https://repositori.mypolycc.edu.my/jspui/handle/123456789/4 Tue, 29 Sep 2026 07:33:12 GMT 2026-09-29T07:33:12Z https://repositori.mypolycc.edu.my/jspui/handle/123456789/10937 Title: OPTIMIZATION OF EFFICIENCY PREDICTION PERFORMANCE FOR SPUN PILE USING HYBRID META-HEURISTIC MACHINE LEARNING ALGORITHMS Authors: Muhammad Nasim Bin Abdul Ghani, Abdul Ghani Abstract: Kajian ini mencadangkan satu rangka kerja pemodelan hibrid bagi meramalkan kecekapan cerucuk berputar dengan menggunakan algoritma pembelajaran mesin yang dioptimumkan melalui teknik metaheuristik berinspirasi semula jadi. Sebanyak 150 rekod ujian Pile Driving Analyzer (PDA) digunakan untuk membangunkan tiga model ramalan iaitu Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM), dan Regression Tree (RT). Setiap model mempunyai struktur fungsi yang berbeza, di mana ANFIS menggunakan fungsi keahlian berbentuk loceng umum, SVM menggunakan kernel fungsi asas radial Gaussian, dan RT menggunakan pembahagian binari berulang. Untuk meningkatkan prestasi model, hiperparameter telah dioptimumkan menggunakan algoritma Sparrow Search (SSA), Whale Optimization Algorithm (WOA), dan Manta Ray Foraging Optimization (MRFO). Penilaian prestasi menunjukkan bahawa model ANFIS yang dioptimumkan dengan SSA mencapai ketepatan tertinggi dengan RMX (RMSE = 0.123, R² = 1.000) dan FMX (RMSE = 0.560, R² = 1.000). Pengesahan telah dijalankan menggunakan data daripada bangunan lain dalam projek yang sama. Ramalan RMX menunjukkan perbezaan peratusan antara 0.0% hingga 3.7%, manakala FMX berada dalam lingkungan 0.1% hingga 6.4%, yang masih diterima untuk aplikasi praktikal. Keputusan ini membuktikan kebolehsesuaian model dalam persekitaran pembinaan yang serupa. Prototaip berasaskan MATLAB ini menawarkan alternatif yang boleh dipercayai dan menjimatkan kos berbanding kaedah ujian cerucuk konvensional dalam kejuruteraan geoteknik. This study proposes a hybrid modeling framework for predicting the efficiency of spun pile foundations by leveraging machine learning algorithms optimized through nature-inspired metaheuristic techniques. Utilizing a dataset comprising 150 Pile Driving Analyzer (PDA) test records, three predictive models were developed, namely Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM), and Regression Tree (RT). Each model incorporates distinct functional structures, with ANFIS using generalized bell-shaped membership functions, SVM employing a Gaussian radial basis function kernel, and RT utilizing recursive binary splitting. To enhance model performance, hyperparameters were optimized using the Sparrow Search Algorithm (SSA), Whale Optimization Algorithm (WOA), and Manta Ray Foraging Optimization (MRFO). Performance evaluation showed that the ANFIS model optimized by SSA achieved the highest predictive accuracy with RMX (RMSE = 0.123, R² = 1.000) and FMX (RMSE = 0.560, R² = 1.000). Validation was conducted using data from another building within the same project. The RMX predictions showed percentage differences ranging from 0.0% to 3.7%, indicating high reliability. For FMX, percentage differences ranged from 0.1% to 6.4%, within acceptable thresholds for practical application. These results demonstrate the model’s robustness in similar construction environments. The MATLAB-based prototype offers a reliable and cost-effective alternative to conventional pile testing methods in geotechnical engineering. Description: This research develops and optimizes hybrid machine learning models using metaheuristic algorithms to predict the efficiency of spun pile foundations. The study utilizes PDA test data and compares ANFIS, SVM, and regression tree models, each optimized with SSA, WOA, and MRFO, to improve predictive accuracy and practical application in geotechnical engineering. Sun, 01 Dec 2024 00:00:00 GMT https://repositori.mypolycc.edu.my/jspui/handle/123456789/10937 2024-12-01T00:00:00Z https://repositori.mypolycc.edu.my/jspui/handle/123456789/10934 Title: Masjid Jamek Tanah Datar Wed, 01 Jan 2020 00:00:00 GMT https://repositori.mypolycc.edu.my/jspui/handle/123456789/10934 2020-01-01T00:00:00Z https://repositori.mypolycc.edu.my/jspui/handle/123456789/10931 Title: CAD CAM by Mastercam 2nd Edition Authors: Hasny Binti Abdul Jalil; Mohd Sallehuddin Bin Yazid; Mohd Hamdan Bin Abdul Razak Abstract: This book is written based on the latest syllabus contents of MasterCam for topic Milling Process. This topic is relevant to student of Mechanical Engineering (Manufacturing) Program. Each topic is carefully written with a combination of notes, examples and tutorials that are suitable for teaching and learning sessions. Constructive criticism and suggestions for improvement of the book will be gratefully acknowledged. Sun, 01 Jan 2023 00:00:00 GMT https://repositori.mypolycc.edu.my/jspui/handle/123456789/10931 2023-01-01T00:00:00Z https://repositori.mypolycc.edu.my/jspui/handle/123456789/10930 Title: WORKBOOK & MIND MAP: MALAYSIAN TAXATION 1 Authors: FARIDAH JAAFAR Abstract: This book is dedicated tostudents taking the Malaysian Taxation 1 course. This workbook is published as an exercise to answer theory based questions. Mon, 01 May 2023 00:00:00 GMT https://repositori.mypolycc.edu.my/jspui/handle/123456789/10930 2023-05-01T00:00:00Z