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All Journal Bulletin of Electrical Engineering and Informatics JOIV : International Journal on Informatics Visualization Eduvest - Journal of Universal Studies
Budhiarti Nababan, Erna
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Aerospace Engineering Computer Science & IT Electrical & Electronics Engineering Health Professions Neuroscience Social Sciences

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Leveraging LRFM Analysis and Synthetic Data for Customer Segmentation Using K-Means Clustering Muhibuddin, Muhibuddin; Budhiarti Nababan, Erna; Fahmi, Fahmi
Eduvest - Journal of Universal Studies Vol. 5 No. 2 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i2.50263

Abstract

This research explores the use of synthetic data in Length Recency Frequency Monetary (LRFM) analysis and K-Means clustering for customer segmentation. It is challenging to access accurate and comprehensive customer data, this study generates synthetic data using Time-series Generative Adversarial Networks (TimeGAN) to supplement or replace original data. LRFM analysis is used to measure customer characteristics based on the dimensions of Length, Recency, Frequency, and Monetary, which are then applied to clustering using the K-Means algorithm. The quality of clustering is evaluated using the Silhouette Coefficient and Davies-Bouldin Index. The results show that the Silhouette Coefficient for synthetic data is 0.42, slightly higher compared to the original data which has a value of 0.41. Meanwhile, the Davies-Bouldin Index for synthetic data is 0.90, slightly higher than the original data which has a value of 0.89. This indicates that synthetic data can mimic the characteristics of real data without compromising the accuracy and quality of clustering. By combining synthetic data, LRFM analysis, and K-Means clustering, this research provides in-depth insights into customer segmentation. The findings are expected to help companies develop more effective marketing strategies, enhance customer retention, and optimize overall customer experience. This study asserts that synthetic data is a valid alternative to real data in customer analysis.
Ambulance integration model in smart city for health services Dewi, Rafiqa; Tulus, Tulus; Zarlis, Muhammad; Budhiarti Nababan, Erna
Bulletin of Electrical Engineering and Informatics Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i3.7202

Abstract

Smart city is a city built to create efficient, sustainable, fair and livable cities. Increasing the percentage of population survival in standard and emergency times or emergency medical services (EMS) is one of the main pillars of a smart city. This study develops previous research. The weakness of this model is that it needs to consider the dynamic travel time factor. The model only considers the fixed travel time between two locations, which may not represent the actual conditions on the highway. It is less flexible because it only finds one threshold value α for all λi. Researchers succeeded in developing a model to overcome the model weaknesses in (4) by adding new constraints that consider ambulance capacity and overcome the shortcomings of the model (5) by adding tighter limits on the value of α to minimize the difference between the values of λi and αλi. In addition, constraints can also be added to ensure that α is a realistic value and meets business requirements.
Advancements in Detection Top Influencer Marketing in the Airline Industry: A Combination of the Leiden Algorithm and Graph Coloring Handrizal, Handrizal; Sihombing, Poltak; Budhiarti Nababan, Erna; Andri Budiman, Mohammad
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3440

Abstract

In recent years, the airline industry has increasingly utilized social media and online platforms to engage customers and enhance brand loyalty. Identifying key influencers within these networks is crucial for optimizing marketing strategies and improving customer engagement. Influencers play a pivotal role in shaping opinions, driving behaviors, and amplifying brand messages within social networks. Consequently, efficient methods for detecting influencers are essential for understanding network dynamics and maintaining a competitive edge. This study introduces a novel contribution to the field of social network analysis by proposing the Leiden Coloring Algorithm, an enhancement of the traditional Leiden algorithm that integrates graph coloring techniques. The scientific contribution of this research lies in improving the precision of community detection and computational performance in large-scale networks. Experimental results on five airline-related datasets demonstrate that the proposed method achieves higher modularity (average 0.9375), faster processing time (average 204.88 seconds), and identifies fewer, more cohesive communities compared to the Louvain Coloring Algorithm. These findings highlight the algorithm's effectiveness in influencer detection and its potential application in community detection, marketing optimization, and strategic decision-making within the airline industry.
Co-Authors Andri Budiman, Mohammad Dewi, Rafiqa Fahmi Fahmi Handrizal Handrizal Muhammad Zarlis, Muhammad Muhibuddin Muhibuddin, Muhibuddin Poltak Sihombing Tulus Tulus