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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.
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Articles 1,172 Documents
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Comparative Analysis of Machine Learning Algorithms for Health Insurance Pricing Bau, Yoon-Teck; Md Hanif, Shuhail Azri
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2282

Abstract

Insurance is an effective way to guard against potential loss. Risk management is primarily employed to protect against the risk of a financial loss. Risk and uncertainty are inevitable parts of life, and the pace of life has led to a rise in these risks and uncertainties. Health insurance pricing has emerged as one of the essential fields of this study following the coronavirus pandemic. The anticipated outcomes from this study will be applied to guarantee that an insurance company's goal for its health insurance packages is within the range of profitability so that the insurance company will also choose the most price-effective course of action. The US Health Insurance dataset was utilized for this study. This health insurance pricing prediction aims to examine four different types of regression-based machine learning algorithms: multiple linear regression, ridge regression, XGBoost regression, and random forest regression. The implemented model's performance is assessed using four evaluation metrics: MAE, MSE, RMSE, and R2 score. Random forest regression outperforms all other algorithms in terms of all four evaluation metrics. The best machine learning algorithm, random forest, is further enhanced with hyperparameter tuning. Random forest with hyperparameter tuning performs better for three evaluation metrics except for MAE. To gain further insights, data visualizations are also implemented to showcase the importance of features and the differences between actual and predicted prices for all the data points.
Optimizing Pigeon-Inspired Algorithm to Enhance Intrusion Detection System Performance Internet of Things Environments Ratnawati, Fajar; Siswanto, Apri; Jaroji, -; Effendy, Akmar; Tedyyana, Agus
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1724

Abstract

Intrusion Detection Systems (IDS) are crucial in maintaining network security and safeguarding sensitive information against external and internal threats. This study proposes a novel approach by utilizing a Pigeon-Inspired Algorithm optimized with the Hyperbolic Tangent Function (Tanh) function to enhance the performance of IDS in threat detection specifically tailored for Internet of Things (IoT) environments. We aim to create a more robust solution for optimizing intrusion detection systems by integrating the efficient and effective Tanh function into the Pigeon-Inspired Algorithm. The proposed method is evaluated on three widely-used datasets in the field of IDS: NSL-KDD, CICIDS2017, and CSE-CIC-IDS2018. Experimental results demonstrate that integrating the Tanh function into the Pigeon-Inspired Algorithm significantly improves the performance of the intrusion detection system. Our method achieves higher accuracy, True Positive Rate (TPR), and F1-score while reducing the False Positive Rate (FPR) compared to traditional Pigeon-Inspired Algorithms and several other optimization algorithms. The Pigeon-Inspired Algorithm optimized with the Tanh function offers an efficient and effective solution for enhancing intrusion detection system performance, specifically in Internet of Things environments. This method holds great potential for application in diverse network environments, bolstering information security and safeguarding systems from evolving cybersecurity threats. By extending the applicability and effectiveness of the Pigeon-Inspired Algorithm optimized with the Tanh function, researchers can contribute to developing more comprehensive and robust security solutions, addressing the ever-evolving landscape of IoT-based cybersecurity threats.
Artificial Neural Network Accuracy Optimization Using Transfer Function Methods on Various Human Gait Walking Environments Indrawati, Ragil Tri; Putri, Farika Tono; Safriana, Eni; Isti Nugroho, Wahyu; Prawibowo, Hartanto; Ariyanto, Mochammad
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2159

Abstract

A bionic leg with ergonomic functionality can increase the user’s independence. However, an ergonomic bionic leg can be challenged to be developed. One of its challenges is related to functionality, where the bionic leg motor can be rugged to adapt to the user. One of the solutions for the bionic leg challenge is the application of a motor driver controlled by the user’s muscle signal. EMG signal can be utilized as the user’s signal source. The EMG signal is then fed back into the motor device. EMG signals obtained during a natural walking environment can result in smooth and natural movement. This study classifies EMG signals into 8 classes: a controlled walking environment (treadmill walking with various speeds) and a natural walking environment (ground walking, upstairs and downstairs walking). This research aims to optimize the ANN method using transfer function variations. The best method will be used to train EMG-driven motors for future studies related to bionic legs. The best ANN parameter in this research using Levenberg-Marquardt backpropagation as a training algorithm with transfer function pairing of the exponential function: Hyperbolic tangent sigmoid transfer function and SoftMax transfer function with 98.8% accuracy and 0.036 MSE value. The best method from the experiment and ANN classification can be used as a training method for a bionic leg in future research.
Bibliometric Analysis of Research Development on the Topic of State Border Development Using VosViewer Daud, Restuardy; Bur, Marzidi; Sunarsi, Denok; Salam, Rudi
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1787

Abstract

The development of national borders is a priority for a country in the interest of sovereignty and prosperity for its citizens. This study examines the development of research that takes the topic of developing national borders. The research aims to discover the development of the number of publications and maps of the development of publications over the last ten years on the topic of development in question. This research method uses descriptive bibliometric analysis, with metadata from 982 research publications sourced and processed from Google Scholar. The results showed that in the period 2012-2022, there was an increase in the development of publications, from 20 publications in 2012 (2.04%) to 182 publications in 2020 (18.54%), or an increase of 8 times compared to publications with the same topic in 2012. The development of mapping research publications based on keywords (co-occurrence) identified a description of the network of relationships between conceptions of national border development and related topics grouped into 10 clusters. Development is the main issue discussed in various studies in the last ten years. From the visualization overlay on co-occurrence, the keyword 'Development' is the most discussed topic and highlights the need for strengthening and improvement in managing national borders. This research also obtained several topics still open for researchers to develop, including infrastructure development and loci in border areas, which are interesting for future research topics. 
Firefly Algorithm for SVM Multi-class Optimization on Soybean Land Suitability Analysis Nurkholis, Andi; Styawati, Styawati; Suhartanto, Alvi
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1860

Abstract

Soybean is the primary source of vegetable protein nutrition, containing fat and vitamins that Indonesian people widely consume. The decline in soybean production in Indonesia every year is due to the reduced area of soybean cultivation, thereby increasing dependence on imports from other countries. Land suitability maps can provide directions for priority locations for soybean cultivation based on land characteristics and weather to produce optimal production. The SVM multi-class algorithm has been applied to classify land suitability data to create a land suitability map but has yet to obtain optimal accuracy, especially for sigmoid kernels. The objective of this study is to enhance the performance of the sigmoid kernel SVM by utilizing the firefly algorithm. The study focuses on evaluating the suitability of soybean cultivation in Bogor and Grobogan Regencies. The results of the tests indicate that the firefly algorithm-optimized SVM (FA-SVM) significantly improves accuracy compared to the SVM without optimization. The accuracy achieved by FA-SVM is 89.95%, while the SVM without optimization only achieves an accuracy of 65.99%. The best parameters produced by the firefly algorithm are C=2.33 and σ=0.45 obtained from firefly customization, and the number of generations is 10. Based on this, the optimization algorithm can be used to produce an optimal model. The best optimal model obtained can be used as a guide for priority locations/areas for soybean cultivation by farming communities, so as to produce maximum soybean productivity.
Teachers’ Acceptance and Readiness to Use Augmented Reality Book to Teach English Vocabulary in Kindergartens Muryanti, Elise; Pransiska, Rismareni; Novrianti, -; Arkanasya Ummayah, Yasifa; Noer Azhari Azman, Mohamed
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.2168

Abstract

English is currently introduced in several Kindergartens in West Sumatra, Indonesia. The focus of introducing English in Kindergartens is introducing simple instruction and vocabulary. The literature review and initial observations show that teaching materials and learning resources to introduce English are still limited. This study aims to explore kindergarten teachers' perspectives on storytelling and using Augmented reality books. This research used a mixing method. The participants in this subject were 57 Kindergarten teachers in West Sumatra, Indonesia. The data of this research was collected through questionnaires and interviews. First, the questionnaire was distributed to explore teachers' acceptance and readiness to use augmented reality storybooks in teaching English vocabulary in a classroom. To validate the data from the questionnaire and interview, they were triangulated. The result shows that most teachers do not have a background in English qualification. They believe that AR can be useful in teaching English vocabulary. Regarding readiness to implement the AR book, the teacher feels optimistic about using the AR stories in the classroom. The teachers believe that AR can motivate children to learn. The respondent teachers accept AR and believe that AR books can benefit children in learning English vocabulary. The ease of using AR and the benefits of AR for teaching vocabulary encourage teachers' readiness to utilize AR storybooks for teaching English vocabulary to Kindergartners. Based on the result of this research, the AR storybook is designed to teach English to Children in Indonesia Kindergarten.
IoT Attack Detection using Machine Learning and Deep Learning in Smart Home S Azli Sham, Sharifah Nabila; Ishak, Khairul Khalil; Mat Razali, Noor Afiza; Mohd Noor, Normaizeerah; Hasbullah, Nor Asiakin
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2174

Abstract

The Internet of Things (IoT) has revolutionized the traditional Internet, pushing past its former boundaries by implementing smart linked gadgets. The IoT is steadily becoming a staple of everyday life, having been implemented into various diverse applications, such as cities, smart homes, and transportation.  However, despite the technological advancements that the IoT brings, various new security risks have also been introduced due to the development of new types of attacks. This prevents current intelligent IoT applications from adaptively learning from other intelligent IoT applications, which leaves them in a volatile state. In this paper, we conducted a structured literature review (SLR) on Smart Home's IoT attack detection using machine learning and deep learning. Four journal databases were used to perform this review: IEEE, Science Direct, Association for Computing Machinery (ACM), and SpringerLink. Sixty articles were selected and studied, where we noted the various patterns and techniques present in the framework of the selected research. We also took note of the different machine learning and deep learning methods, the types of attacks, and the Network layers present in Smart Home. By conducting an SLR, we analyzed the numerous techniques of IoT attack detection for smart homes proposed by various theoretical studies. We enhanced the studied literature by proposing a new solution for better IoT attack detection in smart homes.
Design of Prediction Model using Data Mining for Segmentation and Classification Customer Churn in E-Commerce Mall in Mall Huda, Ilham; Achmad Suhendra, Agus; Arif Bijaksana, Moch
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.2414

Abstract

The classification of churn is driven by the potential risks e-commerce companies face, such as losing customers who discontinue their service usage or churn. Marketing specialists have shifted their efforts from acquiring new customers to retaining existing ones in order to mitigate customer churn. Predictive models are created using data mining techniques to identify customer churn patterns. This study proposes a data mining model aimed at predicting customer behavior, with the processed results utilized as suggestions for improvements and company strategies in customer retention through segmentation and classification. Segmentation and classification involve several variables: Session, Interaction with Application, Actions taken during the interaction, purchasing, claim, and discount. This study employs a clustering technique based on the Recency, Frequency, and Monetary (RFM) model, which considers factors such as the time since the last visit, the number of visits, and the total amount spent by the customer. The classification algorithm model was evaluated by comparing three classification algorithms: decision tree and Support Vector Machine (SVM). The decision tree algorithm had the highest accuracy, achieving an impressive 87% accuracy rate in customer classification. Factors influencing customer churn include purchasing behavior, session activity, claim feature utilization, adding products to cart, and discounts. Improving stock management is crucial to prevent stock shortages, likely to cause churn. Additional measures like sending emails/notifications and offering vouchers/loyalty points can be implemented for customers who added products to their carts but didn't complete the purchase, with a focus on popular products.
Neural Network Based Data Encryption: A Comparison Study among DES, AES, and HE Techniques Yeow, Sin-Qian; Ng, Kok-Why
JOIV : International Journal on Informatics Visualization Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3-2.2336

Abstract

With the improvement of technology and the continuous expansion and deepening of neural network technology, its application in computer network security plays an important role. However, the development of neural networks is accompanied by new threats and challenges. This paper proposes to encrypt the weight data using encryption algorithms and embed image encryption algorithms to improve protected data security further. The purpose is to address the feasibility and effectiveness of using modern encryption algorithms for data encryption in machine learning in response to data privacy breaches. The approach consists of training a neural network to simulate a model of machine learning and then encrypting it using Data Encryption Standard (DES), Advanced Encryption Standard (AES), and Homomorphic Encryption (HE) techniques, respectively. Its performance is evaluated based on the encryption/decryption accuracy and computational efficiency. The results indicate that combining DES with Blowfish offers moderate encryption and decryption speeds but is less secure than AES and HE. AES provides a practical solution, balancing security and performance, offering a relatively swift encryption and decryption process while maintaining high security. However, Fernet and HE present a viable alternative if data privacy is a top priority. Encryption and decryption times increase with file size and require sufficient computational resources. Future research should explore image encryption techniques to balance security and accurate image retrieval during decryption. Advanced privacy-preserving approaches, such as differential privacy and secure multi-party computation, may enhance security and confidentiality in digital encryption and decryption processes.
Optimizing Educational Assessment: The Practicality of Computer Adaptive Testing (CAT) with an Item Response Theory (IRT) Approach Huda, Asrul; Firdaus, Firdaus; Irfan, Dedy; Hendriyani, Yeka; Almasri, Almasri; Sukmawati, Murni
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2217

Abstract

This research aims to develop a Computer Adaptive Test (CAT) system using the Items Response Theory (IRT) approach. This study is part of developing a web-based system using the Research and Development (R&D) method, employing the Four-D (4-D) model. At its core, this system is similar to a Computer-Based Test (CBT). Still, the critical difference lies in its ability to randomize and provide questions that align with the test-taker's skill levels using the Items Response Theory (IRT) algorithm. The system employs the 3-PL model from the Items Response Theory, considering the difficulty level of questions, the discriminative power of questions, and the likelihood of guessing or interference in the questions. The examination system randomly assigns questions to students based on their responses to previous questions, ensuring that each test-taker receives a unique question sequence. The exam concludes when a test-taker accurately estimates their ability, i.e., SE <= 0.01, or when all questions have been answered. The outcome of this research is a Computer Adaptive Test (CAT) system based on the Items Response Theory (IRT), which can be used to assess students' learning outcomes. This research was implemented in the Multimedia Department of SMK Negeri 1 Gunung Talang, with 90 students as the research sample. The evaluation of the practicality of this system received very high scores, indicating that the Computer Adaptive Test (CAT) system based on the Items Response Theory (IRT) is considered highly practical and effective in achieving the established measurement goals.

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