IJAIT (International Journal of Applied Information Technology)
International Journal of Applied Information Technology covers a broad range of research topics in information technology. The topics include, but are not limited to avionics, bio medical instrumentation, biometric, computer network design, cryptography, data compression, digital signal processing, embedded system, enterprise information system, green energy & computing, interactive programming, internet of things, IT management and governance, IT-business strategic alignment, mobile and ubiquitous computing, monitoring system and techniques, multimedia processing, network security, power electronics, remote monitoring and sensing device, robotics and avionics, signal processing circuits, smart cities and smart grids, telecommunication devices & methods, telecommunication fundamentals.
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<![CDATA[Implementation of Data Governance on the Open Government Data Management Platform to Improve Data Quality ]]>
<![CDATA[Khairul Habibie]]>;
<![CDATA[Suhardi Suhardi]]>;
<![CDATA[Wardani Muhamad]]>
<![CDATA[ IJAIT (International Journal of Applied Information Technology) Vol 7 No 02 (2023): Vol 07 No 02 (November 2023) ]]>
Publisher : <![CDATA[School of Applied Science, Telkom University ]]>
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DOI: 10.25124/ijait.v7i02.5979
<![CDATA[Currently, realizing good governance related to data disclosure in government agencies is an initiative as a manifestation of open government data. However, there are still problems with the quality of published data. As a solution, organizations need to establish policies, strategies, and initiatives for data management activities This paper proposes adding data management activities to the platform to enhance the quality of published data. As for the value of the quality of the data tested using the XYZ district budget, there is an increase in the uniqueness quality dimension from valid DQI 98.7203 to 100; the conformity quality dimension has also increased from 94.7368 to 100; the accuracy quality dimension also increased significantly from 0 to 100; integrity quality dimension increased from 66.6667 to 100. As a concern, the validity of the data is by manual checking after deleting data duplication.]]>
<![CDATA[Mobile Application for Simulation of Camera Shot Angles Using 3D Environment Virtual Reality ]]>
<![CDATA[Ady Purna Kurniawan]]>;
<![CDATA[Asaas Putra]]>;
<![CDATA[Sritenaya Geovani Putri]]>;
<![CDATA[Sani Apriliani]]>
<![CDATA[ IJAIT (International Journal of Applied Information Technology) Vol 7 No 02 (2023): Vol 07 No 02 (November 2023) ]]>
Publisher : <![CDATA[School of Applied Science, Telkom University ]]>
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DOI: 10.25124/ijait.v7i02.5984
<![CDATA[This paper addresses the challenges faced by Telkom University in online learning, specifically in practical courses that require hardware. One such course is Videography in the Communication Science Study Program, which aims to equip students with theoretical knowledge and practical techniques for shooting from specific angles or positions using camera devices. To overcome this challenge, the study focuses on developing an Android mobile application that simulates the practical exercises in the Camera Shot Angles course. The application utilizes a virtual 3D environment and offers a VR (Virtual Reality) mode, allowing students to immerse themselves in realistic shooting experiences from various positions and angles. It also includes a comprehensive set of questions to evaluate students' understanding of the course material. The testing results indicate that the application is compatible with mobile devices with a minimum of 4GB RAM and has received positive scores for user experience aspects such as attractiveness, perspicuity, and efficiency. This paper discusses the development process, features, and evaluation of the application, highlighting its potential to enhance the practical learning experience for students in the Communication Science Study Program at Telkom University. By providing a virtual platform for practicing camera shot angles, this application offers a solution to the hardware limitations faced during online learning, enabling students to gain practical skills and knowledge effectively.]]>
<![CDATA[A Review: Data Quality Problem in Predictive Analytics ]]>
<![CDATA[Heru Nugroho]]>
<![CDATA[ IJAIT (International Journal of Applied Information Technology) Vol 7 No 02 (2023): Vol 07 No 02 (November 2023) ]]>
Publisher : <![CDATA[School of Applied Science, Telkom University ]]>
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DOI: 10.25124/ijait.v7i02.5980
<![CDATA[As data size continues to grow, there has been a revolution in computational methods and statistics to process and analyze data into insight and knowledge. This change in the paradigm of analytical data from explicit to implicit raises the way to extract knowledge from data through a prospective approach to determine the value of new observations based on the structure of the relationship between input and output. Data preparation is a very important stage in predictive analytics. To run quality analytical data, data with good quality is needed in accordance with the criteria. Data quality plays an important role in strategic decision making and planning before the digital computer era. The main challenge faced is that raw data cannot be directly used for analysis. One problem that arises related to data quality is completeness. Missing data is one that often causes data to become incomplete. As a result, predictive analysis generated from these data becomes inaccurate. In this paper we will discuss the problems related to the quality of data in predictive analytics through a literature study from related research. In addition, challenges and directions that might occur in the predictive analytics domain with problems related to data quality will be presented.]]>
<![CDATA[Poverty Level Prediction Based on E-Commerce Data Using Naïve Bayes Algorithm and Similarity-Based Feature Selection ]]>
<![CDATA[Pramuko Aji]]>;
<![CDATA[Dedy Rahman Wijaya]]>;
<![CDATA[Elis Hernawati]]>;
<![CDATA[Sherla Yualinda]]>;
<![CDATA[Sherli Yualinda]]>;
<![CDATA[Muhammad Akbar Haikal Frasanta]]>;
<![CDATA[Rathimala Kannan]]>
<![CDATA[ IJAIT (International Journal of Applied Information Technology) Vol 7 No 02 (2023): Vol 07 No 02 (November 2023) ]]>
Publisher : <![CDATA[School of Applied Science, Telkom University ]]>
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DOI: 10.25124/ijait.v7i02.5374
<![CDATA[The poverty rate is an important measure of any country because it indicates how well the economy develops and how well the economic prosperity distributes among citizens. The Central Statistics Agency, or BPS, measures the poverty rates in Indonesia using the concept of the ability to meet demands (basic needs approach). Using this approach, spending becomes a measure of poverty, defined as an economic incapacity to satisfy food and non-food requirements. Thus, the poor are individuals whose monthly per capita spending is less than the poverty threshold. In this study, the machine learning method using Naive Bayes with similarity-based feature selection and e-commerce data has been proposed to predict the poverty level in Indonesia. We proposed the method to be used as a complement to the results of the costly surveys and censuses conducted by BPS. Our experiments show that the classifier shows little relevance between the predicted and the original values or actual poverty prediction based on BPS data. A limited number of features does not necessarily result in poor accuracy, however great accuracy is not always achieved if a lot of features are being used.]]>
<![CDATA[Poverty Level Prediction Based on E-Commerce Data Using Naïve Bayes Algorithm and Similarity-Based Feature Selection ]]>
<![CDATA[Aji, Pramuko]]>;
<![CDATA[Wijaya, Dedy Rahman]]>;
<![CDATA[Hernawati, Elis]]>;
<![CDATA[Yualinda, Sherla]]>;
<![CDATA[Yualinda, Sherli]]>;
<![CDATA[Frasanta, Muhammad Akbar Haikal]]>;
<![CDATA[Kannan, Rathimala]]>
<![CDATA[ IJAIT (International Journal of Applied Information Technology) Vol 07 No 02 (November 2023) ]]>
Publisher : <![CDATA[School of Applied Science, Telkom University ]]>
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DOI: 10.25124/ijait.v7i02.5374
<![CDATA[The poverty rate is an important measure of any country because it indicates how well the economy develops and how well the economic prosperity distributes among citizens. The Central Statistics Agency, or BPS, measures the poverty rates in Indonesia using the concept of the ability to meet demands (basic needs approach). Using this approach, spending becomes a measure of poverty, defined as an economic incapacity to satisfy food and non-food requirements. Thus, the poor are individuals whose monthly per capita spending is less than the poverty threshold. In this study, the machine learning method using Naive Bayes with similarity-based feature selection and e-commerce data has been proposed to predict the poverty level in Indonesia. We proposed the method to be used as a complement to the results of the costly surveys and censuses conducted by BPS. Our experiments show that the classifier shows little relevance between the predicted and the original values or actual poverty prediction based on BPS data. A limited number of features does not necessarily result in poor accuracy, however great accuracy is not always achieved if a lot of features are being used.]]>
<![CDATA[Implementation of Data Governance on the Open Government Data Management Platform to Improve Data Quality ]]>
<![CDATA[Habibie, Khairul]]>;
<![CDATA[Suhardi, Suhardi]]>;
<![CDATA[Muhamad, Wardani]]>
<![CDATA[ IJAIT (International Journal of Applied Information Technology) Vol 07 No 02 (November 2023) ]]>
Publisher : <![CDATA[School of Applied Science, Telkom University ]]>
Show Abstract
|
Download Original
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Original Source
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Check in Google Scholar
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DOI: 10.25124/ijait.v7i02.5979
<![CDATA[Currently, realizing good governance related to data disclosure in government agencies is an initiative as a manifestation of open government data. However, there are still problems with the quality of published data. As a solution, organizations need to establish policies, strategies, and initiatives for data management activities This paper proposes adding data management activities to the platform to enhance the quality of published data. As for the value of the quality of the data tested using the XYZ district budget, there is an increase in the uniqueness quality dimension from valid DQI 98.7203 to 100; the conformity quality dimension has also increased from 94.7368 to 100; the accuracy quality dimension also increased significantly from 0 to 100; integrity quality dimension increased from 66.6667 to 100. As a concern, the validity of the data is by manual checking after deleting data duplication.]]>
<![CDATA[A Review: Data Quality Problem in Predictive Analytics ]]>
<![CDATA[Nugroho, Heru]]>
<![CDATA[ IJAIT (International Journal of Applied Information Technology) Vol 07 No 02 (November 2023) ]]>
Publisher : <![CDATA[School of Applied Science, Telkom University ]]>
Show Abstract
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DOI: 10.25124/ijait.v7i02.5980
<![CDATA[As data size continues to grow, there has been a revolution in computational methods and statistics to process and analyze data into insight and knowledge. This change in the paradigm of analytical data from explicit to implicit raises the way to extract knowledge from data through a prospective approach to determine the value of new observations based on the structure of the relationship between input and output. Data preparation is a very important stage in predictive analytics. To run quality analytical data, data with good quality is needed in accordance with the criteria. Data quality plays an important role in strategic decision making and planning before the digital computer era. The main challenge faced is that raw data cannot be directly used for analysis. One problem that arises related to data quality is completeness. Missing data is one that often causes data to become incomplete. As a result, predictive analysis generated from these data becomes inaccurate. In this paper we will discuss the problems related to the quality of data in predictive analytics through a literature study from related research. In addition, challenges and directions that might occur in the predictive analytics domain with problems related to data quality will be presented.]]>
<![CDATA[Mobile Application for Simulation of Camera Shot Angles Using 3D Environment Virtual Reality ]]>
<![CDATA[Kurniawan, Ady Purna]]>;
<![CDATA[Putra, Asaas]]>;
<![CDATA[Putri, Sritenaya Geovani]]>;
<![CDATA[Apriliani, Sani]]>
<![CDATA[ IJAIT (International Journal of Applied Information Technology) Vol 07 No 02 (November 2023) ]]>
Publisher : <![CDATA[School of Applied Science, Telkom University ]]>
Show Abstract
|
Download Original
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Original Source
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Check in Google Scholar
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DOI: 10.25124/ijait.v7i02.5984
<![CDATA[This paper addresses the challenges faced by Telkom University in online learning, specifically in practical courses that require hardware. One such course is Videography in the Communication Science Study Program, which aims to equip students with theoretical knowledge and practical techniques for shooting from specific angles or positions using camera devices. To overcome this challenge, the study focuses on developing an Android mobile application that simulates the practical exercises in the Camera Shot Angles course. The application utilizes a virtual 3D environment and offers a VR (Virtual Reality) mode, allowing students to immerse themselves in realistic shooting experiences from various positions and angles. It also includes a comprehensive set of questions to evaluate students' understanding of the course material. The testing results indicate that the application is compatible with mobile devices with a minimum of 4GB RAM and has received positive scores for user experience aspects such as attractiveness, perspicuity, and efficiency. This paper discusses the development process, features, and evaluation of the application, highlighting its potential to enhance the practical learning experience for students in the Communication Science Study Program at Telkom University. By providing a virtual platform for practicing camera shot angles, this application offers a solution to the hardware limitations faced during online learning, enabling students to gain practical skills and knowledge effectively.]]>
<![CDATA[Early Estimation of Earthquake Magnitude Using Machine Learning ]]>
<![CDATA[Novianty, Astri]]>;
<![CDATA[Prasasti, Anggunmeka Luhur]]>;
<![CDATA[Saputra, Randy Erfa]]>
<![CDATA[ IJAIT (International Journal of Applied Information Technology) Vol 07 No 02 (November 2023) ]]>
Publisher : <![CDATA[School of Applied Science, Telkom University ]]>
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DOI: 10.25124/ijait.v7i02.5994
<![CDATA[Seismic parameters provide important information that describes the characteristics of an earthquake. The magnitude parameter is one of the essential seismic parameters in making the right decision regarding earthquake disaster mitigation. Determining the magnitude of an earthquake must be done early because this information represents the size of the earthquake and the potential damage it causes. If the determination of the earthquake’s magnitude is delayed, emergencies such as the evacuation of residents and post-disaster recovery may be disrupted. This study attempts to estimate the earthquake magnitude parameters based on Primary (P) wave signals using several machine learning algorithms for regression, such as Neural Network Regression (NNR), Random Forest Regression (RFR), and Support Vector Machine Regression (SVMR). The experimental results show that the RFR can produce the best estimation with an R-squared (R2) value of 0.946 and a root mean square error (RMSE) of 0.087.]]>
<![CDATA[Simplification of Workflow-oriented Security Assessment ]]>
<![CDATA[Yunizal, Edri]]>;
<![CDATA[Wardana, Aulia Arif]]>;
<![CDATA[Niarman, Abdurrahman]]>
<![CDATA[ IJAIT (International Journal of Applied Information Technology) Vol 07 No 02 (November 2023) ]]>
Publisher : <![CDATA[School of Applied Science, Telkom University ]]>
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DOI: 10.25124/ijait.v7i02.6797
<![CDATA[One of the protection mechanisms for organizations to protect their data is through information security risk assessment. The main obstacle in this area is asset dependency. Previous research developments tended to produce models that were difficult to implement because they were only applied to small assets, in contrast to the complexity of implementation in the field. This form of problem solving is a workflow-oriented security assessment solution that provides security rationale from a holistic perspective. The weakness of complexity in workflow oriented then became the basis of this research. The proposed solution is a simplification by using combined nodes that enable a modular concept. The modular concept is then applied to a reliable model, a data flow diagram. The study output shows the contribution of offerings with assessment solutions that consider dependencies by simplifying asset complexity in workflows in a modular manner with data flow diagrams.]]>
