Journal of Advanced Research Design

Evaluating the Adsorption Efficiency of Pelletized and Powdered Clinoptilolite for Methylene Blue

2025-07-03T11:22:43+07:00

The widespread use of dyes in various industries such as clothing, manufacturing, cosmetics, and printing has raised concerns about their negative impact on the environment. Adsorption is a practical method used in industries to treat this type of discharge on a large scale. However, the conventional powder format raises practical concern. This paper examines the adsorption capacity of clinoptilolite prepared as both pellets and powders for removing methylene blue. Both formats showed a consistent increase in adsorption capacity as the mass increased due to the increased number of adsorption sites attracting the methylene blue. However, compared to the powder format, the adsorption capacity of the pelletized clinoptilolite was significantly lower as the mass increased. The difference in adsorption capacity between the pellet and powder widened with increasing mass. This is because increasing the pellet mass only contributes to small increases in surface area from the slight increase in pellet thickness, whereas the powder format effectively uses all the exposed surface area of each powder unit as adsorption sites. Additionally, the methylene blue does not completely penetrate the inner structure of the pellet, further lowering its adsorption capacity. This study suggests that the adsorption capacity of clinoptilolite pellets can be enhanced to match the performance of the powder format if the mass is decreased. It shows that there is significant potential for improving clinoptilolite pellets to perform as well as the powder format.

5G Performance Enhancement Utilizing Adaboost Technique

2025-07-07T09:12:29+07:00

In this paper, the AdaBoost algorithm is involved in modulation techniques, as it works on improving the bit error rate (BER). Inputting a noisy signal received from a sender into AdaBoost yields the original signal after removing noise. This is done through the reconstruction of the constellation diagram of the modulation technique, by eliminating the noise filling the data’s signal space. The AdaBoost algorithm is then added to the fifth generation (5G) mobile systems such as orthogonal frequency division multiplexing (OFDM) and multiple input multiple output OFDM (MIMO-OFDM). It is utilized to enhance the BER performance of the entire system in the presence of different estimation techniques such as Least Squares (LS), Least Mean Squares (LMS) and Recursive Least Squares (RLS) and for different modulation techniques such as 256QAM (Quadrature Amplitude Modulation) and 1024QAM in a Rayleigh fading environment. The obtained results revealed that the highest value of enhancement for the OFDM system is 2 dB, which comes from using both RLS and LS technique with 1024QAM modulation. Also, the highest values of enhancement for the MIMO-OFDM system is 1.9 dB and 2 dB which comes from using LS technique with both 256QAM and 1024QAM modulation.

Productivity Analysis of Piling Work for Coastal Safety Construction in Bay Phase 6 Package 4, Jakarta, Indonesia

2025-01-21T13:45:36+07:00

This study aims to evaluate the productivity of spun pile installation during the construction of coastal safety structures in Jakarta Bay, Phase 6, Package 4. The piling work, scheduled for 303 days with 363 piles to be driven, faced significant deviations between planned and actual productivity rates, leading to delays and increased costs. The primary objective of this research is to identify and analyze the factors causing these discrepancies. Field observations, interviews with site personnel, and secondary data analysis were employed to gather information on productivity levels. The results show that effective productivity was 3.33 piles/day (9.99 m/h), while field and contract productivity were 3.2 m/h and 3.59 m/h, respectively. The discrepancies were largely attributed to factors such as weather conditions, equipment limitations, and community disturbances. This study underscores the need for improved planning, monitoring, and control measures to mitigate these disruptions and enhance piling productivity in future projects. The findings offer valuable insights for optimizing piling operations in similar coastal construction endeavors.

Location-Based Service with Interactive Mapping in Food Feed Mobile App (FeatiGo)

2025-04-07T09:57:43+07:00

Food serves as a cornerstone of human life, connecting sustenance with exploring diverse culinary experiences. With the increasing number of individuals embarking on gastronomic adventures, the demand for accessible and customized food information becomes paramount. Existing mobile applications offer a range of functionalities, primarily focused on posting food information, searching for eateries, filtering options and providing eatery details, frequently facilitated by location-based services. However, there remains a gap in the market for a comprehensive platform that consolidates these features with an interactive map in one mobile app, requiring users to navigate between multiple applications. The study tackles the issue of sifting through vast online food data by developing the FeatiGo mobile app, which uses location-based services and interactive mapping to provide a unified platform for effortless dining discovery. User testing encompassed functionality and instrumental testing alongside usability evaluation, yielding positive results and feedback on usability with a mean satisfaction score of 90.72%. Our app has proven to provide tailored food information, enriching culinary experiences by leveraging location in the digital mobile era.

Feature-based Method using Binary Alphabet-Based Technique in Text Steganography

2025-05-31T10:06:35+07:00

Information security is an important aspect of safeguarding digital assets and ensuring the security of any sensitive information. In this context, steganography is essential in hiding information within apparently harmless carriers such as images, audio files, or text, with the goal of avoiding detection by unauthorized parties. Numerous scholars in the past have strived to apply steganography specifically in the text domain, utilizing distinctive feature-based methods to implant letters that camouflage the concealed message within textual content. The majority of feature-based techniques embed a hidden message by converting it into binary bits. This paper introduces a novel feature-based technique that transforms the hidden message into binary bits, representing each text letter with two binary bits across both uppercase and lowercase alphabets. The technique utilizes an alphabet-based sequence table to correlate the sequence number of letters in the hidden message with the corresponding binary bits. This approach anticipates streamlining the embedding process, generating stego text with a concealed message while minimizing the binary bit length required to cover the message. The novel technique is evaluated against previous techniques in terms of both single-bit and dual-bit feature-based techniques. This efficient approach minimizes the binary bit length needed to cover the message, achieving 100% embedding performance in limited sentences of cover text when implementing the feature-based method.

Comparative Analysis of VGG-16, ResNet50, and EfficientNet-B1 with Optimization Techniques for Crop Disease Detection

2025-07-02T09:46:46+07:00

This paper investigates the application of convolutional neural networks (CNNs) to enhance the identification of leaf diseases in Malaysian agriculture, specifically focusing on tomato and potato crops. The study utilizes pre-trained models VGG16, ResNet50, and EfficientNet-B1, employing transfer learning methods. The PlantVillage dataset, known for its comprehensive collection of annotated images of healthy and diseased plants, forms the basis for training and assessing these models. The research aims to develop a reliable AI system for accurate and efficient disease identification, contributing to sustainable agricultural practices and food security in Malaysia.

Analytical Study on Emerging Trends in Cardiomyopathy Detection through Diverse Database Classification

2025-05-28T10:21:07+07:00

Heart failure (HF) is conditions caused mainly by cardiomyopathy that is a major cause of mortality in the world. New developments in artificial intelligence (AI) here indicate that it can be of big aid when it comes to enhancing the accuracy of cardiomyopathy categorization. This paper provides an extensive analysis of the existing AI approaches towards the classification of cardiomyopathy and categorizes as well as contrasts different classifiers. This means that decision making based on these algorithms can be regarded as accurate or imprecise depending with the chosen datasets used in feeding the algorithm. This research wants to help the scientists in selecting the most suitable classification models for immediate use in the clinical setting. The results shown here clearly prove that machine-learning algorithms coupled with adequate databases can improve diagnostic precision and are feasible for real-time application in the identification of cardiomyopathy subtypes.

A Two-stage Image Encryption Framework using Ensemble Learning-Driven Key Generation and Spiral Ripple Shuffling

2025-06-13T03:49:36+07:00

The surge in the use of digital imaging demands ciphers that are both mathematically rigorous and computationally light. However, there are gaps in earlier chaos- or Machine Learning-based schemes such as inefficient key generation, limited computational scalability and vulnerabilities to advanced attacks. Particularly when integrating machine learning with cryptographic operations by producing fully key-dependent row/column permutations and diffusion in a single pass. This study therefore introduces a two-stage framework that marries an ensemble-learning-driven pseudorandom key generator with a spiral-ripple shuffle followed by XOR diffusion to dismantle pixel correlations at linear-time complexity . Experiments on six benchmark images confirm the design’s statistical resilience with averages of NPCR = 99.57 percent, UACI = 34.63 percent, entropy = 7.52 bits and SSIM quite similar to 0.01 between cipher and plain images. Recovery fidelity remains high (PSNR up to 53.97 dB), while the heaviest image encrypts in 0.94s and lighter images in quite similar to 0.03s on standard desktop hardware. These figures indicate near-ideal diffusion, uniform histogram distribution and negligible perceptual leakage, outperforming recent chaos-IoT ciphers in runtime without sacrificing security metrics. Therefore, the proposed system achieves real-time throughput for megapixel frames, positioning it as a viable candidate for privacy-critical digital image pipelines.

Assessing the Impact of Traffic Noise in Residential Areas: A Case Study of Taman Koperasi Bahagia, Batu Pahat, Malaysia

2025-05-31T10:12:59+07:00

Noise is a by-product of various human activities, and traffic noise is considered the second leading environmental cause of adverse health effects. The increase in road transportation negatively impacts residents' quality of life over time, affecting both health and property values. To assess this, a noise mapping grid was created in a residential area, with data collected over one week during peak hours (7:00 a.m. to 9:00 a.m. and 5:00 p.m. to 7:00 p.m.) to capture maximum traffic noise levels. This data was compared to the standards set by the Department of Environment Malaysia to determine compliance. The results indicated that evening traffic noise levels were higher than morning levels, showing a greater impact on residents in the evening. Additionally, traffic noise during weekdays was higher than on weekends. These findings provide actionable insights for city planners and authorities to develop effective noise control regulations and strategies, ultimately enhancing residents' overall life satisfaction.

Revolutionizing Assessment: Unveiling the Collaborative Portfolio Assessment Learning System Prototype Through Feminist Pedagogy

2025-03-03T13:43:55+07:00

Since 2012, Malaysia has implemented School Based Evaluation (SBA) as a means to enhance the learning and assessment processes for students. Nevertheless, educational institutions continue to face challenges in successfully elucidating and implementing a comprehensive assessment procedure that could enhance the quality of instruction and knowledge acquisition. In addition, they continue to utilize manual file systems for managing and evaluating students' results. Thus, the objective of this project was to develop and apply a concept for a prototype educational system known as Collaborative Portfolio Assessment (CPA) to support students in their knowledge-building drive. Feminist Pedagogy, with its emphasis on collaboration, inclusivity and critical reflection, aligns with the goals of the CPA learning system prototype by empowering Malaysian students to co-construct knowledge through diverse and reflective portfolio processes. By integrating culturally relevant activities and leveraging digital platforms, this approach fosters deeper critical thinking, inclusivity and preparedness for societal and global challenges. The concept for creating this CPA learning system prototype was derived from the knowledge management process model, incorporating the strategies of Computer Supported Collaborative Learning (CSCL) and e-portfolio management system model. This system functions as a tool to evaluate the process by which students construct knowledge within the SBA environment. The study involved a sample of 3 History subject instructors who were interviewed to determine the necessary characteristics for evaluating the SBA e-portfolio management system. In addition, a total of 6 students were engaged to assess the functionality of the CPA prototype learning system. The results suggest that the majority of students agreed that the therapy provided through this CPA prototype learning system was effective in promoting knowledge acquisition during the teaching and learning process. In addition, to establish a cohesive method for developing an educational application, it is possible to integrate a knowledge management model, a computer-supported collaborative learning (CSCL) strategy and an e-portfolio management system. This combination facilitates the generation of material through collaboration, encourages thoughtful learning and establishes centralized repositories of knowledge, leading to a thorough and customized learning experience.

Digital Interventions as Social Support for Adolescent Psychosocial Development and Educational Policy Malaysia: A Conceptual Review

2025-06-02T07:39:21+07:00

The Digital Education Policy (DEP) aims to cultivate a generation of digitally fluent individuals who possess knowledge, value students and enhance skills and infrastructure across preschool to post-secondary education levels. Previous studies highlight the potential of digital interventions in addressing social issues among adolescents, particularly in providing social support. Despite growing use of digital tools in education, limited research has explored how these technologies can be systematically integrated to provide social and emotional support for adolescents. This gap highlights the need to review existing interventions and their potential for fostering psychosocial development. This review grounded in typological approach prior research—particularly studies conducted in Malaysia—to examine policies concerning the integration of digital technology. The effectiveness of interventions by teachers can influence adolescents' psychosocial well-being in addressing contemporary challenges. Hence, the future research of proposed digital intervention could empirically explore, examine and validate as presented in educational system through implementing nationality education policy.

Development of an Advanced Multisystem Aquaponic and Model for the Topic of Educational Aquaponic Design Technology in Malaysia

2025-03-23T10:51:15+07:00

This study aims to develop a Multisystem Aquaponic Model as a teaching aid for the Aquaponic Design topic within the Technology curriculum in Malaysian secondary schools. Specifically, the objectives are to identify the needs and key elements required for the development of the model, to design the Multisystem Aquaponic Model and to validate its use as an instructional tool for teaching Aquaponic Design. Grounded in constructivist learning theory and guided by the Analysis, Design, Development, Implementation, Evaluation (ADDIE) instructional design model, this study adopts a Design and Development Research (DDR) approach utilizing qualitative methods. Data were collected through interviews with three subject-matter experts—secondary school teachers specializing in Design and Technology. The interviews aimed to gather insights into the essential components for model development and to validate the final design. A structured interview protocol was employed to ensure consistency across expert feedback. The findings indicate that the Multisystem Aquaponic Model was well-received and deemed valid for use as a teaching aid. The availability of this model is expected to enhance student achievement, deepen conceptual understanding and serve as a valuable supplementary resource for learners.

The Development of Colorimetric Paper-Based Analytical Device for Detection of Sucrose Adulteration in Malaysian Stingless Bee Honey

2025-08-04T15:20:03+07:00

The growing demand for honey in the market has resulted in tampering honey with foreign substances and increased production of artificial honey. There are lots of laboratory tests for honey adulteration. However, most of the tests are tedious, costly, and complicated. As a result, alternative approaches for faster and simpler detection of honey adulteration should be developed and conducted regularly. Herein, this research attempts to develop paper-based analytical devices (PADs) as a simple, rapid, and inexpensive method for simultaneously detecting sucrose adulteration in stingless bee honey. To develop the PADs, colorimetric paper-based analytical devices (C-PADs) technology was approached. The C-PADs only used a small amount of sample solution (8 microL) for sucrose analyses. Furthermore, no interfering compound was obtained for C-PADs. The storage stability was further investigated, and after 30 days, the stability decreased by 40 percent from its initial value. Finally, the sucrose content of eight stingless bee honey samples was measured using the C-PADs and compared with high-performance liquid chromatography (HPLC). The results for C-PADs corroborated well with HPLC as most of the results are not significantly different at the 95 percent confidence level. Among the eight samples, commercial samples H4, H5, and H8 were positive for honey adulteration. From this research, it can be concluded that the C-PADs have been successfully developed to determine sucrose adulteration in stingless bee honey.