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All Journal Jurnal Sains Matematika dan Statistika BAREKENG: Jurnal Ilmu Matematika dan Terapan Journal of Data Mining and Information Systems
Fairuzindah, Athaya
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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ANALYSIS OF THE EXISTENCE OF THE AGRICULTURAL SECTOR IN MODELING POVERTY IN BENGKULU PROVINCE USING GAUSSIAN COPULA MARGINAL REGRESSION Nugroho, Sigit; Rini, Dyah Setyo; Novianti, Pepi; Crisdianto, Riki; Karuna, Elisabeth Evelin; Fairuzindah, Athaya
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1251-1262

Abstract

Bengkulu Province ranks second in the category of the highest percentage of poor people in the Sumatra region, at 14.62% in March 2022, and sixth in Indonesia, which is undoubtedly one of the fundamental problems that requires mutual attention. The phenomenon of high poverty in Bengkulu Province is inseparable from the lives of people whose main livelihood is in the agricultural sector, especially tenant farmers. Therefore, in this study, the Copula and Gaussian Copula Marginal Regression (GCMR) methods are applied to determine how the agricultural sector affects poverty in Bengkulu Province using secondary data obtained from the Bengkulu Provincial Statistics Agency (Susenas 2022). The results show that the Copula model can identify various types of dependency between the number of poor households in each district/city in Bengkulu Province in 2022 and each of the variables, namely the Number of Agricultural Business Households , the Growth Rate of the Agricultural Sector , the Human Development Index , and the Open Unemployment Rate ( ) by considering the different characteristics of dependency such as top-tail, bottom-tail, or negative dependency. Meanwhile, the GCMR model can provide the direction of influence of the independent variables on the dependent variable Y, where it can be seen that the variables , , and have a negative influence on the variable , whie the variable has a positive impact on the variable . Therefore, in general, it can be concluded that either positive or negative dependencies identified by the Copula model can influence the resulting GCMR model by providing more profound complexity regarding the relationship between the variables analyzed.
Analisis Asosiasi Jenis Kredit Rumah Tangga dengan Jenis Pekerjaan Utama di Provinsi Bengkulu Fairuzindah, Athaya; Marta, Rezkyan; Anjani, Retno Tri; Faeza, Veronnica Noer; Sunandi, Etis; Novianti, Pepi
Jurnal Sains Matematika dan Statistika Vol 11, No 2 (2025): JSMS Juli 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jsms.v11i2.32966

Abstract

Kredit rumah tangga di Provinsi Bengkulu berperan penting dalam mendukung kesejahteraan masyarakat, terutama dalam memenuhi kebutuhan dasar seperti perumahan, pendidikan, dan barang konsumsi. Namun tidak semua jenis pekerjaan utama memiliki kemampuan yang sama dalam memenuhi syarat pengajuan kredit. Dengan  permasalahan   tersebut  penelitian   ini   bertujuan  melihat  asosiasi jenis pekerjaan dan jenis kredit rumah tangga dengan menggunakan metode log linear 2 dimensi. Berdasarkan hasil pengujian asosiasi didapatkan hasil berupa terdapat asosiasi antara jenis pekerjaan dan kredit. Setelah melakukan pengujian dilakukan pemilihan model terbaik dengan menggunakan pengujian Goodness of fit (uji G). Berdasarkan hasil pengujian Goodness of fit (uji G) bahwa model dengan interaksi lebih baik dibandingkan model tanpa interaksi.
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.
Co-Authors Anggraini, Silvia Anjani, Retno Tri Crisdianto, Riki Faeza, Veronnica Noer Islami, Istiqomah Rabithah Alam Karuna, Elisabeth Evelin Marta, Rezkyan Pepi Novianti Rexa, Nafa Rini, Dyah Setyo Sigit Nugroho Sunandi, Etis