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Optimizing retail systems: using big data and power business intelligence for performance insights

10.11591/ijaas.v14.i3.pp945-954
Huu Dang Quoc , Ha Le Viet
In the rapid development of information technology, using enterprise data to support timely management decisions is crucial in helping businesses operate effectively and improve competitiveness. This study uses Microsoft power business intelligence (MPBI) to analyze data in retail systems, allowing managers to grasp the business situation in real time, track advanced sales, optimize inventory control, and analyze customer behavior and supply chain visibility. From the data generated by the business, the study uses the streaming extract transform load (ETL) model to support real-time data aggregation, then converts to the MPBI data visualization system to convert data into visual charts, helping businesses easily monitor, track, analyze, and make decisions to promote business activities. The study proposes a data structure to organize retail information storage. It proposes a system of calculation formulas and data synthesis, making integrate and convert tabular data into visual charts. Through analysis of real data from the LH83 retail system, the study shows the feasibility of implementing a data visualization system and the difficulties encountered when businesses want to deploy this model.
Volume: 14
Issue: 3
Page: 945-954
Publish at: 2025-09-01

Redesign the layout of the raw material warehouse from randomized storage to class-based storage

10.11591/ijaas.v14.i3.pp773-783
Nur Iftitah , Qurtubi Qurtubi , Danang Setiawan , Vembri Noor Helia
The company has a problem of ineffectiveness in the layout of the raw material warehouse due to the use of storage methods that ignore factors such as the type, dimensions, and condition of the goods. This reduces the optimal function of the warehouse and increases the time to retrieve goods. This research aims to redesign the suitable and practical layout of the raw material warehouse by considering its form and function, as well as filling methodological gaps from previous research. The method used is class-based storage. Based on ABC analysis, the category with the highest value is class C goods, with 73 units. Meanwhile, from the fast, slow, non-moving (FSN) analysis, class F (fast-moving) goods have the highest frequency of movement, with a movement percentage of 63% for 10 units of goods. The warehouse slotting analysis shows an increase in the number of shelves from nine to 15 shelves with five different shelf models and layout changes in raw material warehouses 1 and 2. The class-based storage method results in a more organized layout, efficient movement of goods, and faster picking time to optimize warehouse functions.
Volume: 14
Issue: 3
Page: 773-783
Publish at: 2025-09-01

Numerical study of non-linear twisted blades for tidal turbines improvement

10.11591/ijaas.v14.i3.pp894-906
Nu Rhahida Arini , Philips Ade Putera Atmojo , Deni Saputra , Dendy Satrio
Despite the growing demand for renewable energy, the utilization of tidal energy remains underdeveloped due to efficiency limitations in turbine design. Addressing this gap, this study investigates the performance of horizontal-axis tidal turbines (HATT) by comparing two foil designs, National Advisory Committee for Aeronautics (NACA) 2415 and OptA, to optimize energy extraction efficiency. The research employs computational fluid dynamics (CFD) simulations using OpenFOAM to evaluate the effects of foil modifications and non-linear twist distributions on turbine performance across varying tip speed ratios (TSR). The results indicate that the OptA foil significantly improves turbine performance, achieving a 41.4% increase in torque and a 40.2% increase in power coefficient (CP) at TSR 5, which was identified as the optimal operating condition. The OptA foil enhances velocity distribution, reduces flow separation, and improves vortex behavior, leading to greater efficiency and stability. These findings confirm that foil selection and blade design modifications play a critical role in HATT optimization.
Volume: 14
Issue: 3
Page: 894-906
Publish at: 2025-09-01

Fuzzy logic controller-based protection of direct current bus using solid-state direct current breaker

10.11591/ijaas.v14.i3.pp859-868
Eswaraiah Giddalur , Askani Jaya Laxmi
Low-voltage direct current (LVDC) microgrids are increasingly utilized due to their efficiency and compatibility with distributed energy resources (DERs) and direct current (DC) loads, eliminating the need for multiple energy conversions. However, the protection of LVDC systems presents significant challenges, including high fault currents and the vulnerability of electronic devices. Traditional electromechanical circuit breakers are inadequate due to their slow response times. This work presents a protection approach for the DC bus in LVDC microgrids that combines a fuzzy logic controller (FLC) with a solid-state circuit breaker (SSCB). The FLC is designed to detect and respond to faults rapidly by processing input variables such as current magnitude and rate of change of current. The FLC controls the SSCB, which interrupts fault currents quickly and reliably. The proposed system demonstrates optimized fault-clearing times within milliseconds, significantly enhancing the protection and reliability of LVDC microgrids. This novel solution protects critical electronic components while also ensuring the microgrid's operational integrity. The FLC approach is utilized for optimizing fault-clearing duration within milliseconds.
Volume: 14
Issue: 3
Page: 859-868
Publish at: 2025-09-01

Searchable encryption based on a chaotic system and AES algorithm

10.11591/ijaas.v14.i3.pp975-984
Fairouz Sherali , Falah Sarhan
Cloud computing provides on-demand access to computing resources, such as storage and processing power. This technology allows businesses to scale efficiently while reducing infrastructure costs. However, protecting the security and privacy of data has grown to be a top priority. This is where enhancing cloud security with searchable encryption (SE) is crucial. SE effectively secures users’ sensitive data while preserving searchability on the cloud server side. It enables the cloud server to search via encrypted data without disclosing information in plaintext data. SE uses different encryption methods to encrypt data before uploading it to servers. The advanced encryption standard (AES) is a common algorithm for encrypting this data. In this paper, a novel SE method has been presented. The technique exploits the properties of the chaotic map to generate an AES key, which makes the AES algorithm more secure for encrypting the searchable index and uploaded files. We implement and test our method with real data from files. The experimental results show that the proposed method can significantly satisfy a higher level of security as compared to other schemes.
Volume: 14
Issue: 3
Page: 975-984
Publish at: 2025-09-01

A method classifying the domestic tourist destination base similarity measuring

10.11591/ijaas.v14.i3.pp740-750
Nguyen Thi Hoi , Tran Thi Nhung , Bui Quang Truong , Nguyen Quang Trung
The classification problem is crucial in business, providing an effective method for supporting search activities in areas such as e-commerce, education, and marketing. This has become especially important in the wake of the COVID-19 pandemic, which has increased the need to promote and stimulate domestic tourism. This research focuses on recommending tourist destinations based on historical search data related to domestic tourism. The study uses techniques like term frequency-inverse document frequency (TF-IDF) weight vector analysis and similarity measures to calculate recommendation scores. Data was collected from various tourism websites, covering destinations across all 63 provinces and cities in Vietnam. Experiments were conducted using three approaches: cosine similarity, the brute force algorithm, and long short-term memory (LSTM) for long-text processing. The results indicate that similarity-based methods produce recommendations that closely match user preferences. For full-sentence queries, the brute force algorithm delivers more accurate results, while LSTM provides faster processing times. These findings offer businesses multiple strategies for improving recommender systems in practical applications.
Volume: 14
Issue: 3
Page: 740-750
Publish at: 2025-09-01

The impact of fast charging technology on battery longevity in electric vehicles

10.11591/ijaas.v14.i3.pp936-944
Perattur Nagabushanam , Kalagotla Chenchireddy , Radhika Dora , Thanikanti Sudhakar Babu , Vadthya Jagan , Varikuppala Manohar
Fast charging technology has revolutionized the electric vehicle (EV) industry by addressing range anxiety and significantly reducing charging times. However, this convenience introduces challenges concerning battery longevity, as high charging currents and elevated temperatures accelerate battery degradation. This paper investigates the mechanisms through which fast charging impacts lithium-ion batteries, including thermal stress, lithium plating, and mechanical wear. It synthesizes findings from various studies, highlighting how fast charging can shorten battery lifespan by up to 20-30% compared to standard charging methods. Strategies to mitigate these effects, such as advanced materials, adaptive charging protocols, and efficient thermal management systems, are discussed. Furthermore, the paper emphasizes the importance of standards and policies to promote sustainable fast charging practices. By balancing charging speed with long-term battery health, the EV industry can achieve widespread adoption while ensuring sustainability. This work aims to provide a comprehensive understanding of the trade-offs associated with fast charging and offers actionable insights for improving EV battery durability.
Volume: 14
Issue: 3
Page: 936-944
Publish at: 2025-09-01

Large language models and retrieval-augmented generation-based chatbot for adolescent mental health

10.11591/ijaas.v14.i3.pp849-858
Andi Riansyah , Imam Much Ibnu Subroto , Intan Nur'aini , Ratna Supradewi , Suyanto Suyanto
Access to fast and efficient information is crucial in today's digital era, especially for teenagers in obtaining mental health services. The manual method used by Youth Information and Counselling Centre (PIK R) to provide mental health information requires significant time and effort. This research presents an AI-based solution by developing a chatbot system using retrieval-augmented generation (RAG) and large language models (LLM). This chatbot is designed to provide accurate and effective mental health information for teenagers throughout the day. An analysis of a dataset consisting of articles on teenage mental health and data from the Alodokter website was used as the basis for the development of this chatbot. The research results show that the chatbot is capable of providing relevant and accurate information, with evaluations using the recall-oriented understudy for gisting evaluation (ROUGE) score method yielding an average of ROUGE-1 with a precision of 87.8%, recall of 83.0%, and F1-measure of 84.0%; ROUGE-2 with a precision of 82.8%, recall of 76.8%, and F1-measure of 78.2%; and ROUGE-L with a precision of 88.0%, recall of 82.6%, and F1-measure of 83.4%. These findings indicate the potential use of chatbots as an effective tool to support the mental health of adolescents.
Volume: 14
Issue: 3
Page: 849-858
Publish at: 2025-09-01

A bibliometric review of lean principles in highway pavement for productivity improvement

10.11591/ijaas.v14.i3.pp639-649
Pooja P. Gohil , MohammedShakil S. Malek , Deep Shaileshkumar Upadhyaya
A past study of 25 years reveals the positive impact of lean principles on highway pavement productivity. This bibliometric review extracted 389 papers from the Scopus database that revolved around three terms, “lean principles,” “highway pavement,” and “productivity improvement,” and used VOSviewer for scientometric analysis and scientific mapping. Study reveals that addressing this topic on a global scale is of chief significance, given the potential variations in indices of the issue across different countries or provinces. This research undertakes a comprehensive qualitative analysis that highlights diverse indicators that exert influence on the productivity of pavements. Additionally, this analysis also seeks to propose potential avenues for future research within lean construction. An intensive investigation provides four unique clusters of words that have been formed through the process of keyword science mapping within the context of the lean principles, which are road segment, techniques, productivity improvement, and lean. Last but not least, 4 pointers are recommended that will help stakeholders and policymakers assess pavement performance practices, identify areas for improvement, and execute targeted interventions to improve productivity.
Volume: 14
Issue: 3
Page: 639-649
Publish at: 2025-09-01

E-commerce waste management: a systematic review

10.11591/ijaas.v14.i3.pp817-827
Mohd. Suhaimi Shamsuddin , Noor Fadhiha Mokhtar , Safiek Mokhlis , Zuha Rosufila Abu Hasan , NajdahAbd Aziz , Mohamad Nizam Yusof
This paper reviews literature on e-commerce waste management issues and challenges, focusing on potential improvements in Malaysia. It analyzes various sources, including Scopus, Web of Science (WoS), and Google Scholar (GS), using thematic and content analysis based on preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. The review highlights the surge in packaging and electronic waste due to increased e-commerce activity. In response, Malaysia has introduced policies promoting sustainable practices, such as eco-friendly packaging, e-waste regulations, and circular economy (CE) principles. Growing consumer awareness has also driven demand for sustainable e-commerce options. However, the key challenge is to reduce waste generation rather than just managing it. Achieving this will require significant efforts to minimize excessive manufacturing and packaging. The review aims to provide insights for stakeholders to support effective waste management and foster sustainability in the e-commerce sector.
Volume: 14
Issue: 3
Page: 817-827
Publish at: 2025-09-01

Islanding detection of integrated DG system using rate of change of frequency over reactive power

10.11591/ijpeds.v16.i3.pp1637-1644
B. V. Seshu Kumari , Ambati Giri Prasad , S. Sai Srilakshmi , Karri Sairamakrishna Buchireddy , Ch. Rami Reddy
This paper offers a passive islanding detection method that is effective for distributed generation. When a distributed generator (DG) keeps a location powered even when access to the external electrical grid is lost, this circumstance is referred to as islanding. The power distribution system currently includes distributed generators (DGs), which provide inexpensive electricity and have fewer environmental impacts. Sometimes, these DGs continue to supply the nearby loads because of line outages and islands made by system separations. As a result, there are scenarios with unacceptable power quality. The islanding is identified if the result of the rate of change of frequency over reactive power exceeds the threshold value. The MATLAB test results from this study demonstrate the effectiveness of the suggested approach for different islanding and non-islanding scenarios.
Volume: 16
Issue: 3
Page: 1637-1644
Publish at: 2025-09-01

Sulphur corrosion in transformer insulating oils: its effects, detection methods, and mitigation strategies

10.11591/ijaas.v14.i3.pp784-792
Nur Izyan Husnina Zulkefli , Sharin Ab Ghani , Mohd Shahril Ahmad Khiar , Imran Sutan Chairul , Nor Hidayah Rahim , Nur Farhana Mohd Azlan
Oil-immersed transformers are subjected to electrical, thermal, and mechanical stresses over time, which inevitably affect the insulating oil and paper insulation. The presence of sulphur corrosion also degrades the insulating oil and paper insulation. Sulphur corrosion in insulating oils has been a prevalent problem for many years, as it culminates in the failure of oil-immersed transformers. The longevity of oil-immersed transformers is dependent on the integrity of the insulating oil and paper insulation, which can deteriorate owing to sulphur corrosion. The occurrence and accumulation of copper sulphide (Cu2S) can result in transformer malfunctions, which is a significant issue for transformer manufacturers and operators. This paper provides a concise overview of the effects of sulphur corrosion, its detection methods, as well as its mitigation strategies. It is believed that this paper will enhance the understanding of sulphur corrosion in insulating oils, provide the best practices for sulphur corrosion management, and serve as guidance on enhancing transformer reliability and performance.
Volume: 14
Issue: 3
Page: 784-792
Publish at: 2025-09-01

Autonomous navigation system for a rover with robotic arm using convolutional neural networks

10.11591/ijaas.v14.i3.pp724-739
Aziz El mrabet , Hicham Hihi , Mohammed Khalil Laghraib , Mbarek Chahboun , Aymane Amalaoui
The aim of this project is to design and develop an autonomous rover equipped with a KUKA robotic arm. This mobile vehicle will be able to move autonomously thanks to the use of machine learning techniques. It will also be able to detect and retrieve objects using the KUKA arm. The rover will feature Mecanum wheels for improved maneuverability and will be controlled by a Raspberry Pi 3 board, with machine learning algorithms implemented using TensorFlow and Python. The development process will follow the V-methodology. The use of such an autonomous rover and its manipulative capabilities opens the way to many practical applications, including sampling in dangerous or difficult-to-access environments, search and rescue operations in the event of natural disasters or industrial accidents, and inspection and maintenance of industrial or construction sites. The rover could also be used for educational purposes, enabling students to explore the concepts of robotics and artificial intelligence.
Volume: 14
Issue: 3
Page: 724-739
Publish at: 2025-09-01

Artificial neural network based sensorless position estimation and direct torque control for stepper motor

10.11591/ijaas.v14.i3.pp702-710
Nagasridhar Arise , Thiruveedula Madhu Babu , Srinidhi Gollapudi , Tarun Kumar Dommeti , Abhishek Kummari , Mahith Shambukari
This study describes and illustrates how sensorless location estimation is achieved through the application of artificial neural network (ANN) control. Control stepper motor torque directly. Using stepper motors directly leads to a lot of problems; therefore, automated control systems are now commonly preferred. Stepper motors have several drawbacks when used directly, including the potential for steps to occasionally be missing while the motors are running. When physical sensors are not available, the proposed method estimates rotor position and speed using electrical signals and ANN algorithms. Simulation and experiment results demonstrate accurate position estimation (±1.5°) and efficient torque control. The sensorless direct torque control (DTC)-ANN approach increases the performance, reliability, and cost of stepper motors in robotics, computer numerical control (CNC) machines, and 3D printing.
Volume: 14
Issue: 3
Page: 702-710
Publish at: 2025-09-01

Empowering breastfeeding mothers: How self-directed learning boosts confidence-unveiling the two-round Delphi method

10.11591/ijphs.v14i3.25965
Dewi Ariani , Respati Suryanto Dradjat , Kumboyono Kumboyono , Lilik Zuhriyah
Promoting breastfeeding self-efficacy through self-directed learning requires behavior, goal setting, and self-reinforcement. This research aims to collect insights from health professionals on strategies for improving maternal confidence in breastfeeding using self-directed learning and existing knowledge. An in-depth exploration through a two-round Delphi method rooted in the self-efficacy theory of self-directed learning for breastfeeding mothers was conducted, involving expert input and an extensive literature review. Four key documents were identified, each undergoing rigorous expert rating to ensure quality. Six essential elements for health professionals to guide breastfeeding mothers were established, focusing on lactation physiology, successful initiation, confidence building, adversity management, cultural beliefs, and public breastfeeding. Three crucial topics, including prior knowledge, personal attributes, and autonomous processes, were designed to enhance self-efficacy through self-directed learning. In conclusion, the study emphasizes the vital role of health professionals in supporting mothers through comprehensive breastfeeding guidance and encouraging self-directed learning.
Volume: 14
Issue: 3
Page: 1256-1266
Publish at: 2025-09-01
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