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All Journal Dharmakarya : Jurnal Aplikasi Ipteks Untuk Masyarakat Jurnal Sosioteknologi JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Kesehatan Vokasional INTECOMS: Journal of Information Technology and Computer Science Journal of Education and Instruction (JOEAI) Alignment: Journal of Administration and Educational Management Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Teknologi Terapan Jurnal Abdi Insani International Journal of Engineering, Technology and Natural Sciences (IJETS) Jurnal Sistem Komputer dan Informatika (JSON) ARSY : Jurnal Aplikasi Riset kepada Masyarakat Jurnal Ilmiah Wahana Pendidikan Jurnal Kewarganegaraan Electronic Journal of Education, Social Economics and Technology CSRID JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Bakhrun, Akhmad
Politeknik Negeri Bandung, Bandung
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Data Visualization to Analyze Consumer Behavior for Strategic Business Decision Making in the Retail Industry: Walmart Case Study Bakhrun, Akhmad; Maghfyra, Yasyfa; Putri, Rintan Nurhayati; Larassati, Dewi Ayu
CSRID (Computer Science Research and Its Development Journal) Vol. 17 No. 3 (2025): Oktober 2025
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.17.3.2025.354-371

Abstract

This research focuses on data visualization to analyze consumer behavior in an effort to make strategic business decisions in the retail industry, taking the Walmart Case Study. The main objective of this study is to explore customer consumption patterns and generate data-based insights that can be utilized in formulating marketing strategies and managing retail operations. A quantitative approach is applied through systematic stages, including problem identification, literature study, data collection, Extract, Transform, Load (ETL) process, analysis, visualization, and data interpretation. The dataset used includes 50,000 Walmart customer transactions during the period January 2024 to February 2025. The use of interactive data visualization using Microsoft Power BI successfully transformed raw data into strategic insights. Key findings from the analysis indicated that the majority of transactions came from loyal customers at $6.46 million (50.58%), emphasizing the importance of customer retention strategies. In addition, customer purchasing activity was much more dominant on weekdays, with weekday purchases totaling $9.07 million compared to weekend purchases totaling $3.70 million. The data also shows that Generation X dominates the overall purchase value compared to other age groups, with purchases totaling $5.04 million. In addition, in-depth analysis of the most popular product categories, segmentation by gender, and payment method preferences provided comprehensive insights. These visualization results significantly support fast and evidence-based business decision-making. This research contributes to retail business practice through an applicable data visualization approach, and opens up opportunities for further development such as the integration of machine learning for predictive analysis and wider exploration of BI tools to improve the accuracy and scope of business analysis in the future.
Deteksi Dini Anak Disleksia dengan metode Support Vector Machine Ardhian Ekawijana; Akhmad Bakhrun; Zulkifli Arsyad
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 4 No. 1 (2022): September 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v4i1.4776

Abstract

Dyslexia is a brain disorder caused by genetics. People with dyslexia can live a normal life and even have certain advantages if they get the correct education. People with dyslexia often get the predicate stupid because teachers do not know the case of their students. Early detection of dyslexic children can be done with a series of tests so that the system can conclude that the data is dyslexic or not. Support Vector Machine is a data classification method to share dyslexia test results or not. This system is trained with test results data that are already available using the SVM method. This study uses gamification data to detect dyslexic children or not. SVM proves a good level of accuracy in predictions up to 94%.
Perancangan Sistem Pembelajaran Daring Menggunakan Model ADDIE Bakhrun, Akhmad
JOEAI (Journal of Education and Instruction) Vol. 4 No. 2 (2021): JOEAI (Journal of Education and Instruction)
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/joeai.v4i2.2887

Abstract

ABSTRACT The design of the online learning system is not only focused on the software aspect alone but also must pay attention to the teaching materials that will be managed to suit the characteristics and needs of students. This study designed an online learning system using the ADDIE model (Analysis, Design, Develop, Implement, and Evaluate). ADDIE is a model commonly used to design learning. The stages in this model are very similar to the Waterfall model in the field of software engineering. By using the ADDIE model, it is hoped that it will produce an online learning system that is in accordance with the pedagogical aspects of students and technologically in accordance with the concept of software development. The results of the system design are implemented using Moodle for database practicum lectures. Based on the learning outcomes, 100% of students passed the database practicum with an average level of understanding of 94.86%. In conclusion, the ADDIE model can be applied to design an online learning system according to the characteristics of students. Keywords: online learning system, ADDIE, waterfall, Moodle, database
User Engagement Patterns in Viral Social Media Content: A Multinational Comparative Study Based on Interaction Ratios and Data Visualization Robi Rojaya Simbolon; Sarah Fauziah Saefudin; Serani Arta Mauli Silalahi; Akhmad Bakhrun
Electronic Journal of Education, Social Economics and Technology Vol 6, No 2 (2025)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v6i2.704

Abstract

This study explores user engagement patterns in viral social media content through a data visualization dashboard built with Power BI. The dataset comprises 5,000 viral posts across eight countries and four major platforms—Instagram, TikTok, X (Twitter), and YouTube—encompassing ten content hashtags. The analysis covers over 13 billion total views, with an average of 2.56 million views per post and an overall engagement rate of 22.27%. By visualizing metrics such as likes, comments, shares, and views, the dashboard enables multi-dimensional filtering and correlation analysis. The strongest finding is a perfect correlation (CC = 1.00) between views and all engagement types when filtered by content type, highlighting the pivotal role of format (e.g., YouTube Shorts, Photo posts) in driving interactions. High correlations were also found regionally, such as views and comments (CC = 0.92), and views and shares (CC = 0.91), suggesting significant influence of geographic and cultural factors.Further insights show that YouTube leads with 76.29% of total engagements in Brazil, while TikTok and Instagram dominate in the USA. Hashtags also contribute meaningfully, with view-comment correlation reaching 0.88. This dashboard proves valuable not only for tracking metrics but for generating actionable insights to inform content strategy, platform prioritization, and regional targeting. The findings affirm that virality is not incidental but influenced by measurable factors, making data-driven decisions essential for digital success.
Bike Sales Analysis for Understanding Market Trends in Europe via Power BI Dashboard Indriana Kurnia Cahyati; Fitri Nur Waliyaden; Gloria Theodora Wahi Leo; Mery Adelia; Akhmad Bakhrun
Electronic Journal of Education, Social Economics and Technology Vol 6, No 2 (2025)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v6i2.698

Abstract

In the Industry 4.0 era, transforming raw transactional data into strategic insights is vital for maintaining competitiveness. This study leverages Microsoft Power BI to analyze 113,037 bicycle sales records from 2011 to 2016 across Europe using descriptive quantitative methods and interactive dashboards. The analysis reveals that accessories account for the majority of unit sales (77.94%) due to their affordability and repurchase frequency, while high-end bicycles such as Road-150 Red, 62 and Mountain-200 Black, 38 contribute the highest revenue per unit. The adult age group (35–64 years) emerges as the most profitable segment, generating USD 45 million in revenue and USD 18 million in profit. A strong positive correlation (r = 0.87) between age and product price underscores the purchasing power of older consumers. Geographically, the United States dominates with 34.68% of customers and USD 15.9 million in revenue, followed by Australia and Canada. Meanwhile, Europe shows promising potential for future growth. The gender distribution is nearly balanced, with both male and female customers favoring accessories—highlighting opportunities for inclusive and gender-neutral marketing. Power BI’s visualization tools—bar charts, scatter plots, map views, and forecasting—enable dynamic trend analysis and strategic planning. This study contributes academically by enriching the literature on visual analytics in retail and practically by offering a replicable framework for data-driven decision-making in the bicycle market and other consumer goods industries.
NFT Market Segmentation Classification of Buyer Behavior Based on Blockchain Type and Marketplace Platform Using Power BI Interactive Data Visualization Ananda Marcella Suratman; Alyani Intan Shaffira; Farrel Pasya Harramain; Akhmad Bakhrun
Electronic Journal of Education, Social Economics and Technology Vol 7, No 1 (2026)
Publisher : SAINTIS Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33122/ejeset.v7i1.688

Abstract

The Non-Fungible Token (NFT) market has experienced rapid growth and high volatility, creating challenges for stakeholders aiming to understand transaction patterns and market behavior. This study addresses these challenges by employing an interactive Power BI dashboard to analyze and visualize NFT transaction data from 2021 to 2022. Grounded in descriptive and correlation analysis, the research is structured into three primary visual components: market summary, transactional insights, and correlation analysis. Using data visualization techniques and statistical correlation theory, the dashboard enables the identification of key performance indicators such as user activity, platform efficiency, and pricing dynamics. The findings reveal significant growth in NFT adoption, with 7,820 unique buyers, 7,872 sellers, and 4,327 creators contributing to the ecosystem. A strong positive correlation (r = 0.77) between current and sale prices indicates predictive potential in price behavior. Among blockchain platforms, Solana recorded the highest total sales US$13.0 million and royalty distributions 13,000, while LooksRare and Foundation stood out as the most profitable marketplaces for creators. The Collectibles category led in transaction volume, with collections like AbstractVerse attracting notable interest. While monthly transaction volumes varied, the overall market value remained relatively stable, peaking in December 2022. This study contributes to a deeper understanding of NFT market mechanics by combining data-driven analysis with interactive visual tools, offering valuable insights for creators, investors, and analysts navigating the digital asset landscape.
Co-Authors Afifah, Shelvira Azka Alyani Intan Shaffira Ananda Marcella Suratman Ani Rahmani Ardhian Ekawijana Asri Maspupah Beri Noviansyah Beri Noviansyah Carolina Magdalena Lasambouw Cholid Fauzi Daffa Alya Radhwa Taryana Fadhilah, Muhammad Fikri Fakhrudien, Muhammad Hafidz Farrel Pasya Harramain Fitri Nur Waliyaden Gelar, Trisna Gloria Theodora Wahi Leo Hadiningrum, Kunlestiowati Indri Wulandari, Indri Indriana Kurnia Cahyati Iwan Awaludin Joe Lian Min Jonner Hutahaean Joy Philip Nehemia Manjorang Kurniawan, M Teguh Larassati, Dewi Ayu Lena Lestary Maghfyra, Yasyfa Maisevli Harika Mery Adelia Muhammad Danish Al Ghifary Muhammad Fauzan Lubis Muhammad Rizqi Sholahuddin Muhammad Yunus Maulana Neneng Nuryati noviansyah, beri Putri, Rintan Nurhayati Reynaldy Rizki Saputra Robi Rojaya Simbolon Sarah Fauziah Saefudin Sari, Aprianti Nanda Septokasya, Mochamad Fali Serani Arta Mauli Silalahi Setiadi Rachmat Sofy Fitriani Suprihanto Suprihanto Thamrin, Irawan Wulan, Sri Ratna Yadhi Aditya Permana Yusep Rosmansyah Zhafira, Aulia Zulkifli Arsyad