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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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The study on Malaysia Agricultural E-Commerce (AE): Customer Purchase Intention Wah Hen, Kai; Seah, Choon Sen; Witarsyah, Deden; Shaharudin, Shazlyn Milleana; Xia Loh, Yin
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1372

Abstract

Electronic commerce (E-Commerce) became an essential trading platform after the Covid-19 pandemic. From essential products to luxury brands, consumers can find almost everything on the normal E-Commerce platforms with the exception of fresh agricultural products. Agricultural E-Commerce (AE) is introduced to overcome the market needs. Technology Acceptance Model (TAM) is studied and integrated with additional variables to determine the needs of AE in Malaysia. In this study, five variables (product quality, logistic service quality, perceived price & value, platform design quality, and platform security) were studied to determine the Malaysian consumers’ purchase intention towards the AE. Five hypotheses were developed to identify the relationship between the variables. A total of 300 AE users have contributed their perception as respondents in this study through a survey questionnaire. The collected data were processed before the data analysis via Statistical Package for The Social Science (SPSS) version 25.0. Descriptive analysis, and inferential analysis were conducted. The result shows that all five variables are significantly related to the purchase intention towards AE. The product quality has the highest significant value (0.805) towards the purchase intention on AE, followed by logistic service quality, platform security, platform design quality and perceived price and value. Implication, limitation, and recommendation were also being discussed to assist the AE stakeholders in improving their AE.
Factors Influencing Readiness towards Halal Logistics among Food and Beverages Industry in the Era of E-Commerce in Indonesia Muttaqin, Prafajar Suksessanno; Setyawan, Erlangga Bayu; Novitasari, Nia
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.2055

Abstract

Based on Global Islamic Economy Indicator 2019/2020 report, Indonesia is in the fourth position globally as a country that uses a Sharia economic system. Seeing Indonesia's opportunities, it should be able to act as a regional and global halal hub. Efforts to encourage the halal industry through strengthening the halal value chain are one of the strategies to encourage Indonesia to become a global halal hub player. This study utilizes the structural equation modeling to examine relationships among key factors affecting readiness towards halal logistics in the food and beverages industry in Indonesia. 13 key factors are confirmed with measurement-model results, including (1) Cleanliness, (2) Safety, (3) Islamic Dietary Law, (4) Physical Segregation, (5) Material Handlings, (6) Storage and Transport, (7) Packaging and Labelling, (8) Ethical Practices, (9) Training and Personnel, (10) Resource Availability, (11) Innovative Capability, (12) Marketing Performance, (13) Financial Performance. The population in this study is in the food and beverage industries, especially in Semarang, Yogyakarta, Malang, and Surabaya. Cluster random sampling was used in this research with as many as 150 sample respondents. A survey with an online questionnaire was conducted in this research. The structural-model results reveal directions of relationships among key factors. Resource availability, training and personnel, and innovative capability are the most important factor in halal supply chain readiness. Further research can focus on other industrial sectors, such as fashion and tourism, as stated in the 2019-2024 Indonesian Sharia Economic Masterplan
Preliminary study: Readiness of WLAN Infrastructure at Malaysian Higher Education Institutes to support Smart Campus Initiative Rawia, Roziyani; Isa, Mohd Rizal Mohd; Ismaila, M. N.; Sajak, Aznida Abu Bakar; Mustafa, Azmi
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1242

Abstract

Smart campus initiative enables higher education to enhance services, decision-making, and campus sustainability. The initiatives are being actively implemented globally by higher education, including in Malaysia. The recent COVID-19 pandemic has underscored the need for the education sector to explore a digital revolution. The adaptation of digital technologies has improved many aspects, including the teaching and learning experiences and administration tasks, which results in more efficient task handling. This study investigates the readiness of the WLAN infrastructure at Malaysian Public Higher Education Institutes (HEIs) in implementing smart campus initiatives and measures readiness based on the availability of WLAN Infrastructure, WLAN logical architecture and WLAN populated coverage area. This study administered a questionnaire to 19 respondents, all of whom are IT personnel from Malaysian public HEIs to gather preliminary data on the readiness of WLAN infrastructure at Malaysian Public HEI to support the adaptation of smart campus initiatives in their teaching and learning activities. This study is a preliminary study concerning the readiness of WLAN infrastructure at Malaysian Public HEI in adapting smart campus initiatives. The findings show that, even though WLAN service is available at all Malaysian Public HEI, it is essential to enhance the adopted logical architecture and WLAN coverage to prepare HEI to become smart campuses. The findings of this study can provide the fundamental guidelines for the Ministry of Higher Education in determining the baseline of WLAN infrastructure required by Malaysian HEI to support smart campus initiatives.
Investigation of Mobile Cloud Storage Adoption Factors in Higher Education Najwa, Nina Fadilah; Widyasari, Yohana Dewi Lulu; Trisnadoli, Anggy
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1296

Abstract

Mobile cloud storage provides benefits for educational institutions. Several researchers have researched cloud computing adoption, but only a few studies related to how users experience using Personal Cloud Storage Services. This research aims to investigate the adoption of the mobile cloud storage factors following the theory, as well as research that has been previously proven related to user interest in using mobile cloud storage among higher education students. This quantitative research uses data analysis techniques using GSCA to prove the theory and achieve the research goals. The research methodology consists of five main stages, namely the stage of model development and research design, the stage of preparing the instrument and its measurement, the stage of testing the instrument, the stage of survey and results, as well as the stages of analysis and discussion as well as conclusions. Five variables are investigated in this research: knowledge sharing, perceived usefulness, attitude toward using a system, trust, and intention to use. The results of hypothesis testing were conducted using GSCA; three proposed hypotheses were accepted, and one was rejected. The variables the research model can explain are 68%, and the remaining 32% are other variables not used in this study. The characteristics of respondents can provide several ways to increase the adoption of mobile cloud computing by linking research results from inferential analysis and descriptive analysis. Future research can focus on extracting these variables through user interviews regarding students' intentions to use mobile cloud computing.
Feature Selection to Enhance DDoS Detection Using Hybrid N-Gram Heuristic Techniques Maslan, Andi; Mohamad, Kamaruddin Malik Bin; Hamid, Abdul; Pangaribuan, Hotma; Sitohang, Sunarsan
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1533

Abstract

Various forms of distributed denial of service (DDoS) assault systems and servers, including traffic overload, request overload, and website breakdowns. Heuristic-based DDoS attack detection is a combination of anomaly-based and pattern-based methods, and it is one of three DDoS attack detection techniques available. The pattern-based method compares a sequence of data packets sent across a computer network using a set of criteria. However, it cannot identify modern assault types, and anomaly-based methods take advantage of the habits that occur in a system. However, this method is difficult to apply because the accuracy is still low, and the false positives are relatively high. Therefore, this study proposes feature selection based on Hybrid N-Gram Heuristic Techniques. The research starts with the conversion process, package extract, and hex payload analysis, focusing on the HTTP protocol. The results show the Hybrid N-Gram Heuristic-based feature selection for the CIC-2017 dataset with the SVM algorithm on the CSDPayload+N-Gram feature with a 4-Gram accuracy rate of 99.86%, MIB- Dataset 2016 with the 2016 algorithm. SVM and CSPayload feature +N-Gram with 100% accuracy for 4-Gram, H2N-Payload Dataset with SVM Algorithm, and CSDPayload+N-Gram feature with 100% accuracy for 4-Gram. As a comparison, the KNN algorithm for 4-Gram has an accuracy rate of 99.44%, and the Neural Network Algorithm has an accuracy rate of 100% for 4-Gram. Thus, the best algorithm for DDoS detection is SVM with Hybrid N-Gram (4-Gram).
Chest X-ray Image Classification to Identify Lung Diseases Using Convolutional Neural Network and Convolutional Block Attention Module Halim, Chandra; Eka Putra, Nathanael Geordie; Nugroho, Nico Ardian; Suhartono, Derwin
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1136

Abstract

Image classification, the process of categorizing and labeling groups of pixels or vectors within an image based on specific rules, is continuously developed by many researchers in the world to solve many problems. One of those problems is x-ray image classification to determine lung diseases. This research tries to solve the problem of classifying COVID-19, pneumonia, and healthy lungs using x-ray images. The image datasets were collected from several sources. This research aims to build a reliable and robust Convolutional Neural Network (CNN) enhanced with Convolutional Block Attention Module (CBAM) mechanism. CNN is used to do the feature extraction and the classification, whereas CBAM is used to improve the performance of the CNN by focusing on the important features in given data. Research methods are done through extensive data selection, preprocessing, and parameter tuning to achieve a well-performing model. While there is still a lack of research on x-ray classification using the attention mechanism, this research proposes it as the main method. This research also does a further experiment on the effect of the imbalanced dataset on the model. The evaluation is done using a cross-validation method. This research results reach 97.74% of accuracy, precision, recall, and f1-score. This research concludes that CBAM increases the performance of a CNN module. Using a larger dataset can be beneficial in this kind of research as well as evaluation by radiologists.
Mixed Pixel Classification on Hyperspectral Image Using Imbalanced Learning and Hyperparameter Tuning Methods Purwadi Purwadi; Nor Azman Abu; Othman Bin Mohd; Bagus Adhi Kusuma
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1758

Abstract

Hyperspectral image technology in land classification is a distinct advantage compared to ordinary RGB and multispectral images. This technology has a wide spectrum of electromagnetic waves, which can be more detailed than other types of imagery. Therefore, with its hyperspectral advantages, the characteristics of an object should have a high probability of being recognized and distinguished. However, because of the large data, it becomes a challenge to lighten the computational burden. Hyperspectral has a huge phenomenon that makes computations heavy compared to other types of images because this image is 3D. The problem faced in hyperspectral image classification is the high computational load, especially if the spatial resolution of the image also has mixed pixel problems. This research uses EO-1 satellite imagery with a spatial resolution of 30 meters and a mixed pixel problem. This study uses a classification method to lighten the computational burden and simultaneously increase the value of classification accuracy. The method used is satellite image pre-processing, including geometric correction and image enhancement using FLAASH while the corrections are geometric correction and atmospheric correction. Then to lighten the computational burden, the steps carried out are using the Slab and PCA method. After obtaining the characteristics, they are entered into a guided learning model using a support vector machine (SVM) for the five-class or multiclass classification. Moreover, the imbalance learning method is proven to produce increased accuracy. The best results were achieved by the ADASYN method with an accuracy of 96.58%, while the computational time became faster with the feature extraction method.
A Detection and Response Architecture for Stealthy Attacks on Cyber-Physical Systems Tawfeeq Shawly
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1323

Abstract

There has been an increased reliance on interconnected Cyber-Physical Systems (CPS) applications. This reliance has caused tremendous growth in high assurance challenges. Due to the functional interdependence between the internal systems of CPS applications, the utilities' ability to reliably provide services could be disrupted if security threats are not addressed. To address this challenge, we propose a multi-level, multi-agent detection and response architecture built on the formalisms of Hidden Markov Models (HMM) and Markov Decision Processes (MDP). We have evaluated the performance of the proposed architecture on one of the critical smart grid applications, Advanced Metering Infrastructure (AMI). This paper utilizes a simulation tool called SecAMI for performance evaluation. A Stealthy attack scenario contains multiple distinct multi-stage attacks deployed concurrently in a network to compromise the system and stop several critical services in a CPS. The results show that the proposed architecture effectively detects and responds to stealthy attack scenarios against Cyber-Physical Systems. In particular, the simulation results show that the proposed system can preserve the availability of more than 93% of the AMI network under stealthy attacks. A future study may evaluate the effectiveness of various stealthy attack strategies and detection and response systems. The high availability of any AMI should be protected against new attack techniques. The proposed system will also determine a distributed IDS's efficient placement for intrusion detection sensors and response nodes within an AMI.
Illuminance Color Independent in Remote Photoplethysmography for Pulse Rate Variability and Respiration Rate Measurement Suryasari Suryasari; Aminuddin Rizal; Sri Kusumastuti; Taufiqqurrachman Taufiqqurrachman
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1176

Abstract

Remote photoplethysmography (rPPG) is now becoming a new trend method to measure human physiological parameters. Especially due to it noncontact measurement which safe dan suitable to use in this new era condition. Pulse rate variability (PRV) and respiration rate (RR) included as parameters can be measured by using rPPG. PRV and RR are used to measure both physical and psychological wellness of the subject. However, current performance challenges in rPPG algorithm in measuring PRV and RR are illuminance invariant and motion. Especially in different light condition which represent real-life environment, signal-to-noise ratio (SNR) will be affected and directly reduce the measurement accuracy. Therefore in this study, we develop rPPG algorithm and then investigate the performance rPPG in different illuminance scenarios. We perform PRV and RR measurement under each scenario. On this study, for the pulse signal extraction, we were using algorithm is based on the modification of plane orthogonal-to-skin (POS) algorithm. While, for respiration signal extraction is done in CIE Lab color space. Our experimental results show the mean absolute error (MAE) of each measured parameters are 3.25 BPM and 2 BPM for PRV and RR respectively compared with clinical apparatus.  The proposed method proved to be more reliable to use in real environments measurement. However, limitation of our proposed algorithm is still running in offline mode, hence for the future we want try to make our algorithm run in real time.
Automatic Weight of Color, Texture, and Shape Features in Content-Based Image Retrieval Using Artificial Neural Network Akmal Akmal; Rinaldi Munir; Judhi Santoso
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1184

Abstract

Image retrieval is the process of finding images in the database that are similar to the query image by measuring how close the feature values of the query image are to other images. Image retrieval is currently dominated by approaches that combine several different representations or features. The optimal weight of each feature is needed in combining the image features such as color features, texture features, and shape features. In this study, we use a multi-layer perceptron artificial neural network (MLP) method to obtain feature weights automatically and simultaneously look for optimal weights. The color moment is used to find nine color features, Gray Level Co-occurrence Matrix (GLCM) to find four texture features, and Hu Moment to find seven shape features totaling 20 neurons and all of these features become the input layer in our MLP network. Three neurons in output layers become the automatic weight of each feature. These weights are used to combine each feature's role in obtaining the relevant image. Euclidean Distance is used to measure similarity. The average precision values obtained using automatic feature weights are 93.94% for the synthetic dataset, 91.19% for the Coil-100 dataset, and 54.31% for the Wang dataset. These results have an average difference of 5.06% with the target so automatic feature weighting works well. This value is obtained at a hidden layer size of 11 and a learning rate of 0.1. In addition, the use of automatic feature weighting gives more accurate results compared to manual feature weighting.

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