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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 20 Documents
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Search results for , issue "Vol 6, No 2 (2022)" : 20 Documents clear
Performance Evaluation of Successive Interference Cancellation on Gain Ratio Power Allocation using Underwater Visible Light Communication Channel Luthfi Nur'Adli; Arfianto Fahmi; Brian Pamukti
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.994

Abstract

Underwater Visible light communication (UVLC) is a network communication wirelessly where information is transmitted employing light through visible waves; in this case, the light source comes from a light-emitting diode (LED) as a transmitter underwater. VLC has several advantages over radio frequency technology, such as safer communication because light propagation cannot penetrate the wall, so it is difficult to do hacking, easy to get a license, relatively build cheap cost, and has no side effects on health. However, VLC has several limitations, one of which is the narrow bandwidth modulation. VLC undergoes a distribution of modulated bandwidth to allocate against each user. This bandwidth sharing has an impact on reduced system capacity. This study applied non-orthogonal multiple access (NOMA) to increase system capacity. This research analyzes the performance of the two best power allocation methods in a water medium, including gain ratio power allocation (GRPA) and static power allocation (SPA). In the results obtained in the NOMA-UVLC power allocation value, GRPA is more stable than SPA power allocation. Then applying residue in the successive interference cancellation (SIC) process will result in a decrease in system capacity compared to no residue in the SIC process. This study found that the GRPA power allocation is more stable in capacity performance compared to the application of SPA power allocation. Average capacity increase of 48.5% in GRPA power allocation
Confirmatory Factor Analysis: User Behavior M-Commerce Gamification Service in Indonesia Ani Rakhmanita; Ratih Hurriyati; Vanessa Gaffar; Lili Adi wibowo
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.848

Abstract

Gamification of marketing is a fast-growing phenomenon and an innovation for mobile marketing. Gamification is a strategy for increasing the attractiveness of mobile consumers to encourage increased shopping behavior, loyalty, engagement, and product advocacy. Understanding the factors behind the use of gamification services in m-commerce is very interesting, and no one has done any research. This study investigates the theory of self-determination (competence, autonomy, and relatedness) and extrinsic motivation as a predictor of the use of m-commerce gamification services. Data was collected from 400 respondents who had experienced using gamification services on m-commerce. The data was then included in the analysis. Analysis of the data using confirmatory factor analysis to determine the dominant factors of gamification service users. The benefits of factor analysis confirm the dominant factors that motivate users of gamification services in m-commerce. Researchers use AMOS 18 for Windows software to assist in the data processing. The results show that extrinsic motivation is the dominant factor that motivates users of gamification services. This finding provides insight for m-commerce companies and game designers to improve gamification mechanisms based on virtual points to motivate users to be more active and continue using gamification services. For the next research, it is possible to validate the construct by using other theoretical approaches, such as adding flow theory to measure the motivational factors of gamification service users. The research object can use other applications, such as gamification services in health, education, and banking applications.
Data Fairness Transmission and Adaptive Duty Cycle through Machine Learning in wireless Sensor Networks Junheon Jeon; Hyunjoo Park
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.998

Abstract

In this paper, we propose the data fairness transmission and adaptive duty cycle through machine learning in wireless sensor networks. The mechanism of this paper is mainly composed of two parts. The proposed mechanism is based on the sleep-wake structure, which is one of the methods to increase the lifespan of the entire network by efficiently using the energy of the nodes. The first is a mechanism to support priority and data fairness. To this end, data input to the node is divided into priority classes according to transmission urgency and stored. Introduces the concept of cross-layer to rearrange data destined for the same destination. In addition, we propose a fair data transmission mechanism that allows even low-priority data to participate in transmission after a certain period. The second is an adaptive duty cycle mechanism through machine learning. For this purpose, public data related to forest fires are collected. The collected data is refined into data for each forest fire location and data for each forest fire time. For the refined data, an SVM (Support Vector Machine) model of supervised learning is used for machine learning, and a mechanism for adaptively adjusting the duty cycle of each node through the trained model is proposed. The computer language used for machine learning is Python language, and Google's Psychic Learn is used for the machine learning library. It was compared with the existing MAC protocol for evaluation, and it was confirmed that excellent energy efficiency results were obtained.
Classification of Brain Tumors on MRI Images Using DenseNet and Support Vector Machine Agus Eko Minarno; Ilham Setiyo Kantomo; Fauzi Dwi Setiawan Sumadi; Hanung Adi Nugroho; Zaidah Ibrahim
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.991

Abstract

The brain is a vital organ in the human body, performing various functions. The brain has always played a major role in the processing of sensory information, the production of muscular activity, and the performance of high-level cognitive functions. Among the most prevalent diseases of the brain is the development of aberrant tissue in brain cells, which results in the formation of brain tumors. According to data from the International Agency for Research on Cancer (IARC), more than 124,000 people worldwide were diagnosed with brain tumors in 2014, and more than 97,000 people died due to the condition. Current research indicates that magnetic resonance imaging (MRI) is the most effective means of detecting brain cancers. Because brain tumors are associated with significant mortality risk, a large number of brain tumor MRI imaging datasets were used in this research to detect brain cancers using deep learning techniques. To classify three forms of brain tumors, including glioma, meningioma, and pituitary, a deep learning model called DenseNet 201 paired with Support Vector Machines (SVM) was employed in this work included three types of brain tumors. Based on the results of the tests that were conducted, the best accuracy results obtained in this study were 99.65 percent, with a comparison ratio of 80 percent for training data and 20 percent for testing data, oversampled with the SMOTE method, with the best accuracy results obtained in this study being 99.65 percent.
A Blockchain-based Halal Certificate Recording and Verification Prototype Anak Agung Gde Agung; Heru Nugroho; Robbi Hendriyanto
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.995

Abstract

Halal certification assures that a product or a service has been created, processed, and delivered according to Islamic laws. Currently, the certificate is printed on a security paper and includes a QR code that can be used to verify the certificate online. However, there are some problems with the ongoing certificate verification process. The verification site is centralized, creating a single point of failure. The current verification system is also unable to detect the modified printed certificate. The research aims to propose an alternative halal certificate recording and verification system. A smart contract that runs on the Ethereum blockchain is developed and deployed for that purpose. As a result, the average certificate creation cost is US$20.035, and the process requires 5.75 seconds, while verification is free, and the result can be obtained in about one second. Utilizing the blockchain to store and verify the halal certificate increases trust in the product or service since once the data is stored, it cannot be changed and accessible to the public. Nodes around the world replicate the blockchain to ensure service availability. For future consideration, the system can be extended to automate and track the halal application process and integrated as an alternative to the current system by implementing multiple signatures in the smart contract for each party. Furthermore, the system can be integrated with a peer-to-peer sharing system such as IPFS to store the digital certificate
Data Clustering for Identification of Building Conditions Using Hybrid Multivariate Multinominal Distribution Soft Set (MMDS) Method Rohmat Saedudin; Iwan Tri Riyadi Yanto; Avon Budiono; Sely Novita Sari; Mustafa Mat Deris; Norhalina Senan
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.986

Abstract

Identifying building conditions for user safety is an urgent matter, especially in earthquake-prone areas. Clustering buildings according to their conditions in the categories of danger, vulnerable, normal, and safe is important information for residents and the government to take further action. This study introduces a new method, namely hybrid multivariate multinomial distribution with the softest (MMDS) in working on the process of clustering building conditions into the most appropriate category and comparable to the condition data presented in the building data set. Research using the MMDS method is very important to map the condition of existing buildings in an area supported by available data sets. The results of the measurements carried out can provide information related to the building index and were clustered based on the index value of the condition of the building. The dataset used in this study is data on school buildings in the West Java region. There are 286 school building data with four condition parameters: foundation, concrete reinforcement, easel pole, and roof. From existing data and defined condition parameters, buildings can be classified accurately and in proportion to the facts on the ground. This study also compared the proposed method, MMDS, with the baseline method, namely Fuzzy Centroid Clustering (FCC) and Fuzzy k-means Clustering (FKC). The results show that the proposed method is superior to the baseline method with a faster processing time
Intervention Strategies through Interactive Gamification E-Learning Web-Based Application to Increase Computing Course Achievement Noor Zuraidin Mohd Safar; Hazalila Kamaludin; Masitah Ahmad; Muhammad Hanif Jofri; Norfaradilla Wahid; Taufik Gusman
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.1001

Abstract

This study aims to help students improve their knowledge capability based on their active participation through gamification. Gamification is one of the newer methods of education that has the potential to improve student learning. This research looked into gamification's efficacy in student engagement and learning retention during teaching and learning sessions for computer science or information technology courses. The assessment involved in this study is through Pre-Test and Post-Test through instructional intervention by adapting interactive Quizizz gamification e-learning web-based application. The flow of research works begins with a survey of the problem, pre-intervention analysis, and action was taken during the intervention, ending with the implementation and observation phase. The pre and post-analysis of test results and questionnaires were accomplished and discussed. Fifty-six respondents participate in this study. Results show that 87% of the respondents have increased their percentage of marks. In the pre-test result, 56% of the respondents achieved below the 55 marks, while in the post-test, it reduced to 14%. Adoption of other gamification applications, a larger target demographic, and the addition of computer science or information technology courses will help improve the study in the future.
Development of Early Startup Companies' Valuation Model Based on Android Mobile Application: The Angel Investor's Perspective Ratna Candra Sari; Patriani Wahyu Dewanti; M. Andryzal Fajar; Denies Priantinah; Arin Pranesti
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.992

Abstract

This research aims to develop a valuation model for early startup companies based on an Android mobile application (Valuasi app). This application aims to help early startups to evaluate their company performance. This research method uses the research development method. The first stage is to develop a startup valuation model by determining the criteria using the multi-criteria decision making (MCDM) method and weighting the criteria using the simple additive weighting (SAW) method. The instrument and the weight determination of the valuation model have been validated from the perspective of angel investors, practitioners, and academics. The second stage is developing an Android-based startup valuation model application. The third stage is an evaluation by the users of the application. Using the Unified Theory of Acceptance and Use of Technology (UTAUT) model, the results show that a potential user's intent to use the application is affected by the performance expectancy and social influence toward the application. This valuation model is expected to help early startup companies conduct business valuations, so they can attract investors, especially angel investors. In addition, the results showed that there was a positive response from users in using the 'Valuasi app', which was indicated by the positive and significant effect of performance expectations on usage intentions, and a positive and significant influence on social influence and behavioral intentions on user behavior. This research shows that 'Valuasi app' can be used to assess start up valuation. However, further improvements are needed to support application facilities so as to increase the ease of using the "Start Up Valuation App" application
MLP-NARX Bitcoin Price Prediction Model Integrating System Identification Modelling Principles Muhammad Nazrin Farhan Nasarudin; Ihsan Mohd Yassin; Megat Syahirul Amin Megat Ali; Mohd Khairil Adzhar Mahmood; Rahimi Baharom; Zairi Ismael Rizman
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.943

Abstract

Bitcoin is a decentralized digital currency that enables people to exchange value without requiring a third-party intermediary. Due to its many advantages, it has received much interest from institutional and individual investors. Despite its meteoric increase, the price of Bitcoin extremely volatile asset class as it purely relies on supply and demand. This presents an interesting opportunity to create a forecasting model. However, many research papers in this area does not analyse the residuals as part of the forecasting resulting in potentially biased models. In this paper, we demonstrate System Identification (SI) residual analysis techniques to the analysis of our forecasting model. The Multi-Layer Perceptron (MLP) Nonlinear Autoregressive with Exogeneous Inputs (NARX) uses historical price data and several technical indicators to predict the future price movements of Bitcoin. The Particle Swarm Optimization (PSO) algorithm was used to find optimal parameters for the model. The model was able to predict one day ahead price in the prediction test. The model has successfully captured the dynamics of the data through the tests performed on residuals. It is also proving the randomness of residuals, albeit some minor violations.
Genetic Algorithm for Artificial Neural Networks in Real-Time Strategy Games Yudi Widhiyasana; Maisevli Harika; Fahmi Faturahman Nul Hakim; Fitri Diani; Kokoy Siti Komariah; Diena Rauda Ramdania
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.832

Abstract

Controlling each member of the soldiers to carry out battle with Non-Playable Characters (NPC) is one of the secrets to winning Real-Time Strategy games. The game could be more complicated and offer a more engaging experience if every NPC acts like humans rather than machines with patterned behavior. Like people during a war, each army member's command requires rapid reflexes and direction to strike or evade attacks. An intelligent opponent based on ANN as NPC can react quickly to their opponents. The accuracy of ANN could be enhanced by weight modifications using a Genetic Algorithm (GA). The crossover and mutation rates significantly impact GA's performance as an ANN setup. This research aims to find the best crossover and mutation rates in GA as a weight adjustment in ANN. Experiments were conducted using an RTS game simulator using 20 scenarios on a maximum of 4000 iterations. The initial setup of each troop is random, with a seven-unit type available. In this research, the troops won because their men were subjected to fewer attacks than the opposing forces. The GA optimal crossover and mutation rates are determined using troop victories as a baseline. According to the findings, the best crossover rate for GA as an ANN weight adjustment is 0.6, whereas the specific mutation rate is 0.09. The crossover rate of 0.6 has the highest average win value and tends to increase every generation. As for the mutation rate of 0.09, it has the highest average win value. Thus, this preliminary study can develop NPC more humanly.

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