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31,456 Article Results

Enhancing crude palm oil quality detection using machine learning techniques

10.11591/ijai.v14.i4.pp2955-2963
Novianti Puspitasari , Ummul Hairah , Vina Zahrotun Kamila , Hamdani Hamdani , Anindita Septiarini , Amin Padmo Azam Masa
Indonesia, a leading nation in the palm oil industry, experienced a significant increase of 15.62% in crude palm oil (CPO) exports in 2020, effectively meeting the global need for vegetable oil and fat. Therefore, the subjective assessment of CPO quality, influenced by differences in human evaluations, may lead to inconsistencies, necessitating the adoption of machine learning methods. There are several categories of CPO, such as bad and excellent. Machine learning can determine the quality of CPO itself. This study utilizes two distinct categories to measure the quality of CPO. CPO quality data is collected and processed into pre-processing data, in classifying using several methods such as artificial neural network (ANN), k-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), naïve Bayes (NB), and C.45 using the cross-validation evaluation parameter. The best results are obtained by C.45 and DT with an accuracy of 99.98%.
Volume: 14
Issue: 4
Page: 2955-2963
Publish at: 2025-08-01

Interpretable machine learning for academic risk analysis in university students

10.11591/ijai.v14.i4.pp3089-3098
Mukti Ratna Dewi , Mochammad Reza Habibi , Bassam Babgei , Lovinki Fitra Ananda , Brodjol Sutijo Suprih Ulama
Higher education institutions often grapple with issues related to academic risk among their students. These academic risks encompass low academic performance, study delays, and dropouts. One approach to address these challenges is to predict students’ academic performance as accurately as possible by leveraging advanced computational techniques and utilizing academic and non-academic student data. This research aims to develop a model that accurately identifies students with high potential for academic risk while explaining the contributing factors to this phenomenon in the Faculty of Vocational Studies, Institut Teknologi Sepuluh Nopember (ITS). The prediction model is constructed using the light gradient boosting machine (LightGBM) method and is subsequently interpreted using the Shapley additive explanations (SHAP) value. Additionally, an oversampling method, based on synthetic minority oversampling technique (SMOTE), is implemented to address imbalances in the dataset. The proposed approach achieves 96% and 97% accuracy and specificity rates, respectively. Analysis based on SHAP values reveals that extracurricular activities, choice of major, smoking habit, gender, and friendship circle are among the top five factors impacting students’ academic risk.
Volume: 14
Issue: 4
Page: 3089-3098
Publish at: 2025-08-01

Low-cost portable potentiostat for real-time insulin concentration estimation based on electrochemical sensors

10.11591/ijece.v15i4.pp3683-3695
Fitria Yunita Dewi , Harry Kusuma Aliwarga , Djati Handoko
Administering incorrect insulin dosages to diabetic patients can be fatal, leading to severe health consequences. Insulin detection, in conjunction with blood glucose monitoring, can significantly enhance diagnostic accuracy. Electrochemical methods for insulin detection offer a low-cost and portable solution. This study presents an insulin concentration estimation system using a customized electrochemical potentiostat operating in real-time via Bluetooth low energy (BLE). Conventional electrochemical sensing, which relies on calibration curves to determine concentration, poses accuracy limitations in portable devices. To address this, we implement a multiple- predictor approach that incorporates peak currents from multiple cycles of cyclic voltammetry responses and the electroactive surface area of a multi- walled carbon nanotube (MWCNT-COOH) modified screen-printed sensor. This modified sensor enhances sensitivity compared to bare screen-printed carbon sensors, making it suitable for low-volume and portable applications. Through cross-validation, our method demonstrated strong performance, achieving a determination coefficient (R²) greater than 0.90 for all training dataset combinations and greater than 0.85 for all testing dataset combinations. Hypothesis testing further confirmed the statistical significance of the electroactive surface area (p=0.006) as predictor, indicating its meaningful contribution to concentration estimation. This approach improves portable detection performance, supporting the development of affordable and reliable personal insulin monitoring systems.
Volume: 15
Issue: 4
Page: 3683-3695
Publish at: 2025-08-01

Cyber-physical resilience system for anomaly detection in industrial environments

10.11591/ijict.v14i2.pp497-505
Debani Prasad Mishra , Rakesh Kumar Lenka , Rampa Sri Sai Yagyna Duthsharma , Pavan Kumar , Lakshay Bhardwaj , Surender Reddy Salkuti
This work explores the topic of cybersecurity in the context of electric vehicles (EVs). It ensures the resilience of cyber-physical systems against anomalies, which is paramount for maintaining operational efficiency and safety. This paper presents a cyber-physical resilience system (CPRS) customized for anomaly detection. Maintaining operational efficiency and safety in today’s networked industrial contexts requires that cyber-physical systems be resilient to abnormalities. With an emphasis on EVs, this research introduces a unique CPRS designed for anomaly detection in industrial settings. By utilizing the combination of digital and physical elements, the CPRS uses sophisticated monitoring and reaction systems to identify and address irregularities instantly. The process includes creating algorithms for anomaly detection and putting in place a framework that is responsive enough to change with the dangers that it faces. The efficiency of the CPRS in detecting unusual behaviors in EVs is demonstrated by experimental findings, which also improve the overall resilience of the system. Moreover, the research’s ramifications go beyond EVs to include a variety of industrial settings, providing valuable information for the development and execution of resilient cyber-physical systems. This paper highlights the significance of proactive resilience measures in protecting critical infrastructure and advances anomaly detection approaches.
Volume: 14
Issue: 2
Page: 497-505
Publish at: 2025-08-01

Generative Indonesian chatbot for university major selection using transformers embedding

10.11591/ijai.v14.i4.pp3474-3482
Mutiara Auliya Khadija , Bambang Harjito , Morteza Saberi , Astrid Noviana Paradhita , Wahyu Nurharjadmo
Selecting a university major is a crucial decision that impacts students' future career paths and personal fulfillment. Traditional guidance methods often lack the personalization and timeliness needed to support students effectively. This study explores the use of Indonesian generative artificial intelligence (AI) chatbots and transformer embeddings to enhance decision-making for university major selection. By leveraging advanced AI techniques, such as bidirectional encoder representations from transformers (BERT) and Gemini embeddings, the research aims to provide personalized, interactive, and contextually relevant guidance. Experiments showed that BERT embeddings achieved the highest accuracy, with recurrent neural network (RNN) and long short-term memory (LSTM) models also performing well but facing issues with overfitting. Gemini embeddings provided strong performance but slightly less effective than BERT. The findings suggest that BERT-based models with RNN are superior for developing decision-support systems in 92% accuracy. Future work should focus on further optimization and integration of user feedback to ensure the relevance and effectiveness of these AI tools in educational settings.
Volume: 14
Issue: 4
Page: 3474-3482
Publish at: 2025-08-01

Revolutionizing recommendations a survey: a comprehensive exploration of modern recommender systems

10.11591/ijai.v14.i4.pp2579-2589
Prithvi Ram Vinayababu , Pushpa Sothenahalli Krishna Raju
The rapid proliferation of digital information and online services has fundamentally reshaped user interactions with websites, necessitating the evolution of recommender systems. These systems, crucial in domains such as e-commerce, education, and scientific research, serve to enhance user engagement and satisfaction through personalized recommendations. However, it comes up with new challenges, including information overload, prompting the development of recommender systems that can efficiently navigate this vast group to offer more personalized and relevant suggestions. This survey paper explores the dynamic opinion of recommendation systems, addressing the limitations of traditional approaches, the emergence of deep learning models, and the extended potential for additional data. By investigating various recommendation systems and the evolving role of deep learning, this paper illuminates the path toward more accurate, personalized, and effective recommender systems, considering challenges like sparse data and improved context-based recommendations. The study encompasses three primary recommendation approaches: collaborative filtering, content-based filtering, and hybrid systems. It further investigates into the transformation brought about by deep learning models, showcasing how these models intricate user-item interactions. This survey offers a comprehensive exploration of recommendation systems and their advancements in the digital era, providing insights into the future of personalized content delivery.
Volume: 14
Issue: 4
Page: 2579-2589
Publish at: 2025-08-01

Enhanced n-party Diffie Hellman key exchange algorithm using the divide and conquer algorithm

10.11591/ijict.v14i2.pp438-445
Nwanze Chukwudi Ashioba , Patrick Ogholorunwalomi Ejeh , Azaka Maduabuchuku
Cryptographic algorithms guarantee data and information security via a communication system against unauthorized users or intruders. Numerous encryption techniques have been employed to safeguard this data and information from hackers. By supplying a distinct shared secret key, the n-party Diffie Hellman key exchange approach has been used to protect data from hackers. Using a quadratic time complexity, the n-party Diffie-Hellman method is slow when multiple users use the cryptographic key interchange system. To solve this issue, the researchers created an effective shared hidden key for the n-party Diffie Hellman key exchange of a cryptographic system using the divide-and-conquer strategy. The current research recommends the use of the divide and conquer algorithm, which breaks down the main problem into smaller subproblems until it reaches the base solution, which is then merged to generate the solution of the main problem. The comparative analysis indicates that the developed system generates a shared secret key faster than the current n-party Diffie Hellman system.
Volume: 14
Issue: 2
Page: 438-445
Publish at: 2025-08-01

A hybrid framework for enhanced intrusion detection in cloud environments leveraging autoencoder

10.11591/ijict.v14i2.pp555-564
Abinaya Alagarsamy , Thenmozhi Elumalai , S. P. Ramesh , Tamilarasi Karuppiah , Prabu Kaliyaperumal , Rajakumar Perumal
In today’s world, the significance of network security and cloud environments has grown. The rising demand for data transmission, along with the versatility of cloud-based solutions and widespread availability of global resources, are key drivers of this growth. In response to rapidly evolving threats and malicious attacks, developing a robust intrusion detection system (IDS) is essential. This study addresses the imbalanced data and utilizes an unsupervised learning approach to protect network data. The suggested hybrid framework employs the CIC-IDS2017 dataset, integrating methods for handling imbalanced data with unsupervised learning to enhance security. Following preprocessing, principal component analysis (PCA) reduces the dimensionality from eighty features to twenty-three features. The extracted features are input into density-based spatial clustering of applications with noise (DBSCAN), a clustering algorithm. particle swarm optimization (PSO) optimizes DBSCAN, grouping similar traffic and enhancing classification. To address the imbalances in the learning process, the autoencoder (AE) algorithm demonstrates unsupervised learning. The data from the cluster is input into the AE, a deep learning algorithm, which classifies traffic as normal or an attack. The proposed approach (PCA+DBSCAN+AE) attains remarkable intrusion detection accuracy exceeding 98%, and outperforms five contemporary methodologies.
Volume: 14
Issue: 2
Page: 555-564
Publish at: 2025-08-01

Enhanced pre-broadcast video codec validation using hybrid CNN-LSTM with attention and autoencoder-based anomaly detection

10.11591/ijai.v14.i4.pp2864-2875
Khalid El Fayq , Said Tkatek , Lahcen Idouglid
This study presents a machine learning-based approach for proactive video codec error detection, ensuring uninterrupted television broadcasting for TV Laayoune, part of Morocco’s SNRT network. Building upon previous approaches, our method introduces autoencoders for improved anomaly detection and integrates data augmentation to enhance model resilience to rare codec configurations. By combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks with an attention mechanism, the system effectively captures spatial and temporal video features. This architecture emphasizes critical metadata attributes that influence video playback quality. Embedded within the broadcasting pipeline, the model enables real-time error detection and alerts, minimizing manual intervention and reducing transmission disruptions. Experimental results demonstrate a 97% accuracy in detecting codec errors, outperforming traditional machine learning models. This study highlights the transformative role of machine learning in broadcasting, enabling scalable deployment across diverse television networks.
Volume: 14
Issue: 4
Page: 2864-2875
Publish at: 2025-08-01

Automated rice leaf disease detection using artificial intelligence deep learning

10.11591/ijict.v14i2.pp405-415
Suhaila M. P. , Hemalatha S.
As one of the top five rice-producing countries, India relies heavily on rice for both economic management and food needs. To ensure healthy rice plant growth, early detection of diseases and timely treatment are essential. Since manual disease detection is time-consuming and labor-intensive, an automated approach is more practical. This work presents a deep neural network (DNN)-based artificial intelligence (AI) method for recognizing rice leaf diseases. The method detects three common diseases: leaf smut, bacterial leaf blight, and brown spot, as well as healthy images. The approach uses an AI-based attention network and semantic batch normalized DeepNet (AN-SBNDN) combined with a channel attention mechanism to improve disease detection accuracy. Experiments with rice leaf datasets and comparison with conventional networks like residual attention network (Res ATTEN) and dynamic speeded up robust features (DSURF) validate the effectiveness of the method. Key performance metrics include average accuracy, time, precision, and recall, achieved at 21%, 44%, 26%, and 31%, respectively.
Volume: 14
Issue: 2
Page: 405-415
Publish at: 2025-08-01

Cloud application design for financial reporting in Indonesia’s small and medium enterprises

10.11591/ijict.v14i2.pp457-466
Erin Erin , Anderes Gui
Small and medium enterprises (SMEs) in Indonesia are increasingly developing, but the application of information technology (IT) in small medium businesses is still lacking because for small medium business owners, doing their own bookkeeping without a system will maximize profits. However, this makes bookkeeping ineffective and inefficient because it requires manual data input and reconciliation. Utilizing a cloud-based accounting information system (CAIS) can integrate data, increase productivity, and minimize infrastructure costs because there is no need to provide costs for physical infrastructure. In this research, CAIS was designed to produce financial reports that focus on small medium businesses in Indonesia. The method used is a qualitative method by conducting observations through literature study for data collection and the rational unified process (RUP) which is limited to the elaboration stage to produce a prototype design. So, the result of this paper is a system design that can be used as a guide to continue with system development. This system aims to simplify transaction records so that they can be more efficient and effective in producing financial reports. The use of CAIS is also expected to increase profits and maximize the use of internet and technology in small medium businesses.
Volume: 14
Issue: 2
Page: 457-466
Publish at: 2025-08-01

Machine learning application for particle accelerator optimization-a review

10.11591/ijai.v14.i4.pp3014-3021
Isti Dian Rachmawati , Nazrul Effendy , Taufik Taufik
Particle accelerators receive significant attention from researchers. This machine consists of various interdependent elements, so it is complex. Efficient system tuning and diagnostics are essential for utilizing accelerator technology. In addition, machine learning (ML) has been applied in several applications. ML methods such as artificial neural networks, random forest, reinforcement learning, genetic algorithm, and Bayesian optimization have been used for accelerator optimization. The optimization of particle accelerators covers their performance and efficiency. This paper reviews the application of ML techniques in optimizing particle accelerators, highlighting their importance in addressing the complexity inherent in accelerator systems and advancing accelerator science and technology.
Volume: 14
Issue: 4
Page: 3014-3021
Publish at: 2025-08-01

Optimizing traffic lights at unbalanced intersections using deep reinforcement learning

10.11591/ijai.v14.i4.pp2991-3002
Duman Care Khrisne , Made Sudarma , Ida Ayu Dwi Giriantari , Dewa Made Wiharta
Unbalanced intersectional traffic flow increases vehicle delays, fuel consumption, and pollution. This study investigates the application of deep reinforcement learning (DRL) to optimize traffic signal timing at the Pamelisan intersection in Denpasar, Indonesia. Real-world traffic data were incorporated into a SUMO microsimulation environment to train DRL agents using the deep Q-network (DQN) algorithm. Experimental results show that DRL-based optimization reduced the average vehicle waiting time from 594.49 seconds (static control) to 169.44 seconds and 173.10 seconds for agents trained without and with noise, respectively. The average vehicle speed remained stable at 5.6–5.97 m/s across all scenarios, indicating enhanced traffic efficiency without adverse effects. The findings underscore the effectiveness and adaptability of DRL in addressing traffic inefficiencies, optimizing them, and offering a robust solution for dynamic traffic management at unbalanced traffic intersections in urban areas.
Volume: 14
Issue: 4
Page: 2991-3002
Publish at: 2025-08-01

Pioneering the digital readiness for Malaysian museums: custom framework

10.11591/ijict.v14i2.pp728-736
Rehman Ullah Khan , Sabine Chung Sze Yee , Oon Yin Bee
A museum is a hub for public exploration and education of community or country culture and traditions. Digital technologies transform museums into interactive experiences, engaging visitors and bringing cultural values to life. However, Malaysian museums struggle to adopt digital technologies due to limited infrastructure, expertise, exhibition technology, and budgets. These constraints hinder effective audience engagement and limit growth and modernisation efforts. To help Malaysian museums in digitalisation, this study aims to contextualise a digital readiness index (DRI) questionnaire. The findings of this pioneering study have yielded a unique and customised version of the DRI questionnaire specifically designed for Malaysian museums, marking the first-ever initiative of its kind in the country. The DRI serves as a pivotal scale or tool for managers and researchers, facilitating the evaluation and validation of a museum’s digitalisation status while guiding strategic planning for future advancements. This questionnaire enables researchers and museum managers to gain insights into the museums and understand which dimensions require focus and enhancement to ensure a successful and comprehensive transition towards digital transformation.
Volume: 14
Issue: 2
Page: 728-736
Publish at: 2025-08-01

Prediction and classification of diabetic retinopathy using machine learning techniques

10.11591/ijict.v14i2.pp516-528
Makhlouf Chaouki , Mohamed Ridda Laouar , Abbas Cheddad , Bourougaa Salima , Sean Eom
Diabetic retinopathy (DR) is a progressive and sight-threatening complication of diabetes mellitus, characterized by damage to the blood vessels in the retina. Early detection of DR is vital for timely intervention and effective management to prevent irreversible vision loss. This paper provides a comprehensive review of recent advancements in integrating machine learning (ML) and deep learning (DL) techniques for diagnosing DR, aiming to assist ophthalmologists in their manual diagnostic process. The paper presents a comprehensive definition of DR, elucidating the underlying pathological processes, clinical signs, and the various stages of DR classification, ranging from mild non-proliferative to severe proliferative DR. Integrating ML and DL in DR diagnosis has developed the field by offering automated and efficient methods and techniques to analyze retinal images. With high sensitivity and specificity, these techniques demonstrate their efficacy in accurately identifying DR-related lesions, such as microaneurysms, exudates, and hemorrhages. Furthermore, the paper examines diverse datasets employed in training and evaluating ML and DL models for DR diagnosis. These datasets range from publicly available repositories to specialized datasets curated by medical institutions. The role of large-scale and diverse datasets in enhancing model robustness and generalizability is emphasized.
Volume: 14
Issue: 2
Page: 516-528
Publish at: 2025-08-01
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