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p-Index From 2021 - 2026
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All Journal Bulletin of Engineering Science, Technology and Industry International Journal of Applied Artificial Intelligence and Robotics (IJAIC)
Hendrawaty
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering

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FINGERPRINT BASED DOOR SECURITY SYSTEM PROTOTYPE USING THE MINUTIAE METHOD (Case Study: Lhokseumawe State Polytechnic ICT Department Laboratory) Muhammad Nofrizal; Azhar; Hendrawaty
Bulletin of Engineering Science, Technology and Industry Vol. 2 No. 1 (2024): March
Publisher : PT. Radja Intercontinental Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59733/besti.v2i1.26

Abstract

Laboratory security is a very important thing to pay attention to and to create laboratory security there are many things to do, one of which is by utilizing technology to maintain the security of the laboratory because lecturers often store very important items and archives in the laboratory. Security using conventional keys or using manual keys which are widely used by teaching staff or lecturers is easily disabled by criminals. Therefore, technology is needed that can be used to increase security, one of which is fingerprint scanning. System design is carried out through designing Flowcharts, Block Diagram Systems and Data Flow Diagrams. This system was built using the C++ and PHP programming language using the Fingerprint module which has the Minutiae method available in it. When using this system, the fingerprint that is scanned will check the ID into the database. If the ID is correct and meets the requirements to unlock the door, the lock on the door will open. The output result of this system is that this system can work in real time by sending the finger ID and laboratory ID to the webserver for checking, the selenoid lock on this system can also be opened with a push button and on the fingerprint sensor there is a minutiae method for extracting and matching fingerprints. automatically.
Application of K-Means Clustering in the Prevention and Complaint System of Sexual Violence at Politeknik Negeri Lhokseumawe Muhammad Rizka; Hendrawaty; Sepkha Ayunda Sari
International Journal of Applied Artificial Intelligence and Robotics Vol. 1 No. 1 (2025): First Volume and Issue of IJAIC Journal
Publisher : PT. Literasi Teknologi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67745/ijaic.v1i1.6

Abstract

Sexual violence on campus, particularly at Politeknik Negeri Lhokseumawe, is an issue that requires serious attention and effective handling. To improve the current manual complaint management system, the application of information technology through the K-Means Clustering method is necessary for the prevention and complaint system of sexual violence. This approach is expected to assist in grouping complaint data from victims, making it easier to identify levels of sexual violence and to design more targeted responses. This research was conducted by developing and managing a sexual violence complaint system, where the complaints were grouped into three clusters based on the calculation of the data. The clusters were validated using the silhouette method, resulting in a score of 0.50 for cluster 0 (moderate level), 0.20 for cluster 1 (moderate level), and 0.58 for cluster 2 (low level). These results indicate that the K-Means Clustering method is effective in categorizing complaint data and can support more effective handling of sexual violence cases on campus.
Chatbot for Gastric Disease Consultation to Recommend a Healthy Diet Using KNN and NLP Methods Hendrawaty; Azhar Azhar; Rafli Abdul Aziz
International Journal of Applied Artificial Intelligence and Robotics Vol. 1 No. 1 (2025): First Volume and Issue of IJAIC Journal
Publisher : PT. Literasi Teknologi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67745/ijaic.v1i1.7

Abstract

Gastric disease is one of the most common health problems and requires special treatment, including proper dietary arrangements. This research aims to design and build a healthy diet recommendation system using chatbots that utilize Natural Language Processing (NLP) and K-Nearest Neighbor (KNN) methods This system is designed to help people with gastric diseases by providing appropriate dietary recommendations based on the symptoms they are experiencing. In the design stage, the system identifies the symptoms mentioned by the user through a conversation with the chatbot. NLP is used to extract relevant information from the user's input text, while KNN is used to classify symptoms and provide appropriate dietary recommendations. The system was tested using a dataset that had been adjusted to cases of gastric diseases. The test results show that this chatbot system has a classification accuracy of 96.36%, with a confusion matrix that shows good performance in identifying symptoms and providing recommendations. The system is able to understand the context of the conversation well through NLP, while KNN provides accurate classification based on the available datasets. With these results, this system is expected to be an effective tool for gastric disease sufferers in managing their diet better and appropriately.
Spatial Analysis of Residential Areas in Densely Populated Regions of Bireuen City Using K-Medoids Clustering Ulfi Sahara; Muhammad Rizka; Hendrawaty
International Journal of Applied Artificial Intelligence and Robotics Vol 2 No 1 (March 2026)
Publisher : PT. Literasi Teknologi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67745/ijaic.v2i1.17

Abstract

The rapid population growth in Bireuen City, Aceh, poses significant challenges for urban planning, particularly in managing high population density and various social issues. Based on data from the Central Bureau of Statistics, Aceh’s population increased by 2%, reaching 5.37 million people in 2019, with a population density ratio of 95 people per km². Bireuen City, with a population of 471,635, is experiencing rapid growth across diverse occupational backgrounds. This study aims to develop a web-based population density mapping system using the K-Medoids method to overcome the limitations of the K-Means method in handling outliers. The data used include district names, area size, number of ID cards, number of households, and total population. The K-Medoids algorithm groups the data into the nearest clusters, and the process is repeated until the clustering results become stable. The clustering results indicate three main clusters: Cluster 0 (medium density, Silhouette Score 0.70), Cluster 1 (low density, Silhouette Score 0.20), and Cluster 2 (high density, Silhouette Score 1). These findings are expected to assist the Central Bureau of Statistics in planning residential areas and managing urban development more effectively and efficiently.
A Hybrid K-Means and Agglomerative Hierarchical Clustering Method for Zakat Mal Recipient Classification Hafidh Asyi; Mulyadi; Hendrawaty
International Journal of Applied Artificial Intelligence and Robotics Vol 2 No 1 (March 2026)
Publisher : PT. Literasi Teknologi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67745/ijaic.v2i1.18

Abstract

Grouping and determining zakat mal recipients is an important stage in the accurate and precise distribution of zakat mal. The process of determining zakat mal recipients needs to be taken into account considering that the number of zakat mal recipients in Aceh Besar exceeds the available quota in each zakat mal distribution period in 2022. This research aims to using KnA method in the process of determining zakat mal recipients. This research uses criteria data that has been determined by Baitul Mal Aceh Besar for 17 groups of zakat mal recipients. The KnA method is used to rank zakat recipients mal in the process of determining zakat mal recipients based on criteria for each group. Testing the results of the KnA method was carried out by assessing using original data on five zakat mal recipients for each group. The results of this research show that there are five groups with five zakat mal recipients in the same order with a percentage of 29%, three groups with three zakat mal recipients in the same order with a percentage of 17%, four groups with two zakat mal recipients in the same order with a percentage of 23%, four groups with one zakat mal recipient in the appropriate order with a percentage of 23%, and two groups with zero zakat mal recipients in the appropriate order with a percentage of 11%. Based on the research results, it can be concluded that the use of the KnA method in determining zakat mal recipients has a relatively low level of accuracy with only five of the seventeen groups in the appropriate order for five sample data with an accuracy percentage of 29%.
Co-Authors Azhar Azhar Azhar Hafidh Asyi Muhammad Nofrizal Muhammad Rizka MULYADI Rafli Abdul Aziz Sepkha Ayunda Sari Ulfi Sahara