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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
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Deep Metric Learning with Augmented Latent Fusion and Response-Based Knowledge Distillation on Edge Device for Paddy Pests and Disease Identification Darmawan, Hendri; Yuliana, Mike; Hadi, Mochammad Zen Samsono; Sangaiah, Arun Kumar
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.8.3-2.3104

Abstract

The health of paddy fields significantly impacts rice yields and the economic stability of farmers. Limited number of experts available to watch these issues poses a challenge. Consequently, a reliable diagnostic system is necessary to find pests and diseases in rice crops. In this study, we propose deep metric learning with augmented latent fusion (FADMAKA) combined with a response-based knowledge distillation (KD) approach. The student model, which processes single RGB input images, is trained using soft latent labels derived from four augmented input from the teacher model. Our method delivers a high validation accuracy of 0.973, keeps an accuracy of 0.782 on the unseen data, and with rapid inference time of 38.911 milliseconds. This approach’s accuracy outperforms SoftMax deep learning classification with fine-tuning, which only has a maximum accuracy of 0.739 on the unseen data with computation time of 36.224 ms, and the DML with augmented latent fusion with k-NN classifier on the same base model, which achieves an accuracy of 0.78 with computation time of 124.977 ms. Our proposed model has 0.12 giga floating point operations per second (GFLOPs) that is suitable for edge devices with low computational resources. Following the modeling phase, we deployed the highest-accuracy student model to a Raspberry Pi 4B device equipped with a camera. This system can provide biological agent-based recommendations for identified pest and disease threats in rice fields. Our approach not only improved accuracy but also proved efficiency, enabling farmers to identify pests and disease without relying on internet connectivity. 
Multi-spatial Resolution Imagery to Estimate Above-Ground Carbon Stocks in Mangrove Forests Purnamasari, Eva; Kamal, Muhammad; Wicaksono, Pramaditya; Hidayatullah, Muhammad Faqih; Susetyo, Bigharta Bekti
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.8.3.2237

Abstract

Mangroves are a type of vegetation that can absorb carbon and have an essential role in controlling CO2 levels in the atmosphere. Mangroves can absorb carbon better than terrestrial ecosystems because of their ability to bury carbon in sediment. This research aims to compare and measure the carbon stock content above the surface of mangroves in the field using multi-spatial resolution imagery, namely, Landsat 8 OLI, Sentinel 2A, and Planetscope. Field carbon calculations were carried out using the allometric method based on mangrove species. The calculation results are then linked through regression analysis with the vegetation index Difference Vegetation Index (DVI) results. The total carbon obtained from PlanetScope imagery was 535.27 tons, Sentinel 2A imagery was 549.23 tons, and Landsat 8 OLI imagery was 533.57 tons. Among the three images used, based on Sentinel 2A statistical analysis reflects the possibility of overfitting or the best with higher r and R2 values in the calculations. However, based on SE accuracy tests, PlanetScope has better accuracy than the other two images. Apart from that, the accuracy test results using a 1:1 goodness of fit plot for each image, the distribution pattern of mangrove carbon stock estimates shows that the entire model in mapping mangrove carbon stocks is over-estimated. The overestimated results are possible because more objects around the mangrove, especially canopy density, are recorded by remote sensing sensors compared to tree diameter as input for field carbon results.
Audio Signal Classification using Mel-Frequency Cepstrum Coefficients and Deep Neural Network for Noon Saakin or Tanween Tajweed Rule Dataset Irawan, Genta Hayindra; Mubarok, Husni; Hoeronis, Irani
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.9.3.2875

Abstract

The Al-Qur’an serves as a fundamental guide for Muslims, requiring both comprehension and practice. Accurate recitation according to tajweed rules is essential for a deeper understanding of its meaning. Despite the growing focus on classification across various modalities, studies specifically targeting audio objects remain relatively limited, motivating further exploration in this area. This study focused on the classification of the tajweed rule as the decided audio object, leveraging the potential of Natural Language Processing (NLP) to support Qur’an research and studies, as well as developing applications that may help learners understand the Qur’an, so further study is needed on the recognition of tajweed reading rules, one of which is the noon saakin or tanween tajweed rule. Audio features were extracted using Mel-Frequency Cepstrum Coefficients (MFCC) technique, which has been widely adopted in various study within the scope of audio processing tasks. These features were subsequently used to train a classification model based on Deep Neural Network (DNN) algorithm. Experiment results demonstrate that the DNN classification model produces an accuracy of 71% and f1 score respectively for iqlab of 0.8, idgham of 0.46, idzhar of 0.77, and ikhfa of 0.72. The results of testing the model with new foreign data, each class tested with one data has successful rate of 50%. These findings indicate that the classification model needs to be further improved in terms of its design or diversity of the audio data, especially model improvements in the recognizing idgham, idzhar, and ikhfa laws.
A Review on Deep Learning Approaches and Optimization Techniques For Political Security Threat Prediction Zaabar, Liyana Safra; Mat Razali, Noor Afiza; Ishak, Khairul Khalil; Abdullah, Nor Asiakin; Wook, Muslihah
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.8.3.2204

Abstract

In an era of complex geopolitical dynamics and evolving security threats, the accurate prediction and proactive management of political security risks are imperative. This article provides a comprehensive review of the application of deep learning methodologies and optimization techniques to enhance political security threat prediction. Beginning with analyzing the dynamic landscape of political security threats, the paper emphasizes the necessity for adaptive, data-driven predictive tools. It then delves into the fundamentals of deep learning, elucidating core principles, notable architectural frameworks, and their diverse applications across domains. Expanding upon this foundation, the study evaluates the suitability of deep learning models for addressing the multifaceted challenges associated with political security threat prediction. To maximize the utility of these models, the article explores optimization techniques encompassing hyperparameter tuning, transfer learning, and ensemble strategies, assessing their effectiveness in fine-tuning predictions and bolstering the resilience of threat prediction systems. This review involved the utilization of four journal databases: IEEE, Science Direct, Association for Computing Machinery (ACM), and SpringerLink. We analyzed and examined 39 articles, paying close attention to the different patterns and techniques found within the chosen research framework. Through a critical synthesis of existing research, this review offers insights into the strengths, limitations, and future directions of deep learning-based political security threat prediction, contributing to the ongoing discourse on leveraging artificial intelligence for safeguarding global stability and security.
Discovering the Fascinating Pattern in the Geometric Representation of Non-intersecting Chords on the Circle within the Motzkin Sequence Ibrahim, Haslinda; Mohd Darus, Maizon; Karim, Sharmila
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.9.2.2766

Abstract

This study explores the Motzkin sequence and introduces a new sequence, the Wing sequence, derived from a novel geometric representation of directed paths. The main objective is to investigate patterns in the geometric representation of the Motzkin sequence and analyze these representations to construct the Wing sequence. The Motzkin sequence, which counts the number of ways to draw non-intersecting chords between  points on a circle serves as the basis for this study. The geometric representation of these chords, particularly for  and , is analyzed to reveal patterns and properties that could be modified to generate a new sequence. Based on the analysis, we tried some iterations for  and  to develop initial ideas for constructing the Wing sequence. The technique involved modifying the geometric representation of the Motzkin sequence to derive the Wing sequence. This process included transforming the non-intersecting chords into a circular representation into a linear arrangement. We then removed any representations without chords and only considered those with chords. Next, we transformed the chords into directed paths. Since these directed paths only connected two points, we combined them to form directed paths that passed through all points at least once. These results identified a pattern between the first and second points, leading to the Fist-Second-Third-Points (FSTP) technique for constructing the Wing sequence. The main findings include deriving a general formula for the Wing sequence, establishing a recurrence relation, and constructing a generating function. These results highlight the applicability of the geometric representation technique in discovering new sequences and enhancing geometric representation techniques in mathematical research. The techniques developed in this study can be applied to other geometric problems, offering researchers a more intuitive understanding of structures. Further exploration of these results may open up various applications for combinatorial structures, such as network routing issues, project scheduling, and key generation involving data security.
Application of Cognitive Load Theory in VR Development and Its Impact on Learning: A Perspective on Prior Knowledge, Learning Interest, Engagement, and Content Comprehension Sulisworo, Dwi; Erviana, Vera Yuli; Robiin, Bambang
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.8.2.2467

Abstract

This research examines the utilization of Virtual Reality (VR) and its implications for the learning process, specifically focusing on learning interest, prior knowledge, learning engagement, and content comprehension. The central objective is to establish a comprehensive model that unravels the intricate interplay between these factors within the context of VR-based learning. The study also aims to shed light on the impact of integrating Cognitive Load Theory into VR development and its effects on the learning experience. Adopting an observational design, this study elucidates the intricate relationships among learning interest, prior knowledge, learning engagement, and content comprehension in VR-based education. The VR technology employed in this research has previously undergone rigorous feasibility testing. The VR application was designed following cognitive load theory principles. Its immersive content offers users a lifelike immersion into the natural habitats of diverse animal species across various global regions. By leveraging VR technology, elementary school students engage in a more profound and authentic learning journey. A total of 85 participants, encompassing fourth and fifth-grade elementary school students, were involved in the study. These students were drawn from schools situated in rural areas in particular regions in Indonesia and had moderate to low economic backgrounds. The variables under examination include Prior Knowledge, Learning Interest, Engagement, and Content Comprehension as learning outcomes. Data analysis was conducted utilizing a blend of linear regression and path analysis techniques, with a confidence level of 95%. The Guttman scale questionnaire was used, and total scores were transformed into a ratio scale through a conversion process. The study reveals a positive correlation between learning interest and learning outcomes, highlighting that a strong interest in a subject contributes to improved learning results. Additionally, both learning interest and prior knowledge influence learning engagement. Students with higher learning interests and prior knowledge are more likely to actively engage in the learning process actively, underscoring internal factors' role in motivating participation. Learning engagement moderates the relationships between learning interest, prior knowledge, and learning outcomes. By enhancing the effect of learning interest and prior knowledge on learning outcomes, engagement enables more comprehensive and practical information processing.
PRiSm: Policy Recommendation Systems in Cadastral Survey Using National Public Opinion Big Data Lee, Kihoon; Chang, Yohan
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.8.3-2.3023

Abstract

Cadastral surveys are vital for assessing individual properties and generating national statistics. In South Korea, rapid territorial changes have exposed personal-level issues, while current systems limited public engagement capabilities have constrained policymaking. Although efforts have been made to bridge this gap, they have been hindered by the lack of an effective medium. This study introduces a novel framework, PRiSm (Policy Recommendation Systems in Cadastral Survey), which leverages National Public Opinion Big Data. We collected and analyzed two key data sources: 1) public opinion data from 2018 to 2023, which correlates strongly with cadastral resurveys across South Korea, and 2) content from 54 major news media outlets over the same period. The first data set represents bottom-up opinions at the individual level, while the second reflects top-down perspectives on national issues. The PRiSm system, developed in this study, utilizes Natural Language Processing (NLP) and advanced Machine Learning (ML) techniques, including Word2Vec and a Genetic Algorithm for hyperparameter optimization, to process over a thousand inquiries and news articles. Our results highlight how different groups engage in discussions shaped by their interests and concerns, revealing key sensitivities and recommending terms invaluable for stakeholders and policymakers. We anticipate that PRiSm will offer meaningful insights for the public and decision-makers. Additionally, with more advanced ML and/or Deep Learning algorithms, there is significant potential for further advancements in NLP within the PRiSm framework.
Assessment of Post-Disaster Building Damage Levels Using Back-Propagation Neural Network Prediction Techniques Wibowo Almais, Agung Teguh; Fajrin, Rahma Annisa; Naba, Agus; Sarosa, Moechammad; Juhari, Juhari; Susilo, Adi
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.9.3.2711

Abstract

Indonesia is susceptible to natural disasters, with its geographical location being one of the contributing factors. To mitigate the harmful effects of natural catastrophes, a disaster emergency response must be undertaken, consisting of steps taken immediately following the event. These operations include rescuing and evacuating victims and property, addressing basic needs, providing protection, and restoring buildings and infrastructure. Accurate data is required for adequate recovery after a disaster. The Badan Penanggulangan Bencana Daerah (BPBD) oversaw disaster relief efforts, but faulty damage assessments slowed restoration. Surveyor subjectivity and differing criteria result in discrepancies between reported damage and reality, generating issues during the post-disaster reconstruction. The objective of this study is to develop a prediction system to measure the extent of damage caused by natural disasters to buildings. The five criteria that decide the level of building damage after a disaster are building conditions, building structure condition, physical condition of severely damaged buildings, building function, and other supporting conditions. The data used are from the BPBD of Malang city from 2019 to 2023. This system would allow surveyors to make speedy and objective evaluations. Five different models were tested using the Neural Network Backpropagation approach. Model A2 produces the highest accuracy of 93.81%. A2 uses a 40-38-36-34 hidden layer pattern, 1000 epochs, and a learning rate 0.1. These findings can lay the groundwork for advanced prediction models in post-disaster building damage evaluation research.
Structural Equations Modeling Approach: Issues in selecting a University Simarmata, Justin Eduardo; Mone, Ferdinandus; Chrisinta, Debora
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.8.3.2162

Abstract

Higher education is a continuation of secondary education that is organized to prepare students to become members of the community with academic or professional abilities. Therefore, selecting a university is essential for helping students acquire knowledge and skills and prepare themselves to be independent individuals. This study intends to determine the distribution of decision-making students who select the university based on factors of academic ability, individual characteristics, psychology, instrumental, and environmental factors. The study's methodology, which involves the collection of primary data from 474 respondents from high schools in the Indonesian-Timor Leste border region, is robust and rigorous, instilling confidence in the reader. The method used for data analysis is quantitative, which involves applying structural equation modeling to identify factors that influence students’ selection of universities. The study results showed that out of the six structural models formed, they could provide evidence that factors such as academic ability, individual characteristics, psychology, instrumental factors, and environment influence students’ selection of universities. This can be seen from the test values of the regression model formed in the structural modeling, which gives a P-value of less than 5%. The variable that provided the largest percentage, partially influencing student interest in selecting a state university, was the school environment, which was 98%. The value was based on the path coefficient of structural equation modeling. These findings have significant implications for the design of university admission processes and the development of student support programs.
The Development of a Hybrid Learning Management System with Learning Styles for Creative Learning to Undergraduate Students Anugrah, Septriyan; Supendra, Dedi; Jalinus, Nizwardi; Syahril, Syahril; Austin, Diah Anggraini
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.9.3.2670

Abstract

Distance learning often poses challenges for teachers, particularly in understanding the diverse learning styles of their students. The inability to recognize and accommodate these differences can lead to monotonous teaching approaches, which hinder students' comprehension and creativity. This study aims to evaluate the application of learning style concepts developed by Peter Honey and Alan Mumford, implemented in a Learning Management System (LMS) designed by the researcher to support creative learning practices. The research method used is research and development with the Plomp model. This study involved 219 students enrolled in an introductory programming course at Universitas Negeri Padang. The results indicate that respondents' experiences in six aspects—attractiveness, clarity, efficiency, reliability, stimulation, and novelty—received favorable evaluations. This study emphasizes the importance of understanding students' learning styles and using appropriate media to create an environment that supports exploring ideas and innovation, enabling students to develop their creativity optimally while ensuring equal opportunities for all students to grow and succeed.

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