinearity. Based on the analysis comparing the Mean Squared Error (MSE) values, the Jackknife Ridge Regression method yields the smallest MSE value, 0.004. Both methods are effective in addressing multicollinearity and identifying significant predictor variables for OUR in Sumatra Island, namely the Human Development Index (HDI), average years of schooling, number of poor people, Life Expectancy (LE), population density and inactive population
Co-Authors Aflah, Ukasyah Afni, Della Nur Agwil, Winalia Andini, Agita Anggraini, Silvia Anjani, Retno Tri Ayu Lestari, Wina Azizy, Nala Kamila Damayanti, Putri Dian Agustina Dzakirah, Qanitahudz Fachri Faisal Fadila, Risfa Faeza, Veronnica Noer Fairuzinda, Athaya Fairuzindah, Athaya Fhaeza, Veronnica Noer Firdaus Firdaus Firlian, Lutfiah Herlin Fransiska Hermalia, Hermalia Idhia Sriliana Islami, Istiqomah Rabithah Alam Jose Rizal M. Zidan Mezilano Y Marta, Rezkyan Muhammad Hafiz Nurul Hidayati Pandu Winata, Aji Pepi Novianti Popita, Resi Rexa, Nafa Ridho, Muhammad Kevyn Sigit Nugroho Simanullang, Aditama Rouliber Sonia, Gita Syah, Vivi Elvira Saputri Syahada, Sri Wanti, Novelisa Syendra Yulian Utami, Rahmi