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International Journal of Applied Artificial Intelligence and Robotics (IJAIC)
Published by PT. Literasi Teknologi Informatika
ISSN : 31247520     EISSN : 31241212     DOI : http://dx.doi.org/10.67745/ijaic.v2i1
Core Subject :
The International Journal of Applied Artificial Intelligence and Robotics (IJAIC) (E-ISSN: 3124-1212) is a peer-reviewed journal that focuses on the advancement and application of artificial intelligence (AI), machine learning, and robotics across various sectors of society. This journal serves as a platform for researchers, academics, and industry practitioners to publish original research, reviews, and theoretical works that address current challenges and innovations in intelligent systems, autonomous machines, and human-robot interaction. With an interdisciplinary scope, the journal encourages contributions that bridge the gap between theoretical AI frameworks and real-world implementations in fields such as healthcare, manufacturing, education, transportation, and smart environments. Applied Artificial Intelligence and Robotics Society is committed to fostering impactful scientific exchange by upholding rigorous peer-review standards and embracing open-access principles that promote transparency, accessibility, and global collaboration in the field of intelligent technologies.
Arjuna Subject : -
Articles 11 Documents
 1   2 
Implementation of the Combined K-Means and Agglomerative Hierarchical Clustering Method (KNA) for Grouping and Determining Recipients of Zakat Mal Hafidh Asyi; Hendrawaty; Mulyadi
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.v1i1.3

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%.
Mobile Web-Based Campus Life Introduction Information System Using the K-Means Method mulyadi; Zulfan Khairil Simbolon; Iqbal Panutan
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.v1i1.5

Abstract

The introduction of new student campus life (PKKMB) is an annual activity carried out by the Lhokseumawe State Polytechnic campus which aims to prepare new students to go through the transition process to become mature and independent students, as well as accelerate the process of adapting students to a new environment and providing provisions for their success in pursuing education. at University. PKKMB must be carefully planned with several stages in it so that it can be used as a momentum for new students to get accurate information about the education system in higher education, both in the academic and non-academic fields. The small result of the lack of preparation of the activity implementers, sometimes making several stages in this activity, especially in the grouping of new student data, is not optimal, causing several problems such as lack of funding for activities and human errors. The purpose of this research is to build a mobile web-based information system that is useful for making it easier for executors to carry out several stages in this activity, especially in the grouping of new students. So that the purpose of this activity can be maximally accepted both for new students and executors. To collect the data needed in building this system is by conducting interviews with activity executors, and observations. The target of this research is a system website that provides features to facilitate several stages in PKKMB activities. At the stage of grouping new student data, this system uses the K-Means method to cluster new student data with national exam scores and report cards to produce clusters according to the predicate smart, medium, and less, then the results of the clusters will be grouped into 30 groups. according to their respective selection paths.
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.
Decision Support System for Determining Tourist Attractions in Aceh Using the Android-Based Simple Multi-Attribute Rating Technique (SMART)­ Mahdi; Muhammad Arhami; Nurul Azkiya
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.v1i1.8

Abstract

Choosing a tourist attraction often involves many complex factors that make it difficult for tourists to make tourist choices. Various aspects such as facilities, distance, transportation, entrance fees, parking spaces and road accessibility need to be evaluated systematically to facilitate decision making. This research develops an Android-based Decision Support System (DSS) using the Simple Multi-Attribute Rating Technique (SMART) method. The SMART method is used to assess tourist attractions based on six main criteria and several sub-criteria. The system development process includes planning, user interface design, implementation, and testing. Criterion weights are normalized to produce an objective final score. Functionality testing is carried out using the black-box method to ensure the reliability and effectiveness of the application. The application developed successfully underwent functionality testing with satisfactory results, achieving a pass percentage of 95.5%.
Evaluating the Impact of Behavioural Features on Hindi Speech Emotion Recognition: A Multimodal Deep Learning Approach Sujata Kotian; Santosh Singh
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.v2i1.9

Abstract

Context: Speech Emotion Recognition (SER) is an important part of affective computing, but that it cannot work effectively in low resource languages like Hindi. The available SER systems have focused on low-level speech features (acoustic and prosodic) and little has been done to investigate the high-level behavioural speech features (e.g., pauses and rhythm) even though they are significant in human emotional communication. Objective: This study aimed to explore the hypothesis, whether explicit behavioral speech features can enhance Hindi SER performance, as well as study their joint role in complementing acoustic and prosodic features within a multimodal deep learning system. Method: A curated Hindi emotional speech corpus of 2,370 utterances of 25 speakers composed of seven emotion classes was studied through a controlled experimental study. The acoustic, prosodic and behavioral features were obtained and represented with a dual-branch multimodal deep learning framework that included CNN/transformer and BiLSTM-attention modules. Results: The entire multimodal model had an accuracy of 83.9% and a macro-F1 of 0.81, which was significantly higher than the acoustics-only and acoustics-prosodic baselines. The behavioral features provided significant progress to low-arousal emotions, including sadness and neutral, and medium to large effect sizes. Conclusions: The results show that Hindi SER accuracy and strength is significantly increased by behavioral speech cues. To practitioners, the findings provide justification to apply behavior-aware SER in practice, whereas to researchers, they show the necessity to explicitly model the behavioral characteristics in low-resource and culturally diverse languages.
The Role of Artificial Intelligence in Transforming Entrepreneurs’ Strategic Decisions fatma chikhaoui
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.v2i1.10

Abstract

In a globalized economic environment characterized by rapid transformation, intensified competition, and growing uncertainty, entrepreneurs face increasingly complex decision-making challenges. These conditions demand strategic choices that are rapid, precise, and grounded in reliable information. Digital transformation has profoundly disrupted traditional management practices, introducing a wide range of innovative technological tools designed to enhance organizational efficiency, responsiveness, and competitiveness. Among these technologies, Artificial Intelligence (AI) has emerged as a major driver of transformation, reshaping the way entrepreneurs collect, analyze, and interpret strategic information, identify emerging opportunities, and anticipate potential risks. This conceptual article is based on an integrative review of literature in entrepreneurship, strategic management, and information systems. It highlights the essential role of AI in entrepreneurial decision-making by drawing on existing literature in entrepreneurship, strategic management, and information systems. It emphasizes AI’s ability to support strategic choices through advanced data-processing techniques, predictive modeling, and automated analytics. The article adopts a structured conceptual approach, synthesizing different perspectives to clarify the mechanisms through which AI supports opportunity recognition, risk anticipation, and strategic decision-making under uncertainty. By enabling the extraction of relevant insights from large, diverse, and complex datasets, AI enhances forecasting accuracy, optimizes internal performance, and strengthens the agility of decision-making processes. The main contribution of this paper lies in proposing a conceptual framework and clarifying the theoretical mechanisms through which AI influences entrepreneurial decision-making, thereby offering a structured perspective on AI as a strategic enabler of more proactive, informed, and innovation-oriented decisions.  
An Intelligent Student Attendance System Based on Facial Image Recognition Using YOLOv5: A Case Study at Politeknik Negeri Lhokseumawe Heri Maulana; Azhar Azhar; Rahmad Hidayat; Eni Mawardhaningrum
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.v2i1.15

Abstract

You Only Look Once (YOLO) is an effective deep learning method for real-time object detection. This method is applied to build an accurate, fast facial recognition model, with a case study on an attendance system based on facial images at the Lhokseumawe State Polytechnic. The main objective is to implement and evaluate the YOLOv5s model's performance on the attendance system using facial images with high accuracy. The research process includes collecting facial image datasets from 40 subjects with various viewing angles to train the YOLOv5s model. This model is specifically configured to detect one class of objects, namely faces, and then integrated into the system to function as the main face detector. Model performance is evaluated quantitatively using a confusion matrix to measure key metrics such as accuracy, precision, recall, and F1 score. The evaluation results show that the developed YOLOv5s model has excellent performance. This model achieved 90% accuracy, with a precision and recall of 92%. The balanced F1-score value (92%) proves that the YOLOv5s method has a high level of accuracy for detecting faces. The high-performance metrics confirm that this method is the right solution for building an attendance system based on accurate facial images.
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%.

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