Ungku Omar Polytechnic

This research examines how curing temperature and duration influence the electrical and mechanical behavior of hybrid graphene nanoplatelet and silver (GNP/Ag) conductive ink. The ink was formulated from GNPs, silver flakes and silver acetate printed on copper substrates, then cured 240 °C, 250 °C, and 260 °C for one to three hours. Electrical resistance was measured using a Two-Point probe, while natural frequency was obtained from experimental modal analysis (EMA) on stainless-steel (SUS304) cantilever beams laminated with printed ink. The results show that the higher curing temperature and longer curing time reduce resistivity and increase natural frequency, with the best performance observed at 260 °C for 3 hours (8.4×10⁻⁶ Ω.m and a 4.2 Hz increase). These findings confirm that a direct relationship between conductivity and stiffness, where conditions that promote stronger particle bonding also raise structural rigidity. The main contribution of this research is the joint evaluation of curing effects on both electrical and vibrational responses, offering a combined electro-mechanical perspective that is not often explored in GNP/Ag ink research. The results provide practical guidance for selecting curing conditions based on the required balance between conductivity and mechanical stability in flexible and stretchable electronic applications.

The construction industry is a major contributor to global carbon emissions, necessitating accurate predictive models for sustainable development. This study compares the performance of fine tree regression (Rtree) and adaptive neuro-fuzzy inference system (ANFIS) in predicting carbon footprints across four stages of residential construction: production, transportation, operational, and destruction. A dataset of 2000 observations was used, with an 80 to 20 split for training and testing. The models were evaluated using root mean square error (RMSE) and mean absolute percentage error (MAPE). Results indicate that ANFIS outperforms Rtree in all stages. ANFIS achieved an RMSE of 0.5142 at the production stage compared to 0.5317 for Rtree. ANFIS obtained an RMSE of 447.07 in the transportation stage, while Rtree recorded 492.23. The operational stage showed an RMSE of 1179.3 for ANFIS versus 1386.5 for Rtree. At the destruction stage, ANFIS demonstrated superior accuracy with an RMSE of 0.0610 compared to 0.0631. The findings suggest that ANFIS provides more precise predictions and is a reliable model for estimating carbon footprints in residential construction. This study contributes to sustainable construction by offering an efficient tool for reducing environmental impact.

The issue of network security is extremely urgent; an effective intrusion detection system is becoming increasingly important for modern network security. Thus, interpretable AI methods have played a role in real-world IDS. This study used two machine learning models to analyze the UKM-IDS20 and NSL-KDD datasets, namely LightGBM and CNN, mainly for training and evaluating binary and multi-label network traffic data. Moreover, interpretable artificial intelligence techniques such as SHAP and LIME were required to interpret the models. SHAP and LIME highlight the different feature importance patterns among models and enhance artificial intelligence models' transparency, understandability, and credibility. During the usage of SHAP, for the LightGBM binary classification model, the top 5 features contribute more than 72% to the interpretability of the model. Among them, the feature dst_bytes has a relatively significant impact. The same situation is that in the LIME analysis stage, the contribution value of dst_bytes is also the highest, which fully demonstrates that there is a high degree of consistency between SHAP and LIME. Moreover, in the NSL-KDD dataset, the outputs of CNN models such as Normal, DoS, Probe and R2L all achieved perfect scores (100%). Only the performance of the U2R class declined, with an accuracy of 80% and a recall rate of 36%. LightGBM has achieved excellent performance on the UKM IDS dataset, with a Metasploit accuracy score of 99%, which indicates that compared with CNN, LightGBM performs better in both binary classification and multi-classification scenarios. Therefore, adaptive intrusion detection systems are crucial for protecting computer networks. Furthermore, the continuous update of the IDS dataset also enables the proposed system to be applied to the datasets related to recent attacks for testing.

Kehidupan beragama merujuk kepada cara seseorang atau kelompok menjalani hidup mereka berdasarkan ajaran dan nilai-nilai agama yang dianuti. Ini melibatkan pelbagai aspek kehidupan, termasuklah amalan ibadah. Islam dengan jelas menyebutkan bahawa ibadah itu menuntut seluruh kehidupan manusia untuk mengabdikan diri dan mentaati Allah SWT, tetapi ramai juga yang mengabaikan ibadah yang ditetapkan oleh ajaran Islam. Oleh itu, kajian berkaitan ibadah ini telah dijalankan bertujuan untuk mengenalpasti tahap pengamalan ibadah Islam anggota Askar Wataniah di politeknik Negeri Perak serta mengkaji perbezaan tahap amalan ibadah berdasarkan jantina dan hubungan antara pendidikan agama dengan amalan ibadah mereka. Kajian berbentuk kajian tinjauan dan menggunakan borang soal selidik sebagai instrumen kajian. Sampel kajian ini tertumpu kepada anggota Askar Wataniah yang beragama Islam di politeknik Negeri Perak yang terdiri daripada 2 buah politeknik iaitu Politeknik Ungku Omar, Ipoh seramai 46 orang, dan Politeknik Sultan Azlan Shah, Behrang seramai 51 orang, menjadikan jumlah respondennya ialah 97 orang. Data yang diperolehi daripada soal selidik dikumpul dan dianalisis menggunakan perisian Statistical Packages for Social Science Version 22.0 (SPSS v22.0) bagi mendapatkan kekerapan, peratusan, skor min, ujian-t serta korelasi pearson. Hasil kajian mendapati tahap pengamalan ibadah Islam adalah tinggi. Ini menunjukkan bahawa kehidupan beragama anggota Askar Wataniah adalah baik dan mempunyai kesedaran untuk menunaikan amalan ibadah yang telah ditetapkan oleh syariat. Dapatan juga menunjukkan bahawa tidak terdapat perbezaan yang signifikan secara statistik antara min kedua-dua kumpulan jantina dan tidak terdapat perkaitan linear yang signifikan antara tahap pendidikan agama dan amalan ibadah. Hasil kajian ini diharapkan dapat memberikan pandangan yang lebih mendalam tentang bagaimana anggota Askar Wataniah mengimbangi antara tugas ketenteraan dan tanggungjawab agama, serta membantu pihak berkuasa dalam merangka program-program yang dapat menyokong kehidupan beragama mereka.

Kajian ini bertujuan untuk menilai tahap pemahaman dan penghayatan pelajar terhadap konsep Rukun Negara melalui kursus Falsafah dan Isu Semasa di Politeknik Malaysia. Sebagai asas perpaduan nasional dan kesejahteraan negara, Rukun Negara memainkan peranan penting dalam membentuk jati diri generasi muda. Walau bagaimanapun, kajian terdahulu menunjukkan bahawa tahap penghayatan dalam kalangan pelajar masih boleh dipertingkatkan. Oleh itu, kajian ini meneliti hubungan antara amalan pengajaran pensyarah dengan tahap pemahaman serta penghayatan pelajar terhadap prinsip Rukun Negara. Kajian ini menggunakan pendekatan kuantitatif melalui kaedah tinjauan soal selidik yang melibatkan 176 orang pelajar Ijazah Sarjana Muda dari empat politeknik yang menawarkan kursus ini. Data dianalisis menggunakan statistik deskriptif dan inferensi, termasuk analisis korelasi Pearson. Hasil kajian menunjukkan bahawa tahap pemahaman dan penghayatan pelajar terhadap Rukun Negara adalah tinggi, dengan skor min keseluruhan antara 4.67 hingga 4.77. Selain itu, analisis korelasi Pearson (r = 0.752, p < 0.01) menunjukkan hubungan positif yang signifikan antara amalan pengajaran pensyarah dengan tahap pemahaman pelajar terhadap prinsip Rukun Negara. Dapatan ini membuktikan bahawa pendekatan pengajaran yang interaktif, reflektif, serta berasaskan teknologi mampu meningkatkan keberkesanan pembelajaran, sekali gus menyumbang kepada pembentukan warganegara yang bertanggungjawab dan beretika. Oleh itu, kajian ini mencadangkan agar pendekatan pengajaran yang terbukti berkesan ini diperluaskan ke kursus-kursus lain yang berkaitan dengan pembangunan sahsiah dan pengukuhan nilai kenegaraan, bagi memastikan pendidikan tinggi yang lebih holistik dan berkesan.

The increasing effects of climate change require the accurate measurement and prediction of carbon emissions from building construction operations. This study aims to assess the performance of Gaussian Process Regression (GPR) and Support Vector Machine (SVM) in predicting emissions and determine which model is best suited for improving sustainable construction practices. The GPR and SVM models were trained and tested using empirical datasets with varying parameter values. Model performance is evaluated using correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE). The analysis reveals that GPR consistently performs better than SVM across diverse datasets, especially in intricate and varied data situations. The superior performance of GPR highlights its robustness and versatility in handling the complexities of construction-related emissions data. The results indicate that GPR has the potential to assist significantly many stakeholders in the construction sector, including planners and environmental managers, in making better-informed decisions that are in line with carbon reduction objectives. Further studies could examine the incorporation of further predictive variables into the GPR model, such as economic indicators and the effects of material innovation.

This study investigates the potential enhancement of classification accuracy in timber defect identification through the utilization of deep learning, specifically residual networks. By exploring the refinement of these networks via increased depth and multi-level feature incorporation, the goal is to develop a framework capable of distinguishing various defect classes. A sequence of ablation experiments was conducted, comparing our proposed framework's performance (R1, R2 and R3) with the original ResNet50 architecture. Furthermore, the framework’s classification accuracy was evaluated across different timber species and statistical analyses such as independent t-tests and one-way ANOVA tests were conducted to identify the significant differences. Results showed that while the R1 architecture demonstrated slight improvement over ResNet50, particularly with the addition of an extra layer ("ConvG"), the latter still maintained superior overall performance in defect identification. Similarly, the R2 architecture, despite achieving notable accuracy improvements, slightly lagged behind R1. Integration of fully pre-activation activation functions in the R3 architecture yielded significant enhancements, with a 14.18% increase in classification accuracy compared to ResNet50. The R3 architecture showcased commendable defect identification performance across various timber species, though with slightly lower accuracy in Rubberwood. Nonetheless, its performance surpassed both ResNet50 and other proposed architectures, suggesting its suitability for timber defect identification. Statistical analysis confirmed the superiority of the R3 architecture across multiple timber species and this underscores the significance of integrating network depth and fully pre-activation activation functions in improving classification performance. In conclusion, while the wood industry has made strides towards automation in timber grading, significant challenges remain. Overcoming these challenges will require innovative approaches and advancements in image processing and artificial intelligence to realize the full potential of automated grading systems.

The distribution of zakat in Malaysia should be able to reduce poverty in society and help zakat recipients in fulfilling their needs. The study is to know about student’s perception towards services quality of zakat distribution. The purpose of this research is to determine the level of student’s perception towards services quality of zakat distribution and to determine the relationship between tangibles, reliability, responsiveness, assurance and empathy towards student’s perception on zakat distribution. A student’s survey questionnaire was utilized as the primary research instrument for this study. The questionnaire for this study consisted of 35 items in three parts that were Part A, Part B and Part C by using the 5 Likert scale. This study involved students in all department at Polytechnic Ungku Omar (PUO) which are from 3rd to 5th semester who receiving zakat in session December 2021 until June 2023. Descriptive analysis for 255 respondents was obtained from questionnaire based on mean of the main research variables. The overall mean of student’s perception is 4.2840, tangibles is 4.0980, reliability is 4.3080, responsiveness is 4.2100, assurance is 4.2820 and empathy is 4.3040. The study shows that medium relationships among responsiveness, reliability, assurance and students’ perception towards services quality of zakat distribution. Therefore, the institution should improve all service quality to gain stronger relationship such as by sending staff to attend courses to be more open in solving problems, training staff to have more skills and be prepared to face the zakat contestants, provide classes and instructors for new staff to be more vulnerable knowledge related to zakat. Thus, students will feel trust and relying solely on the institution. Lastly, the researchers need to expand the number of respondents in order to know more results about zakat perception among students and including more institutions.