Article Per Year (5 Year)
p-Index From 2021 - 2026
2.986
P-Index
This Author published in this journals
All Journal JOIV : International Journal on Informatics Visualization INVOTEK: Jurnal Inovasi Vokasional dan Teknologi JITSI : Jurnal Ilmiah Teknologi Sistem Informasi International Journal of Advanced Science Computing and Engineering
Aldo Erianda, Aldo
Unknown Affiliation
Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Transportation

Published : 18 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 18 Documents
Search

 1   2 
The Effects of Imbalanced Datasets on Machine Learning Algorithms in Predicting Student Performance Sujon, Khaled Mahmud; Hassan, Rohayanti; Khairudin, Alif Ridzuan; Moi, Sim Hiew; Mohd Shafie, Muhammad Luqman; Saringat, Zainuri; Erianda, Aldo
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.8.3-2.2449

Abstract

Predictive analytics technologies are becoming increasingly popular in higher education institutions. Students' grades are one of the most critical performance indicators educators can use to predict their academic achievement. Academics have developed numerous techniques and machine-learning approaches for predicting student grades over the last several decades. Although much work has been done, a practical model is still lacking, mainly when dealing with imbalanced datasets. This study examines the impact of imbalanced datasets on machine learning models' accuracy and reliability in predicting student performance. This study compares the performance of two popular machine learning algorithms, Logistic Regression and Random Forest, in predicting student grades. Secondly, the study examines the impact of imbalanced datasets on these algorithms' performance metrics and generalization capabilities. Results indicate that the Random Forest (RF) algorithm, with an accuracy of 98%, outperforms Logistic Regression (LR), which achieved 91% accuracy. Furthermore, the performance of both models is significantly impacted by imbalanced datasets. In particular, LR struggles to accurately predict minor classes, while RF also faces difficulties, though to a lesser extent. Addressing class imbalance is crucial, notably affecting model bias and prediction accuracy. This is especially important for higher education institutes aiming to enhance the accuracy of student grade predictions, emphasizing the need for balanced datasets to achieve robust predictive models.
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.
Test Case Prioritization for Software Product Line: A Systematic Mapping Study Idham, Muhammad; Halim, Shahliza Abd; Jawawi, Dayang Norhayati Abang; Zakaria, Zalmiyah; Erianda, Aldo; Arss, Nachnoer
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.7.3-2.1340

Abstract

Combinatorial explosion remains a common issue in testing. Due to the vast number of product variants, the number of test cases required for comprehensive coverage has significantly increased. One of the techniques to efficiently tackle this problem is prioritizing the test suites using a regression testing method. However, there is a lack of comprehensive reviews focusing on test case prioritization in SPLs. To address this research gap, this paper proposed a systematic mapping study to observe the extent of test case prioritization usage in Software Product Line Testing. The study aims to classify various aspects of SPL-TCP (Software Product Line – Test Case Prioritization), including methods, criteria, measurements, constraints, empirical studies, and domains. Over the last ten years, a thorough investigation uncovered twenty-four primary studies, consisting of 12 journal articles and 12 conference papers, all related to Test Case Prioritization for SPLs. This systematic mapping study presents a comprehensive classification of the different approaches to test case prioritization for Software Product Lines. This classification can be valuable in identifying the most suitable strategies to address specific challenges and serves as a guide for future research works. In conclusion, this mapping study systematically classifies different approaches to test case prioritization in Software Product Lines. The results of this study can serve as a valuable resource for addressing challenges in SPL testing and provide insights for future research.
Cardio-Respiratory Motion Prediction Analysis: A Systematic Mapping Study Mohd Fuaad, Nur Atiqah; Hassan, Rohayanti; Ahmad, Johanna; Kasim, Shahreen; Erianda, Aldo
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.4814

Abstract

Cardio-respiratory motion prediction analysis is a crucial medical application for enhancing the precision and effectiveness of medical imaging and patient diagnosis, particularly in the cardiac and respiratory context. This systematic mapping study reviews 23 selected research papers to provide a comprehensive overview of emerging trends and future directions in the field, which also highlights challenges and limitations frequently encountered in cardio-respiratory motion prediction and identifies key machine learning, deep learning, and computational paradigm methodologies examining their application frequencies. In addition, the study analyses the number of performance metrics used alongside validation techniques, which are essential for assessing the accuracy and reliability of the predictive models. Furthermore, it explores the most utilized data types and imaging modalities in this domain, such as X-ray, CT, MRI, and ultrasound, discussing their respective advantages and limitations. Ethical considerations, including patient privacy, data security, informed consent, and the potential for bias, are also addressed. This study aims to deepen the understanding of the landscape of cardio-respiratory motion prediction, guiding future research and the development of more effective, reliable predictive models to enhance medical imaging and patient care, providing valuable insights for researchers, practitioners, and technologists in the field.
Agent-Oriented Modelling for Blockchain Application Development: Feasibility Study LiBin, Michelle Ten; WaiShiang, Cheah; Khairuddin, Muhammad Asyraf B; Mit, Edwin; Erianda, Aldo
JOIV : International Journal on Informatics Visualization Vol 5, No 3 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.3.670

Abstract

Blockchain application development has received much attention nowadays. As development is complex and challenging, a systematic approach is needed to improve the product, services, and process quality. Despite the introduction of techniques, there are still inadequate models for demonstrating the blockchain's internal architecture. Hence, there is a gap when developing the blockchain application, a gap in the modelling environment of a blockchain development application. This paper introduces a new insight into blockchain application development through Agent-Oriented Modelling (AOM). AOM is a methodology for complex socio-technical system development, and we believe that it can reduce the complexity of implementing the blockchain application. In this paper, the AOM is used to model a blockchain-based "win a fortune" system, which includes smart contract development. It showcases the feasibility of adopting AOM to model a blockchain enabling application. A usability survey among the novices has further validated the usability and benefits of AOM in the blockchain enabling application development.
A Microarray Data Pre-processing Method for Cancer Classification Hui, Tay Xin; Kasim, Shahreen; Md Fudzee, Mohd Farhan; Abdullah, Zubaile; Hassan, Rohayanti; Erianda, Aldo
JOIV : International Journal on Informatics Visualization Vol 6, No 4 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.4.1523

Abstract

The development of microarray technology has led to significant improvements and research in various fields. With the help of machine learning techniques and statistical methods, it is now possible to organize, analyze, and interpret large amounts of biological data to uncover significant patterns of interest. The exploitation of microarray data is of great challenge for many researchers. Raw gene expression data are usually vulnerable to missing values, noisy data, incomplete data, and inconsistent data. Hence, processing data before being applied for cancer classification is important. In order to extract the biological significance of microarray gene expression data, data pre-processing is a necessary step to obtain valuable information for further analysis and address important hypotheses. This study presents a detailed description of pre-processing data method for cancer classification. The proposed method consists of three phases: data cleaning, transformation, and filtering. The combination of GenePattern software tool and Rstudio was utilized to implement the proposed data pre-processing method. The proposed method was applied to six gene expression datasets: lung cancer dataset, stomach cancer dataset, liver cancer dataset, kidney cancer dataset, thyroid cancer dataset, and breast cancer dataset to demonstrate the feasibility of the proposed method for cancer classification. A comparison has been made to illustrate the differences between the dataset before and after data pre-processing.
Acceptance and Implications of Holography Technology for Presenting Minangkabau Traditional Clothing in Museums: A Technology Acceptance Model (TAM) Afyenni, Rita; Erianda, Aldo; Firosha, Ardian; Hidayat, Rahmat; Gusman, Taufik
International Journal of Advanced Science Computing and Engineering Vol. 7 No. 3 (2025)
Publisher : SOTVI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/ijasce.7.3.267

Abstract

This study investigates the use of holography technology to present Minangkabau traditional clothing in museums, applying the Technology Acceptance Model (TAM) to assess public response. Survey results show high acceptance, with strong ratings for cultural authenticity and educational value. Perceived ease of use and usefulness significantly influence positive attitudes and behavioral intention, with higher acceptance among females, younger age groups, and those with higher education. Despite these positive outcomes, technical challenges remain, including high implementation costs, limitations in visual clarity, and concerns about digital authenticity. The findings imply that holography can effectively bridge traditional heritage with modern, interactive experiences, making museums more engaging and accessible. The study recommends further research to address technical improvements, cost efficiency, and broader implementation, supporting holography as a strategic tool for cultural preservation and educational innovation.
Sistem Pakar Diagnosa Kerusakan Smartphone Menggunakan Metode Certainty Factor Pratama, Ilham Agus; Erianda, Aldo; Syawaldipa, Ardi
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 7 No 1 (2026)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.7.1.556

Abstract

Smartphones are multifunctional telecommunication devices that have become an essential part of everyday life. However, with the increasing use of smartphones, damage to these devices often occurs and is difficult for lay users to detect. To assist Pagaruyung Ponsel employees or cashiers in diagnosing smartphone damage without needing to rely on expert technicians, this study developed a computer-based expert system. This system combines the Forward Chaining and Certainty Factor (CF) methods to accurately detect smartphone damage. By utilizing expert knowledge, this system provides appropriate solutions based on detected symptoms. The system's accuracy test results showed a value of 85%, which proves the system's effectiveness in providing accurate diagnoses. It is hoped that this system can facilitate independent smartphone diagnosis and repair anytime and anywhere, through a website-based platform. The implementation of this expert system with the Forward Chaining and Certainty Factor (CF) methods is expected to increase the speed and efficiency in handling smartphone damage problems
Co-Authors Ab Razak, Mohd Faizal Abdullah, Zubaile Ade Irma Suryani Adiya, M. Hasmil Ahmad Firdaus, Ahmad Ahmad, Johanna Alde Alanda, Alde Arss, Nachnoer Azmi, Shahdatunnaim Baharum, Zirawani Che Dalim, Che Samihah Deden Witarsyah Defni, - Dwiny Meidelfi Ferda Ernawan Firosha, Ardian Fitri, Redhatul Gusman, Taufik Halim, Shahliza Abd Hui, Tay Xin Huriati, Putri Ibrahim, Najwa Sabirah Ismail, Shahrinaz Jaber, Mustafa Musa Jawawi, Dayang Norhayati Abang Jubair, Mohammed Ahmed Kamarudin, Nur Khairani Khairuddin, Muhammad Asyraf B Khairudin, Alif Ridzuan LiBin, Michelle Ten Mit, Edwin Mizan, Muhammad Thaqiyuddin Mohd Farhan MD Fudzee, Mohd Farhan Mohd Fauzi, Abdullah Munzir Mohd Fuaad, Nur Atiqah Mohd Shafie, Muhammad Luqman Moi, Sim Hiew Mostafa, Salama A Muhammad Idham Nasirudin, Mohd Asrul Osman, Mohd Zamri Othman, Muhammad Fakri Pratama, Ilham Agus Puspita, Kartika PUTRI WAHYUNI Rahmat Hidayat Rasyidah Rasyidah, - Rika Idmayanti Rita Afyenni Rohayanti Hassan, Rohayanti Saringat, Zainuri Senan, Norhalina Shahreen Kasim, Shahreen Sujon, Khaled Mahmud Sukma, Fanni Sumema Syawaldipa, Ardi WaiShiang, Cheah Yance Sonatha Yuris Alkhalifi Zakaria, Zalmiyah Zazkia, Rahmi