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.
Articles
61 Documents
<![CDATA[Analysis of Rainfall Prediction Using Fuzzy Time Series Method in Medan City ]]>
<![CDATA[Zikri, Syaftial]]>
<![CDATA[ Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 5, No 1 (2024) ]]>
Publisher : <![CDATA[Al'Adzkiya ]]>
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DOI: 10.55311/aiocsit.v5i1.319
<![CDATA[The increasingly significant climate change causes high rainfall variability, thus requiring an accurate prediction method for disaster mitigation planning and water resource managment. This study aim to analyze rainfal prediction in Medan City using Fuzzy Time Series (FTS) methode. Historical rainfall data for Medan City for a certain period is collected and processed to build an FTS model. The fuzzification process is carried out to convert numerical data into fuzzy values, then the time series relationship is identified to predict the next rainfall value. Based on Chen's fuzzy time series with the detemination of the average-based interval, the Medan City rainfall forecast based on January 2019-December 2023 data obtained the forecast results for January 2024 is 386.7 mm. From the result of tests that have caried out, the best number of sampels be used in the Medan City rainfall case is 60 data, namely the period January 2019 - December 2023.]]>
<![CDATA[Implementation of Case Based Reasoning and Forward Chaining Algorithm to Diagnose Brocoly Plant Disease ]]>
<![CDATA[Aulia, M. Syahdan]]>;
<![CDATA[Hasdiana, Hasdiana]]>;
<![CDATA[Dalimunthe, Yulia Agustina]]>
<![CDATA[ Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 2, No 1 (2021) ]]>
Publisher : <![CDATA[Al'Adzkiya ]]>
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DOI: 10.55311/aiocsit.v2i1.93
<![CDATA[Doing a broccoli crop farming business is very promising, but there is little knowledge that farmers and the community have about the types of broccoli plant diseases that often attack these plants. By utilizing an expert system, which is able to make a rapid, precise, and accurate diagnosis of the symptoms caused, it is hoped that it will be able to help farmers in anticipating losses caused by disease attacks. Using the Forward Chaining method to determine conclusions starting from a set of facts by looking for rules that match the allegations, and using the Case Based Reasoning method that can solve new case problems by looking for similarity values on a case basis. From the sample cases taken are three symptoms of black rot disease, three symptoms of clubroot, and three symptoms of powdery mildew. Produces a value of 81.81% for black rot, then the system will provide a solution for the disease. Keyword: Broccoli Plant Diseases , Case Based Reasonin, Forward Chaining, Expert System.]]>
<![CDATA[IoT-Based Smart Class System ]]>
<![CDATA[Ramadhani, Fanny]]>;
<![CDATA[Satria, Andy]]>
<![CDATA[ Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 6, No 2 (2025) ]]>
Publisher : <![CDATA[Al'Adzkiya ]]>
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DOI: 10.55311/aiocsit.v6i2.352
<![CDATA[The rapid development of Internet of Things (IoT) technology has significantly transformed various sectors, including education. This study proposes an IoT-Based Smart Class System designed to enhance the effectiveness, efficiency, and interactivity of the learning environment. The proposed system integrates IoT devices such as sensors, microcontrollers, and networked actuators to monitor and control classroom conditions, including lighting, temperature, occupancy, and learning equipment usage in real time. Data collected from these devices are transmitted to a centralized platform for processing, visualization, and decision support. The system enables automated classroom management, improves energy efficiency, and supports data-driven decision-making for educators and administrators. Experimental results and system evaluation indicate that the implementation of the IoT-based smart classroom improves learning comfort, optimizes resource utilization, and provides a scalable solution for modern educational environments. The findings demonstrate that IoT technology has strong potential to support smart education initiatives and the development of intelligent learning spaces.]]>
<![CDATA[Application of Data Mining in Determining the Performance of Family Planning Field Officers Using the C4.5 Algorithm ]]>
<![CDATA[Sulaiman, Oris Krianto]]>;
<![CDATA[Siambaton, Muhammad Zulfan Syuri]]>
<![CDATA[ Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 6, No 2 (2025) ]]>
Publisher : <![CDATA[Al'Adzkiya ]]>
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DOI: 10.55311/aiocsit.v6i2.353
<![CDATA[The performance of Family Planning Field Officers plays a crucial role in the success of family planning programs. Accurate and objective performance evaluation is essential to support effective decision-making and policy formulation. This study applies data mining techniques to determine the performance of Family Planning Field Officers using the C4.5 decision tree algorithm. The dataset used in this research consists of officer performance indicators, including service coverage, counseling activities, reporting accuracy, and community participation. The C4.5 algorithm is employed to classify officer performance into predefined categories based on these attributes. The resulting decision tree provides interpretable classification rules that can support managerial decision-making. Experimental results show that the proposed model achieves satisfactory classification accuracy and demonstrates the effectiveness of the C4.5 algorithm in extracting meaningful patterns from performance data. This study highlights the potential of data mining approaches to enhance performance evaluation systems in public service institutions, particularly in the field of family planning management.]]>
<![CDATA[Comparative Analysis of Radix Sort, Quick Sort, and Bubble Sort Algorithms in Data Sorting Based on Array Size and Time ]]>
<![CDATA[Fadhillah, Kurnia Wati]]>;
<![CDATA[Ulga, Nandy Thaher]]>;
<![CDATA[Oktaviansyah, Raffi Ramadhan]]>;
<![CDATA[Sulistia, Farah]]>;
<![CDATA[Amanda, Jeni]]>;
<![CDATA[Jayadi, Akhmad]]>
<![CDATA[ Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 6, No 2 (2025) ]]>
Publisher : <![CDATA[Al'Adzkiya ]]>
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DOI: 10.55311/aiocsit.v6i2.354
<![CDATA[An algorithm is a series of logical actions used to solve important problems in contemporary programming and data processing. The purpose of this study is to compare the time efficiency of three sorting algorithms: Bubble Sort, Radix Sort, and Quick Sort. All algorithms are used on small (10-100 elements), medium (1,000-10,000 elements), and large (more than 100,000 elements) arrays, with execution time using Java. The results show that Radix Sort and Quick Sort are generally more efficient and scalable than Bubble Sort, especially for large arrays and random or semi-sorted data. Radix Sort excels on small and medium arrays under various conditions, while Quick Sort excels on large arrays in the average and nearly sorted cases. Although Bubble Sort can be the fastest in the best case for large arrays, its performance drops drastically in the average and nearly sorted cases. In conclusion, the selection of the best sorting algorithm depends heavily on the type of input data, such as its size and the degree of initial sorting.]]>
<![CDATA[Development of an Android-Based Smart Health Monitoring Device for Heartbeat Detection ]]>
<![CDATA[Zulherry, Andi]]>;
<![CDATA[Gunawan, Muhammad]]>
<![CDATA[ Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 6, No 2 (2025) ]]>
Publisher : <![CDATA[Al'Adzkiya ]]>
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DOI: 10.55311/aiocsit.v6i2.355
<![CDATA[This research presents the development of a smart health monitoring system designed to detect and monitor heartbeat patterns using Android-based technology. The increasing prevalence of cardiovascular diseases necessitates accessible and user-friendly monitoring solutions for early detection and continuous health assessment. This study aims to design and implement a portable heartbeat detection device integrated with an Android application, enabling real-time monitoring and data analysis. The system utilizes pulse sensor technology to capture heartbeat signals, which are then processed by a microcontroller and transmitted wirelessly to an Android smartphone via Bluetooth connectivity. The developed application features an intuitive user interface that displays heart rate measurements, stores historical data, and provides alert notifications when abnormal patterns are detected. System testing was conducted to evaluate accuracy, reliability, and user experience across various conditions. Results demonstrate that the device achieves accurate heartbeat detection with minimal deviation from standard medical equipment, offering a practical and cost-effective solution for personal health monitoring. This research contributes to the advancement of mobile health (mHealth) technology, providing individuals with greater autonomy in managing their cardiovascular health while facilitating early intervention opportunities. The system's portability, affordability, and ease of use make it particularly suitable for home-based health monitoring and remote patient care applications.]]>
<![CDATA[Development of A Smart Monitoring System for IoT – Based Tide Observation ]]>
<![CDATA[Sari, Indah Purnama]]>;
<![CDATA[Manurung, Asrar Aspia]]>
<![CDATA[ Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 6, No 2 (2025) ]]>
Publisher : <![CDATA[Al'Adzkiya ]]>
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DOI: 10.55311/aiocsit.v6i2.351
<![CDATA[This research presents the development of a smart monitoring system for real-time tide observation using Internet of Things (IoT) technology. The system is designed to monitor sea level fluctuations continuously and transmit data wirelessly to a cloud-based platform for remote access and analysis. The hardware consists of ultrasonic sensors for water level measurement, a microcontroller for data processing, and wireless communication modules for data transmission. The collected data can be accessed through a web-based dashboard or mobile application, enabling users to monitor tidal patterns from anywhere at any time. The system also incorporates alert notifications when water levels reach predetermined thresholds, providing early warning capabilities for coastal communities. Testing results demonstrate that the system can accurately measure tidal changes with minimal error and successfully transmit data in real-time. This IoT-based tide monitoring system offers a cost-effective and efficient solution for oceanographic observation, coastal management, and disaster mitigation applications. The implementation of this technology contributes to improved maritime safety, fishing activities planning, and environmental monitoring in coastal areas.]]>
<![CDATA[Performance Analysis of YOLO11 for Welding Defect Detection Under Low-Light Conditions ]]>
<![CDATA[Yonky Pernando]]>
<![CDATA[ Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 7, No 1 (2026) ]]>
Publisher : <![CDATA[Al'Adzkiya ]]>
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DOI: 10.55311/aiocsit.v7i1.370
<![CDATA[This study aims to analyze the impact of image enhancement techniques on welding defect detection performance using a deep learning-based YOLO11L model. The dataset consists of 1392 welding images categorized into four classes: Good, Crack, Porosity, and Bad, with a significant class imbalance. Five image enhancement methods were evaluated, namely Zero-DCE, RETINEX, CLAHE, Supervision, and Gamma Correction, and compared against a no-enhancement baseline. Image quality was assessed using SSIM, and PSNR, while detection performance was evaluated using Precision, Recall, F1-Score, and mAP50. The results show that Gamma Correction achieves the best image quality improvement, with an average SSIM of 0.569, and a PSNR of 18.862 dB. However, contrasting results are observed at the detection stage, where 0.7772 and 0.6969, respectively, for the Gamma Correction-based model while for the baseline model without enhancement outperforms the enhanced model, achieving a mAP50 of 0.7098 and an F1-Score of 0.6965. This finding reveals a paradox where improved visual image quality does not necessarily lead to better object detection performance. This study highlights the importance of end-to-end evaluation in computer vision systems, particularly in industrial inspection applications, and demonstrates that original images, which are closer to the pretrained data distribution, may yield better detection results than heavily enhanced images.]]>
<![CDATA[Development of an Augmented Reality (AR) Anatomy Application to Enhance Practical Skills in Vocational Health Education ]]>
<![CDATA[Yoga Sahria]]>
<![CDATA[ Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 7, No 1 (2026) ]]>
Publisher : <![CDATA[Al'Adzkiya ]]>
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DOI: 10.55311/aiocsit.v7i1.380
<![CDATA[Vocational health education, particularly diploma-level nursing programs, faces challenges in anatomy learning that still rely on conventional methods using mannequin models and two-dimensional atlases. This study aimed to develop and evaluate an Android-based Augmented Reality (AR) anatomy application designed for vocational nursing students at STIKES Al Islam Yogyakarta. The development process employed the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation). Usability evaluation was conducted using the User Experience Questionnaire (UEQ) and the System Usability Scale (SUS) involving 40 students. The UEQ results showed an overall score of 1.80 (Excellent category), with the Novelty scale achieving the highest score (1.93) and the Efficiency scale the lowest (1.68). The average SUS score was 81.3, categorized as Excellent/Grade A. Effectiveness testing using pre-test and post-test assessments demonstrated an improvement in practical examination scores from an average of 55.4 to 81.1, with an N-gain score of 0.57 (moderate category). The findings indicate that the AR anatomy application is feasible for use and effective in improving the practical skills of vocational nursing students.]]>
<![CDATA[A Structured Data Wrangling Pipeline for TikTok Datasets Using Pandas Python ]]>
<![CDATA[Agus Suharto]]>;
<![CDATA[Muhammad Syarif Hartawan]]>
<![CDATA[ Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal Vol 6, No 1 (2025) ]]>
Publisher : <![CDATA[Al'Adzkiya ]]>
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DOI: 10.55311/aiocsit.v6i1.397
<![CDATA[This study aims to develop a structured data wrangling pipeline for TikTok datasets using the Pandas Python library. The purpose of the research is to transform raw social media data into clean, consistent, and analyzable formats that can support academic inquiry into digital engagement patterns.The methodology consists of five stages: data loading, cleansing, transformation, feature engineering, and validation. Raw TikTok data, including video metadata, user interactions (likes, comments, shares), and hashtags, were processed to remove inconsistencies, handle missing values, and standardize formats. Feature engineering was applied to derive analytical variables such as engagement rate, posting frequency, and hashtag clustering. Validation ensured structural integrity, completeness, and consistency of the dataset, enabling reliable statistical analysis. The results demonstrate that systematic wrangling improves dataset quality, enhances interpretability, and enables advanced analysis of user behavior and content trends. By applying Pandas-based operations, the study provides a reproducible framework that bridges technical rigor with methodological transparency. This research contributes to the academic field of social media analytics by offering a practical pipeline for TikTok data preparation. It highlights the importance of data wrangling not merely as a preparatory step, but as a methodological foundation for evidence-based digital research.]]>
