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All Journal MDP Student Conference Al-Aqlu : Jurnal Matematika, Teknik dan Sains
Jesisca, Jesisca
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Mathematics

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Analisis Pola Pembelian Pelanggan dengan Algoritma Apriori pada Perusahaan Distributor Besi Jesisca, Jesisca; Dafid, Dafid
MDP Student Conference Vol 4 No 1 (2025): The 4th MDP Student Conference 2025
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/mdp-sc.v4i1.11095

Abstract

In the digital era, transaction data analysis has become crucial for enhancing business competitiveness. PT XYZ, a steel distributor in Palembang City, faces challenges in understanding customer purchasing patterns, which impacts the optimization of marketing strategies and sales volume. This study aims to analyze customer purchasing patterns using the Apriori algorithm to develop more effective marketing strategies. The research follows the CRISP-DM methodology, which includes data collection, cleaning, analysis, and visualization through an interactive dashboard. Sales transaction data from August 2023 to August 2024 is analyzed to identify frequently purchased product combinations. The results indicate that the Apriori algorithm generates association rules that can be leveraged to design bundling packages that align with customer preferences. With the interactive dashboard, the analysis results can be visually presented, supporting faster and more accurate business decision-making. This study offers a data-driven strategy to improve operational efficiency, optimize sales, and strengthen the company's competitiveness.
ANALISIS ISU SOSIAL MAHASISWA BERBASIS DATA MEDIA SOSIAL MENGGUNAKAN LATENT DIRICHLET ALLOCATION (LDA): Analysis Of Student Social Issues Based On Social Media Data Using Latent Dirichlet Allocation (LDA) Jesisca, Jesisca; Yonny, Chodri Dwi
Al-Aqlu: Jurnal Matematika, Teknik dan Sains Vol. 4 No. 1 (2026): Januari 2026
Publisher : Yayasan Al-Amin Qalbu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59896/aqlu.v4i1.485

Abstract

This study aims to identify the most frequently discussed social issues among university student on social media platforms such as TikTok and Instagram. Data were collected via a questionnaire distributed to 50 students, with 49 valid responses analyzed. The Latent Dirichlet Allocation (LDA) method based on linear algebra was applied to classify text data into several main topics. The result revealed three primary clusters of social issues: (1) gender equality and discrimination within the campus environment, (2) educational costs, mental health, and student welfare, and (3) campus politics and career opportunities. Campus politics emerged as the most dominant topic among others. These findings suggest that student actively utilize social media to express their views and respond to institutional policies that directly affect their academic lives. Furthermore, this research demonstrates the effectiveness of the LDA method in analyzing social data to reveal digital communication patterns among student
Co-Authors Dafid Dafid Rizaldy Pinzon Yonny, Chodri Dwi