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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 1,172 Documents
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Enhancing Land Management through U-Net Deep Learning: A Case Study on Climate-Related Land Degradation in Berembun Forest Reserve in Malaysia Chew, Yee Jian; Ooi, Shih Yin; Mohd-Razali, Sheriza; Pang, Ying Han; You Lim, Zheng
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2948

Abstract

In the face of accelerating climate change, effective management of land resources needs innovative technological approaches. This study, conducted in the Berembun Forest Reserve, Jelebu, Malaysia, leverages advancements in geospatial technology and machine learning to address the pressing issue of land degradation, focusing on forested areas vulnerable to landslides. Utilizing high-resolution Unmanned Aerial Vehicle (UAV) imagery, the U-Net convolutional neural network model is employed for the precise classification and early detection of landslide-induced land degradation. Through a systematic analysis of 15 high-quality UAV images of 5472 x 3647 pixels, segmented into 256 x 256-pixel patches, the U-Net model demonstrated remarkable accuracy, achieving a mean Intersection-over-Union (IoU) of 0.9466. These findings underscore the model's potential to significantly enhance land management practices by providing timely and cost-effective landslide detection. Adopting such deep learning techniques is a pivotal shift towards more sustainable and resilient land management strategies in the era of climate change. This research showcases the practical application of machine learning in environmental monitoring and paves the way for future innovations. Implications for further research include integrating additional spectral bands, addressing environmental variability, and expanding applications across diverse landscapes to improve environmental monitoring, conservation efforts, and resilience strategies. Developing automated frameworks for real-time data processing and model deployment could further revolutionize the field, enabling more responsive and efficient land management practices.
Unveiling Gold Membership Classification Using Machine Learning Christiano Tjokro, Vincencius; Oetama, Raymond Sunardi; Prasetiawan, Iwan
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2023

Abstract

The main challenge in loyalty programs is selecting customers with limited funding. To address it, we explore various machine learning-based classification models. This study aims to enhance the effectiveness of a marketing strategy that promotes gold membership to customers with prior transaction history. Previously, much research applied decision trees, random forests, and logistic regression for classification, but gradient boosting is still unpopular. However, in this study, the Gradient Boost algorithm exhibits the best performance among these models, achieving an impressive accuracy of around 88%. This result underscores the model's capability to classify customers, thereby suggesting its potential to significantly enhance the marketing strategy's effectiveness. The analysis identifies crucial features that influence the model's predictive capabilities. Notably, the recency of the last visit, the number of transactions involving wine and meat, marital status, and the number of offline store transactions are identified as influential factors. Leveraging machine learning techniques enables the automation of the customer selection process, facilitating the attraction of a more extensive customer base. By targeting those customers most likely to respond positively to the gold membership offer, efficient resource allocation can be achieved. This research provides valuable insights and practical recommendations for implementing an effective marketing strategy under resource constraints. Combining machine learning algorithms and feature identification enables efficient targeting of potential customers, maximizing the impact of the gold membership offering. Implementing the findings of this study could lead to increased customer acquisition and improved overall business performance.
An Improved Okta-Net Convolutional Neural Network Framework for Automatic Batik Image Classification Elvitaria, Luluk; Ahmad, Ezak Fadzrin; Samsudin, Noor Azah; Ahmad Khalid, Shamsul Kamal; Salamun, -; Indra, Zul
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2591

Abstract

Batik is one of Indonesia's most important cultural arts and has received recognition from UNESCO. Batik has high artistic and historical value with a variety of patterns. Currently, Indonesia has 5,849 batik motifs which are generally classified based on shape, color, motif and symbolic meaning. The diversity of batik motifs makes it difficult for ordinary people to fully recognize them. This paper intends to develop an automatic framework for classifying batik motifs as a solution to overcome this issue. To develop this classification automation framework, the paper proposes a new architecture based on deep learning, which is named Okta-net. The architecture consists of 8 convolutional layers with separate convolution operations (SeparableConv2D). The output of the last convolution block will be fed to the fully connected layer using global average pooling. Meanwhile, in developing a deep learning model to classify batik image patterns, a dataset of 5 batik classes (motifs) was organized, consisting of 4,284 batik images. Through a series of experiments carried out, the proposed Okta-Net architecture succeeded in achieving satisfactory results with a validation accuracy of 93.17%, Precision of 91.60%, Recall of 92.28%, F-1 Score of 91.54%, and a loss of just 0.12%. Thus, it can be concluded that Okta-Net architecture can help preserve Indonesia's batik cultural heritage by accurate batik motif’s classification. Apart from that, based on a comparison of research outcomes, Okta-Net outperformed most of earlier studies, the majority of which had an accuracy of below 90%.
Performance Analysis Of Machine Learning Algorithms Using The Ensemble Method On Predicting The Impact Of Inflation On Indonesia's Economic Growth Abdulloh, Ferian Fauzi; Aminuddin, Afrig; Rahardi, Majid; Harianto, Fetrus Jari
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2567

Abstract

The warning of a global recession expected in 2023 is currently the world's concern. Global financial institutions have raised interest rates to lower inflation, which has led to this problem. This study aims to evaluate the effect of interest rates and inflation on Indonesia's economic growth and compare the performance of machine learning models, specifically Random Forest and XGBoost, in analyzing the impact of inflation. A qualitative methodology was used for the literature survey, while the quantitative approach involved the implementation of machine learning algorithms using the Ensemble Method. The results show that Random Forest performs better than XGBoost in predicting the impact of inflation on economic growth, with MSE values of 0.799 and 0.864 and MAE of 0.576 and 0.619, respectively. In addition, the R-squared value of Random Forest 0.908 is also higher than that of XGBoost 0.901, indicating that the model can better explain the variation in the target data. The practical implication of this study is that the Random Forest model can be more effectively used in analyzing the impact of inflation on Indonesia's economic growth. Recommendations for future research include exploring other methods and using more extended time series to deepen the understanding of the relationship between interest rates, inflation, and economic growth.
DDOS Attack Analysis on IoT Device for Smart Home Environment and A Proposed Detection Technique Ibrahim, Mohd Izwan; Darus, Mohamad Yusof
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2175

Abstract

This study is grounded in a comprehensive review of literature on smart homes and Distributed Denial of Service (DDoS) attacks. To evaluate the defensive capabilities of pfSense and Suricata, a simulated Slowloris DDoS attack was performed on a smart home network, both with and without these security measures. Data was collected for each attack instance, followed by an analysis of the attack's effectiveness and the botnets' responses to refine DDoS assault strategies targeting smart home networks. The results revealed that the network was highly vulnerable without defense mechanisms, collapsing under the attack. In contrast, implementing pfSense and Suricata enabled swift detection and mitigation, neutralizing the attack within 15 seconds. Further testing involved five different scenarios, each assessing the ability of these systems to detect and block DDoS attacks. In all cases, the attacks were identified within 60 seconds. Attackers varied HTTP headers to flood IP-based cameras with packets ranging from 500 to 3000. The findings highlight the significant vulnerability of IoT devices in smart homes to cyber threats. However, deploying pfSense and Suricata proved to be a practical approach for detecting and mitigating DDoS attacks. The research underscores the importance of selecting high-quality hardware, evaluating IoT security features, and adopting proactive security practices to bolster smart home security.
Determining the Grade of Robusta Coffee Beans of Lampung, Bengkulu, and South Sumatra Provinces by Using the Analytical Hierarchy Process (AHP) Yuniarthe, Yodhi; Syarif, Admi; Gitosaputro, Sumaryo; Warsito, Warsito
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2667

Abstract

Coffee is an important commodity for the world business community. One of the world's leading coffee producers is Indonesia. In Indonesia, several provinces produce coffee beans, especially in Sumatra island. They generally cultivate robusta-type coffee. The determination of coffee quality here is still done manually. Recently, along with the increasing recognition of computers, several decision-support system approaches have been introduced, including the Analytical Hierarchy Process (AHP). This research aims to implement the AHP to assess Indonesian robusta coffee beans (Lampung, Bengkulu, and South Sumatra). The researchers use a systematic process, including the preparation stage, data collection using datasets, determination of criteria and alternatives, hierarchical structure, creation of matrices to compare pairs, calculation of priority vectors and eigenvector values, and accuracy testing. This research uses six criteria with 19 sub-criteria and seven alternatives. From the rankings calculated using the AHP method for coffee production areas, the best quality coffee bean is in West Lampung, with the highest value of  0.28. The results of this study are compared with those given by an expert. The results show the MAPE error of 4.42%, a very accurate category.  Thus, it is shown that this method provides excellent results. Future research can be conducted to develop a more sophisticated and efficient AHP method for multi-criteria decision-making in various fields such as business management, engineering, environment, and health.
Addressing Challenges and Enhancing Sustainability in the Food Supply Chain Management for the Malaysian Armed Forces Based on IoT Technologies Sallehudin, Muhammad Izzat; Hashim, Hani Kalsom; Shamsudheen, Mohd Iqbal; Razali, Mohd Norsyarizad; Omar, Nor Bahiyah
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2259

Abstract

The critical nature of the food supply chain issues within the Malaysian Armed Forces necessitates careful consideration to establish a well-structured and organized sustainable food supply. The primary source of frustration arises from the contractor's failure to adhere to contractual obligations, resulting in inadequate supplies, delivery delays, and provisions that do not meet the specified requirements. These shortcomings indirectly impede the management process. The aim of this paper is to identify the relationship between delivery handling, quality control, condition of storage, food supply chain management, and contract management towards the quality of military fresh rations. It is also focusing on improving food supply chain management in MAF, especially the quality of military fresh rations. In addition, this study proposes potential solutions to address these issues, providing a clear path for improvement. The research methodology for this study will employ a qualitative approach. The primary data will be gathered via questionnaire surveys and subsequently analyzed using SPSS. Finally, the study concludes with some recommendations for future research, highlighting areas for further investigation.
RC5 Performance Enhancement Based on Parallel Computing Abead, Suaad Ali; Ali, Nada Hussein M.
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2675

Abstract

This study aims to enhance the RC5 algorithm to improve encryption and decryption speeds in devices with limited power and memory resources. These resource-constrained applications, which range in size from wearables and smart cards to microscopic sensors, frequently function in settings where traditional cryptographic techniques because of their high computational overhead and memory requirements are impracticable. The Enhanced RC5 (ERC5) algorithm integrates the PKCS#7 padding method to effectively adapt to various data sizes. Empirical investigation reveals significant improvements in encryption speed with ERC5, ranging from 50.90% to 64.18% for audio files and 46.97% to 56.84% for image files, depending on file size. A substantial improvement of 59.90% is observed for data files sized at 1500000kb. Partitioning larger files notably reduces encryption time, while smaller files experience marginal benefits. Certain file types benefit from both strategies. Evaluation metrics include encryption execution time and throughput, consistently demonstrating ERC5's superiority over the original RC5. Moreover, ERC5 exhibits reduced power consumption and heightened throughput, highlighting its multifaceted benefits in resource-constrained environments. ERC5 is developed and tested on various file types and sizes to evaluate encryption speed, power consumption, and throughput. ERC5 significantly improves encryption speed across different file types and sizes, with notable gains for audio, image, and large data files. While partitioning smaller files only slightly improves encryption time, larger files partitioning yields faster results. Future research could explore ERC5 optimizations for different computing environments, its integration into real-time encryption scenarios, and its impact on other cryptographic operations and security protocols.
Examining the Impact Factors Influencing Higher Education Institution (HEI) Students’ Security Behaviours in Cyberspace Environment Syed Zulkiplee, Syed Muzammer; Mohd Shukran, Mohd Afizi; Isa, Mohd Rizal Mohd; Adib Khairuddin, Mohammad; Wahab, Norshahriah; Hidayat, Hendra
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2296

Abstract

The Internet’s increasing connectivity through devices and systems, particularly with the Internet of Things (IoT), has expanded the threat landscape, making cybersecurity a constantly evolving field. Phishing is a common and emerging cyber-attack that attempts to deceive individuals and persuade them to disclose sensitive information, such as passwords, financial information, or personal data. Researchers have studied phishing extensively in recent years to understand its mechanisms, strategies, and potential solutions. This research examines essential factors that affect how online users behave regarding security in cyberspace, focusing on phishing attacks through the Health Belief Model (HBM). Understanding what influences users' security behaviors is crucial for building strong defenses. A survey was sent to students via WhatsApp and email, with 252 participants. The results were analyzed using quantitative methods. Principal Component Analysis (PCA) revealed that perceived barriers, self-efficacy, and privacy concerns were the main determinants of students' security behaviors. Students were particularly concerned about the misuse of their personal information. Despite varying levels of formal cybersecurity education, most students demonstrated confidence in configuring web browser security settings. The findings underscore the importance of tailored educational interventions and user-friendly security tools. Future research could explore additional security issues such as spyware, adware, and spam attacks. Additionally, leveraging machine learning and deep learning algorithms offers promising avenues for enhancing phishing detection and mitigation strategies. Furthermore, this study contributes to understanding cybersecurity behaviors, providing valuable insights for policymakers, educators, and developers to foster a safer online environment.
Implementation of Word Trends Using a Machine Learning Approach with TF-IDF and Latent Dirichlet Allocation Rifaldi, Dianda; Fadlil, Abdul; Herman, -
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2452

Abstract

In today's technological age, the prevalence of social media has become ubiquitous, facilitating the easy dissemination of information and communication. This has led to the uploading of various content, including opinions on mental health, particularly in Indonesia. Mental health refers to an individual's emotional, psychological, and social well-being, commonly affecting individuals from adolescence to adulthood. This research utilized Twitter data on mental health issues gathered from October to November 2022, employing TF-IDF and Latent Dirichlet Allocation (LDA) to conduct topic modeling for word trend analysis based on user-generated content. The sentiment analysis concept was used to label text as either negative or positive sentiment. Subsequently, TF-IDF weighed the word frequency in the documents/tweets, categorizing the data based on the resulting sentiments. Manual labeling ensured accuracy, avoiding potential errors from libraries provided in the Indonesian language. Employing these two topic modeling techniques, conclusions were drawn for each concept, aiming to identify word trends, mainly focusing on mental health discourse within Twitter user-generated content. Results indicated the synchronicity of the keyword 'mental health' with word trends generated by LDA. At the same time, TF-IDF produced word trends based on positive and negative labels, revealing commonly used terms by Twitter users to express these concerns. Furthermore, subsequent research can be experimented by comparing topic modeling techniques using Latent Semantic Allocation (LSA), Probabilistic Latent Semantic Analysis (pLSA), and Hierarchical Dirichlet Process (HDP), where LSA and pLSA present approaches closely aligned with LDA.

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