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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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Identification of Indonesian Traditional Foods Using Machine Learning and Supported by Segmentation Methods Rangkuti, Abdul Haris; Kerta, Johan Muliadi; Mogot, Roderik Yohanes; Athala, Varyl Hasbi
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2545

Abstract

Traditional food is essential in preserving cultural heritage and is a vital part of Indonesian cuisine. In this research, we implement a methodology to identify the traditional Indonesian food using machine learning algorithms supported by various segmentation methods. This research aims to provide an efficient and accurate approach to classifying traditional foods, which can contribute to promoting and preserving Indonesia's culinary heritage. To conduct this research, we conducted experiments on 34 types of conventional Indonesian food originating from various provinces in Indonesia. The analysis of food images involved several segmentation algorithms, including Sobel, Prewitt, Robert, Scharr, and Canny filters. After the segmentation process, we proceeded with feature extraction and classification using traditional machine learning algorithms such as the Random Forest algorithm, Decision Tree, and derivatives of the SVM algorithm. These algorithms aimed to recognize the 34 types of traditional food. After conducting several experiments, we found that Random Forest with Robert's segmentation method was the highest-performance algorithm. It produced extraordinarily accurate results on the test dataset, with an accuracy performance of 85.52%, recall of 84.63%, precision of 83.77%, and an f1 score of 82.49%. Additionally, the best-performing algorithms with execution time averaged less than 1 minute. Another experimental result showed that the Random Forest algorithm with the Canny operator achieved an accuracy of 81.51%, recall of 84.97%, precision of 86.8%, and an f1 score of 85.61% on the test dataset. Furthermore, the Random Forest algorithm with the Sobel operator achieved accuracy results of 78.4%, recall of 65.3%, precision of 62.3%, and an f1 score of 63.71%.  In the SVM algorithms derivative, the Sigmoid SVM combined with the Scharr operator achieved the highest performance in its category across all classification metrics. In conclusion, this research offers valuable insights into classifying traditional Indonesian dishes using traditional machine learning algorithms. Simultaneously, this research aims to promote the appropriate and effective preservation and recognition of traditional Indonesian food.
Two-Way Thesis Supervisor Recommendation System Using MapReduce K-Skyband View Queries Dasri, Dasri; Annisa, Annisa; Haryanto, Toto
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2800

Abstract

Timely graduation is an important indicator of the quality of higher education. Yet, many students struggle to complete their studies on time due to challenges in finding relevant research topics and suitable supervisors. This study developed a two-way supervisor recommendation system that considers the preferences and expertise of both students and supervisors. The main contribution of this research is the comparison of Block Nested Loop (BNL) k-skyband and MapReduce k-skyband algorithms. The recommendation model developed in this study uses course syllabi to obtain research topics and academic grades to determine students' interests in research topics. A total of 239 research topics were obtained from 37 courses. Optimal recommendations were achieved with a k value of 16. Implementing the MapReduce algorithm in this recommendation model demonstrated a computation speed 8 times faster than the BNL k-skyband approach, making it effective in handling large datasets. The proposed recommendation system received positive feedback from students, with scores of 3.5 for relevance, 3.7 for topic diversity, 3.4 for serendipity, and 3.5 for novelty. These findings suggest that the proposed recommendation system can support students in their research endeavors and improve the overall supervision process in academic settings, with potential for widespread implementation across various study programs. Thus, contributing to the overall improvement of higher education quality.
Building Historical Narratives: The Development of Virtual Reality Learning Media for Exploring Historical Sources Bung Hatta's Birthplace Ofianto, Ofianto; Ningsih, Tri Zahra; Mulyani, Fini Fajri; Putri, Suci Kurnia
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3118

Abstract

The shortcomings in the history of education, constrained by conventional teaching approaches, inadequate utilization of historical sites, and logistical hurdles, diminish student engagement and comprehension. This study aims to develop a virtual reality (VR) learning medium around Bung Hatta's birth house, aiming to enhance students' historical understanding through captivating and immersive experiences. We adapted the Borg and Gall development model into four phases: preliminary study, strategic planning, development, and validation. We utilized 360-degree pictures in conjunction with extensive historical data regarding Bung Hatta's birthplace, integrating these elements into an immersive virtual reality environment. We used expert validation to guarantee material accuracy, subsequently doing practical testing with history educators and students to evaluate usability and overall effectiveness. The findings revealed a practicality score of 87%, highlighting the substantial influence of VR media on enhancing student motivation and understanding of historical material. This technology facilitates the virtual investigation of significant historical locations, effectively surmounting geographical and financial barriers and enhancing accessibility, engagement, and the relevance of history for learners. This method enhances critical thinking and deepens appreciation of national heritage while offering a strong framework for incorporating VR into educational curricula. Further research must examine the long-term impacts of VR on various learning outcomes and its relevance across the educational curriculum. Moreover, integrating VR technology in diverse historical disciplines may augment its effectiveness and applicability in education. Virtual reality techniques improve students' understanding of history while strengthening 21st-century skills such as critical thinking and creativity
Measuring Score of Ethnic Tolerance Index among Peacekeepers using a MyETI System Dashboard Wan Husin, Wan Norhasniah; Kamarudin, Nur Diyana; Hilmi, Muhammad Amjad; Zainurin, Siti Juwairiah; Jamilah, Maryam
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2261

Abstract

In post-conflict nations, the state-building process might take up to ten years to provide positive outcomes. This scenario has become increasingly challenging due to the escalation of intolerance among different ethnic groups, leading to incidents of communal violence in the aftermath of the war. Therefore, it is imperative to have ethnic tolerance and cultural understanding in peacekeeping operations that occur in a more intricate setting. The presence of ethnic tolerance among peacekeepers is crucial for ensuring the efficacy of peacekeeping missions. The increase of intolerant perspectives often causes the beginning of ethnic conflicts in multi-ethnic societies. Therefore, the main objective of peacekeepers deployed in these countries is to reinstate peace and security. This study proposes employing an online analysis to assess the ethnic tolerance index among peacekeepers accurately. The suggested method entails collecting and analyzing real-time survey data via a MyETI system dashboard, which may precisely evaluate the ethnic tolerance index score among Malaysian individuals. The MyETI e-survey has 103 questionnaires organized into four main categories: ethnic cross-relationships, governance, ethnic tolerance, and religious beliefs. To achieve the study's goal, 103 Malaysian peacekeepers who have previously been deployed to different United Nations Peacekeeping Operations (UNPKO) will be requested to answer the questions using the MyETI dashboard. The results could enhance the ethical guidelines for cultural competence, prioritizing understanding ethnic tolerance in peacekeeping operations or deployments.
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.
A Conceptual Framework for Personalized Early Prediction of Asthma Exacerbation Attacks Using Proximal Policy Optimization Aliyu, Dahiru Adamu; Patah Akhir, Emelia Akashah; Osman, Nurul Aida; Yahaya, Saidu; Adamu, Shamsudden; Mamman, Hussaini
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2944

Abstract

Asthma, a chronic respiratory ailment affecting millions worldwide, presents significant challenges due to the unpredictable nature of exacerbation episodes. Existing methodologies struggle to accurately predict exacerbations individually, particularly across diverse patient demographics. This paper introduces an innovative conceptual framework for the early prediction of asthma exacerbations, leveraging advanced reinforcement learning (RL) techniques, specifically proximal policy optimization, along with patient-specific data and environmental factors. The primary goal is to revolutionize asthma management by providing customized predictions and tailored reward mechanisms that enable proactive interventions and optimize resource allocation. The framework comprises critical components such as patient profiling through a mobile application, trigger identification, a RL-based predictive model, an early warning mechanism, and a personalized reward scheme. Data for patient profiling is gathered through a mobile application, which includes medical history, demographics, symptoms, and triggers. Profiling forms the foundation for the prediction model, enabling it to identify subtle patterns and anticipate exacerbation events more accurately and efficiently. The significant contributions of this research include offering a novel approach by incorporating custom reward functions to enhance learning across heterogeneous patient populations, tailoring interventions to unique triggers and symptom presentations, and addressing challenges associated with patient diversity. By addressing the limitations of existing methodologies and offering a comprehensive solution, this research promises significant improvements in asthma care and healthcare delivery, paving the way for future advancements in personalized medicine and predictive healthcare systems.
Flexible Semantic Qur’an Question Answering Using Graph-Based Summarization and KNN Wardani, Dewi; Abdurrahman Syah, Hafidz; Wijayanto, Ardhi; Harjito, Bambang
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.1907

Abstract

Researchers in the computer science field have been attracted by Qur'an-based research. This research area focuses on representing the ontology-based Qur'an. A semantic-based search will be beneficial in extracting information from the Quran, which has complex knowledge and language. This work aims to develop flexible semantic Qur'an question-answering by applying graph-based summarization and K-nearest neighbors (KNN) methods to add flexibility to semantic-based searches in the Indonesian Language. Meanwhile, the Qur'an is based on a unique Arabic language. It is a part of the complexity of this work. The graph-based summarization method effectively summarizes a complex question. It was proved by ROUGE testing with F1, precision, and recall scores of 72%, 62%, and 72%, respectively. The KNN method evaluated by the expert resulted in an average approval percentage on the 1st, second, and third topics of 62.11%, 66.15%, and 19.61%. As for other issues related to the questions, 70% needs to be displayed. The analysis of the obtained result indicates that the classification step needs to be improved in the tiny dataset. This work will contribute to Qur'an Question Answering as it is considered that the Qur'an is a different object compared to the other content of Question Answering. The Qur'an is an object that contains a massive amount of multi-interpretation. Lots more work in the future. The dataset is also limited by the scope of the theme of this research, which is only the pillars of Islam, so many topics still need to be included in the dataset
Application of Digital Teaching Materials Based on Flipped Learning Model in Civics Education in Elementary School Waldi, Atri; Supendra, Dedi; Rivelia, Katherine Putri; Anggraeni, Aisyah; Febriani, Rika
JOIV : International Journal on Informatics Visualization Vol 9, No 1 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.1.2229

Abstract

This research aims to improve students' understanding of Pancasila Student Profile Values through the implementation of Flipped Learning by combining it with digital teaching materials according to the characteristics of students in elementary schools. In addition, this research also aims to create practical digital teaching materials for elementary school students in Padang City on Civic Education learning. This research is a development research using the 4D development model (Define, Design, Develop and Disseminate). This study involved a sample of elementary school students in Padang City who measured the practicality of the developed teaching materials assessed through a structured evaluation process. The results showed a high practicality score of 96%, which categorized the digital teaching materials as very practical for use in the classroom. In addition, researchers also measured the impact of the implementation of these teaching materials on student learning outcomes by obtaining significant results; 87% of students achieved scores above the threshold of completeness, with an average score of 88. The findings suggest that the integration of Flipped Learning with digital teaching materials not only facilitates a deeper understanding of Pancasila values but also positively affects students' overall performance. The implications of this study highlight the potential for further research to explore the long-term effects of digital teaching materials and Flipped Learning on different subjects and levels of education. Future research could also investigate the scalability of these materials in different educational contexts and their effectiveness in fostering critical thinking and civic engagement among students.
Improving Data Reliability Assessment in ETL Processes through Quality Scoring Technique in Data Analytics Atika Razali, Nor Famiera; Baharom, Salmi; Abdullah, Salfarina; Admodisastro, Novia Indriaty
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.3632

Abstract

The foundation of a relevant and accurate data analysis is reliable data. Technique and measurement are essential to evaluate current data quality regarding reliability and establish a baseline for ongoing improvement initiatives. Without tools or visualizations, data engineers may find it challenging to monitor and maintain the reliability of the massive data from the extraction, transformation, and loading (ETL) data load process. Data reliability assessment is a helpful technique in analyzing the quality of data reliability and information on the present state of data before commencing any analytics. The proposed technique hinges on the metric and measurement defining data reliability and the dashboard platform where the integration with the user in dictating the weight of data and the final output, which is the final data reliability score, will be projected. The score obtained affirms whether improvements are needed on the data or if an organization can proceed with data analytics. The technique considers the data extraction, transformation, and loading (ETL) procedures used to gather datasets. Data significance or weight was determined according to the analytics needs and preferences, indicating an acceptable score for generating insights. Ultimately, when utilizing the data reliability assessment metrics technique, we are credited with an overall picture of our data’s reliability aspect, as only one look is offered based on the intended analysis. This new approach boosts the confidence among data practitioners and stakeholders, especially those relying on findings generated from data analysis. Furthermore, the overview assists in enhancing the current state of data, where the derived score helps identify possible areas of improvement in the ETL process. Accuracy and efficiency assessment of the proposed technique also showed positive feedback in measuring the method in measuring the reliability of data.
Classification of Skin Cancer Images Using Convolutional Neural Network with ResNet50 Pre-trained Model Minarno, Agus Eko; Lusianti, Aaliyah; Azhar, Yufis; Wibowo, Hardianto
JOIV : International Journal on Informatics Visualization Vol 8, No 4 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.4.2156

Abstract

The skin, an astonishingly expansive organ within the human body, plays a pivotal role in safeguarding us against the environment's harsh elements. It acts as a formidable barrier, shielding our delicate internal systems from the scorching heat of the sun and the harmful effects of relentless exposure to light. Nevertheless, it is not impervious to damage, especially when subjected to excessive sunlight and the potentially hazardous ultraviolet (UV) radiation that accompanies it. Prolonged UV exposure can wreak havoc on our skin cells, potentially setting the stage for the development of skin cancer. This condition demands prompt and accurate diagnosis for effective treatment. To address the pressing need for swift and precise skin cancer diagnosis, cutting-edge technology has come to the fore in the form of deep learning systems. These sophisticated systems have been meticulously designed and trained to classify skin lesions autonomously with remarkable accuracy. The Convolutional Neural Network (CNN) architecture is a formidable choice for handling image classification tasks among the array of deep learning techniques. In a recent breakthrough study, a CNN-based model was meticulously constructed to explicitly classify skin lesions, leveraging the power of a pre-trained ResNet50 architectural model to augment its capabilities. This groundbreaking ResNet50 architecture was meticulously trained to classify seven distinct skin lesions, surpassing the performance of its predecessor, MobileNet. The results achieved in this endeavor are nothing short of impressive. The overall accuracy of the ResNet50 model stands at a commendable 87.42% when tasked with classifying the seven diverse classes within the dataset. Delving further into its proficiency, we find that the Top2 and Top3 accuracy rates soar to an astounding 95.52% and 97.86%, respectively, illustrating the model's exceptional precision and reliability.

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