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All Journal JOIV : International Journal on Informatics Visualization INVOTEK: Jurnal Inovasi Vokasional dan Teknologi International Journal of Advanced Science Computing and Engineering
Aldo Erianda, Aldo
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Transportation

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The Rise of Deep Learning in Cyber Security: Bibliometric Analysis of Deep Learning and Malware Kamarudin, Nur Khairani; Firdaus, Ahmad; Osman, Mohd Zamri; Alanda, Alde; Erianda, Aldo; Kasim, Shahreen; Ab Razak, Mohd Faizal
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.8.3.1535

Abstract

Deep learning is a machine learning technology that allows computational models to learn via experience, mimicking human cognitive processes. This method is critical in the development of identifying certain objects, and provides the computational intelligence required to identify multiple objects and distinguish it between object A or Object B. On the other hand, malware is defined as malicious software that seeks to harm or disrupt computers and systems. Its main categories include viruses, worms, Trojan horses, spyware, adware, and ransomware. Hence, many deep learning researchers apply deep learning in their malware studies. However, few articles still investigate deep learning and malware in a bibliometric approach (productivity, research area, institutions, authors, impact journals, and keyword analysis). Hence, this paper reports bibliometric analysis used to discover current and future trends and gain new insights into the relationship between deep learning and malware. This paper’s discoveries include: Deployment of deep learning to detect domain generation algorithm (DGA) attacks; Deployment of deep learning to detect malware in Internet of Things (IoT); The rise of adversarial learning and adversarial attack using deep learning; The emergence of Android malware in deep learning; The deployment of transfer learning in malware research; and active authors on deep learning and malware research, including Soman KP, Vinayakumar R, and Zhang Y.
Systematic Literature Review on Augmented Reality with Persuasive System Design: Application and Design in Education and Learning Nasirudin, Mohd Asrul; Md Fudzee, Mohd Farhan; Senan, Norhalina; Che Dalim, Che Samihah; Witarsyah, Deden; Erianda, Aldo
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.8.2.2702

Abstract

Augmented Reality (AR) is an innovative technology that has gained significant scholarly attention. It uses computer-generated sensory inputs like visuals, sounds, and touch to enhance how we perceive the real world, providing a transformative impact on human sensory experiences. Motivated by the possibilities of augmented reality (AR) in the realm of the educational learning environment, this research aims to document the evolving landscape of augmented reality (AR) applications in education and training, with a specific emphasis on the incorporation of persuasive system design (PSD) elements. The study also explores the diverse technologies and methodologies for developing these applications. A systematic literature review was conducted, analyzing 44 articles following the protocol for PRISMA assessments. Four research questions were formulated to investigate trends in AR applications. Between 2016 and 2023, publications on AR applications doubled, with a significant focus on the educational field. Marker-based AR methods dominated (68.49%), while markerless methods constituted 31.51%. Unity and Vuforia were the most used platforms, accounting for 77.27% of applications. Most research papers assessed application effectiveness subjectively through custom-made questionnaires. University students were identified as the primary target users of AR applications. Only a few applications integrated persuasive elements, even for adult users. This highlights the need for further studies to fully grasp the possibilities of combining persuasive system design with augmented reality applications in education
Augmented Reality Applications for Cultural Heritage Using Kinect Sensor Afyenni, Rita; Erianda, Aldo; Wahyuni, Putri; Firosha, Ardian; Gusman, Taufik; Sumema
International Journal of Advanced Science Computing and Engineering Vol. 6 No. 3 (2024)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.6.3.221

Abstract

There are quite a number of advantages gained from Microsoft Kinect which is a motion sensing device that also includes AR applications to aid in the preservation and promotion of cultural assets. In this work, a batik model was developed with the help of Microsoft Kinect by utilizing its core components consisting of a depth sensor, an RGB camera and a microphone array to allow motion tracking, gesture recognition, and voice command functionalities of the device.The depth sensing features of the device, in particular the structural light employed, transition to Time-of-Flight technology has improved the efficiency and applicability of the device towards AR. Kinect has the ability to create an immersive AR experience by accurately mapping virtual objects to the user’s environment, leveraging body joint tracking to create a 3D skeletal model. It is required to take into consideration the kinnect positioning technique which has importance in allowing 3D objects and models to align. Therefore, the conclusion addresses Kinect as it relates to human-computer interaction, in particular, concerning its potential to change body interaction in real time and how it sets the stage for future systems that require an active interaction across a number of fields including culture and heritage.
Implementation of The Moving Average Method for Forecasting Inventory in CV. Tre Jaya Perkasa Huriati, Putri; Erianda, Aldo; Alanda, Alde; Meidelfi, Dwiny; Rasyidah, -; Defni, -; Suryani, Ade Irma
International Journal of Advanced Science Computing and Engineering Vol. 4 No. 2 (2022)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (366.89 KB) | DOI: 10.62527/ijasce.4.2.77

Abstract

The supply chain is an organization's place to distribute production goods and services to customers. This chain is a network of various organizations that are interrelated and have the aim of carrying out the procurement or supply of goods. Inventory is storing goods in the form of raw materials, semi-finished goods or finished goods that will be used in the production or distribution process. CV. Tre Jaya Perkasa is a company engaged in the distribution of goods such as snacks, drinks and daily necessities. CV. Tre Jaya Perkasa is located in Solok, West Sumatra, Indonesia. From January 2020 to June 2021, CV. Tre Jaya Perkasa has made more than 10 thousand transactions. Based on the sales data, each period (month) of sales transactions can increase and decrease, and the company must plan product sales in the coming period. To maximize profits and minimize losses, a strategy is needed to maintain the availability of goods that are often purchased by customers. From historical transaction data, the company can predict how much stock should be provided for transactions in the coming period. The method used is the moving average method, to measure the error rate of forecasting, MAD, MSE and MAPE are used. Based on the research that has been done, then carried out on the product Trick Potato Biscuit BBQ 24 BOX X 10X18 forecasting comparison between using 3 periods and 5 periods, using 5 product data that are most often purchased by buyers, it was found that the average value of MAD, MSE and MAPE closer to 0 is to use 3-period forecasting.
Boundary Value Analysis Techniques for Testing Geographic Information System Applications for Public Facilities Idmayanti, Rika; Meidelfi, Dwiny; Erianda, Aldo; Sukma, Fanni; Zazkia, Rahmi
International Journal of Advanced Science Computing and Engineering Vol. 4 No. 3 (2022)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (575.419 KB) | DOI: 10.62527/ijasce.4.3.91

Abstract

The Geographic Information System for Public Facilities is a system that makes it easier for the public to search and find locations. The more people use this system, the more problems will arise. Because people still use the manual method, namely by asking about the location of public facilities in the surrounding community. This is very ineffective and accurate. Therefore, a public facility information system was built using CI 3, Java, and PHP in the form of android. The method used in this development system is the waterfall method. With this GIS, the public can search for the location of public facilities, find public facilities, and provide a route to the location using the Tracking process to find out where the current position is.
Systematic Literature Review: Digitalization of Rural Tourism Towards Sustainable Tourism Rasyidah; Erianda, Aldo; Alanda, Alde; Hidayat, Rahmat
International Journal of Advanced Science Computing and Engineering Vol. 5 No. 3 (2023)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.5.3.134

Abstract

In the context of rural tourism, the internet is crucial. It is vital to create and implement new technology in order to assist digital tourism in tourist communities and undeveloped, frontier, and remote locations.  The utilization of big data can enhance the precision of predicting tourist flows, providing valuable insights to assist and enhance destination management, planning, and advertising. It can also ease mobility and encourage visitors to be distributed according to time. In addition to supporting visitors with specific access needs and keeping management informed about visitor behavior, artificial intelligence (AI) and automation can also be very helpful in the tourist industry by enabling those with limited mobility to travel the world. In this sense, as the sharing and gig economies grow along with technology, we have more options in our everyday lives—as long as they are properly set up and maintained. Therefore, this paper aims to study the research on internet criteria based on AlUla framework to achieve sustainable tourism in rural areas and to identify the key journals, articles and authors. The findings in this research are that there has been an increase in the number of journals post COVID19, where the country that produces the most journals is China and the author that is most cited is Pesonen JA. To achieve the goal of sustainable digital rural tourism, infrastructure is needed in the form of internet penetration, internet speed and usability, and internet security level.
Sales Segmentation Analysis of Tobacco Products Using the K-Means Clustering Method Sonatha, Yance; Erianda, Aldo; Fitri, Redhatul
invotek Vol 24 No 2 (2024): INVOTEK: Jurnal Inovasi Vokasional dan Teknologi
Publisher : Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/invotek.v24i2.1221

Abstract

Technological advancements have encouraged businesses to optimize data utilization, including in sales analysis. This study analyzes sales transaction data of tobacco products at Tobacco Shop Taste using the K-Means Clustering method. By implementing the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework, the sales data were categorized into three groups: highly sold, moderately sold, and less sold. These clustering results support stock management, marketing strategies, and data-driven decision-making. A web-based system was developed, providing real-time monitoring of analysis results, which distinguishes this study from existing solutions by enabling store management to promptly respond to sales trends. This study significantly contributes to the application of data mining technology in the tobacco retail sector, despite being limited to a single store and basic variables. Future development opportunities include integrating broader datasets and analyzing external variables to enhance the accuracy and relevance of the findings.
Systematic Literature Review of Gender Bias within Video Games Character Design Ibrahim, Najwa Sabirah; Senan, Norhalina; Othman, Muhammad Fakri; Azmi, Shahdatunnaim; Erianda, Aldo; Gusman, Taufik
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3297

Abstract

Gender bias in video games refers to the unequal treatment and discrimination that players experience based on gender, which is often normalized within the gaming community. Gender bias is widespread in Multiplayer Online Battle Arena (MOBA) games, where it can take many different forms. Common examples include assumptions made about players' abilities, character design in games, and the roles given to characters according to gender. This situation has created an unwelcoming environment, especially for female players, leading to feelings of exclusion. This study conducts a systematic literature review to examine gender bias in MOBA games, explicitly focusing on character representation, hypersexualized character models, and gameplay mechanics. By analyzing data from peer-reviewed articles, theses, and research papers, the study highlights the recurring patterns of bias and identifies gaps in current approaches. Although prior studies have explored the elements that contribute to gender bias, few studies have offered practical solutions to mitigate this bias. However, there is still a lack of research proposing a practical game design framework that integrates strategies to reduce this bias. In conclusion, efforts to address gender bias are not only significant in terms of design ethics, but also a good strategy in expanding the game's audience. This study identifies possible solutions that might help future research and be developed into a conceptual framework model that developers can understand to create a more inclusive, fair, and profitable gaming environment in the long term.
Comparative Analysis of Robust Imputation Techniques for Enhancing Cervical Cancer Prediction with Missing Data Mizan, Muhammad Thaqiyuddin; Ernawan, Ferda; Kasim, Shahreen; Erianda, Aldo; Mohd Fauzi, Abdullah Munzir
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.4501

Abstract

Handling missing data is a critical challenge in machine learning applications, as it can significantly affect the accuracy and reliability of predictive models. Addressing this issue is crucial for developing robust systems that can deliver high-performance results. This study provides a comparative analysis of the robust imputation technique for cervical cancer prediction with incomplete information. This study has investigated the importance of robust imputation techniques, particularly Soft Imputer, in addressing missing data challenges and enhancing model performance. This study investigates the impact of various imputations across five distinct approaches: KNN imputer, PCA imputer, MICE imputer, XGBoost imputer, LightGBM imputer, and feature selection methods. These imputation data are tested on several machine learning models such as Random Forest (RF), Extreme Gradient Boosting (XGB), Decision Tree (DT), Support Vector Classifier (SVC), Logistic Regression (LR), Extra Trees Classifier (ETC), CatBoost Classifier, Stochastic Gradient Descent (SGD), and Gradient Boosting (GB) for improving classification accuracy of cervical cancer prediction. The evaluation reveals that the soft imputer method achieves a balanced and effective handling of missing data, significantly improving the reliability of the models. Among the tested methods, LightGBM and XGBoost deliver strong results, each achieving an average accuracy of 96.91%. MICE demonstrated the lowest average accuracy at 95.94%, although it still performs reliably in managing missing data. The findings provide valuable insights for enhancing predictive accuracy in future work by integrating advanced imputation strategies for high-dimensional and complex datasets.
Brain Tumor Classification based on Convolutional Neural Networks with an Ensemble Learning Approach through Soft Voting Puspita, Kartika; Ernawan, Ferda; Alkhalifi, Yuris; Kasim, Shahreen; Erianda, Aldo
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.4609

Abstract

The brain is a vital organ that serves various purposes in the human body. Processing sensory data, generating muscle movements, and performing complex cognitive tasks have all historically relied heavily on the brain. One of the most common conditions affecting the brain is the growth of abnormal tissue in brain cells, leading to the development of brain tumors. The most common forms of brain tumors are pituitary, glioma, and meningioma, which are major global health issues. From these issues, there is a need for appropriate and prompt handling before the brain tumor disease becomes more severe. Quick handling is through an early disease detection approach, and computer vision is one of the trending early disease detection methods that can predict diseases using images. This research proposes a model in computer vision, namely the Convolutional Neural Network (CNN), with a soft voting ensemble learning strategy to classify brain tumors. The dataset consists of 7,023 images without tumors and MRI brain tumors such as glioma, meningioma, and pituitary with a resolution of 512x512 pixels. This experiment investigates classifier models such as VGG16, MobileNet, ResNet50, and DenseNet121, each of which has been optimized to maximize performance. The proposed soft voting ensemble strategy outperformed existing methods, with an accuracy of 97.67% and a Cohen's Kappa value of 0.9688. The proposed soft voting ensemble method approach has proven effective in improving the accuracy.
Co-Authors Ab Razak, Mohd Faizal Ade Irma Suryani Ahmad Firdaus, Ahmad Alde Alanda, Alde Azmi, Shahdatunnaim Che Dalim, Che Samihah Deden Witarsyah Defni, - Dwiny Meidelfi Ferda Ernawan Firosha, Ardian Fitri, Redhatul Gusman, Taufik Huriati, Putri Ibrahim, Najwa Sabirah Kamarudin, Nur Khairani Mizan, Muhammad Thaqiyuddin Mohd Farhan MD Fudzee, Mohd Farhan Mohd Fauzi, Abdullah Munzir Nasirudin, Mohd Asrul Osman, Mohd Zamri Othman, Muhammad Fakri Puspita, Kartika PUTRI WAHYUNI Rahmat Hidayat Rasyidah Rasyidah, - Rika Idmayanti Rita Afyenni Senan, Norhalina Shahreen Kasim, Shahreen Sukma, Fanni Sumema Yance Sonatha Yuris Alkhalifi Zazkia, Rahmi