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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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Econometric Model Using Arbitrage Pricing Theory and Quantile Regression to Estimate the Risk Factors Driving Crude Oil Returns Maitra, Sarit; Mishra, Vivek; Kundu, Sukanya; Chopra, Manav
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.8.1.2268

Abstract

This work presents a novel approach to determining the risk and return of crude oil stocks by employing Arbitrage Pricing Theory and Quantile Regression. Arbitrage Pricing Theory identifies the risk factors likely to impact crude oil returns. Subsequently, Quantile Regression estimates the relationship between the selected factors and the returns across different distribution quantiles. The West Texas Intermediate (WTI) crude oil price is used in this study as a benchmark for crude oil prices. WTI’s price fluctuations can significantly impact the performance of global crude oil stocks and, subsequently, the global economy. Various statistical measures are used in this study to determine the proposed model's stability. The results show that changes in WTI returns can have varying effects depending on market conditions and levels of volatility. This study emphasizes the influence of structural discontinuities on returns. These are likely generated by changes in the global economy and the unpredictable demand for crude oil during the pandemic. The inclusion of pandemic, geopolitical, and inflation-related explanatory variables adds uniqueness to the study as it considers current global events that can affect crude oil returns. Findings show that the key factors that pose significant risks to returns are industrial production, inflation, the global price of energy, the shape of the yield curve, and global economic policy uncertainty. This implies that while making investment decisions in WTI futures, investors should pay particular attention to these elements.
Enhancing Early Detection of Melanoma: A Deep Learning Approach for Skin Cancer Prediction al Huda, Md Sadi; Ali, Md. Asraf; Hossain, Ajran; Tuz Johora, Fatama; Liew, Tze Hui; Sadib, Ridwan Jamal; Hossen, Md. Jakir; Ahmed, Nasim
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.8.3-2.3081

Abstract

Melanoma, a form of skin cancer, is a substantial global public health threat due to its rising prevalence and the potential for severe outcomes if not promptly identified and managed. Detecting skin cancer lesions in their first stages enhances patient outcomes and decreases mortality rates. The core issue investigated in this research paper is the enduring problem of early skin cancer prediction. In the past, individuals often lacked awareness of their skin cancer condition until it had reached late stages. Consequently, this resulted in delayed diagnoses, which restricted the available treatment options and perhaps led to worse outcomes.  This research focuses on finding key attributes and methods in a specialized dataset to effectively differentiate between benign and potentially malignant skin lesions, particularly the implementation of an early-stage skin cancer prediction model. It aims to accurately categorize skin mole pictures as benign or malignant using a Convolutional Neural Network (CNN) model built within the PyTorch framework. The primary aim of this study was to enhance the accuracy and effectiveness of diagnosing skin problems by implementing deep learning algorithms to automate the process of showing such conditions. The model underwent training using 3600 skin mole images sourced from the ISIC-Archive on a GPU RTX 3080. Its outstanding performance is shown by an F1 score of 0.8496 and an accuracy rate of 85%. This research aims to create a predictive model and offer a practical solution that healthcare professionals can readily use for early skin cancer prediction.
Comparison of Machine Learning as an Inference Engine to Improve Expert Systems in Dengue Disease Istiadi, -; Marisa, Fitri; Joegijantoro, Rudy; Suksmawati, Affi Nizar; Rahman, Aviv Yuniar
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.9.3.3192

Abstract

Dengue disease remains a significant public health challenge in tropical and subtropical regions, with rising incidence and mortality rates over the past few decades. While expert systems have been developed for early detection, traditional approaches often rely on rigid rule-based inference engines, which are limited by their dependence on expert-defined structures and lack adaptability to evolving knowledge sources. This study introduces a novel approach to enhance the flexibility and adaptability of expert systems by integrating machine learning (ML) techniques into the inference engine, leveraging the growing availability of medical record data as a dynamic knowledge source. Using a dataset of 90 medical records, balanced to 126 items via the Synthetic Minority Over-sampling Technique (SMOTE), we evaluated the performance of multiple ML algorithms, including Decision Trees (DT), Support Vector Machines (SVM), and Artificial Neural Networks (ANN), against traditional models like Naive Bayes (NB) and K-Nearest Neighbors (KNN). The DT, SVM, and ANN models demonstrated exceptional performance, achieving average accuracy, precision, recall, and F1 scores of 97.73%, 98.33%, 97.22%, and 97.41%, respectively. The key innovation of this research lies in developing an adaptive inference engine that can dynamically learn from medical data, reducing reliance on static rule bases and enabling the expert system to evolve with new knowledge. This approach improves diagnostic accuracy and provides a scalable and flexible framework for addressing other infectious diseases. By bridging the gap between expert systems and machine learning, this study paves the way for more intelligent, data-driven healthcare solutions with significant implications for public health and disease management.
Applying Deep Learning Models to Breast Ultrasound Images for Automating Breast Cancer Diagnosis Khaleefah, Shihab Hamad; Lojungin, Eva Cabrini; Mostafa, Salama A.; Baharum, Zirawani; Aldulaimi, Mohammed Hasan; Ghazal, Taher M.; Alo, Salam Omar; Hidayat, Rahmat
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.8.3-2.1912

Abstract

Breast cancer is a result of uncontrolled human cell division. The vast growth of breast cancer patients has been an issue worldwide. Most of the patients are women, but breast cancer also affects men with a much lesser percentage. Breast cancer might lead to death for those who are suffering from it. Numerous types of research have been done to make an early diagnosis of breast cancer. It has been proven that the tumor can be detected by using an ultrasound image. Artificial Intelligence techniques have been used to detect breast cancer fundamentally. This paper studies the effectiveness of deep learning (DL) techniques in automating breast cancer diagnosis. Subsequently, the paper evaluates the diagnosis performance of three DL models utilizing the criteria of accuracy, recall, precision, and f1-score. The Densenet-169, U-Net, and ConvNet DL models are selected based on the examination of the related work. The DL diagnosis process involves identifying two types of breast cancer tumors: benign and malignant. The evaluation outcomes of the DL models show that the most effective model for diagnosing breast cancer among the three is the ConvNet, which achieves an accuracy of 91%, a recall of 83%, a precision of 85%, and an F1-score of 83%.
Software Agent Simulation Design on the Efficiency of Food Delivery Ismail, Shahrinaz; Mostafa, Salama A; Baharum, Zirawani; Erianda, Aldo; Jaber, Mustafa Musa; Jubair, Mohammed Ahmed; Adiya, M. Hasmil
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.8.1.2648

Abstract

Food delivery services have gained popularity since the emergence of online food delivery. Since the recent pandemic, the demand for service has increased tremendously. Due to several factors that affect how much time additional riders spend on the road; food delivery companies have no control over the location or timing of the delivery riders. There is a need to study and understand the food delivery riders' efficiency to estimate the service system's capacity. The study can ensure that the capacity is sufficient based on the number of orders, which usually depends on the number of potential customers within a territory and the time each rider takes to deliver the orders successfully. This study is an opportunity to focus on the efficiency of the riders since there is not much work at the operational level of the food delivery structure. This study takes up the opportunity to design a software agent simulation on the efficiency of riders' operations in food service due to the lack of simulation to predict this perspective, which could be extended to efficiency prediction. The results presented in this paper are based on the system design phase using the Tropos methodology. At movement in the simulation, the graph of the efficiency is calculated. Upon crossing the threshold, it is considered that the rider agents have achieved the efficiency rate required for decision-making. The simulation's primary operations depend on frontline remotely mobile workers like food delivery riders. It can benefit relevant organizations in decision-making during strategic capacity planning.
Predicting and Explaining Customer Response to Upselling in Telecommunications: A Malaysian Case Study Abdullah, Railey Shahril; Shastera Nulizairos, Nur Shaheera; Mohd Ariffin, Nor Hapiza; Witarsyah, Deden; Maskat, Ruhaila
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.8.3-2.2823

Abstract

This research explores the predictive capabilities of XGBoost (XGB) and Random Forest (RF) models for customer upsell responses, emphasizing the use of Explainable Artificial Intelligence (XAI) techniques to gain insights. Initially trained without hyperparameter tuning, both models were later optimized using 5-fold cross-validation. While RF consistently achieved high accuracy (0.99), XGB exhibited lower accuracy (0.85) yet demonstrated superior precision and recall. Post-tuning, XGB maintained its competitive edge despite a slight decrease in ROC-AUC scores (0.76 and 0.75 versus RF's 0.67 and 0.72), indicating proficiency in classifying positive cases. XAI techniques complemented XGB’s prediction, revealing significant predictors such as inactive duration in days, race (Chinese), total communication count, age, and active period in days. Lesser predictive value was attributed to factors such as race (Indian), gender (female), and region (northern). While the feature importance plot provided a broad overview, it did not detail specific attribute relationships to predictions. To address this, a summary violin plot was employed to illustrate how feature importance varies with actual values, enhancing the understanding of each feature's impact. Results indicated that longer inactivity periods negatively influenced predictions, while non-Chinese ethnicity, higher communication frequency, and younger age were associated with positive outcomes. Dependence plots further elucidated these relationships, highlighting how older non-Chinese customers and those with shorter inactive periods and frequent communication were more likely to accept offers. Local explanations using Shapley's force plot and LIME offered deeper insights into specific instances. Overall, the study underscores the complementary use of XAI techniques to understand a model’s predictions.
Exploring Digital Competency as a Fundamental Job Competency in Higher Education Choi, Seongyune; Jang, Yeonju; Kim, Hyeoncheol
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.9.3.4253

Abstract

In the era of artificial intelligence, emerging digital technologies have revolutionized the nature of workplaces, making digital competency (DC) an increasingly essential competency in the modern job market. However, there are discrepancies between the existing and required levels of DC among employees, highlighting the need for proper and early educational interventions to foster this competency in higher education. In response to this need, this study aims to explore the degree of commitment (DC) among university students in work contexts. This study first developed an instrument to assess the level of DC and conventionally stressed job competencies—cognitive, interpersonal, and self-leadership—and applied it to 4,297 first-year university students. The study first compared the students' DC levels with other job competencies and found that their DC levels were lower than those of other competencies. Additionally, the study investigated the relationship between DC and other job competencies, identifying the prerequisite role of DC in affecting other competencies. Finally, the study also explored factors that promote DC and found that students' interest in emerging information and communication technologies is the most prominent indicator of their DC level. We also examined the effect of experience and attitude toward learning programming on the DC level and found that they were also significant factors. In particular, learning both block-based and text-based programming languages was the most effective means to improve DC. Accordingly, the practical implications for future studies and stakeholders regarding students' DC in higher education were discussed.
Optimizing Genetic Algorithm by Implementation of An Enhanced Selection Operator BinJubier, Mohammed; Ismail, Mohd Arfian; Othman, Muhaini; Kasim, Shahreen; Amnur, Hidra
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.8.3-2.3449

Abstract

The Traveling Salesman Problem (TSP) represents an extensively researched challenge in combinatorial optimization. Genetic Algorithms (GAs), recognized for their nature-inspired approach, stand as potent heuristics for resolving combinatorial optimization problems. Nevertheless, GA exhibits inherent deficiencies, notably premature convergence, which diminishes population diversity and consequential inefficiencies in computational processes. Such drawbacks may result in protracted operations and potential misallocation of computational resources, particularly when confronting intricate NP-hard optimization problems. To address these challenges, the current study underscores the pivotal role of the selection operator in ameliorating GA efficiency. The proposed methodology introduces a novel parameter operator within the Stochastic Universal Selection (SUS) framework, aimed at constricting the search space and optimizing genetic operators for parent selection. This innovative approach concentrates on selecting individuals based on their fitness scores, thereby mitigating challenges associated with population sorting and individual ranking while concurrently alleviating computational complexity. Experimental results robustly validate the efficacy of the proposed approach in enhancing both solution quality and computational efficiency, thereby positioning it as a noteworthy contribution to the domain of combinatorial optimization.
A Comparative Study of Feature Selection Technique for Predicting the Professional Tennis Matches Outcome in a Grand Slam Tournament Ruslan, Nur Amira Sariaty; Zainol, Zuraini; Abdul Rauf, Ummul Fahri
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.8.1.2198

Abstract

Tennis is one of the world's most played sports, attracting many spectators to participate in the game. One of the most essential strokes in a tennis match is serve performance. This research is intended to determine the most critical strokes in tennis serve performance in predicting the tennis match outcome. This research focuses on the Grand Slam Tournaments of the Australian Open, French Open, Wimbledon, and US Open. The data are collected on the tennis serve performances such as Percentage First Serve In (PFSI), Percentage First Serve Won (PFSW), Percentage First Serve Return Won (PFSRW), Aces, and many more. For one tournament, it consists of 254 observations. This study applied feature selection methods available in R programming, such as Correlation Matrix, Relative Importance Metrics, Boruta, MARS, and cForest. Selecting the most essential and correlated variables with the match status can improve the model and help produce better results. This might help the practitioners to apply this method to obtain the closest result to the actual outcome when we include the most correlated variables in the model. From the result obtained, variables of first and second serve, either win on serve or return serve, are identified as the most critical attributes in the tennis match. As a future implication, we suggest that these are all the factors the players need to pay extra attention to in winning the tennis match. 
Technologies on Intelligent Financial Risk Early Warning in Higher Education Institutions: A Systematic Review Chao, Yu; Elias, Nur Fazidah; Yahya, Yazrina; Jenal, Ruzzakiah
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.8.3.1536

Abstract

Financial risk early warning (FREW) is critical for developing Higher Educational Institutions (HEIs). This review uses the Systematic Literature Review (SLR) method to discuss the current research status, leading causes, early warning techniques, and algorithms of financial risk management in HEIs. Based on the WoS database, 139 articles meeting the research criteria were selected from 451 relevant literature for in-depth analysis. The results show that the current research on financial risk management in HEIs mainly focuses on developing risk identification, assessment, and early warning models. The primary sources of university financial risk include the instability of fundraising and distribution, decreased financial allocation, and intensified market competition. In response to these risks, scholars have proposed various early warning models and technologies, such as univariate, multivariable, and artificial neural network models, to predict and manage these risks better. In terms of methodology, this review provides a comprehensive perspective on the study of university financial risk through quantitative and qualitative analysis. This study reveals this field's main research trends and gaps through literature screening and cluster analysis. Finally, this study discusses the practical significance of financial risk management in HEIs, highlighting its role in the stability and growth of these institutions. It suggests future research directions, especially in improving the accuracy and applicability of the Early Warning System (EWS), to further enhance the financial stability of HEIs. This literature review has crucial theoretical value for the academic community and provides practical guidance for HEI administrators.

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