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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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Hybrid Approach with Distance Feature for Multi-Class Imbalanced Datasets Hartono, Hartono; Ongko, Erianto
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1292

Abstract

The multi-class imbalance problem has a higher level of complexity when compared to the binary class problem. The difficulty is due to the large number of classes that will present challenges related to overlapping between classes. Many approaches have been proposed to deal with these multi-class problems. One is a hybrid approach combining a data-level approach and an algorithm-level approach. This approach is done by the ensemble on the classifier and also oversampling on the minority class. SMOTE is an oversampling method that provides good performance, but this method is necessary to determine the best sample used in the interpolation process to generate new samples. The need for determining the best sample is related to the overlap between classes that always accompanies the multi-class imbalance problem. The existence of overlap requires efforts to determine the safe region to synthesize the sample in the oversampling process in SMOTE. The safe region is considered the best for synthesizing samples due to the lower tendency of overlapping. It can be done by constructing distance features to determine the safe region. The sample with the best distance and the lowest imbalance ratio will be selected as a sample in the over-sampling process with SMOTE. The main contribution of this research is the proposed method of Hybrid Approach with Distance Feature so that it can determine safe samples, with the main advantage being in addition to handling multi-class imbalances, it is also better for handling overlapping. The results of this study will be compared with Multiple Random Balance (MultiRandBal) which performs a random oversampling process. The results showed that the Augmented R-Value, Class Average Accuracy, Class Balance Accuracy, and Hamming Loss obtained in this method was better than the random oversampling process. These results also show that the Hybrid Approach with Distance Feature provides better results in handling multi-class imbalances when compared to MultiRandBal.
Knowledge Management Factors and Its Impact on Organizational Performance: A Systematic Literature Review Slamet Darmawan; Novia Agusvina; Sofian Lusa; Dana Indra Sensuse
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1644

Abstract

Knowledge management can help organizations to improve their performance. Many studies show that knowledge management impacts organizational performance. Human capital is considered a mediating role in knowledge management's impact on organizational performance, but it is still blurred, and only a few studies are related to this issue. Moreover, various factors influence knowledge management, such as organizational structure, culture, technology, strategy, trust, and leadership, but maybe other factors have not been identified. This factor can help knowledge management impacts organizational performance. This study was conducted to determine how the human capital role mediates the impact of knowledge management on organizational performance and determine another factor that affects knowledge management, which can impact organizational performance. This study was based on the Systematic Literature Review (SLR), which includes 37 articles published from 2016 to 2021. The study showed that human capital mediates the impact of knowledge management on organizational performance directly and indirectly through innovation. Meanwhile, organizational structure, culture, trust, leadership, human behavior, human resources practices, technology, and strategy are identified as factors that affect knowledge management, whereas human resources practices affect human behavior and leadership. Finally, we proposed a conceptual model that described how knowledge management factors impact human capital and organizational performance. This research can contribute to enriching knowledge management theory and be used to give recommendations for improving the implementation of knowledge management. Further research involves data collection, and empirical analysis needs to be conducted in an organization to examine the conceptual model.
Predicting the Welfare Cost of Premature Deaths Based on Unsafe Sanitation Risk using SutteARIMA and Comparison with Neural Network Time Series and Holt-Winters Suwardi Annas; Ansari Saleh Ahmar; Rahmat Hidayat
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.%v.%i.1003

Abstract

Unhealthy and unsafe sanitation will make it easier for various diseases to attack the body. In addition, unsafe sanitation will also affect a country's economy, including declining welfare, tourism losses, and environmental losses due to the loss of productive land. The research aimed to estimate the welfare cost of premature deaths based on unsafe sanitation risks using the SutteARIMA, Neural Network Time Series, and Holt-Winters. The study analyzed estimates and projections of the welfare cost of premature deaths based on the risks of unsafe sanitation of BRICS countries (Brazil, Russia, Indonesia, China, and South Africa). The data in this research used secondary data. Secondary time series data was taken from the Environment Database of the OECD. Stat. (Mortality and welfare cost from exposure to environmental risks). The data on the study was based on variables: welfare cost of premature deaths, % GDP equivalent, risk: unsafe sanitation, age: all, sex: both, unit: percentage, and data from 2005 to 2019. The three forecasting methods (SutteARIMA, Neural Network Time Series, and Holt-Winters) were juxtaposed in fitting data to see the forecasting methods' reliability and accuracy. The accuracy of forecasting results was compared based on MAPE and MSE values. The results of the research showed that the SutteARIMA and NNAR(1,1) methods were best used to predict the welfare cost of premature deaths in view of unsafe sanitation risks for BRICS countries.
End-To-End Evaluation of Deep Learning Architectures for Off-Line Handwriting Writer Identification: A Comparative Study Suteddy, Wirmanto; Agustini, Devi Aprianti Rimadhani; Adiwilaga, Anugrah; Atmanto, Dastin Aryo
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1293

Abstract

Identifying writers using their handwriting is particularly challenging for a machine, given that a person’s writing can serve as their distinguishing characteristic. The process of identification using handcrafted features has shown promising results, but the intra-class variability between authors still needs further development. Almost all computer vision-related tasks use Deep learning (DL) nowadays, and as a result, researchers are developing many DL architectures with their respective methods. In addition, feature extraction, usually accomplished using handcrafted algorithms, can now be automatically conducted using convolutional neural networks. With the various developments of the DL method, it is necessary to evaluate the suitable DL for the problem we are aiming at, namely the classification of writer identification. This comparative study evaluated several DL architectures such as VGG16, ResNet50, MobileNet, Xception, and EfficientNet end-to-end to examine their advantages to offline handwriting for writer identification problems with IAM and CVL databases. Each architecture compared its respective process to the training and validation metrics accuracy, demonstrating that ResNet50 DL had the highest train accuracy of 98.86%. However, Xception DL performed slightly better due to the convergence gap for validation accuracy compared to all the other architectures, which were 21.79% and 15.12% for IAM and CVL. Also, the smallest gap of convergence between training and validation accuracy for the IAM and CVL datasets were 19.13% and 16.49%, respectively. The results of these findings serve as the basis for DL architecture selection and open up overfitting problems for future work.
Internet of Things (IoT) Innovation and Application to Intelligent Governance Systems: A Case Study on DISHUB for Transport Vehicles Abdul Azis; Dwi Krisbiantoro; Riyanto -
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1282

Abstract

Vehicles can be used to facilitate humans in carrying out their daily activities. Motorized vehicles are divided into 3: land, sea, and air. Apart from the many benefits that can be obtained, motorized vehicles have dangers related to high-risk accidents. The highest cause of motorized vehicle accidents on land is road damage due to overload vehicles. Brake failure due to overload vehicles also contributes to vehicle accidents. This research aims to create an Internet of Things (IoT) based application to detect motorized vehicle load conditions. It was combined with several other technologies to produce a tool for detecting motor vehicle load conditions. The Extreme Programming Method is being used in this research. The Extreme Programming method is considered more suitable for completing this research because the communication with stakeholders is quite different. The Extreme Programming Method enables it to go back to the next step if discrepancies are encountered in making the system. The result of this research is an IoT-based tool called e-overload. It can detect vehicle loads, provide information for the drivers, and inform the results to related officers at the same time. E-overload tool will enable the drivers to get real-time information on the load on their vehicles. Officers will get additional evidence and the latest position of the vehicle to carry out actions against motorists who operate their vehicles with excessive loads.
The Joint Decision-Making Support through Piecewise Objective Optimization Model for Integrated Supplier Selection, Inventory Management, and Production Planning Involving Discounts - Widowati; - Sutrisno; Robertus Heri Soelistyo Utomo
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1328

Abstract

The decision-makers in manufacturing industries continuously optimize every supply-chain part to achieve optimal profit. In this paper, three crucial activities in the supply chain are observed as profit contributors: supplier selection, inventory management, and production planning. Decision-making support is needed to optimize those activities, especially when prices/costs involve discounts. Therefore, this study aims to develop integrated decision-making support for supplier selection, inventory management, and production planning involving discounted prices. The problem was considered with multi-supplier, multi-raw material, multi-product, and multi-observation time instant. The objective was based on maximizing the profit for the entire activity, i.e., from the raw material procurement and storage to the production. This supply chain was modeled as mixed-integer linear programming with a piecewise objective function representing the profit, which was maximized. It was also modeled with a bunch of constraint functions, including product demand satisfaction. The proposed model was tested with computational simulations using randomly generated supply chain data. The primal simplex algorithm was also employed to calculate the real value of the optimal decision, which was combined with the Branch-and-Bound approach to calculate the appropriate integer solution. The results showed that the optimal decision was achieved, namely (1) The optimal quantity of raw materials ordered to each supplier, (2) The optimal production quantity, and (3) The optimal inventory level, which provided the maximal profit for the whole optimization time horizon. This indicated that the proposed decision-making support model is implementable for industrial decision-makers.
CNN with Batch Normalization Adjustment for Offline Hand-written Signature Genuine Verification Fatihia, Wifda Muna; Fariza, Arna; Karlita, Tita
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1443

Abstract

Signature genuine verifications of offline hand-written signatures are critical for preventing forgery and fraud. With the growth of protecting personal identity and preventing fraud, the demand for an automatic system for signature verification is high. The signature verification system is then studied by many researchers using various methods, especially deep learning-based methods. Hence, deep learning has a problem. Deep learning requires much training time for the data to obtain the best model accuracy result. Therefore, this paper proposed a CNN Batch Normalization, the CNN architectural adaptation model with a normalization batch number added, to obtain a CNN model optimization with high accuracy and less training time for offline hand-written signature verification. We compare CNN with our proposed model in the experiments. The research method in this study is data collection, pre-processing, and testing using our private signature dataset (collected by capturing signature images using a smartphone), which becomes the difficulties of our study because of the different lighting, media, and pen used to sign. Experiment results show that our model ranks first, with a training accuracy of 88.89%, an accuracy validation of 75.93%, and a testing accuracy of 84.84%—also, the result of 2638.63 s for the training time consumed with CPU usage. The model evaluation results show that our model has a smaller EER value; 2.583, with FAR = 0.333 and FRR = 4.833. Although the results of our proposed model are better than basic CNN, it is still low and overfitted. It has to be enhanced by better pre-processing steps using another augmentation method required to improve dataset quality. 
IP-Light Technologies Gen 2: Intervention Tools on IPLT for Trauma, Phobia, and Psychological Problems Ifdil, Ifdil; Fadli, Rima Pratiwi; Zola, Nilma; Ismail, Izwah Binti; Hadi, M Fahli Zatra
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1343

Abstract

As the fourth industrial revolution guides humanity toward the next stage of civilization, there have been various changes across multiple domains of life. The youngest generations now face a new era in which unexpected problems continuously arise and evolve. Consequently, innovation is needed, especially in psychology and technology. This innovation is important because of the increasing number of issues that plague human life, especially psychological problems, trauma, and phobias. However, modern psychology has not kept pace with these developments. Based on this, the researchers developed a tool called IP-Light Technologies. This tool was developed using a research and development approach. Product testing in the form of prototypes is limited and widely carried out in West Nusa Tenggara, West Sumatra, DKI Jakarta, and Bali. As a result of testing, the final prototype was widely produced. The perceptual light prototype comprises a set of lamps and a multiplicity of LED lights extending in horizontal wings spaced concerning the main handle; an elongated handle extending from the main handle in the opposite direction, vertically to horizontally; a linear array of illuminated displays located on the display surface of the handle; and numerous control switches mounted on the casing. The device further comprises a power supply, and control circuit wherein the LEDs on both wings are arranged in an array configured so that the combination of each bulb may project high-intensity light. A lamp clip with a spring design can be clipped on the edge of a table or any other surface.
Roboswab: A Covid-19 Thermal Imaging Detector Based on Oral and Facial Temperatures I Nyoman Gede Arya Astawa; I.D.G Ary Subagia; Felipe P. Vista IV; IGAK Cathur Adhi; I Made Ari Dwi Suta Atmaja
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1505

Abstract

The SARS-CoV-2 virus has been the precursor of the coronavirus disease (COVID-19). The symptoms of COVID-19 begin with the common cold and then become very severe, such as those of Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). Currently, polymerase chain reaction (PCR) is used to detect COVID-19 accurately, but it causes some side effects to the patient when the test is performed. Therefore, the proposed "Roboswab" was developed that uses thermal imaging to measure non-contact facial and oral temperature. This study focuses on the performance of the proposed equipment in measuring facial and oral temperature from various distances. Face detection also involves checking whether the subject is wearing a mask or not. Image processing methods with thermal imaging and robotic manipulators are integrated into a contact-free detector that is inexpensive, accurate, and painless. This research has successfully detected masked or non-masked faces and accurately detected facial temperature. The results showed that the accurate measurement of facial temperature with a mask is 90% with an error of +/- 0.05%, while it was 100% without a mask. On the other hand, the oral temperature was measured with 97% accuracy and an error of less than 5%. The optimal distance of the Roboswab to the face for measuring temperature is an average of 60 cm. The Roboswab tool equipped with masked or non-masked face detection can be used for early detection of COVID-19 without direct contact with patients.
A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data Suboh, Syahirah; Aziz, Izzatdin Abdul; Shaharudin, Shazlyn Milleana; Ismail, Saidatul Akmar; Mahdin, Hairulnizam
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1297

Abstract

In data analysis, recognizing unusual patterns (outliers’ analysis or anomaly detection) plays a crucial role in identifying critical events. Because of its widespread use in many applications, it remains an important and extensive research brand in data mining. As a result, numerous techniques for finding anomalies have been developed, and more are still being worked on. Researchers can gain vital knowledge by identifying anomalies, which helps them make better meaningful data analyses. However, anomaly detection is even more challenging when the datasets are high-dimensional and multivariate. In the literature, anomaly detection has received much attention but not as much as anomaly detection, specifically in high dimensional and multivariate conditions. This paper systematically reviews the existing related techniques and presents extensive coverage of challenges and perspectives of anomaly detection within high-dimensional and multivariate data. At the same time, it provides a clear insight into the techniques developed for anomaly detection problems. This paper aims to help select the best technique that suits its rightful purpose. It has been found that PCA, DOBIN, Stray algorithm, and DAE-KNN have a high learning rate compared to Random projection, ROBEM, and OCP methods. Overall, most methods have shown an excellent ability to tackle the curse of dimensionality and multivariate features to perform anomaly detection. Moreover, a comparison of each algorithm for anomaly detection is also provided to produce a better algorithm. Finally, it would be a line of future studies to extend by comparing the methods on other domain-specific datasets and offering a comprehensive anomaly interpretation in describing the truth of anomalies.

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