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Artificial Intelligence Systems and Its Applications (AISA)
Published by CV. Cognispectra Publishing
ISSN : -     EISSN : 31100457     DOI : https://doi.org/10.65917/aisa.v1i2.2025
Core Subject : Science,
Computer Science & IT
Artificial Intelligence Systems and Its Applications (AISA) is an international, peer-reviewed journal publishing cutting-edge original research in Artificial Intelligence (AI) and its applications. The journal explores theory, methodologies, and real-world applications of AI in various domains, including but not limited to machine learning, natural language processing, AI-driven embedded systems, AI-integrated solutions, and computational social science. AISA aims to serve both academic researchers and industry practitioners by providing an effective platform for the timely dissemination of advanced AI innovations and emerging trends. The journal welcomes contributions that address fundamental challenges in AI, interdisciplinary approaches, and critical applications of AI across different fields. Scope AISA invites high-quality submissions in the following areas: Artificial Intelligence and Its Applications – Core advancements and breakthroughs in AI technologies. Machine Learning and Its Applications – Algorithms, models, and learning paradigms. Natural Language Processing (NLP) – Language understanding, text generation, and conversational AI. AI Embedded Systems – AI in IoT, robotics, and smart hardware solutions. AI Integrated Systems – AI-powered automation, decision-making, and intelligent computing. Computational Social Science – AI applications in social media analytics, sentiment analysis, and human behavior modeling. Publication Information Peer-Reviewed: Ensuring rigorous evaluation and high-quality contributions. Open Access: Providing unrestricted access to cutting-edge AI research. Frequency: Published quarterly with special issues on emerging AI trends. AISA welcomes original research papers, review articles, and case studies that contribute to the advancement and practical implementation of AI technologies. The journal aims to bridge the gap between theoretical AI advancements and their real-world applications, fostering innovation in artificial intelligence systems globally.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 15 Documents
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Detecting Indonesian Online Gambling Promotions in Digital Images: An OCR-LSTM Pipeline Ariefin Nur Hidayat; Endang Wahyu Pamungkas
Artificial Intelligence Systems and Its Applications Vol. 2 No. 1 (2026): Vol. 2, No. 1, June 2026
Publisher : CV Cognispectra Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65917/aisa.v2i1.68

Abstract

The rapid spread of online gambling promotions via digital images on social media renders manual identification processes highly inefficient, necessitating an automated detection system. This study aims to implement a system to identify online gambling promotions in digital images using a combination of Optical Character Recognition (OCR) and Long Short-Term Memory (LSTM) methods. The research utilizes a dataset of 2,200 images, evenly balanced between gambling and non-gambling categories. The proposed system involves several key stages: image preprocessing using the Contrast Limited Adaptive Histogram Equalization (CLAHE) method to improve contrast, text extraction via PaddleOCR, text and vocabulary correction using FastText, and finally, contextual classification using the LSTM architecture. The testing results on 330 test data demonstrated excellent and stable model performance in recognizing text patterns. The model successfully achieved an impressive accuracy rate of 94%, with a precision of 0.96, a recall of 0.92, and an F1-score of 0.94. In conclusion, the combination of OCR and LSTM technologies is proven to be highly effective for automatically detecting online gambling promotional content.
Hikmah AI: Developing a Specialized Large Language Model for Islamic Knowledge and Information Retrieval Muhammad Rakha Abimanyu; Endang Wahyu Pamungkas
Artificial Intelligence Systems and Its Applications Vol. 2 No. 1 (2026): Vol. 2, No. 1, June 2026
Publisher : CV Cognispectra Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65917/aisa.v2i1.69

Abstract

This research aims to develop an Islamic-based Large Language Model (LLM) system called Hikmah AI, designed to provide valid, contextual Islamic information sourced from the Qur'an, Hadith, and trusted Islamic literature. The background of this research is driven by the increasing public demand for Islamic knowledge in the digital era, while many available sources remain unstructured and lack credibility. The system was developed using the OpenAI GPT-5 model integrated with the Retrieval-Augmented Generation (RAG) method based on hybrid search (vector and keyword). The development followed the Waterfall methodology, encompassing the stages of requirements analysis, design, implementation, testing, and maintenance. The testing process employed evaluation metrics consisting of Precision, Recall, F1-Score, Relevance, and Accuracy to assess the quality and relevance of the model's responses to Islamic queries. The test results demonstrate that the system successfully achieved Precision, Recall, and F1-Score of 100% across all test queries, along with an average Relevance and Accuracy of 86.2%. Expert validation by an Islamic scholar concluded that the system is highly feasible, with citations consistent with the original scriptural texts and no substantive errors found. The results of this research demonstrate that Hikmah AI has successfully become an innovative solution for providing accurate, accessible Islamic information in accordance with authentic Islamic sources.
Web-Based Fertilizer Recommendation System Using Soil, Crop, And Environmental Parameters With Random Forest Classifier Romi Anjarianto
Artificial Intelligence Systems and Its Applications Vol. 2 No. 1 (2026): Vol. 2, No. 1, June 2026
Publisher : CV Cognispectra Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65917/aisa.v2i1.70

Abstract

The agricultural sector plays a crucial role in food security where crop productivity is highly dependent on the availability of nutrients in the soil such as Nitrogen (N), Phosphorus (P), and Potassium (K). However, inappropriate fertilization practices, whether insufficient or excessive, can damage soil structure and reduce crop yields. Farmers often have difficulty in precisely determining the most appropriate type of fertilizer based on specific soil and weather conditions. This study aims to build an intelligent recommendation system based on Machine Learning that can accurately predict fertilizer types. The algorithm used in this classification is the Random Forest Classifier which was chosen because of its ability to handle complex data sets and minimize overfitting. The software development method applied is the Agile method which allows for an iterative development process and is responsive to changing needs. The parameters used as input values include temperature, air humidity, soil moisture, soil color, plant type, and N, P, and K levels. The results of model testing using evaluation measurements show that the Random Forest algorithm is able to provide an accuracy level of 91.25% in predicting the right fertilizer class for various types of agricultural crops. This system can help farmers make more efficient and data-driven fertilization decisions
Emotionally-Aware Chatbots: A Survey Endang Wahyu Pamungkas; Ikhlasul Amal
Artificial Intelligence Systems and Its Applications Vol. 2 No. 1 (2026): Vol. 2, No. 1, June 2026
Publisher : CV Cognispectra Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65917/aisa.v2i1.71

Abstract

Textual conversational agents, also known as chatbot technology, have garnered significant attention in both academia and industry in recent times. Chatbots have found wide application as agents for human communication in various services, such as booking assistance, customer service, and even personal companionship. The primary challenge in chatbot development lies in creating a machine capable of human-like interaction to enhance user engagement. Several studies have highlighted the importance of incorporating emotion in humanizing machines, including chatbots. However, to the best of our knowledge, there has been no focused work in this specific area. Therefore, this paper aims to provide a systematic review of approaches to building emotionally-aware chatbots (EACs). We propose three key focuses for studying EACs. Firstly, we explore the history and evolution of EACs. Next, we review the approaches adopted by previous studies in building EACs. Lastly, we delve into available resources for constructing EACs, with a particular emphasis on datasets and affective resources. Our investigation reveals that early EAC development predominantly relied on a rule-based approach, while more recent works have embraced neural-based approaches. We observe that most EACs incorporate emotion classifiers within their architecture, leveraging various existing affective resources. We argue that the field of EAC development will continue to attract increasing attention from researchers, as evidenced by recent studies proposing new datasets for building EACs in diverse languages.
Applying Explainable Artificial Intelligence Principles to Interface Design: Improving User Trust and Understandability in a Chicken Weight Monitoring System Divi Galih Prasetyo Putri; Maritza Angelina Az Zahra; Margareta Hardiyanti
Artificial Intelligence Systems and Its Applications Vol. 2 No. 1 (2026): Vol. 2, No. 1, June 2026
Publisher : CV Cognispectra Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65917/aisa.v2i1.72

Abstract

Artificial Intelligence (AI) has been increasingly adopted in smart farming to support monitoring and decision-making processes. However, many AI-based systems still operate as black boxes, making their outputs difficult for end users to understand and potentially reducing user trust. Although Explainable Artificial Intelligence (XAI) has been proposed to improve transparency, studies integrating XAI principles into interface design and evaluating their effects on user experience remain limited, particularly in smart farming contexts. This study investigates the implementation of XAI principles in redesigning the interface of a chicken weight monitoring system and evaluates their effects on user trust, understandability, and usability. A concurrent embedded mixed methods approach with a within-subject and counterbalanced design was conducted involving 16 participants. The redesigned interface incorporated human-centered XAI principles and was evaluated using the Trust in Automation Scale (TiAS), an understandability questionnaire, and the System Usability Scale (SUS). The results showed statistically significant improvements across all evaluated aspects (p < 0.001). Trust increased from 50.52 to 73.82, understandability from 54.25 to 81.88, and usability from 43.28 to 74.38, with large effect sizes observed in all measurements. Qualitative findings indicated that clearer and contextual explanations improved users’ interpretation of system outputs. These findings suggest that integrating XAI principles into interface design can support more transparent and understandable interaction in AI-based monitoring systems.

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