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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 62 Documents
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Search results for , issue "Vol 8, No 1 (2024)" : 62 Documents clear
Asana and Trello: A Comparative Assessment of Project Management Capabilities Kamila, Jihan Syafa; Marzuq, Muhammad Falah
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2595

Abstract

Project management tools are essential for streamlining project management activities and providing a variety of functionalities to assist organizations in executing projects efficiently. The selection of an appropriate tool is crucial, given the many options available in the market. This scholarly article employs a comparative analysis methodology to scrutinize two prominent project management tools, Asana and Trello. The aim is to assist companies and organizations in making informed decisions based on their specific needs. The comparative analysis delves into the strengths and weaknesses of Asana and Trello, assessing their features, functionalities, and suitability in the context of knowledge management areas. Both tools are evaluated for their capability to address project management challenges and improve organizational processes. The study concludes that the choice between Asana and Trello hinges on factors such as project scale, organizational requirements, and the preferred level of complexity. With its comprehensive features, Asana emerges as ideal for larger, agile-oriented projects. In contrast, Trello's simplicity and user-friendly interface suit relatively smaller projects well. This analysis provides valuable insights for organizations to align their project management tools with specific project conditions, facilitating optimising project execution processes to meet their unique goals and requirements. In terms of features, Asana outshines Trello by providing a more extensive range of functionalities that effectively support the mapping of knowledge management areas.
Programming Language Selection for The Development of Deep Learning Library Rachmawati, Oktavia Citra Resmi; Barakbah, Ali Ridho; Karlita, Tita
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2437

Abstract

Recently, deep learning has become very successful in various applications, leading to an increasing need for software tools to keep up with the rapid pace of innovation in deep learning research. As a result, we suggested the development of a software library related to deep learning that would be useful for researchers and practitioners in academia and industry for their research endeavors. The programming language is the core of deep learning library development, so this paper describes the selection stage to find the most suitable programming language for developing a deep learning library based on two criteria, including coverage on many projects and the ability to handle high-dimensional array processing. We addressed the comparison of programming languages with two approaches. First, we looked for the most demanding programming languages for AI Jobs by conducting a data-driven approach against the data gathered from several Job-Hunting Platforms. Then, we found the findings that imply Python, C++, and Java as the top three. After that, we compared the three most widely used programming languages by calculating interval time to three different programs that contain an array of exploitation processes. Based on the result of the experiments that were executed in the computer terminal, Java outperformed Python and C++ in two of the three experiments conducted with 5,4047 milliseconds faster than C++ and 231,1639 milliseconds faster than Python to run quick sort algorithm for arrays that contain 100.000 integer values. 
Smart Contract and IPFS Decentralized Storage for Halal Certification Process Agung, Anak Agung Gde; Yuniar, Irna; Hendriyanto, Robbi
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1497

Abstract

The halal industry today has achieved rapid development. Halal product is mandatory for Muslims and a big business for Indonesia. For others, it affirms the product's quality assurance and becomes a trending lifestyle. The product owner must submit an application and undergo several processes to obtain a halal certificate. However, there are challenges in the certification process and documentation. The proposed system automates the flow between certification processes through digital signing and stores the certificate and fatwa file. The study investigates the utilization of blockchain to manage the process and the integration of decentralized storage (IPFS) to store the digital version of the fatwa and certificate. A smart contract is designed and deployed on the Ethereum blockchain, and the transaction time and cost are analyzed. A smart contract enforces that certain actions are executed once the required conditions are fulfilled. The proposed system would cost 24.6 USD and require 227 seconds on average for the system setup. Each submission requires 9.86 USD and takes 92 seconds on average. Verification is free, and the average result can be obtained in one second. The appointed officer sets each entity to interact with the contract, and the digital documents (fatwa and certificate) are available online using IPFS. Progress of the certification is transparent to the public, increasing the public's trust. The study demonstrates a smart contract's capability to manage a product's certification process.
Minimum, Maximum, and Average Implementation of Patterned Datasets in Mapping Cryptocurrency Fluctuation Patterns Parlika, Rizky; Mustafid, Mustafid; Rahmat, Basuki
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1543

Abstract

Cryptocurrency price fluctuations are increasingly interesting and are of concern to researchers around the world. Many ways have been proposed to predict the next price, whether it will go up or down. This research shows how to create a patterned dataset from an API connection shared by Indonesia's leading digital currency market, Indodax. From the data on the movement of all cryptocurrencies, the lowest price variable is taken for 24 hours, the latest price, the highest price for 24 hours, and the time of price movement, which is then programmed into a pattern dataset. This patterned dataset is then mined and stored continuously on the MySQL Server DBMS on the hosting service. The patterned dataset is then separated per month, and the data per day is calculated. The minimum, maximum, and average functions are then applied to form a graph that displays paired lines of the movement of the patterned dataset in Crash and Moon conditions. From the observations, the Patterned Graphical Pair dataset using the Average function provides the best potential for predicting future cryptocurrency price fluctuations with the Bitcoin case study. The novelty of this research is the development of patterned datasets for predicting cryptocurrency fluctuations based on the influence of bitcoin price movements on all currencies in the cryptocurrency trading market. This research also proved the truth of hypotheses a and b related to the start and end of fluctuations.
The Impact of Online Learning on NDUM Students During COVID-19 Iskandar, Sari Nashikim Radin; Adib, Mohammad Khairuddin; Isa, Mohd Rizal Mohd; Ali, Sharifah Aishah Syed; Shukran, Mohd Afizi Mohd; Maskat, Kamaruzaman
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1238

Abstract

One of the impacts of the COVID-19 outbreak was the closure of numerous education facilities, including schools and universities. Due to the closing of these institutions, the method used for teaching and learning changed from physical face-to-face lecturing to online contactless learning. This helps curb the spread of infections while ensuring that teaching and learning continue as usually as possible. However, questions arise not only about the effectiveness of online learning but also about the impact of online learning on education stakeholders, namely students and educators. This study aims to assess the effects of the lockdown during COVID-19 on National Defense University of Malaysia (NDUM) students. A link pointing to a custom-built questionnaire was forwarded to students through email and WhatsApp. At the end of the survey period, 445 students responded to the questionnaire. The simple percentage distribution was employed to evaluate the student's learning status and their expectations. Based on the analysis, during the lockdown, students faced issues involving technical, time management, social interactions, and surrounding (home-related) issues. In contrast, during the lockdown, students were also keen to learn new technological skills and favorable towards the ability to replay lectures and class materials. These valuable insights on the impact of online learning on students are essential due to the advancement of technology in education, not only in Malaysia but in other nations as well.
An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation Vu, Van Vien; Le, Phuoc Tai; Do, Thi Mai Thom; Nguyen, Thi Thuy Hieu; Tran, Nguyen Bao Minh; Paramasivam, Prabhu; Le, Thi Thai; Le, Huu Cuong; Chau, Thanh Hieu
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2641

Abstract

This review article looks at the developing field of artificial intelligence and machine learning in maritime and marine environment management. The marine industry is increasingly interested in applying advanced AI and ML technologies to solve sustainability, efficiency, and regulatory compliance issues. This paper examines maritime and marine AI and ML applications using a deep literature review and case study analysis. Modeling ship fuel consumption, which impacts the environment and operating expenses, is a top responsibility. The study demonstrates that ML approaches such as Random Forest and Tweedie models can estimate ship fuel use. Statistical analysis demonstrates that the Random Forest model beats the Tweedie model regarding accuracy and consistency. For the training and testing datasets, the Random Forest model has high R2 values of 0.9997 and 0.9926, indicating a solid match. Low Root Mean Square Error (RMSE) and average absolute relative deviation (AARD) suggest that the model accurately reflects fuel use variability. While still performing well, the Tweedie model has lower R2 values and higher RMSE and AARD values, suggesting reduced accuracy and precision in fuel consumption prediction. These findings provide light on the potential applications of artificial intelligence and machine learning in maritime and marine environment management. Advanced analytics enables decision-makers to analyze fuel consumption patterns better, increase operational efficiency, and decrease environmental impact, thus improving maritime sustainability.
Predicting Battery Storage of Residential PV Using Long Short-Term Memory Rakasiwi, Rizky Khaerul Maulana; Kurnianingsih, Kurnianingsih; Suharjono, Amin; Enriko, I Ketut Agung; Kubota, Naoyuki
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1603

Abstract

Solar power panels, or photovoltaic (PV), have recently grown rapidly as a renewable alternative energy source, especially since the increase in the basic electricity tariff. PV technology can be employed instead of the state electricity company to reduce the electricity used. Indonesia is one of the countries that have great potential in producing electricity from PV technology, considering that most of Indonesia's territory gets sunlight for most of the year and has a large land area. Considering the benefits of PV technology, it is necessary to carry out predictive monitoring and analysis of the energy generated by PV technology to maximize energy utilization in the future. The Internet of Things (IoT) and cloud computing system was developed in this research to monitor and collect data in real-time within 27 days and obtained 7831 data for each parameter that affects PV production. These data include data on the light intensity, temperature, and humidity at the location where the PV system is installed. The feature selection results using Pearson correlation revealed that the light intensity parameter significantly impacted the PV production system. This research used the Long Short-Term Memory (LSTM) method to predict future PV production. By tuning hyperparameters using 3000 epochs, the resulting RMSE value was 171.5720. The results indicated a significant change in the RMSE value compared to 100 epochs of 422.5780. This model can be applied as a forecasting system model at electric vehicle charging stations, given the increasing use of electric vehicles in the future.     Keywords— Forecasting; energy; Photovoltaic; LSTM; Internet of Thing. 
Automatic Cell Planning Method for Radio Network Optimization Putri, Hasanah; Ahmad, Izanoordina; Hikmaturokhman, Alfin; Haura Putri, Dwi
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1913

Abstract

As the first step in building a wireless communication network, wireless network optimization is crucial since it determines how the network will be built scientifically. Numerous challenges remain in the way of the Radio Network's deployment in Indonesia, not the least of which is the still-uneven coverage region. The Kiaracondong region in Bandung is one of the numerous areas in Indonesia that are still considered to be "bad spot areas" as a result. Based on the findings of the driving test conducted in the Kiaracondong sub-district, the KPI target was not fulfilled for the RSRP, SINR, and Throughput parameters. Therefore, this study primarily focuses on the physical tuning optimization using the Automatic Cell Planning (ACP) method for the LTE wireless network optimization. To assess the quality of the LTE network before and after optimization, the results of the ACP optimization simulation will be compared with the results of the existing or non-ACP site simulation and the results of the operator's ACP implementation. As a result, Area 1 has an average RSRP of -72.79 dBm, area 2 -73.17 dBm, and area 3 -68.22 dBm. Additionally, the average SINR in areas 1,2 and 3 is 8 dB, 6.58 dB, and 8.17 dB, respectively. The average downlink throughput in area 1 is 42652.66 Kbps, area 2 is 34420.88 Kbps, and area 3 is 43882.92 Kbps. Finally, the average throughput uplink for areas 1 to 3 is 51651.24 Kbps, 47895.99 Kbps, and 49648.84 Kbps, respectively.
Econometric Model Using Arbitrage Pricing Theory and Quantile Regression to Estimate the Risk Factors Driving Crude Oil Returns Maitra, Sarit; Mishra, Vivek; Kundu, Sukanya; Chopra, Manav
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2268

Abstract

This work presents a novel approach to determining the risk and return of crude oil stocks by employing Arbitrage Pricing Theory and Quantile Regression. Arbitrage Pricing Theory identifies the risk factors likely to impact crude oil returns. Subsequently, Quantile Regression estimates the relationship between the selected factors and the returns across different distribution quantiles. The West Texas Intermediate (WTI) crude oil price is used in this study as a benchmark for crude oil prices. WTI’s price fluctuations can significantly impact the performance of global crude oil stocks and, subsequently, the global economy. Various statistical measures are used in this study to determine the proposed model's stability. The results show that changes in WTI returns can have varying effects depending on market conditions and levels of volatility. This study emphasizes the influence of structural discontinuities on returns. These are likely generated by changes in the global economy and the unpredictable demand for crude oil during the pandemic. The inclusion of pandemic, geopolitical, and inflation-related explanatory variables adds uniqueness to the study as it considers current global events that can affect crude oil returns. Findings show that the key factors that pose significant risks to returns are industrial production, inflation, the global price of energy, the shape of the yield curve, and global economic policy uncertainty. This implies that while making investment decisions in WTI futures, investors should pay particular attention to these elements.
Software Agent Simulation Design on the Efficiency of Food Delivery Ismail, Shahrinaz; Mostafa, Salama A; Baharum, Zirawani; Erianda, Aldo; Jaber, Mustafa Musa; Jubair, Mohammed Ahmed; Adiya, M. Hasmil
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2648

Abstract

Food delivery services have gained popularity since the emergence of online food delivery. Since the recent pandemic, the demand for service has increased tremendously. Due to several factors that affect how much time additional riders spend on the road; food delivery companies have no control over the location or timing of the delivery riders. There is a need to study and understand the food delivery riders' efficiency to estimate the service system's capacity. The study can ensure that the capacity is sufficient based on the number of orders, which usually depends on the number of potential customers within a territory and the time each rider takes to deliver the orders successfully. This study is an opportunity to focus on the efficiency of the riders since there is not much work at the operational level of the food delivery structure. This study takes up the opportunity to design a software agent simulation on the efficiency of riders' operations in food service due to the lack of simulation to predict this perspective, which could be extended to efficiency prediction. The results presented in this paper are based on the system design phase using the Tropos methodology. At movement in the simulation, the graph of the efficiency is calculated. Upon crossing the threshold, it is considered that the rider agents have achieved the efficiency rate required for decision-making. The simulation's primary operations depend on frontline remotely mobile workers like food delivery riders. It can benefit relevant organizations in decision-making during strategic capacity planning.

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