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Applied AI and Machine Learning Journal
Published by Goodwood Publishing
ISSN : -     EISSN : 31249167     DOI : https://doi.org/10.35912/aiml
Core Subject :
Applied AI and Machine Learning Journal (AIML) is a peer-reviewed, open-access scholarly journal dedicated to publishing high-quality original research papers, review articles, and case studies in the fields of artificial intelligence (AI) and machine learning (ML). The journal aims to advance theoretical foundations, innovative methodologies, and real-world applications of intelligent systems that contribute to technological and scientific progress. AIML serves as an interdisciplinary academic platform for academics, researchers, and practitioners to exchange ideas, foster collaboration, and disseminate cutting-edge research findings. The journal covers a broad range of topics, including deep learning, natural language processing, computer vision, robotics, data analytics, and intelligent decision support systems, reflecting the rapidly evolving landscape of AI and ML research. By encouraging global scholarly contributions, Applied AI and Machine Learning Journal (AIML) seeks to promote the ethical, responsible, and sustainable development of artificial intelligence and machine learning technologies. The journal aims to bridge theory and practice by supporting research that delivers meaningful technological innovation and positive societal impact at local, national, and global levels.
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Articles 9 Documents
 1 
The effect of Artificial Intelligence (AI) and Customer Experience (CX) use in telemedicine on customer satisfaction moderated by service duration Lusitania Ayu Widyastuti; Rudy C. Tarumingkeng
Applied AI and Machine Learning Journal Vol 1 No 1 (2025): December
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/aiml.v1i1.3763

Abstract

Purpose: This study investigates the effects of Artificial Intelligence (AI) use and Customer Experience (CX) in telemedicine services on customer satisfaction, with service duration as a moderating variable. Methods: A quantitative approach was applied using survey data from 121 active telemedicine users. Data were analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM) with SmartPLS 4, including measurement model evaluation, structural analysis, and moderation testing. Results: The results show that AI use has a positive but insignificant effect on customer satisfaction. In contrast, Customer Experience has a positive and significant effect on customer satisfaction, indicating its central role in telemedicine services. Service duration significantly and negatively moderates the relationship between AI use and customer satisfaction, suggesting that longer AI-based service processes reduce satisfaction. However, service duration does not significantly moderate the relationship between Customer Experience and customer satisfaction. Conclusion: Customer satisfaction in telemedicine is influenced more by experiential quality than by AI adoption alone. Effective AI implementation should emphasize service efficiency to enhance satisfaction. Limitation: This study is limited to a single telemedicine platform and uses a cross-sectional design, which may limit generalizability. Contribution: This research highlights the importance of Customer Experience and demonstrates the conditional effect of service duration on AI-driven telemedicine satisfaction.
Expert system for early detection of autism in children using forward chaining method based on android Yulita Yulita
Applied AI and Machine Learning Journal Vol 1 No 1 (2025): December
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/aiml.v1i1.3773

Abstract

Purpose: This study aims to design and develop an Android-based expert system for early detection of Autism Spectrum Disorder in children using the Forward Chaining inference method. The system supports parents and educators in recognizing symptoms and bridging the gap between early identification and professional intervention. Methodology/approach: This research adopts a research and development approach using a prototype model. Data were collected through literature review, observation, and expert interviews with child development specialists. The expert system applies Forward Chaining using a rule-based knowledge base covering three ASD severity levels and 27 validated symptoms. System performance was evaluated. Results/findings: The study developed an Android-based expert system to identify autism symptoms and classify severity levels. The system achieved 92% accuracy compared with expert diagnoses, while functional testing confirmed all features operated correctly, including symptom input, diagnostic results, and online clinic reservation. Conclusions: The Android-based expert system using the Forward Chaining method is effective and reliable for supporting early autism detection. Its logical and transparent inference process makes it suitable for non-expert users, while the integration with healthcare services strengthens early intervention efforts. Limitations: The system relies on predefined rules and symptom data, which may not capture the full variability of autism manifestations. The application also does not replace professional clinical diagnosis and is limited to early screening purposes. Contribution: This study contributes a lightweight and accessible mobile expert system for autism detection, integrating diagnostic support with professional access, delivering practical value for parents, educators, and early childhood intervention programs.
Traffic density prediction using the YOLO algorithm to improve traffic management in Bandar Lampung City Iqbal Gymnastiar Purdadi; Isnandar Agus
Applied AI and Machine Learning Journal Vol 1 No 1 (2025): December
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/aiml.v1i1.3774

Abstract

Purpose: This study aims to develop a traffic density prediction system in Bandar Lampung City to address increasing congestion caused by the rapid growth of vehicles that exceeds road capacity. The system is intended to support real-time monitoring, improve traffic management efficiency, and facilitate data-driven decision-making for adaptive traffic light control and route diversion. Research Methodology: The study employed an experimental approach combined with prototyping. Vehicle detection was performed using the YOLO algorithm on CCTV footage collected from congestion-prone areas. The resulting data were processed and visualized through a web-based dashboard. System performance was evaluated based on vehicle detection accuracy and real-time processing speed under various traffic conditions. Results: The developed system successfully detected vehicles from CCTV footage in real-time and displayed traffic density information through an interactive web dashboard. The system enabled adaptive traffic management by providing authorities with accurate and timely data on congestion patterns. Conclusions: The study demonstrates that integrating YOLO-based vehicle detection with a web-based dashboard improves traffic management efficiency in Bandar Lampung City. Real-time monitoring and data visualization enhance the ability of authorities to make informed, timely decisions, contributing to more effective traffic control. Limitations: The study is limited by the use of CCTV footage from selected congestion-prone areas, a relatively small dataset, and potential variability in detection accuracy under extreme weather or low-light conditions. Contribution: This research provides a practical model for real-time traffic monitoring and management using YOLO and web-based visualization. The system offers a replicable framework for other urban areas facing similar traffic congestion challenges and supports data-driven policymaking.
Application of Fuzzy Matching in chatbot development to improve user experience on e-commerce sites (Case study: Cutiw Fashion Store) Syifa Rahma Nisa; Rionaldi Ali
Applied AI and Machine Learning Journal Vol 1 No 1 (2025): December
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/aiml.v1i1.3775

Abstract

Purpose: In the rapidly developing digital era, e-commerce websites face challenges in providing responsive and personalized customer service. This study aims to develop a web-based chatbot for the Fashion Cutiw Store by implementing the Fuzzy String Matching method to enhance user experience. Methods: The research involves designing and implementing a web-based chatbot integrated with the Fuzzy String Matching method. This approach enables the chatbot to understand and respond to customer inquiries despite variations in wording or typographical errors, thereby improving the accuracy and relevance of responses. Results: The evaluation results indicate that the chatbot employing Fuzzy String Matching successfully improves user satisfaction through more natural and efficient interactions. The chatbot is able to deliver product information quickly and accurately while handling diverse user input formats. Conclusions: The implementation of a web-based chatbot using the Fuzzy String Matching method effectively enhances customer service performance in e-commerce. It reduces reliance on manual customer support and provides faster, more reliable responses to customer inquiries. Limitation: This study is limited to a single e-commerce platform and focuses primarily on text-based interactions. The chatbot’s performance may vary when handling complex queries or expanding to other product categories without further training and development. Contribution: This research contributes to the development of adaptive automated customer service systems in the e-commerce sector, demonstrating the effectiveness of Fuzzy String Matching in improving chatbot responsiveness and user experience.
Blibiometric analysis of detection lung cancer Fiqqi Ahludzikri; MS. Hasibuan; RZ Abdul Aziz; Joko Triloka
Applied AI and Machine Learning Journal Vol 1 No 1 (2025): December
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/aiml.v1i1.3776

Abstract

Purpose: This study aims to analyze global research trends in lung cancer detection using a bibliometric approach. It focuses on identifying publication growth, dominant research themes, citation patterns, and collaboration networks to better understand the direction and innovation of lung cancer detection research. Methods: A bibliometric analysis was conducted using publication records retrieved from the Scopus database covering the period from 2019 to 2024. Key indicators such as publication output, citation counts, keyword co-occurrence, and author collaboration networks were analyzed. Results: The results indicate a steady increase in publications related to lung cancer detection over the analyzed period. Major research themes include circulating tumor DNA, early detection strategies, next-generation sequencing, and liquid biopsy technologies. The analysis also reveals strong international collaboration networks, highlighting the global nature of lung cancer research and the collective effort to improve detection technologies. Conclusion: The study concludes that research on lung cancer detection is rapidly expanding, driven by technological advancements and growing interest in non-invasive diagnostic approaches. Emerging technologies are expected to play a critical role in enhancing early diagnosis and reducing lung cancer mortality rates. Limitation: This study is limited by its reliance on a single database (Scopus) and a relatively short time frame, which may not capture all relevant publications or long-term research trends. Contribution: This research provides a comprehensive baseline reference for scholars and practitioners, offering valuable insights into current research directions and supporting future advancements in early lung cancer detection methods.
The Influence of Intellectual Capital on Corporate Financial Performance Dimas Novianto Kurniawan; Supriyadi Supriyadi; Rhoma Iskandar
Applied AI and Machine Learning Journal Vol 1 No 2 (2026): June
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/aiml.v1i2.4173

Abstract

Purpose: This study aims to examine and obtain empirical evidence on the effect of intellectual capital on the financial performance of food and beverage manufacturing companies listed on the Indonesia Stock Exchange (Bursa Efek Indonesia/BEI) during 2020–2024. Research Methodology: A quantitative approach was used with secondary data from annual financial reports. Intellectual capital was measured using the Value-Added Intellectual Coefficient (VAIC™), while financial performance was assessed through Return on Assets (ROA). The sample consisted of 12 companies, with 60 firm-year observations, analyzed using descriptive statistics, Pearson’s correlation, and simple linear regression with SPSS. Results: The study found that intellectual capital has a negative and statistically insignificant effect on financial performance (? = ?4.144E?05, t = ?0.232, p = 0.817), with an R² of 0.001, indicating that intellectual capital explains only 0.1% of the variation in ROA. The Pearson correlation between VAIC™ and ROA was ?0.030 (p = 0.409).. Conclusions: The findings suggest that intellectual capital does not significantly influence financial performance in the food and beverage sector. Other factors may explain the majority of the variation in ROA. The study contributes to the accounting literature by providing empirical evidence on intellectual capital’s role in financial performance in post-pandemic Indonesia. Limitations: The study is limited by its sample size and sector focus, and the VAIC™ method may not fully capture the true value of human capital. Contributions: This research adds to the understanding of intellectual capital’s influence on financial performance in the Indonesian food and beverage industry.
The Effect of Earnings Per Share (EPS), Debt to Equity Ratio (DER), and Return on Equity (ROE) on Stock Prices Diaz Bayu Samudra; Zaharuddin Zaharuddin; Supriyadi Supriyadi
Applied AI and Machine Learning Journal Vol 1 No 2 (2026): June
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/aiml.v1i2.4174

Abstract

Purpose: This study examines the effect of Earnings Per Share (EPS), Debt to Equity Ratio (DER), and Return on Equity (ROE) on the stock prices of state-owned enterprises listed on the Indonesia Stock Exchange from 2020 to 2024. Research Methodology: Using purposive sampling, 16 firms were selected from a pool of 20, and data were collected over five years. This research employed descriptive and verification methods, analyzing secondary data through regression, correlation, F-test, t-test, and determination analyses to test the hypotheses. Results: The findings reveal that EPS, DER, and ROE simultaneously influence stock prices, with EPS having a positive significant effect and DER and ROE showing significant negative effects. Conclusions: This study concludes that EPS is a crucial factor in determining stock prices, while high DER and ROE may negatively impact investor perception. Limitations: This study is limited by its focus on state-owned enterprises, which may not represent the broader market, and by its reliance on secondary data, which could introduce reporting biases. Contributions: The findings provide valuable insights for investors and policymakers on the key financial indicators affecting stock prices, emphasizing the importance of monitoring EPS, DER, and ROE in evaluating the financial health of state-owned companies.
Optimizing Profitability: The Role of Working Capital and Accounts Receivable Turnover Bagus Setiawan; Desy Arigawati; Nadia Rista
Applied AI and Machine Learning Journal Vol 1 No 2 (2026): June
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/aiml.v1i2.4177

Abstract

Purpose: This study investigates the effect of working capital turnover and accounts receivable turnover on profitability, measured by Return on Assets (ROA), in consumer goods manufacturing firms listed on the Indonesia Stock Exchange during the 2021–2024 period. Research Methodology: A sample of 47 companies was selected through purposive sampling using secondary data from published financial reports. The analysis applies descriptive statistics, correlation, and multiple regression techniques to assess the relationship between these variables. Results: The findings reveal that both working capital turnover and accounts receivable turnover have a significant impact on profitability when considered jointly. Individually, working capital turnover has a positive and significant effect on ROA, while accounts receivable turnover shows no significant influence. Conclusions: This study concludes that working capital turnover significantly improves profitability, while accounts receivable turnover has no significant effect. However, managing both working capital and accounts receivable turnover together can positively impact overall financial performance. Limitations: This study is limited to consumer goods manufacturing companies listed on the Indonesia Stock Exchange during the 2021–2024 period, which may not fully represent other sectors. Contributions: The study contributes to understanding the importance of efficient working capital management for profitability, providing insights for managers to optimize financial performance.
LLM-Driven Sentiment Analysis in Customer Service Dashboards: A Framework for Real-Time Feedback Intelligence Khonqulova Nilufar Ravshanovna
Applied AI and Machine Learning Journal Vol 2 No 1 (2026): December
Publisher : Goodwood Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35912/aiml.v2i1.4258

Abstract

Purpose: This study develops and evaluates a conceptual framework for integrating Large Language Model (LLM) driven sentiment analysis into customer service dashboards to enable real-time, emotionally intelligent feedback monitoring. The framework addresses gaps in traditional dashboards that fail to capture contextual and affective dimensions of customer experience.Research.Methodology: A conceptual and analytical approach synthesizes literature on LLM based sentiment analysis, aspect based opinion mining, customer service automation, and dashboard design. A five-layer integration architecture covering data ingestion, processing, LLM analysis, visualization, and human in the loop is proposed and evaluated for real-time enterprise feedback intelligence.Results: The framework introduces six capabilities real-time negative trend detection, emotionally weighted ticket prioritization, automated escalation, aspect-based sentiment disaggregation, sentiment-trajectory agent performance evaluation, and predictive customer satisfaction modeling. An LLM output schema defines sentiment polarity, emotion, urgency, service aspect, customer risk, and recommended action. Key challenges, including privacy, bias, hallucination, latency, computational cost, and over-reliance, are discussed.Conclusions: LLM driven sentiment analysis can transform dashboards into emotionally aware decision-support systems, combining contextual understanding with operational metrics, human oversight, and continuous validation.Limitations: The framework remains conceptual and untested in live deployments, with implementation feasibility and user acceptance to be empirically examined.Contributions: This study provides a structured, practitioner oriented framework consolidating NLP, customer experience, and information systems knowledge for multilingual, multichannel service environments.

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