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JOIV : International Journal on Informatics Visualization
Published by Politeknik Negeri Padang
ISSN : 25499610     EISSN : 25499904     DOI : -
Core Subject : Science,
Computer Science & IT
JOIV : International Journal on Informatics Visualization is an international peer-reviewed journal dedicated to interchange for the results of high quality research in all aspect of Computer Science, Computer Engineering, Information Technology and Visualization. The journal publishes state-of-art papers in fundamental theory, experiments and simulation, as well as applications, with a systematic proposed method, sufficient review on previous works, expanded discussion and concise conclusion. As our commitment to the advancement of science and technology, the JOIV follows the open access policy that allows the published articles freely available online without any subscription.
Arjuna Subject : -
Articles 33 Documents
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Search results for , issue "Vol 9, No 6 (2025)" : 33 Documents clear
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.
The Development of Affine Transformation Method Using Scale Invariant Feature Transform (SIFT) Hartika Zain, Ruri Hartika; Yuhandri, Yuhandri; Sovia, Rini
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.9.6.3653

Abstract

Carving is a technique used to create decorative images on wood, stone, and other materials. In Indonesia, wood is a popular choice because of its durability and attractive grain. Examples of wood carvings include floral designs. The carving process can involve changes in color, texture, and scale, which may affect the carving's size and appearance and cause dimensional changes in certain materials. This study addresses the issue of quality control in wood carving on thin veneer layers. Free wood-carving data are provided as 200 flower images that can be used as input images. Affine transformation is used to determine the system behavior and the material transfer function during the production process. Additionally, we propose extending the affine transformation method to use the Scale-Invariant Feature Transform (SIFT). Affine transformations enable correlation analysis, outlier removal, and feature orientation in the affine domain. The SIFT algorithm accounts for scale, rotation, brightness, and perspective. Applications using ASIFT can efficiently process images and handle those with different pixel sizes to create new carvings. Training samples used to update the filter model are changed to the same pose. This enables the flower wood carving filter to represent objects with 98% accuracy. The model is then used to predict the class of the flower-carving data and to compute the distance between the template image's features and those of the input flower-wood-carving image. This research project has successfully developed an Affine Transformation method using SIFT features to create a new engraving application based on the ASIFT approach. 
Implementation of Customer Segmentation Model using K-Means and DBSCAN for Fashion Industry Product Transaction William, William; Johan, Monika Evelin
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.9.6.2978

Abstract

The use of online marketplaces is rapidly expanding in Indonesia, particularly within the fashion industry. To develop effective marketing strategies, it is essential to understand consumer behaviour through customer segmentation. With a deeper understanding of consumer behaviour, XYZ company, which is engaged in the fashion industry, can improve the effectiveness of marketing strategies and respond to consumer needs more accurately to achieve a significant increase in sales. This study aims to implement a customer segmentation model using clustering methods with machine learning algorithms, specifically K-Means and DBSCAN, following the CRISP-DM Data Mining Framework for data processing. The research utilizes purchasing transaction data from XYZ fashion industry, applying pre-processing techniques such as Standard Scaler and PCA before clustering. The K-Means and DBSCAN algorithms are implemented and evaluated using Silhouette Score and Davies-Bouldin Index matrices. Results show that the K-Means algorithm outperformed DBSCAN, achieving an optimal cluster number of k=7 with a Silhouette Score of 0.549 and a Davies-Bouldin Index of 0.593, compared to DBSCAN's Silhouette Score of 0.29 and Davies-Bouldin Index of 0.92. The final implementation involves creating a dashboard that automatically processes data and generates clusters to support customer segmentation decisions. The model was deployed through a simple website using FastAPI for backend Python execution and React with TypeScript for the front end. Future studies could address limitations by incorporating recent datasets to improve model accuracy, exploring alternative algorithms like Gaussian Mixture Models (GMM) for additional insights, and focusing on robust deployment strategies for real-world applications within the fashion industry.

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