Al'Adzkiya International of Computer Science and Information Technology Journal
Computer Science, Computer Engineering and Informatics: Data Science Artificial Intelligence, Machine Learning, Neural Network, Computer Architecture, Parallel and Distributed Computer, Pervasive Computing, Computer Network, Embedded System, Human—Computer Interaction, Virtual/Augmented Reality, Computer Security, Software Engineering (Software: Lifecycle, Management, Engineering Process, Engineering Tools and Methods), Programming (Programming Methodology and Paradigm), Data Engineering (Data and Knowledge level Modelling, Information Management (DB) practices, Knowledge Based Management System, Knowledge Discovery in Data), Network Traffic Modelling, Performance Modelling, Dependable Computing, High Performance Computing, Computer Security, , Networking Technology, Optical Communication Technology, Next Generation Media, Robotic Instrumentation, Information Search Engine, Multimedia Security, Computer Vision, Distributed Computing System, Mobile Processing, Next Network Generation, Computer Network Security, Natural Language Processing, Cognitive Systems. Management Informatics, Information System and developmental economics : Human-Machine Interface, Stochastic Systems, Information Theory, Intelligent Systems, IT Governance, Smart City, e-Learning, Business Intelligence, Information Retrieval, Business Process, Financial Technology (Fintech). Telecommunication and Information Technology: Modulation and Signal Processing for Telecommunication, Information Theory and Coding, Antenna and Wave Propagation, Wireless and Mobile Communications, Radio Communication, Communication Electronics and Microwave, Radar Imaging, Distributed Platform, Communication Network and Systems, Telematics Services and Security Network. Instrumentation and Mathematics: Optimal, Robust and Adaptive Controls, Non Linear and Stochastic Controls, Modelling and Identification, Robotics, Image Based Control, Hybrid and Switching Control, Process Optimization and Scheduling, Control and Intelligent Systems, Artificial Intelligent and Expert System, Fuzzy Logic and Neural Network, Complex Adaptive Systems.
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<![CDATA[Design of Sentiment Analysis on Indodax Instagram Social Media Comments About Cryptocurrency Using Naïve Bayes Classifier ]]>
<![CDATA[Prayoga, Dimas]]>
<![CDATA[ Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 5, No 2 (2024) ]]>
Publisher : <![CDATA[Al'Adzkiya ]]>
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DOI: 10.55311/aiocsit.v5i2.325
<![CDATA[In today's digital era, social media such as Instagram has become the main platform for many individuals to interact and express opinions online. One application that is often the subject of conversation is Indodax, a well-known digital asset trading platform in Indonesia. This research aims to evaluate the sentiment of Instagram users towards Indodax services through a sentiment analysis approach using Naive Bayes Classifier. The data collected consists of Instagram users' comments, which are analyzed to assess the tendency of their sentiments, whether positive or negative towards Indodax services. This method applies probability and statistical concepts to classify sentiments based on the words present in the comments. It is hoped that the results of this study can provide insights for Indodax to improve the quality of their services based on the perceptions of users. Based on the experiments conducted, the Naive Bayes Classifier method shows fairly accurate classification results, so it can support sentiment analysis related to the Indodax application.]]>
<![CDATA[Predicting the Risk of Online Sales Fraud with the Naïve Bayes Approach on Facebook Social Media ]]>
<![CDATA[Pasha, Leony Ayu Diah]]>
<![CDATA[ Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 5, No 2 (2024) ]]>
Publisher : <![CDATA[Al'Adzkiya ]]>
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DOI: 10.55311/aiocsit.v5i2.320
<![CDATA[The rapid development of digital shopping media is accompanied by increasing cases of online fraud, especially through social media platforms such as Facebook. This study aims to develop a prediction model for the risk of online sales fraud using the Naïve Bayes algorithm. The data used is the data of buying and selling transactions that occur through the Facebook marketplace. The data has been collected on the Kaggle platform so that it can be used directly. Data in the form of extracted features include seller characteristics, products sold, number of transactions, device usage and other fraud indicators. Important features that affect the potential for fraud are identified and used in the machine learning process. The results of the study show that the Naïve Bayes model is able to provide accurate predictions in identifying the risk of online sales fraud, with a satisfactory accuracy rate of 95%. The results of the study are expected to contribute to the development of a more effective fraud detection system and increase user confidence in making online transactions.]]>
<![CDATA[Analysis and Comparison of the Performance of the K-Means Algorithm and the X-Means Algorithm in Clustering Disease Types in Mitra Medika Hospital ]]>
<![CDATA[Afdilla, Herdawani]]>
<![CDATA[ Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 5, No 2 (2024) ]]>
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DOI: 10.55311/aiocsit.v5i2.321
<![CDATA[The system currently used by the hospital is still manual in managing patient data and information. What happens at Mitra Medika Hospital is that it is difficult to provide medical needs related to the diseases experienced by patients, considering that there are many types of diseases, so they provide many medical needs. Several inpatients have used BPJS facilities for various diseases suffered by the patient to carry out further examinations so that they can recover from the disease they are suffering from. Mitra Medika Hospital only looks at medical needs based on the disease suffered by the patient, however, seeing the large amount of patient history data makes it very difficult for Mitra Medika Hospital to find out the groups of diseases that patients often experience. This research uses a quantitative approach which starts from a theoretical framework, expert ideas, and researchers' understanding based on their experience, then developed into problems and solutions that are proposed to obtain justification (verification) or assessment in the form of empirical data support in the field. Here we apply a data mining pattern where data mining is extracting very large data (big data). Cluster 0: Of 245 Men (Suffering from 1-5 Diseases) Cluster 1: Of 255 Women (Suffering from 6-10 Diseases) Using the K-Means Algorithm and X-Means Algorithm can produce clustering. By using disease history data, you can apply the K-Means Algorithm and X-Means Algorithm methods to determine clusters. By using web programming, we can produce an analysis and comparison of the performance of the K-Means algorithm and the X-Means algorithm in clustering disease types in hospitals. Medika Partners.]]>
<![CDATA[Implementation of a Web-Based Academic and Non-Academic Achievement Information System at the Student Affairs and Alumni Office of Universitas Muhammadiyah Sumatera Utara Using the Rapid Application Development (RAD) Method ]]>
<![CDATA[Dhillon, Ravindra Singh]]>
<![CDATA[ Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 5, No 2 (2024) ]]>
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DOI: 10.55311/aiocsit.v5i2.322
<![CDATA[This research discusses the implementation of a web-based academic and non-academic achievement information system at the Student Affairs and Alumni Office of Universitas Muhammadiyah Sumatera Utara. The system is designed to address issues in managing student achievement data, which was previously handled manually, often resulting in data inaccuracies, delayed reporting, and a lack of convenience in accurately monitoring student achievements. Using the Rapid Application Development (RAD) methodology, the system development is conducted quickly and iteratively through planning, design, and prototype construction phases that allow users to test it directly. RAD was chosen because it enables results that meet user needs in a relatively short time and facilitates adjustments throughout the development process. This information system allows the student affairs office to record, process, and report student achievement data more efficiently. Additionally, the system includes features for searching and tracking achievements, assisting the university in analyzing and evaluating student accomplishments in both academic and non-academic areas. The results of this research show that the developed system can provide accurate, real-time data and contributes to the improvement of service quality at the Student Affairs and Alumni Office. It is expected that this system will enable more transparent, effective student achievement data management and support the enhancement of graduate quality.]]>
<![CDATA[Performance Analysis of the K-Medoids Algorithm in Clustering Able and Disabled Students at MAN 1 Panyabungan ]]>
<![CDATA[Lubis, Putri Augesti]]>
<![CDATA[ Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 5, No 2 (2024) ]]>
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DOI: 10.55311/aiocsit.v5i2.323
<![CDATA[In implementing the Smart Indonesia Program (PIP), the problem faced at MAN 1 Panyabungan was that the school had difficulty in determining students who were entitled to receive the Smart Indonesia Program (PIP), this was due to the many criteria that had to be considered in determining aid recipients. The large amount of student data and the many variables used in determining recipients of the Smart Indonesia Program (PIP) have become an obstacle for MAN 1 Panyabungan. Classifying student data is very important because the process of determining scholarship recommendations involves various criteria that need to be considered and takes quite a long time, but the results do not necessarily provide the right and accurate decision. Implementing applications and systems can be a solution to speed up correct and fast decision making, and can provide the best results in selecting students according to the criteria set by the school. In grouping capable and incapable students, the K-Medoids algorithm is used. The criteria used in the data mining process are the average report card score, parents' occupation, income and number of parents' dependents.]]>
