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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.
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Articles 51 Documents
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Search results for , issue "Vol 8, No 3 (2024)" : 51 Documents clear
UI/UX Redesign of SH-UPI App Using Design Thinking Framework Fuada, Syifaul; Setyowati, Endah; Restyasari, Nissa; Heong, Yee Mei; Hasugian, Leonardi Paris
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.8.3.2094

Abstract

The rise of smart home technology has significantly impacted people’s behavior and lifestyle, especially when controlling household electronics remotely. Smart homes have become increasingly commoditized in the last decades, resulting in several vendors offering various commercial products and their variants. Universitas Pendidikan Indonesia (UPI) has created the smart home platform “SH-UPI,” which includes the Smart LED Bulb RGB. The platform’s mobile app can be downloaded from the Play Store or the official website (www.sh-upi.com). The SH-UPI is one of Indonesia's leading smart homes that adopted Internet-of-Things (IoT). However, customers have reported issues with the app’s user interface (UI) and user experience (UX), prompting a redesign to address these concerns and stay competitive. This study employs the design thinking method of empathizing, defining, imagining, prototyping, and testing. The define stage to prototype stage resulted in a newly designed prototype for the SH-UPI app. Testing involved evaluating the prototype using metrics like the User Experience Questionnaire (UEQ) and System Usability Scale (SUS). The test results showed an increase in the UEQ parameter value, exceeding the initial average scores of -0.8 to 0.8. Additionally, there was an improvement compared to benchmark scores, which initially ranged from below average to poor but now range from above average to excellent. The SUS score also improved, rising from 59.75 (grade D) to 83.625 (grade A). The findings of this study can be a valuable resource for developing SH-UPI apps with significantly enhanced UI/UX.
Analyzing The Impact of Project-Based Learning STEAM Flipped Classroom on Computer Architecture and Organization Courses in Higher Education Ekayana, Anak Agung Gde; Parwati, Ni Nyoman; Agustini, Ketut; Ratnaya, I Gede
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.8.3.2540

Abstract

This study examines the differences in students' creative thinking skills between the project-based learning STEAM Flipped Classroom and the direct learning STEAM Flipped Classroom model by paying attention to the role of academic self-efficacy as a moderator variable. The research population consists of students in informatics engineering study programs in the Denpasar area of Bali. The study used a quasi-experimental 2 x 2 factorial design. Data collection is carried out through questionnaires and test instruments. Data analysis used descriptive and inferential statistics, as well as analysis of covariance. The results showed a significant difference in students' creative thinking skills between those who learned with the STEAM Flipped Classroom project learning model and the STEAM Flipped Classroom direct learning model. In addition, there are differences in students' creative thinking skills based on the level of academic self-efficacy. The empirical application of the STEAM Flipped Classroom project model is proven to help students generate new ideas, develop ideas, and improve their thinking skills. However, the findings suggest that flexible thinking skills must be further enhanced. Students with high self-efficacy tend to be more proactive in providing constructive ideas in project-based learning activities. This research implies that it is necessary to actively motivate and share experiential stories with students who have low self-efficacy. In the future, I suggest that universities adopt the innovative learning model of project-based learning STEAM flipped Classroom to improve the creative thinking skills of informatics engineering students at the college level.
Wireless Volume Corrector for Natural Gas Flow Metering Using ESP32 Microcontroller and Open-Source Web Server Handaja, Suka; Dewi, Astrie Kusuma; Triyanto, Roni Heru
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.8.3.2160

Abstract

The measurement of gas flow in energy transactions from an energy-producing company to an energy user is essential to evaluate, considering that measurement errors can accumulate, and losses can occur, which will be ongoing. The gas measurement process begins with measuring pressure and temperature parameters and the gas flow volume transacted. Then, the measured gas volume will be converted into a standard volume as the basis for gas buying and selling transactions (custody). This article conveys that a wireless volume corrector has been designed using the ESP32 microcontroller with Wi-Fi communication that can be integrated with the internet to support automatic meter reading systems. The measured data is collected in a database server using XAMPP, an open-system web server. Data stored on the database server is displayed via a web browser with a display of the last 5 data entered the web server. The web browser display is refreshed every second so that the display on the web browser is a live or online display. This research resulted in temperature, pressure, actual flow, and volume standard measurements with an error below 0.1%, which met the metrology requirements, instilling confidence in the system's reliability. This research proves that realizing a volume corrector with IoT technology can be done cheaply.
The Implementation and Empirical Analysis of Android Learning Application toward Performance among Students Electronics Engineering Education Hidayat, Hendra; Harmanto, Dani; Orji, Chibueze Tobias; Anwar, Muhammad
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.8.3.2235

Abstract

The integration of technology into the realm of education is experiencing exponential growth, and an ever-evolving selection of media formats is being created to facilitate teaching and learning in a more effective manner. The objective of this research endeavor is to ascertain the degree to which the implementation of learning applications influences the academic achievement of students enrolled in electrical engineering-related programs. To accomplish this objective, learning methodologies and self-directed learning must be implemented as variables that impact students' academic performance. To facilitate this inquiry, a total of 339 representative samples of participants were collected. The collected data were subjected to analysis using the SmartPLS 4.0 software and the Structural Equation Model (SEM) with partial least square (PLS) correction. Following a thorough analysis, it was determined that the data provided an accurate representation of the population. The findings of this study have practical implications-students who engage in self-directed learning and implement effective learning strategies will see a substantial improvement in their overall learning outcomes. Students desire easy access to a variety of educational resources and materials, according to the findings. This aspiration motivates the proliferation of mobile media devices. To facilitate students' access to a diverse range of learning strategies, instructors possess the ability to provide accommodation. These applications benefit students by streamlining the process of obtaining access to learning-supporting materials and resources
Performance Comparison of GLCM Features and Preprocessing Effect on Batik Image Retrieval Azhar, Yufis; Akbi, Denar Regata
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.8.3.2179

Abstract

The use of the Grey-Level Co-occurrence Matrix (GLCM) for feature extraction in image retrieval with complex motifs, such as batik images, has been widely used. Some features often extracted include energy, entropy, correlation, and contrast. Other than these four features, the addition of dissimilarity and homogeneity features to the GLCM method is proposed in this study. Preprocessing methods such as Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) are also used to see whether the two methods can increase the precision value of the retrieval results. This study used the Batik 300 dataset, which consists of 50 classes. Batik was chosen because this type of image has complex patterns and motifs so that it will maximize the role of the GLCM method itself. In addition, Batik is also a world heritage art, so its sustainability needs to be maintained. The test results show that adding dissimilarity and homogeneity features and using the CLAHE method in the preprocessing step can improve model performance. Combining these two methods has produced higher precision values than not using either. Batik, a globally recognized art form, holds the status of a world heritage, necessitating the preservation of its sustainability. Test results have demonstrated that incorporating dissimilarity and homogeneity features, alongside using the CLAHE method during the preprocessing stage, leads to enhanced model performance. The amalgamation of these two methods has yielded precision values that surpass those achieved when either method is used in isolation.
Serial Multimodal Biometrics Authentication and Liveness Detection Using Speech Recognition with Normalized Longest Word Subsequence Method Andrian, Rafi; Putra Kusuma, Gede
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.8.3.2247

Abstract

Biometric authentication aims to verify whether an entity matches the claimed identity based on biometric data. Despite its advantages, vulnerabilities, particularly those related to spoofing, still exist. Efforts to mitigate these vulnerabilities include multimodal approaches and liveness detection. However, these strategies may potentially increase resource requirements in the authentication process. This paper proposes a multimodal authentication process incorporating voice and facial recognition, with liveness detection applied to voice data using speech recognition. This paper introduces Normalized Longest Word Subsequence (NLWS), a combination of Intersection Over Union (IOU) and the longest common subsequence, to compare the prompted system sentence with the user's spoken sentence at speech recognition. Unlike the Word Error Rate (WER), NLWS has a measurable range between 1 and 0. Furthermore, the paper introduces decision-level fusion in the multimodal approach, employing two threshold levels in voice authentication. This approach aims to reduce resource requirements while enhancing the overall security of the authentication process. This paper uses cosine similarity, Euclidean distance, random forest, and extreme gradient boosting (XGBoost) to measure distance or similarity. The results show that the proposed method has better accuracy compared to unimodal approaches, achieving accuracies of 98.44%, 98.83%, 97.46%, and 99.22% using cosine similarity, Euclidean distance, random forest, and XGBoost calculations. The proposed method also demonstrates resource savings, reducing from 5.19 MB to 0.792 MB, from 7.3294 MB to 1.9437 MB, from 6.6512 MB to 1.3284 MB, and from 7.8632 MB to 2.1517 MB in different distance or similarity measurements
Atomic Structure Simulation and Properties’ Prediction using Machine Learning on Neodymium Oxide Nanoparticles Zinc Tellurite Glasses Aided by FTIR and TEM Analysis Nazrin, S.N.; Zaman, Halimah Badioze; Jothi, Neesha; Jouay, Doha; Lahrach, Badreddine; Halimah, M.K.
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.8.3.3097

Abstract

The optical, structural, and physical characteristics of zinc tellurite glasses doped with neodymium oxide nanoparticles, which are produced by the melt-quenching method, were examined in this work. The amorphous character of the glasses was verified by XRD analysis. Using the Pair Distribution Function (PDF) and Monte Carlo simulations and visualisation for precise molecule distribution representation, an intuitive Python interface was created to emphasize these features. The density increased with increasing Nd2O3 concentrations, from 5346 to 5606 kg/cm2. Density data was used to infer the molar volume. The best projected density was achieved by the Gradient Boosting Regressor model, with a R2 of 0.9988 and an RMSE of 0.0032; the best predicted molar volume was achieved by linear regression, with a R2 of 1 and an RMSE of 2.67e-15. These models successfully represent the correlations between dopant concentration and glass properties, advancing our knowledge of the optical properties for further glass technology research.
Deep Learning Models for Dental Conditions Classification Using Intraoral Images Makarim, Ahmad Fauzi; Karlita, Tita; Sigit, Riyanto; Bayu Dewantara, Bima Sena; Brahmanta, Arya
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.8.3.1914

Abstract

This paper presents the digitalization of dentistry medical records to support the dentist in the patient examination process. A dentist uses manual input to fill out the evaluation form by drawing and labeling each patient’s tooth condition based on their observations. Consequently, it takes too long to finish only one examination. For time efficiency, using AI-based digitalization technology can be a promising solution. To address the problem, we made and compared several classification models to recognize human dental conditions to help doctors analyze patient teeth. We apply the YOLOv5, MobileNet V2, and IONet (proposed CNN model) as deep learning models to recognize the five common human dental conditions: normal, filling, caries, gangrene radix, and impaction. We tested the ability of YOLO classification as an object detection model and compared it with classification models. We used a dataset of 3.708 intraoral dental images generated by various augmentation methods from 1.767 original images. We collected and annotated the dataset with the help of dentists. Furthermore, the dataset is divided into three parts: 90% of the total dataset is used as training and validation data, then divided again into 80% training data and 20% validation data. 10% of the total dataset will be used as testing data to compare classification performance. Based on our experiments, YOLOv5, as an object detection model, can classify dental conditions in humans better than the classification model. YOLOv5 produces an 82% accuracy testing value and performs better than the classification model. MobileNet V2 and IONet only get 80% and 70% testing accuracy. Although statistically, there is not much of a difference between the test accuracy values for YOLOv5 and MobileNet v2, the speed in classifying dental objects using YOLOv5 is more efficient, considering that YOLOv5 is an object detection model. There are still challenges with the deep learning technique used in this research, but these can be addressed in further development. A more complex model and the enlargement of more data, ensuring it is varied and balanced, can be used to address the limitations. 
Modified Alexnet Architecture for Classification of Cassava Based on Leaf Images Sholihin, Miftahus; Md Fudzee, Mohd Farhan; Ismail, Mohd Norasri; Wati, Efi Neo; Arshad, Mohamad Syafwan; Gusman, Taufik
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.8.3.2966

Abstract

The objective of this study is to address the drawbacks of conventional classification approaches through the implementation of deep learning, specifically a modified AlexNet. The primary aim of this study is to precisely categorize the four distinct varieties of cassava, namely Manggu, Gajah, Beracun, and Kapok. The cassava dataset was obtained from farmers in Lamongan, Indonesia, and was used as a source of information. Data collection on cassava leaves was carried out with agricultural research specialists. A total of 1,400 images are included in the dataset, with 350 images corresponding to each variety of cassava produced. The central focus of this research lies in a comprehensive evaluation of the modified AlexNet architecture's performance compared to the original AlexNet architecture for cassava classification. Multiple scenarios were examined, involving diverse combinations of learning rates and epochs, to thoroughly assess the robustness and adaptability of the proposed approach. Among the evaluation criteria that were rigorously examined were accuracy, recall, F1 score, and precision. These metrics were used to determine the predictive capabilities of the model as well as its potential utilization in the actual world. The results show that the modified AlexNet design has better performance than the original AlexNet for recall, accuracy, precision, and F-1 score, all achieving a rate of 87%. In situations where a learning rate of 0.0001 and an epoch count of 150 are utilized, the performance of the approach stands out significantly, displaying an excellent level of competency. Nevertheless, it is crucial to recognize that distinct fluctuations in performance were noted within particular contexts and with diverse learning rates.
A Convolutional Neural Network-based Intelligence System for the Identification of Copra Maturity Levels Latumakulita, Luther Alexander; Paat, Frangky J; Budiman, Glenn; Tooy, Dedie; Koibur, Mayko Edison; Islam, Noorul
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.8.3.2574

Abstract

The North Sulawesi Province, widely recognized as the Coconut Waving Province owing to its substantial coconut tree population, primarily depends on copra production. This research presents a novel methodology for determining copra maturity levels by utilizing a Convolutional Neural Network (CNN) on digital photographs, classifying them into three distinct stages: raw, half-ripe, and ripe. By employing a rigorous 10-fold cross-validation technique, our models demonstrated remarkable performance. Notably, even the model with the lowest performance achieved a commendable accuracy of 87.78% during the training and validation phases. The model that exhibited the highest level of performance achieved a perfect accuracy rate of 100%. Moreover, when subjected to real-world testing situations using novel data, the model with the lowest performance exhibited a noteworthy accuracy of 83.34%. In contrast, the highest-performing model achieved a flawless accuracy of 100%. Based on the findings above, an online system has been built that leverages the most optimal model, facilitating the assessment of copra maturity in real-time. The prospects encompass the integration of this methodology into copra sorting machinery, thereby yielding advantages for both agricultural producers and industrial sectors. This research enhances copra quality control processes and promotes sustainability in the copra industry. Further research could explore refining the CNN model to accommodate a broader range of copra variations and investigating automation possibilities in copra production processes. These endeavors would advance the efficacy and applicability of copra maturity classification methods, fostering continued innovation in the industry.

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