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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
 103   104   105   106   107 
Predicting Battery Storage of Residential PV Using Long Short-Term Memory Rakasiwi, Rizky Khaerul Maulana; Kurnianingsih, Kurnianingsih; Suharjono, Amin; Enriko, I Ketut Agung; Kubota, Naoyuki
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.8.1.1603

Abstract

Solar power panels, or photovoltaic (PV), have recently grown rapidly as a renewable alternative energy source, especially since the increase in the basic electricity tariff. PV technology can be employed instead of the state electricity company to reduce the electricity used. Indonesia is one of the countries that have great potential in producing electricity from PV technology, considering that most of Indonesia's territory gets sunlight for most of the year and has a large land area. Considering the benefits of PV technology, it is necessary to carry out predictive monitoring and analysis of the energy generated by PV technology to maximize energy utilization in the future. The Internet of Things (IoT) and cloud computing system was developed in this research to monitor and collect data in real-time within 27 days and obtained 7831 data for each parameter that affects PV production. These data include data on the light intensity, temperature, and humidity at the location where the PV system is installed. The feature selection results using Pearson correlation revealed that the light intensity parameter significantly impacted the PV production system. This research used the Long Short-Term Memory (LSTM) method to predict future PV production. By tuning hyperparameters using 3000 epochs, the resulting RMSE value was 171.5720. The results indicated a significant change in the RMSE value compared to 100 epochs of 422.5780. This model can be applied as a forecasting system model at electric vehicle charging stations, given the increasing use of electric vehicles in the future.     Keywords— Forecasting; energy; Photovoltaic; LSTM; Internet of Thing. 
Automatic Cell Planning Method for Radio Network Optimization Putri, Hasanah; Ahmad, Izanoordina; Hikmaturokhman, Alfin; Haura Putri, Dwi
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.8.1.1913

Abstract

As the first step in building a wireless communication network, wireless network optimization is crucial since it determines how the network will be built scientifically. Numerous challenges remain in the way of the Radio Network's deployment in Indonesia, not the least of which is the still-uneven coverage region. The Kiaracondong region in Bandung is one of the numerous areas in Indonesia that are still considered to be "bad spot areas" as a result. Based on the findings of the driving test conducted in the Kiaracondong sub-district, the KPI target was not fulfilled for the RSRP, SINR, and Throughput parameters. Therefore, this study primarily focuses on the physical tuning optimization using the Automatic Cell Planning (ACP) method for the LTE wireless network optimization. To assess the quality of the LTE network before and after optimization, the results of the ACP optimization simulation will be compared with the results of the existing or non-ACP site simulation and the results of the operator's ACP implementation. As a result, Area 1 has an average RSRP of -72.79 dBm, area 2 -73.17 dBm, and area 3 -68.22 dBm. Additionally, the average SINR in areas 1,2 and 3 is 8 dB, 6.58 dB, and 8.17 dB, respectively. The average downlink throughput in area 1 is 42652.66 Kbps, area 2 is 34420.88 Kbps, and area 3 is 43882.92 Kbps. Finally, the average throughput uplink for areas 1 to 3 is 51651.24 Kbps, 47895.99 Kbps, and 49648.84 Kbps, respectively.
Optimizing Artificial Neural Network for Customer Churn: Advanced Data Balancing and Feature Selection Hermawan, Aditiya; Wijaya, Willy; Daniawan, Benny
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3064

Abstract

Customers are valuable assets in the dynamic business world. However, service dissatisfaction often leads them to switch to competitors, a phenomenon known as customer churn. In the telecommunications industry, churn poses a significant challenge as it directly impacts revenue and influences other customers within their social networks to do the same. Consequently, predicting churn has become essential, with numerous researchers employing various methods to classify potential churners. This study builds upon prior research that utilized Artificial Neural Networks (ANN) or Deep Learning to predict churn, achieving an accuracy of 88.12%. To improve model performance, this research implements an Artificial Neural Network (ANN) as the primary algorithm, along with Random Over-Sampling (ROS) and Synthetic Minority Oversampling Technique (SMOTE) for data balancing, and three feature selection methods: Minimum Redundancy Maximum Relevance (mRMR), Lasso Regression, and XGBoost. The results demonstrate a 0.38% increase in accuracy compared to previous studies. The finding suggests opportunities for further exploration. Future studies can consider alternative feature selection techniques, such as Wrapper Methods or Heuristic/Metaheuristic approaches, which may produce more optimal feature combinations. Other data balancing methods, such as Undersampling techniques (e.g., Random Undersampling, Tomek Links) or Hybrid Methods (e.g., SMOTE combined with Tomek Links), could be explored to address imbalanced datasets effectively. These approaches are expected to provide better combinations and to improve overall prediction performance, enabling researchers to develop more robust and accurate models for customer churn prediction in subsequent studies.
Collaborative Intrusion Detection System with Snort Machine Learning Plugin Priambodo, Dimas Febriyan; Faizi, Achmad Husein Noor; Rahmawati, Fika Dwi; Sunaringtyas, Septia Ulfa; Sidabutar, Jeckson; Yulita, Tiyas
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2018

Abstract

The increasing prevalence of cybercrime and cyber-attacks underscores the imperative need for organizations to implement robust network security measures. Nevertheless, current Intrusion Detection Systems (IDS) often rely on single-sensor or multi-sensor in the same type of IDS, including Host-Based IDS (HIDS) or Network-Based IDS (NIDS), which inherently possess limited detection capabilities. To address this limitation, this research combines NIDS and HIDS components into a collaborative-IDS system, thus expanding the scope of intrusion detection and enhancing the efficacy of the established attack mitigation system. However, the integration of NIDS and HIDS introduces formidable challenges, notably the elevated rates of False Positive and False Negative alerts. To surmount these challenges, the researcher employs machine learning techniques in the form of Snort plugins and comparison methods to heighten the precision of attack detection. The obtained results unequivocally illustrate the effectiveness of this approach. Using a Support Vector Machine for static analysis of the NSL-KDD dataset attains an outstanding 99% detection rate for Denial of Service (DoS) attacks and an impressive 98% detection rate for Probe attacks. Furthermore, in dynamic real-time attack simulations, the machine learning plugins exhibit remarkable proficiency in detecting various types of DoS attacks, concurrently offering more comprehensive identification of SYN Flooding DoS attacks compared to the Snort community rules set. These findings signify a significant advancement in intrusion detection, paving the way for more robust and accurate network security systems in an era of escalating cyber threats.
Enhancing Contactless Respiratory Rate Measurement Accuracy: Integration of 24GHz FMCW Radar and XGBoost Machine Learning Arisandy, -; Erfianto, Bayu; Setyorini, -
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.8.4.2654

Abstract

Advancements in non-contact vital sign monitoring are crucial for enhancing patient measurements' accuracy and overall patient experiences. This research explores the integration of 24GHz Frequency-Modulated Continuous-Wave (FMCW) radar with the XGBoost machine learning algorithm to improve the detection of respiratory rate (RR). This innovative approach offers a promising alternative to traditional contact-based methods. The study utilizes FMCW radar to detect respiratory motion, while signal patterns are analyzed using XGBoost to ensure accuracy across various healthcare environments. The method involves collecting signals, pre-processing to remove noise and irrelevant data, and extracting features to be analyzed by the XGBoost algorithm. The collected dataset, which includes controlled and randomized respiratory rates from a diverse subject pool, establishes a solid basis for the algorithm's training and validation, ensuring extensive adaptability and precision. Empirical results show that XGBoost surpasses other machine learning models' accuracy and reliability. Importantly, this method significantly reduces error margins compared to established benchmarks, leading to substantial improvements in RR measurement. The implications of this study are wide-ranging, indicating that such a system could significantly enhance patient care standards by providing continuous, accurate, and non-intrusive monitoring, especially in settings where traditional methods are impractical or uncomfortable. Future research should aim to refine the system's real-world applicability, assess long-term reliability, and optimize the technology for integration into existing healthcare frameworks, thereby further transforming the landscape of patient monitoring technologies.
Grouping of Image Patterns Using Inceptionv3 For Face Shape Classification Hidayat, Tonny; Astuti, Ika Asti; Yaqin, Ainul; Tjilen, Alexander Phuk; Arifianto, Teguh
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.7.4.1743

Abstract

The human face is an extraordinary part where nearly everybody is not quite the same as each other. One perspective that should be visible plainly is the shape. Face shape grouping can be used for amusement, security, or excellence. One technique that can be utilized in picture grouping is the InceptionV3 model. InceptionV3 is the structure of the Convolutional Neural Network (CNN) created by Google, which can tackle picture examination and item discovery issues. This engineering is utilized to order face shapes into five classes: Round, Heart, Square, Oblong, and Oval. At that point, the Google Pictures dataset goes through the pre-handling stage, and the Shrewd Edge Identifier is applied to each picture. Hair turns into a commotion. Consider recognizing the side of the face because it does not make any difference what the hairdo resembles. What is important is the side of the face. When there is a dataset of elongated class and heart class with a comparable hairdo, InceptionV3 will identify the component and expect the two pieces of information to come from a similar class. The exchange learning strategy is done in preparation for the last Layer of ImageNet's InceptionV3 model. This strategy puts the high precision level with an exactness of 93% preparation and testing between 88% - 98%. InceptionV3 could arrange upwards of 692 from 747 datasets or around 92.65%. The most reduced information class is the heart class, where out of 150 information, InceptionV3 can characterize upwards of 130 information.
Bibliometric Analysis of AI-Based Prototype Proposal for User Security Awareness in Healthcare Pratama, I Putu Agus Eka; Widyantara, I Made Oka; Linawati, Linawati; Gunantara, Nyoman
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3319

Abstract

In the realm of public healthcare, integrating information technology (IT) must be judiciously balanced with heightened security awareness among users, given the escalating frequency of cyberattacks targeting this sector. Despite the availability of various product and service solutions aimed at enhancing user security awareness, these efforts have yet to yield optimal outcomes. There is a pressing need for innovative approaches to bolster healthcare user security awareness through IT, particularly leveraging the rapidly advancing field of artificial intelligence (AI). This study conducts a comprehensive review of prior research on the application of AI, specifically Large Language Models (LLM), within the domain of healthcare cybersecurity from 2014 to 2024. The objective is to ascertain the volume of publications, trace the evolution of publication trends, and assess the potential and positioning of research in this area. Employing a bibliometric analysis methodology, this study analyzes a dataset comprising 1000 related publications indexed by Google Scholar. The findings reveal that publications concerning applying LLM AI in healthcare cybersecurity constituted 12.82% in 2023, with a significant increase to 87.18% in 2024, representing a 6.8-fold rise. The mapping of publication developments is categorized into 24 clusters, with large language models, healthcare, retrieval-augmented generation, LLM, artificial intelligence, and cybersecurity emerging as the six most frequently discussed keywords in the research landscape. Consequently, this study underscores the substantial potential for current and future research on the application of AI in healthcare cybersecurity, advocating for the development of AI-based solutions to enhance healthcare user security awareness.
Assessing the Multifaceted Determinants of Collaborative Competence Among Students in the Digital Learning: A Comprehensive Analysis Saputra, Indra; Sari, Resti Elma; Mahniza, Melda; Hayatunnufus, Hayatunnufus; Rahmiati, Rahmiati; Yanita, Merita; Yupelmi, Mimi
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.8.4.2202

Abstract

The study background related to the students' collaborative competence in digital learning is still relatively low. The objective of this study is to examine the elements influencing collaborative competence. The study involved 107 cosmetology and beauty education students. The method used was a survey with data collection using questionnaires developed based on predetermined variable indicators. The analysis of data employed Structural Equation Model-Partial Least Square (SEM-PLS) with Smart PLS 4.0 software. The SEM results describe the Convergent Validity (Loading Factor and Average Variance Extracted) and Discriminant Validity (Fornell Larcker Criterion and Cross Loading), which states that the measurement model is valid. Furthermore, the Composite reliability and Cronbach's Alpha conclude that the measurement model is reliable. The analysis results indicate a positive and significant correlation of predictor variables, including project-based learning, social media, instructional approach, and material relevance to collaborative competence. Based on the variable analysis, material relevance becomes the highest aspect, followed by project-based learning, which increases the collaborative competence of students. Conversely, social media as a mediator variable weakens the level of correlation of the predictor variables to collaborative competence. This study contributes to understanding factors affecting students' collaborative competence in digital learning environments, with significant implications for educators, institutions, and policymakers in shaping digital learning frameworks enhancing collaborative competence. Future research, including longitudinal studies, could investigate the lasting impact of digital learning environments on developing collaborative competence over time.
Introversion-Extraversion Prediction using Machine Learning Fieri, Brillian; La'la, Joshua; Suhartono, Derwin
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.7.4.1019

Abstract

Introversion and extroversion are personality traits that assess the type of interaction between people and others. Introversion and extraversion have their advantages and disadvantages. Knowing their personality, people can utilize these advantages and disadvantages for their benefit. This study compares and evaluates several machine learning models and dataset balancing methods to predict the introversion-extraversion personality based on the survey result conducted by Open-Source Psychometrics Project. The dataset was balanced using three balancing methods, and fifteen questions were chosen as the features based on their correlations with the personality self-identification result. The dataset was used to train several supervised machine-learning models. The best model for the Synthetic Minority Oversampling (SMOTE), Adaptive Synthesis Sampling (ADASYN), and Synthetic Minority Oversampling-Edited Nearest Neighbor (SMOTE-ENN) datasets was the Random Forest with the 10-fold cross-validation accuracy of 95.5%, 95.3%, and 71.0%. On the original dataset, the best model was Support Vector Machine, with a 10-fold cross-validation accuracy of 73.5%. Based on the results, the best balancing methods to increase the models’ performance were oversampling. Conversely, the hybrid method of oversampling-undersampling did not significantly increase performance. Furthermore, the tree-like models, like Random Forest and Decision Tree, improved performance substantially from the data balancing. In contrast, the other models, excluding the SVM, did not show a significant rise in performance. This research implies that further study is needed on the hybrid balancing method and another classification model to improve personality classification performance.
Comparative Analysis of Machine Learning Algorithms for Cross-Site Scripting (XSS) Attack Detection Hamzah, Khairatun Hisan; Osman, Mohd Zamri; Anthony, Tumusiime; Ismail, Mohd Arfian; Abdullah, Zubaile; Alanda, Alde
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.3451

Abstract

Cross-Site Scripting (XSS) attacks pose a significant cybersecurity threat by exploiting vulnerabilities in web applications to inject malicious scripts, enabling unauthorized access and execution of malicious code. Traditional XSS detection systems often struggle to identify increasingly complex XSS payloads. To address this issue, this research evaluated the efficacy of Machine Learning algorithms in detecting XSS threats within online web applications. The study conducts a comprehensive comparative analysis of XSS attack detection using four prominent Machine Learning algorithms, which consist of Extreme Gradient Boosting (XGBoost), Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). This research utilizes a comparative methodology to assess the selected Machine Learning algorithms by analyzing their performance metrics, including confusion matrix, 10-fold cross-validation, and assessment of training time to thoroughly evaluate the models. By exploring dataset characteristics and evaluating the performance metrics of each selected algorithm, the study determined the most robust Machine Learning solution for XSS detection. Results indicate that Random Forest is the top performer, achieving 99.93% accuracy and balanced metrics across all criteria evaluated. These findings will significantly enhance web application security by providing reliable defenses against evolving XSS threats.

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