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 51 Documents
 1   2   3   4   5 
Search results for , issue "Vol 8, No 3 (2024)" : 51 Documents clear
Fermented and Unfermented Cocoa Beans for Quality Identification Using Image Features Basri, Basri; Indrabayu, Indrabayu; Achmad, Andani; Areni, Intan Sari
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.8.3.2578

Abstract

Fermented cocoa bean products are one of the high-quality requirements of the cocoa processing industry. On an automated industrial scale, early identification of cocoa bean quality is essential in the processing industry. This study aims to identify the condition of quality cocoa beans based on fermentation and non-fermentation characteristics. This study applies analysis based on static images taken using a camera with a distance variation of 5 cm, 10 cm, and 15 cm in both classes, with 500 image data each. The Feature extraction Approach uses the Oriented Gradient (HOG) method with a Support Vector Machine (SVM) classification technique. Image analysis of both object classes was also performed with a color change to show the dominance of the color pattern on the skin of the cocoa beans to be analyzed. The results showed that fermented cocoa beans show a color pattern and texture that tends to be darker and coarser than non-fermented cocoa beans. Computational results with performance analysis using Receiver Operating Characterisic (ROC) on both classes showed the results that the distance of 5 cm and 15 cm has 100% accuracy, but based on the best performance, comprehensively seen in terms of Precision, Recall, and F1-Score shows the best value is at a distance of 15 cm. The results of this research based on the literature review conducted have better achievements, thus enabling further research on the development of conveyor models with real-time video data for automation systems.
Indonesian Fake News Classification Using Transfer Learning in CNN and LSTM Praha, Tohpatti Crippa; Widodo, Widodo; Nugraheni, Murien
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.8.2.2126

Abstract

Fake news spreads quickly and is challenging to stop due to the ease of accessing and sharing information online. Deep learning techniques are a method that can be used to identify fake news quickly and accurately. The types of neural networks commonly utilized in deep learning architectures include Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), which can perform well when managing the task of classifying fake news, according to several pertinent studies. Regarding handling instances of Indonesian fake news classification, this study compares how well the CNN and LSTM models perform. However, given that Indonesian is a low-resource language with scant documentation, it is challenging to build an adequate data set. At the same time, the CNN and LSTM classification models require significant training data. We proposed a transfer learning method by combining two classification models with a pre-trained IndoBERT language model. 1340 news text data were used, including 643 actual news texts from CNN Indonesia, Liputan6, and Detik and 697 fake news texts from TurnBackHoax. As a result, the performance of the combination of the LSTM classification model with IndoBERT outperformed that of the CNN classification model with IndoBERT, which only produced an accuracy of 92.91%, down by 6%, and was able to produce an accuracy of up to 97.76%, an increase of 4.8% from before. Furthermore, the results show that the LSTM classification model outperforms the CNN classification model in capturing the representation created by IndoBERT. Additionally, these insights may serve as a basis for future research on identifying fake news in Indonesia, helping to improve methods for combatting misinformation in Indonesia.
Distribution Model of Personal Protective Equipment (PPE) Using the Spatial Dominance Test and Decision Tree Algorithm Purwayoga, Vega; Yuliyanti, Siti; Nurkholis, Andi; Gunawan, Harry; Sokid, Sokid; Kartini, Nuri
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.8.3.2471

Abstract

The COVID-19 case has developed positively, but preventive measures must be taken to anticipate SARS-CoV-2 mutations. Anticipation can include policies, preparing health workers, and providing personal protective equipment. Personal Protective Equipment (PPE) availability is a big challenge in handling pandemics, especially COVID-19. The level of need for PPE in an area depends on the number of COVID-19 cases. This research provides a solution to overcome the availability of PPE by applying the concept of cross-regional collaboration. Areas with low COVID-19 case rates can help areas with high COVID-19 case rates by sending PPE assistance. Implementing the cross-regional collaboration concept is assisted by the spatial dominance test algorithm, namely the spatial skyline query. Spatial Skyline Query works by searching for the most ideal area. The ideal area is an area with low COVID-19 case criteria. The low number of positive cases, death cases, probable cases, and close contact cases supports the low number of COVID-19 cases. Areas with the highest number of recovered cases are also priorities. The SSQ model was developed into two models for searching priority areas for PPE assistants. The first model is Sort Filter Skyline 1 (SFS1), and the second is Sort Filter Skyline 2 (SFS2). SFS1 is a form of SFS algorithm optimization that searches for the best 50% of all regions. SFS2 modifies SFS1 by selecting areas whose distance is <= the average distance of the area to the Health Crisis Centre of the Ministry of Health of the Republic of Indonesia. This research involves searching for priority areas and applying a prediction algorithm to extract knowledge built from the prediction model. The algorithm used is C5.0. The data used to apply the prediction algorithm results from the application of SFS1 and SFS2. The results of testing the prediction model by the C5.0 algorithm produced an accuracy of 77.26% for SFS1 data and 92.01% for SFS2. The average rules resulting from the C5.0 algorithm are three for SFS1 and two for SFS2.
Spatial-Temporal Visualization of Dengue Fever Vulnerability in Kediri Using Hierarchy Clustering Based on Mobile Devices Hamida, Silfiana Nur; Fariza, Arna; Basofi, Arif
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.8.3.2195

Abstract

In Indonesia, Dengue Fever (DF) is a contagious disease that is a significant issue in public health. The Kediri Regency in East Java, as reported by the Ministry of Health in 2019, had the highest number of DF cases. If not addressed promptly, DF can lead to outbreaks, creating a health emergency. The lack of a thorough investigation into the diversity of risk within a spatial and temporal region exacerbates this issue. Therefore, spatial-temporal analysis is crucial in developing a warning system to prevent and control DF. This paper proposes a method that combines the Euclidean Distance calculation with the Hierarchical Clustering method. We collected data from the Kediri Regency health department and conducted pre-processing and classification processes, considering the number of DF victims, death rate, population, rainfall, and public facilities. The hierarchical clustering algorithm was used to categorize the 344 village analyses into low, medium, and high vulnerability categories. This method allows for a comparison of yearly single, average, complete, and centroid linkage in DF vulnerability levels. We also employed spatial-temporal visualization based on cellular applications to create a clear picture of areas vulnerable to DF. The experimental results in clustering showed a satisfactory level of matching, with variant values calculated using the hierarchical clustering method. The variants for single linkages were 0.113; for average linkages, they were 0.120; for complete linkages, they were 0.178; and for centroid linkages, they were 0.106. The grouping validation results indicated that the centroid linkage method produced the best variant level. We suggest further enhancing the methods with better process steps using other pre-processing methods to improve the validation quality.
Participation of Citizen as Social Capital in LAPOR! Application in Indonesia Febriani, Rika; Luthfi, Zaky Farid; Waldi, Atri
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.8.3.2239

Abstract

Citizen engagement is crucial for the success of smart cities. In urban areas, communities as social capital has an interpersonal bond that unites people. Due to the complexity of urban issues like pollution, waste, and population density, citizens can monitor the government's work priorities to solve the problem. This article focuses on smart cities from a human-centered perspective, emphasizing the role of information technology. In Indonesia, citizens' rights awareness is facilitated by the National Public Service Complaint Management System (SP4N) or People's Aspiration and Online Complaints Service (LAPOR!), which acts as social capital. This article explores five key aspects of smart cities and smart citizenship: governance, citizen participation, infrastructure, public spaces, and urban livability. While ICT-driven approaches can swiftly transform cities, a community-based approach fosters long-term shifts. Meanwhile, limited citizen involvement contributes to ecosystem degradation in Indonesia and underscores the urgency and importance of citizen engagement in urban planning. In democratic societies, citizens hold power, and concepts of smart citizenship encompass civic intelligence and, at the same time, also value local knowledge. In Indonesia, LAPOR! reflects smart citizenship, enabling transparent reporting of aspirations and complaints. However, factors like limited access and education of citizens affect its use. This research used qualitative methods with content analysis methods. Qualitative content analysis is used to examine the textual content of websites.  Using LAPOR! SP4N in Indonesia to see the implementation of the concept of smart society and smart city. Participatory processes should contribute substantially to urban planning and governance. Information technology serves as a temporary solution for long-term urban planning issues. Broader concerns like public policy and participatory democracy must also be considered. In conclusion, this article explores the dynamic relationship between smart cities and smart citizenship, highlighting the importance of active citizen engagement and the potential of technology to empower communities for positive urban transformations.
Remote Laboratory Based on the Internet of Things for E-Learning: A Development Model of Newton’s Law Experiment Asrizal, -; Khairat, Raudhatul; Yohandri, -
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.8.3.2241

Abstract

Remote laboratory is a development of digital technology to support the quality of learning in this digital era. However, scientific processes often cannot be accommodated in digital spaces such as e-learning. This research highlights a remote laboratory system that can accommodate scientific process improvement in e-learning.  The research objective is to develop and determine the performance of the remote laboratory system of Newton’s Law experiment based on IoT for e-learning as an experiment development model. Research methods can be classified into design and development, abbreviated as DDR. The remote laboratory system is designed and developed in six phases. This system is developed by five main components, namely, a photodiode sensor, MCU nodes, motor drivers, stepper motor, and ESP 32 CAM. The results indicate that the remote laboratory system of Newton's law experiment has demonstrated positive performance, and the accuracy and precision of measurement from the remote laboratory system are classified as high. Accordingly, the remote laboratory system of Newton's law experiment can be used as an alternative to support scientific processes in e-learning. It is expected to serve as a guide for virtual laboratory design, enlightening the audience on the potential of this system. It is used extensively for experimental teaching in modern physics education. The success in designing and developing an experimental model of Newton's law by implementing a remote laboratory based on IoT provides a good opportunity to develop various more sophisticated physics experimental systems to support the science process and e-learning.
Drone Kit-Python for Autonomous Quadcopter Navigation Pulungan, Ali Basrah; Putra, Zaki Yuanda; Sidiqi, Adam Rasyid; Hamdani, Hamdani; Parigalan, Kathleen E
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.8.3.2301

Abstract

Using Python scripts over the MAVLink protocol, developers can use the open-source DroneKit Python software framework to enable autonomous drone operations. This framework provides excellent flexibility and power to facilitate automated drone control. The built quadcopter has an X configuration and uses a DJI F450 frame with some modifications. Interestingly, the drone has legs made of aluminum on both sides to help with smooth takeoffs and landings. The frame is 45 cm diagonal length and 30 cm vertical height. The drone was given an additional weight in a 15 x 18 x 12.5 cm box. The propeller used in this investigation is a 9x6 carbon-based model. The x2216 1400kV brushless motor that is being used is from Sunnysky, and it comes with an Electronic Speed Controller (ESC) with a 30A rating. A 4-cell 14.8V Lithium-Polymer (Li-Po) battery with a 7200mAh capacity powers the drone. Apart from that, the drone weighs 1573g in total. The results are obtained by self-measurement and flight measurement data (FMU). Six attempts were made, and the results showed that the second flight had the longest flight time and the highest altitude. In particular, the Flight Measurement Unit (FMU) reported that the flight lasted 81 seconds and reached an altitude of 0.93 meters. In contrast, the self-measurement data reported that the flight lasted 85 seconds and reached an altitude of 1.5 meters.
Classification of Air Pollutant Index on Data with Outliers and Imbalance Class Problem Krisbiantoro, Dwi; Waluyo, Retno; Hasanah, Uswatun; Pratama, Irfan; Sarmini, Sarmini
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.8.3.1993

Abstract

The problem of air pollution has become a global issue that has received attention from various countries. Jakarta, Indonesia's capital city, is unavoidable from the same problem. This study will use four parameters of substances PM10, SO2, CO, O3, and nitrogen dioxide to categorize Jakarta's air quality (NO2). The data used is daily data taken from the Air Quality Monitoring Station in Jakarta throughout 2020. The methods used include SVM, Random Forest, Logistic Regression, KNN, CART, and Stacking Algorithm. At the data preparation stage, we found missing values, outliers, and class imbalance problems. Before applying machine learning methods and evaluating accuracy, we used data pre-processing techniques such as the MissForest method, median substitution, and ADASYN. The results prove that the original dataset has a higher accuracy score (0.882 – 0.977) than the balanced dataset (0.829 – 0.976). According to the evaluation results, the Random Forest method has the highest accuracy score for original and balanced datasets. The overall result is better than the identical research, which produces 96.61% accuracy using a neural network. It shows that preprocessing steps such as missing values handling an imbalanced class handling is essential in classification performance.
Development of Smart Simulator for Electronic Fuel Injection (EFI) fuel system based on Quick Response Code (QR Code) for Learning Media Hidayat, Nuzul; Wakhinuddin, Wakhinuddin; Lapisa, Remon; Milana, Milana; Muslin, Muslim; Sardi, Juli; Wirdianto, Eri
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.8.3.2209

Abstract

This research aims to develop learning media in the form of an intelligent fuel system simulator, namely electronic fuel injection (EFI) on motorbikes, which focuses on developing the addition of Quick Response (QR) codes as additional information from the simulator, which is connected to video on the YouTube platform. With the help of the QR code, it is scanned using a smartphone so that the QR code can be connected directly to YouTube, providing additional information about the Smart simulator EFI system on motorbikes. This research was carried out by applying the Research and Development method and following the Plomp model, which consists of the following stages: (1) preliminary investigation, (2) designing and making a prototype, and (3) assessment. The existing simulator was developed by adding a QR code, and the QR code will be connected to videos that have been uploaded on the YouTube platform. QR codes are created using the online QR code generator platform. Assessment of the smart simulator is carried out through a questionnaire filled out by media experts and subject experts, as well as through observation sheets during smart simulator testing. The research results are a smart simulator product for EFI fuel on motorbikes equipped with a QR code. Evaluation by media and material experts shows that the smart simulator is declared valid. Meanwhile, the results of observations during product testing show that the smart simulator can describe the characteristics of the EFI fuel system on a motorbike according to the actual situation.
An Automated Face Detection and Recognition for Class Attendance Horn Boe, Chang; Ng, Kok-Why; Haw, Su-Cheng; Naveen, Palanichamy; Abdulwahab Anaam, Elham
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.8.3.2967

Abstract

Class attendance is a crucial indicator of students' seriousness towards learning. Many institutions continue to use manual methods, which are usually error-prone and unproductive. By leveraging computer vision algorithms, the system accurately captures and verifies the identity of students attending class. This paper aims to investigate and create an automated facial recognition system for classroom attendance to increase the precision and effectiveness of the attendance tracking system. To achieve this, we propose a system using computer vision technologies, namely Histogram of Oriented Gradients (HOG) with Support Vector Machine (SVM) for face detection and deep Convolutional Neural Networks (CNN) for face identification. The facial recognition system simplifies attendance recording, requiring participants to only gaze into the camera for the system to record their presence automatically. The system is rigorously tested and evaluated, and its accuracy is compared to our institution's current QR code attendance method. The study results reveal that the recommended approach is more accurate and competent than the existing procedures. The system allows for precise attendance records with real-time face detection and recognition capabilities. This technology ensures accurate and reliable attendance data, empowering organizations to make informed decisions, effectively manage resources, and provide a seamless experience for all students. In addition, a similar attendance system can be deployed for any event in an organization, thereby enhancing overall operational efficiency.

Page 3 of 6 | Total Record : 51

 1   2   3   4   5 

Filter by Year

2024 2024


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