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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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Literature Reviews of RBV and KBV Theories Reimagined - A Technological Approach Using Text Analysis and Power BI Visualization Arief, Ikhwan; Hasan, Alizar; Putri, Nilda Tri; Rahman, Hafiz
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1940

Abstract

Over the years, the Resource-Based View (RBV) and Knowledge-Based View (KBV) have solidified their roles as pivotal paradigms in strategic management literature. With an emphasis on Small and Medium Enterprises (SMEs), this study uses text analysis and Microsoft Power BI to explore these concepts innovatively. The study implements a systematic literature review, extracting data from Scopus, Web of Science, and DOAJ databases to assemble a comprehensive literature corpus. The methodology incorporates text analysis to draw out key themes, relationships, and trends, and these are subsequently visualized using Power BI to create an engaging, interactive representation of data. Components like word clouds, co-occurrence networks, and trend lines are generated, while Power BI's dynamic filtering and drill-down functionalities facilitate thorough data investigation. The results display significant overlap between RBV and KBV, denoting possible integration junctures for these theories within the domain of strategic management. Additionally, the study underscores the relevance of these insights for SMEs, emphasizing the part played by unique resources, encompassing knowledge assets, in catalyzing innovation and fostering a competitive edge. The study concludes by recognizing the significant theoretical and practical implications of integrating text analysis and Power BI in conducting literature reviews. This methodology bolsters our understanding of RBV and KBV, offering small and medium-sized enterprises a beneficial instrument to traverse these intricate theories. The study suggests that future research could broaden the application of this methodological approach to encompass other strategic management theories.
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.
Predictive Wireless Received Signal Strength Using Friis Transmission Technique Rawi, Roziyani; Mohd Isa, Mohd Rizal; Ismail, Mohd Nazri; Abu Bakar Sajak, Aznida; Yahaya, Yuhanim Hani
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.8.2.2178

Abstract

A good WLAN performance is crucial in determining the quality of experience (QoE) among the campus community. Proper WLAN planning and design should be done beforehand to ensure good WLAN performance. Various studies have discussed different methods of conducting WLAN planning to predict WLAN's best performance, including using artificial intelligence and mathematical approaches. One of the processes involved in performing WLAN planning is measuring performance parameters. Signal strength is one of the vital parameters to be measured in determining the excellent performance of WLAN in a particular area. When deploying a WLAN design in two different environments, the signal strength outcomes can differ due to various factors, including obstacles and path loss propagation issues within the deployment area. Higher Learning Institutions (HLIs) present a unique challenge as their building designs vary to accommodate student needs. As a result, the selection of materials used will also be different, affecting the WLAN performance. A detailed study should investigate the effect of path loss propagation and the type of obstacle that affects WLAN performance in HLI. Thus, this study focuses on predicting received signal strength using Friis Transmission and studying the effect of path loss propagation on WLAN performance. The simulated model significantly affects signal strength when the signal passes through different types of building material (non-LOS) and line-of-sight (NLOS), where concrete walls substantially affect the received signal strength between transmitters. The proposed model can assist network planners in designing robust WLAN infrastructure by improving signal strength, particularly in the HLI WLAN environment. 
Automated Matching Skills to Improve the Accuracy of Job Applicant Selection Using Indonesian National Work Competency Standards Ajhari, Abdul Azzam; Priambodo, Dimas Febriyan; Yulianti, Henny
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.8.2.2017

Abstract

The high number of cyberattack anomalies and data leaks in Indonesia increases the need for cybersecurity in various companies. Cybersecurity capabilities and skills in Indonesia are divided into three categories based on the Indonesian National Work Competency Standards (SKKNI), namely Security Operation Center (SOC), Cybersecurity test/Penetration testing (Pentest), and Information Security Audit. Although various approaches have been applied in different companies to select job applicants, a new method with automated matching is explored in this study. This method matches the skills possessed by prospective job applicants with the profile of their job task requirements based on the SKKNI Decree of the Minister of Manpower of the Republic of Indonesia using Machine Learning (ML) models. The empirical comparison of results comes from automated matchmaking processed by Multinomial Naive Bayes (MNB) and Decision Tree algorithm models. Before modeling, the data is trained and evaluated for testing. Then to assess the most optimal algorithm between MNB and Decision Tree, a confusion matrix is proposed and used to find the best model. From the evaluation results, both models performed well and were highly accurate during training and test evaluation. The Decision Tree model performs slightly better than the MNB model, but both still provide satisfactory results in classifying data based on the Indonesian National Work Competency Standards (SKKNI) categories. This study offers a solution to minimize the number of potential applicants who are not competent in the three SKKNI cybersecurity job categories due to the mismatch of their abilities and skills.
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.
Implementation of Multi Extension in Blockchain-Based IoT Platform for Industrial IoT Devices Prayudi, Agus; Sukaridhoto, Sritrusta; Udin Harun Al Rasyid, Muhammad; Hakim, Oktafian Sultan; Yohanie Fridelin Panduman, Yohanes; Putri Nourma Budiarti, Rizqi
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.8.2.2401

Abstract

The rise of the Internet of Things (IoT) has led to the creation of technologies to improve human life. IoT involves integrating the Internet with the physical world, spanning applications like smart homes, industries, supply chains, academia, and more. By the end of 2020, around 212 billion IoT devices were globally deployed, presenting substantial opportunities for manufacturers and diverse applications. There have been numerous implementations of IoT across various fields, including Blockchain IoT (B-IoT), Artificial Intelligence of Things (AIoT), Digital Twin, and new communication protocols like the Matter protocol. We conducted a comprehensive testing of the blockchain (B-IoT) extension system on various bandwidths and scenarios, such as blockchain API execution time, speed, retention performance, and smart contract vulnerability testing. Our testing has been successful, and several messaging systems were used. Kafka was recommended to overcome the pending transaction problem caused by unprocessed messages. Our smart contract exhibited high severity. The Artificial Intelligence of Things extension, tested on real environments for person and vehicle counters, has shown successful results. Digital Twin, integrated into the IoT platform to perform and control 3D assets such as the postgraduate PENS building, has demonstrated efficient performance. Matter protocol achieved an average task execution speed of 0.48 tasks per second. Matter P2P communication was also successfully tested in this research by implementing the Access Control List (ACL) command.
Big Mart Sales Data Visualization and Correlation Arista, Artika; Theresiawati, Theresiawati; Seta, Henki Bayu
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.8.2.1780

Abstract

The amount of unprocessed data available every day is growing. This massive amount of data needs to be effectively assessed to give results that are extremely useful. In the present day, it is crucial for inventory management and demand forecasting to collect sales data for commodities or things, together with all their numerous dependent or independent parts. In a Big Mart Company, the use of sales forecasting is to estimate numerous goods that are readily available and supplied at multiple retailers in different towns. As the number of products and outlets increased drastically, it became increasingly difficult to forecast them manually. As a result, it is necessary to see to what extent the relationship between several variables, including price, popularity, time of day, outlet type, outlet location, etc., affects the appeal of a product. In this research, a data cleaning process was carried out, and data visualization using scatter plots, as well as finding Pearson correlations. The raw processing the data with study of a case big mart sales data is taken from the Kaggle website [https://www.kaggle.com/datasets/sandeepgauti/bigmart-sales]. The Pearson correlation test determines a lack of connection between the two Item_Weight and Item_Outlet_Sales variables. There is a strong but negative correlation where if Item_Visibility decreases, Item_Outlet_Sales also decreases. Positive relationships exist between the two Item_MRP and Item_Outlet_Sales variables. In addition to the correlation test, descriptive statistical analysis is also performed here. With this simple data processing, the raw data will be better organized and easier to analyze, read, and use.
Comparison of the Packet Wavelet Transform Method for Medical Image Compression Atmaja, I Made Ari Dwi Suta; Triadi, Wilfridus Bambang; Astawa, I Nyoman Gede Arya; Radhitya, Made Leo
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.1732

Abstract

Medical images are often used for educational, analytical, and medical diagnostic purposes. Medical image data requires large amounts of storage on computers. Three types of codecs, namely Haar, Daubechies, and Biorthogonal, were used in this study. This study aims to find the best wavelet method of the three tested wavelet methods (Haar, Daubechies, and Biorthogonal). This study uses medical images representing USG and CT-scan images as testing data. The first test is carried out by comparing the threshold ratio. Three threshold values are used, namely 30, 40, and 50. The second test looks for PSNR values with different thresholds. The third test looks for a comparison of the rate (image size) to the PSSR value. The final test is to find each medical image's compression and decompression times. The first compression ratio test results on both medical images showed that CT scan images on Haar and Biorthogonal wavelets were the best, with an average compression ratio of 40.76% and a PSNR of 33.77. The PSNR obtained is also getting more significant for testing with a larger image size. The average compression time is 0.52 seconds, and the decompression time is 2.27 seconds. Based on the test results, this study recommends that the Daubechies wavelet method is very good for compression, which is 0.51 seconds, and the Biorthogonal wavelet method is very good for medical image decompression, which is 1.69 seconds.
Detecting Distributed Denial-of-Service (DDoS) Attacks Through the Log Consolidation Processing (LCP) Framework Khairuddin, Mohammad Adib; Mohd Isa, Mohd Rizal; Mohd Shukran, Mohd Afizi; Ismail, Mohd Nazri; Maskat, Kamaruzaman
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.8.3.2184

Abstract

One major problem commonly faced by organizations is a network attack especially if the network is vulnerable due to poor security policies. Network security is vital in protecting not only the infrastructure but most importantly, the data that moves around the network and is stored within the organization. Ensuring a secure network requires a complex combination of hardware including both network and security devices, specialized applications such as web filtering and log management, and a group of well-trained network administrators and highly skilled analysts.  This paper aims to present an alternative to the current log management solution. A hindrance to the current log management solution is the difficulty in amalgamating and correlating a vast number of logs with different formats and variables. This paper uses a novel framework called Log Consolidation Processing (LCP) based on the System Information Event Management (SIEM) technology, to monitor the behavior and the fitness of a network. LCP provides a flexible and complete solution to collect, correlate, and analyze logs from multiple devices as well as applications. An experiment testing the effectiveness of LCP in detecting DDoS attacks in a campus network environment was conducted, demonstrating a highly successful rate of detection. Besides threat detection and avoidance through log monitoring and analysis, other benefits of implementing the LCP framework are also included. This paper concludes by mentioning suggested enhancements for the LCP framework.

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