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All Journal CESS (Journal of Computer Engineering, System and Science) MIND (Multimedia Artificial Intelligent Networking Database) Journal Journal of Appropriate Technology for Community Services Jurnal Teknik Informatika (JUTIF) International Journal of Applied Information Systems and Informatics
Ramdani, Cecep Muhamad Sidik
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Journal : International Journal of Applied Information Systems and Informatics

 1 
Development of Information System Classification of Community Complaints Based on Keyword Case Study: District Pangandaran Ramdani, Cecep Muhamad Sidik; Rachman, Andi Nur
Journal of Applied Information System and Informatic (JAISI) Vol 2, No 1 (2024): Mei 2024
Publisher : Deparment Information System, Siliwangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jaisi.v2i1.10795

Abstract

The growing technology of the public complaint system requires the development to solve the problems that exist in Pangandaran Regency. One of the problems is not applying the classification of complaints based on keywords. So that people still have difficulty understanding the functions of government agencies correctly because there are several government agencies that have similar functions. The development of the complaints system can minimize these errors. System development used is the Extremme Programming method which has four frameworks including planning, design, coding and testing, using UML modeling (Unified Modeling Language). Extreme Programming is the development of the previous method, the Agile method. For testing applications using Black Box Testing is done only to observe the results of execution through test data and check the functional of the software. So that the results obtained are classifiers of public complaints that are directly conveyed to the relevant agencies running well.
Evaluation of Information Technology Governance at DISKOMINFO Tasikmalaya City Using COBIT 2019 Rachmasari Biduri, Nadia; Ramdani, Cecep Muhamad Sidik
Journal of Applied Information System and Informatic (JAISI) Vol 1, No 1 (2023): November 2023
Publisher : Deparment Information System, Siliwangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jaisi.v1i1.8992

Abstract

The Office of Communication and Informatics (DISKOMINFO) is an agency engaged in the fields of communication, informatics, coding and statistics. Based on the results of interviews with the Head of Application and Informatics DISKOMINFO Tasikmalaya City, it is known that there are obstacles related to limited resources. Starting from human resources, equipment, budget, and also other supporting facilities. So that an evaluation of information technology governance is needed to determine the capabilities possessed by the information technology. This study uses the COBIT 2019 framework using the RACI diagram as a mapping reference for observation and questionnaire distribution. The domains used are BAI02 (Managed Requirements Definition), DSS02 (Managed Service Requests and Incidents), and MEA01 (Managed Performance and Conformance Monitoring). The results of this study are to determine the capability level in each domain so that the current conditions of the Tasikmalaya City DISKOMINFO are obtained. After carrying out the analysis, it was found that the service performance from the BAI02 domain was at level 4, the service performance from the DSS02 domain was at level 2, and the service performance from the MEA01 domain was at level 3. The results of this service performance measurement made a recommendation to be implemented to increase the value information technology governance in accordance with the needs of DISKOMINFO Tasikmalaya City. Capability level objectives can be increased by carrying out activities that are not yet optimal by the agency until it reaches the full value for each level.
Implementation Of C5.0 Classification And Support Vector Machine Algorithm With Correlation-Based Feature Selection In Application Liver Disease Rachman, Andi Nur; Ramdani, Cecep Muhamad Sidik; Insani, Muhammad Hanif
Journal of Applied Information System and Informatic (JAISI) Vol 2, No 1 (2024): Mei 2024
Publisher : Deparment Information System, Siliwangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37058/jaisi.v2i1.10848

Abstract

Liver disease is a general term that refers to a number of disorders or problems that affect the liver. The liver is an important organ in the human body and has many diverse functions, including food processing, protein production, toxin removal, and energy storage. Therefore, when the liver experiences disorders or disease, it can have a serious impact on the overall health and function of the body. Liver disease is a significant global health problem. Early detection as well as classification of liver disease can provide valuable guidance for effective treatment. Based on the problems above, the aim of this research is to create a liver disease classification model using C5.0 and Support Vector Machine with Radial Basis Function (RBF) and Sigmoid kernels. With data obtained from the liver disease dataset. The two methods will be compared and we will find out which one produces the best results. The method used is also optimized with CFS (Correlation Based Feature Selection) feature selection. The results of the classification process, namely the C5.0 Model and Support Vector Machine (RBF) with CFS have a similar accuracy of 76%, while the Support Vector Machine (Sigmoid) has an accuracy of 70%, without feature selection the C5.0 algorithm has an accuracy of 66% , Support Vector Machine between RBF and sigmoid kernels has an accuracy of 69% and 55%.
Co-Authors Acep Irham Gufroni Anshary, Muhammad Adi Khairul BISRY, AHMAD Fazamin Bin Wan Hamzah, Wan Mohd Amir Hendar Nuryaman Insani, Muhammad Hanif Jatnika, Galih Rahmat Nafis Sjamsuddin, Irfan Nazar, Nizar Fathun Nugraha, Yuki Rizki Adam Rachman, Andi Nur Rachmasari Biduri, Nadia Salsabil, Fauzan Alif Shofa, Rahmi Nur Siti Yuliyanti, Siti Yahya, Aria Priawan