cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Rahmat Hidayat
Contact Email
mr.rahmat@gmail.com
Phone
-
Journal Mail Official
rahmat@pnp.ac.id
Editorial Address
-
Location
Kota padang,
Sumatera barat
INDONESIA
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 48 Documents
 1   2   3   4   5 
Search results for , issue "Vol 9, No 2 (2025)" : 48 Documents clear
Wireless Data Communications in WSN Networks Using UAV Miptahudin, Rd Apip; Suryani, Titiek; Wirawan, Wirawan
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2940

Abstract

This research explores the impact of environmental and technical factors on air-to-ground (A2G) wireless communications using drones, specifically tackling challenges like multipath propagation, Doppler effects, and geographical variability. The study aims to analyze performance determinants of A2G communications, develop a simulation model to predict communication issues and offer recommendations for optimizing interactions between drones and ground stations. The methodology includes data collection from field tests and simulations, focusing on various environmental and weather conditions. Statistical data analysis, including regression and hypothesis testing, is employed to interpret the results. Key findings reveal that factors such as operational altitude, drone speed, and weather conditions—mainly rain—significantly affect throughput, latency, and packet loss. Optimal communication performance is achieved at a drone height of 120 meters, with rural environments offering the best conditions for data transmission. Conversely, urban settings experience decreased throughput and increased latency due to physical obstructions like buildings and infrastructure. These insights highlight the need for adaptive communication technologies and comprehensive testing across diverse conditions. The research advocates further exploring advanced antenna technologies, dynamic operational adjustments informed by real-time environmental data, and robust security measures to enhance communication reliability. In conclusion, this study establishes a strong foundation for future advancements in drone communication technologies, aiming to improve the safety and efficiency of drone operations across various applications. The findings serve as a roadmap for developing innovative solutions to address the inherent challenges of A2G communications in varying operational environments.
Visualization of Accounting and Indigenous People Research: A Bibliometric Review Using R Thahirah, Khadijah Ath; Triyuwono, Iwan; Mulawarman, Aji Dedi; Achsin, M.
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2878

Abstract

This study aims to map out the evolution of research trends on accounting and Indigenous peoples by using bibliometric analysis. Most bibliometric literature articles rely on basic graphical representations generated by computer systems. The methodology for conducting bibliometric analysis presented in this paper consists of three stages, namely data collection, software selection and analysis. This study used published papers from the Scopus database was carried out on 13 June 2024 and found 42 indexed research publications on the topic of accounting and indigenous people between 1999 and 2023. The map of research development in the field of accounting and Indigenous people is obtained through the export process, which was analyzed using the R Biblioshiny application program. The findings demonstrated a development trend with a static increase in the number of publications about accounting and research on Indigenous people. Besides, the results show that the journal with the most publication and impact is the Accounting, Auditing, and Accountability Journal. The country with the most objects of study is Australia. The development of research related to accounting and Indigenous People was growing, although not too massive. Along with these conditions, various trends in   Accounting and Indigenous People Research topics grew. The results of this study also indicate that the most widely used topic keywords are Accounting, Indigenous, People, and Research.  The findings of this study provide scholars with a comprehensive understanding of the current research work in the field of accounting and indigenous people and its future directions.
Addressing Challenges and Enhancing Sustainability in the Food Supply Chain Management for the Malaysian Armed Forces Based on IoT Technologies Sallehudin, Muhammad Izzat; Hashim, Hani Kalsom; Shamsudheen, Mohd Iqbal; Razali, Mohd Norsyarizad; Omar, Nor Bahiyah
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2259

Abstract

The critical nature of the food supply chain issues within the Malaysian Armed Forces necessitates careful consideration to establish a well-structured and organized sustainable food supply. The primary source of frustration arises from the contractor's failure to adhere to contractual obligations, resulting in inadequate supplies, delivery delays, and provisions that do not meet the specified requirements. These shortcomings indirectly impede the management process. The aim of this paper is to identify the relationship between delivery handling, quality control, condition of storage, food supply chain management, and contract management towards the quality of military fresh rations. It is also focusing on improving food supply chain management in MAF, especially the quality of military fresh rations. In addition, this study proposes potential solutions to address these issues, providing a clear path for improvement. The research methodology for this study will employ a qualitative approach. The primary data will be gathered via questionnaire surveys and subsequently analyzed using SPSS. Finally, the study concludes with some recommendations for future research, highlighting areas for further investigation.
Development of a Life Story-Based Digital Counseling Model to Detect Student Depression Using LSTM Jiwa Permana, Agus Aan; Sudarma, Made; Sukarsa, I Made; Hartati, Rukmi Sari
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2642

Abstract

This research aims to develop an LSTM-based model to help counselors analyze depressive symptoms in students based on their life stories. Depression often occurs among students, which can affect their lives. However, counselling can overcome these mental problems. In order to support the Indonesian government's programs in the field of mental health, concrete steps are needed. One concrete effort is to prevent children from experiencing depression. Depression can be recognized early through a counselling approach. Currently, counselling can be done using digital counselling technology. Therefore, a reliable model is needed to help counsellors. This research used 2,551 tweets about someone's life story from 2,581 datasets. ANN method with LSTM (Long Short-Term Memory) architecture. This counselling is effective in helping individuals resolve psychological and emotional problems, especially depression. The advantage of LSTM is that it can delete data that is no longer relevant. This method effectively processes, predicts, and classifies data based on a certain time sequence. The dataset was taken from Twitter(X) and then validated by experts before being trained with the model. As a result, the model can recognize the depression levels with a test accuracy of 86%. This research has implications in psychology regarding cases of student mental health in realizing the vision of Indonesia in 2045.
Detection of Oil Palm Fruit Ripeness through Image Feature Optimization using Convolutional Neural Network Algorithm Setiawan, Dedy; Eko Prasetyo Utomo, Pradita; Alfalah, Muksin
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2687

Abstract

The increase in the need for raw materials for palm oil products in the form of food and non-food is felt by the people of Indonesia and other countries. For this reason, triggering oil palm farmers in Indonesia must be able to maximize their production. Currently, oil palm farmers in Indonesia still need help knowing the level of sustainability of oil palm fruit to maintain their production. This research was conducted to identify the maturity level of oil palm fruit using practical images for oil palm farmers in Indonesia. The Convolutional Neutral Network (CNN) algorithm is the research method used to identify pictures of oil palm fruit. The dataset collection comprised 400 images of oil palm fruits divided into three types of classes, namely images of raw, ripe, and rotten oil palm fruits. The dataset was taken from various internet sources, and photos were taken directly using a mobile phone camera according to a predetermined class. This study found that identifying the maturity level of oil palm fruit using the Convolutional Neural Network (CNN) algorithm obtained a high accuracy of 98% in the training process and 76% in the model testing process. The findings of this study can also inspire further research in optimizing image features and using the Convolutional Neural Network (CNN) algorithm more efficiently. This could include a reduction in model training time, the number of parameters, or the development of other techniques that improve algorithm performance.
Chest X-Ray Images Clustering using Convolutional Autoencoder for Lung Disease Detection Syafira, Putri Amanda; Yudistira, Novanto; Kurnianingtyas, Diva
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2478

Abstract

In healthcare, medical imaging is commonly used for health assessments. One of the most commonly used types of medical imaging is X-ray imaging. One area that often undergoes examination using this modality is the lungs, where healthcare professionals use X-ray images to interpret the results. However, prolonged interpretation of X-ray results by healthcare professionals and other work activities can lead to errors and potentially result in invalid disease identification. There is a need for a system that can classify the detection results from these images to assist healthcare professionals in their tasks. Various methods can be used for this purpose, such as classification, clustering, segmentation, etc. However, data labeling requires significant resources and costs, especially with large-scale datasets. One possible solution is to use an unsupervised learning approach to address this. One method under unsupervised learning is clustering, which allows the system to process and understand data patterns without needing external annotations or manual labeling. This research uses an autoencoder as a subcategory of unsupervised learning. This is because autoencoders can automatically extract relevant features from the data without needing external label guidance. The research utilizes a dataset consisting of 700 X-ray images of the chest, including 500 images showing disease and 200 normal X-ray images. This research aims to determine the effectiveness of clustering methods using an autoencoder model in grouping X-ray image results. The research conducted two experiments. In the first experiment, an autoencoder with 18 Layers was used, resulting in the best performance with a value of K=15 and a rand index of 76%. In the second experiment, an autoencoder with a reduced number of Layers (11 Layers) was used, and it achieved the best performance with a value of K=15 and a rand index of 87%.
Leveraging ESRGAN for High-Quality Retrieval of Low-Resolution Batik Pattern Datasets Azhar, Yufis; Marthasari, Gita Indah; Regata Akbi, Denar; Minarno, Agus Eko; Haqim, Gilang Nuril
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3202

Abstract

As one of the world's cultural heritages in Indonesia, batik is one of the quite interesting research subjects, including in the realm of image retrieval. One of the inhibiting factors in searching for batik images relevant to the query image input by the user is the low resolution of the batik images in the dataset. This can affect the dataset's quality, which automatically also impacts the model's performance in recognizing batik motifs with complex details and textures. To address this problem, this study proposes using the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) method to increase the resolution of batik images. By increasing the resolution, it is hoped that ESRGAN can clarify the details and textures of the initial low-resolution image so that these features can be extracted better. This study proves that ESRGAN can produce high-resolution batik images while maintaining the details of the batik motif itself. The resulting image's high PSNR and low MSE values confirm this. The implementation of ESRGAN has also been proven to improve the performance of the image retrieval system with an increase in precision and average precision values between 1-5% compared to other methods that do not implement it.
YoloV8, EfficientNetv2, and CSP Darknet Comparison as Recognition Model’s Backbone for Drone-Captured Images Kridalukmana, Rinta; Eridani, Dania; Septiana, Risma; Windasari, Ike Pertiwi
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2880

Abstract

Artificial intelligence (AI) has recently empowered drones to support smart city apps and recognize on-the-ground objects or events. Various pre-trained backbones are available to develop object recognition models, and some of them could boost the models’ accuracy. Consequently, it becomes difficult for practitioners to select a suitable backbone as a feature extractor during recognition model development. Hence, this research aims to provide a benchmark examining the performance of three popular backbones in supporting recognition models using images captured by drones as the dataset. This research used the UAV-AUAIR dataset and compared three deep learning backbone architectures as the feature extractor, namely YoloV8_s, EfficientNetv2_s, and CSP_DarkNet_l. The head part of each selected backbone was replaced with YoloV8Detector architecture, provided by Keras-CV, to perform the inference tasks. The models generated during training were evaluated against four measurement methods: loss function, intersection over union (IOU), across-scale mean average precision (mAP), and computational performance. The results showed that the model generated using EfficientNetv2_s backbone outperformed the others in most criteria, except the computational performance and detecting small objects, which was won by YOLOV8_s and CSP_Darknet_l, respectively. Thus, EfficientNetv2_s and CSP_DarkNet_l can be considered if app development concerns accuracy. Meanwhile, YoloV8_s is far better when computational performance is essential, as its prediction time achieved 0.8 seconds per image. This study is essential as a reference for practitioners, particularly those who want to develop an object-recognition model based on a pre-trained backbone.
A Deep Learning Approach Using VGG16 to Classify Beef and Pork Images Zulfikar, Wildan Budiawan; Angelyna, Angelyna; Irfan, Mohamad; Atmadja, Aldy Rialdy; Jumadi, Jumadi
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2848

Abstract

There are 87.2% of the Muslim population in Indonesia, which makes Indonesia one of the countries with the largest Muslim population in the world. As a Muslim, it is supposed to carry out and stay away from the commands that Allah SWT commands, one of which is in QS. Al-maidah:3, one of the commands in the verse is not to consume haram food such as pork. Even so, it turns out that many traders in Indonesia still cheat to get more significant profits, namely by counterfeiting beef and pork. The lack of public knowledge supports this situation to differentiate between the two types of meat. Therefore, the classification process is used to distinguish the two kinds of meat using the convolutional neural network approach with VGG16 with several preprocessing stages. Two primary stages are used during the preprocessing stage: scaling and contrast enhancement. The VGG16 algorithm gets very good results by getting an accuracy value of 99.6% of the test results using 4,500 images for training data and 500 images for testing data. To compare the effectiveness of these techniques, it is recommended to use alternative CNN architectures, such as mobilNet, ResNet, and GoogleNet. More investigation is also required to gather more varied datasets, enabling the ultimate goal to achieve the best possible categorization, even when using cell phone cameras or with dim or fuzzy photos.
Attendance System Leveraging Haar Cascade Detection And CNN-Based Facenet Recognition Technology Syarif, Muhammad Adib; Gunawan, Wawan
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2464

Abstract

The objective of this research is to investigate face identification methods in the context of employee recognition as a solution to the problem of attendance that still uses manual methods or applications without identity validation. The main goal is to achieve optimal accuracy and consistency in the identification process using Convolutional Neural Networks (CNN) with FaceNet and Haar Cascade. This research focuses on the challenge of managing employee attendance, particularly for those who are working remotely, which can be vulnerable to fraudulent activity. The proposed solution combines facial recognition to enhance identity verification, attendance tracking, and assist companies in achieving their goals. The study employed a dataset of 1,050 employee face data and divided it into three scenarios for training and testing ratios: the first scenario (80:20), the second scenario (70:30), and the third scenario (60:40). The results indicate that the model in the first scenario had the highest accuracy value of 98% and outperformed the models in the second and third scenarios in terms of precision, recall, and f1-score, with values of 98.60%, 98.70%, and 98.60%, respectively. The results indicate that the model used in the first scenario is the most effective in classifying predicted cases and consistently predicting employee identification. Based on these findings, we recommend implementing suggestions such as adding datasets and analyzing important classes to improve the accuracy and generalization of face identification models in the context of employee recognition. Combining facial recognition improves identity verification and attendance tracking, making it easier for companies to manage employee attendance with greater effectiveness.­

Page 1 of 5 | Total Record : 48

 1   2   3   4   5 

Filter by Year

2025 2025


Reset
Filter By Issues
All Issue Vol 9, No 6 (2025) Vol 9, No 5 (2025) Vol 9, No 4 (2025) Vol 9, No 3 (2025) Vol 9, No 2 (2025) Vol 9, No 1 (2025) Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow Vol 8, No 4 (2024) Vol 8, No 3 (2024) Vol 8, No 2 (2024) Vol 8, No 1 (2024) Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien Vol 7, No 4 (2023) Vol 7, No 3 (2023) Vol 7, No 2 (2023) Vol 7, No 1 (2023) Vol 6, No 2-2 (2022): A New Frontier in Informatics Vol 6, No 1-2 (2022): Data Visualization, Modeling, and Representation Vol 6, No 4 (2022) Vol 6, No 3 (2022) Vol 6, No 2 (2022) Vol 6, No 1 (2022) Vol 5, No 4 (2021) Vol 5, No 3 (2021) Vol 5, No 2 (2021) Vol 5, No 1 (2021) Vol 4, No 4 (2020) Vol 4, No 3 (2020) Vol 4, No 2 (2020) Vol 4, No 1 (2020) Vol 3, No 2-2 (2019): Internet of Things and Smart Environments Vol 3, No 4 (2019) Vol 3, No 3 (2019) Vol 3, No 2 (2019) Vol 3, No 1 (2019) Vol 2, No 4-2 (2018): Cyber Security and Information Assurance Vol 2, No 3-2 (2018): The Diversity in Information Systems Vol 2, No 4 (2018) Vol 2, No 3 (2018) Vol 2, No 2 (2018) Vol 2, No 1 (2018) Vol 1, No 4-2 (2017): The Advancement of System and Applications Vol 1, No 4 (2017) Vol 1, No 3 (2017) Vol 1, No 2 (2017) Vol 1, No 1 (2017) More Issue