Saranan HAMKA terhadap Perpaduan Bangsa Serumpun Malaysia – Indonesia

Hamka adalah tokoh Nusantara yang sinonim dengan gelaran ulama dan menulis lebih dari 100 buah buku dalam pelbagai bidang. Bukan setakat sifat ulamak yang menjadikan beliau dikagumi tetapi kemampuannya sebagai pemimpin, penulis, pemikir, pujangga dan pejuang bangsa menjadikannya milik masyarakat Nusantara. Penulisan ini akan memperlihatkan bagaimana sifat keperibadian dan karisma Hamka dalam memperjuangkan penyatuan bangsa serumpun Malaysia-Indonesia berdasarkan ilham dan cita-cita sepanjang tempoh kehidupannya. Timbulnya isu-isu yang menggugat keharmonian kedua negara ternyata telah mencabar semangat serumpun yang diwarisi sejak ratusan tahun. Roh dan semangat serumpun semakin meminggir malahan tidak disedari oleh sebahagian generasi baru kerana identiti negara bangsa yang bersifat patriotik. Hamka telah menyedari bakal berlakunya pemecahan identiti bangsa dan kerosakan terhadap nilai serumpun sekiranya tidak dipertahankan oleh Malaysia dan Indonesia. Melalui kajian dan mengabstrakkan hasil-hasil karya Hamka sama ada dalam bidang sejarah, politik, novel, falsafah, tafsir dan biografi, beberapa elemen yang menyatukan bangsa serumpun akan dilihat sebagai bukti bahawa Hamka mempunyai cita-cita dan misi dalam menyatukan bangsa serumpun. Melalui penulisannya, Hamka menjadikan Melaka sebagai lambang kejatuhan bangsa serumpun di samping memperjuangkan pemikiran agama sebagai penyatuan. Selain perjuangan menangkis pengaruh luar yang melemahkan bangsa, Hamka menjadikan bahasa, tulisan dan sastera sebagai lambang penyatuan bangsa serumpun. Penolakan terhadap sikap sentrik dan pengukuhan identiti bangsa serumpun merupakan perjuangan dalam memastikan umat Melayu dan Islam di kedua negara bersatu. Jumpaan terhadap perjuangan Hamka melalui karya dalam bidang sejarah, bahasa, budaya dan agama ini boleh dijadikan panduan dalam konteks negara serumpun bagi memastikan perhubungan negara bangsa yang lebih harmoni dan beridentitikan dunia Melayu

Deep neural network method for the prediction of xylitol production

Bio-based chemical products such as xylitol have achieved remarkable attentions both in pharmaceutical and food industries due to their several advantages such as sugar substitute that can help diabetic patients and help in preventing tooth decay problem. To produce xylitol, recently, microbial host such as E. Coli often used as it is predicted that E. Coli can produce high level of xylitol. Therefore, metabolic engineering need to be done towards E. Coli and powerful tools are needed to manipulate, simulate and analyse the E. Coli metabolic pathway. Artificial intelligence methods such as deep neural network offer an efficient and powerful approach to be used to analyse the xylitol production value and at the same time to predict which genes and pathway that give biggest effect in the process to produce xylitol in E. Coli. Results show that, with an absence of genes pgi, tkt and tala, xylitol production can be boosted up to the higher level

Mini-batch k-Means versus k-Means to Cluster English Tafseer Text: View of Al-Baqarah Chapter

Al-Quran is the primary text of Muslims' religion and practise. Millions of Muslims around the world use al-Quran as their reference guide, and so knowledge can be obtained from it by Muslims and Islamic scholars in general. Al-Quran has been reinterpreted to various languages in the world, for example, English and has been written by several translators. Each translator has ideas, comments and statements to translate the verses from which he has obtained (Tafseer). Therefore, this paper tries to cluster the translation of the Tafseer using text clustering. Text clustering is the text mining method that needs to be clustered in the same section of related documents. The study adapted (mini-batch k-means and k-means) algorithms of clustering techniques to explain and to define the link between keywords known as features or concepts for Al-Baqarah chapter of 286 verses. For this dataset, data preprocessing and extraction of features using TF-IDF (Term Frequency-Inverse Document Frequency), and PCA (Principal Component Analysis) applied. Results show two/three-dimensional clustering plotting assigning seven cluster categories (k=7) for the Tafseer. The implementation time of the mini-batch k-means algorithm (0.05485s) outperforms the time of the k-means algorithm (0.23334s). Finally, the features 'god', 'people', and 'believe' was the most frequent features

K-means variations analysis for translation of English Tafseer Al-Quran text

Text mining is a powerful modern technique used to obtain interesting information from huge datasets. Text clustering is used to distinguish between documents that have the same themes or topics. The absence of the datasets ground truth enforces the use of clustering (unsupervised learning) rather than others, such as classification (supervised learning). The “no free lunch” (NFL) theorem supposed that no algorithm outperformed the other in a variety of conditions (several datasets). This study aims to analyze the k-means cluster algorithm variations (three algorithms (k-means, mini-batch k-means, and k-medoids) at the clustering process stage. Six datasets were used/analyzed in chapter Al-Baqarah English translation (text) of 286 verses at the preprocessing stage. Moreover, feature selection used the term frequency–inverse document frequency (TF-IDF) to get the weighting term. At the final stage, five internal cluster validations metrics were implemented silhouette coefficient (SC), Calinski-Harabasz index (CHI), C-index (CI), Dunn’s indices (DI) and Davies Bouldin index (DBI) and regarding execution time (ET). The experiments proved that k-medoids outperformed the other two algorithms in terms of ET only. In contrast, no algorithm is superior to the other in terms of the clustering process for the six datasets, which confirms the NFL theorem assumption

Disaster Management in Malaysia: An Application Framework of Integrated Routing Application for Emergency Response Management System

Malaysia has experienced various disasters either natural or manmade disaster. One of the critical phases in Disaster Management System life cycle is response phase. In this phase, connectivity analysis such as a navigation service to help emergency rescue (ER) units reach at disaster area on time is necessary. Nowadays, commercial navigation system seems not appropriate to be used by ER units as they have different preferences. In addition, location information that is vital was not fully utilized in disaster management, especially in doing multi-task analysis. Thus, the real potential of GIS technology in managing spatial data including real-time (moving objects) data of ER units may influence the quality of the service. However, the services should be supported by a good data model. In order to eliminate inappropriate information, incomplete data, and overloaded information from Database Management System (DBMS) sent to the user, this paper will present the framework of integrated routing application for emergency response units embedded with context-aware

Prediction of bioprocess production using deep neural network method

Deep learning enhanced the state-of-the-art methods in genomics allows it to be used in analysing the biological data with high prediction. The training process of neural network with several hidden layers which has been facilitated by deep learning has been subjected into increased interest in achieving remarkable results in various fields. Thus, the extraction of bioprocess production can be implemented by pathway prediction in genomic metabolic network in eschericia coli. As metabolic engineering involves the manipulation of genes which have the potential to increase the yield of metabolite production. A mathematical model of this network is the foundation for the development of computational procedure that directs genetic manipulations that would eventually lead to optimized bioprocess production. Due to the ability of deep learning to be well suited in terms of genomics, modelling for biological network can be implemented. Each layer reveal the insight of biological network which enable pathway analysis to be implemented in order to extract the target bioprocess production. In this study, deep neural network has been to identify any set of gene deletion models that offers optimal results in xylitol production and its growth yield

Kesan perencatan madu gelam terhadap kehilangan tulang pada tikus periodontitis

Periodontitis adalah penyakit kronik yang melibatkan kehilangan tulang dan inflamasi pada tisu periodontium. Interleukin-6 (IL-6) dan Tumor Necrosis Factor-α (TNF-α) merupakan penanda pro-inflamasi yang penting yang terlibat dalam periodontitis. Sebanyak 20 ekor tikus Sprague-Dawley dibahagikan kepada empat kumpulan iaitu: Kumpulan kawalan dengan salin normal (CS); kumpulan kawalan dengan madu Gelam 3 g/mL (CH); kumpulan ujian periodontitis dengan salin normal (TS); dan kumpulan ujian periodontitis dengan madu Gelam 3 g/mL (TH). Benang bersaiz 4/0 diikat pada molar pertama gigi tikus sebelah kiri bagi tujuan rangsangan penyakit periodontitis. Madu Gelam diberi secara paksa oral selama 15 hari. Selepas 15 hari, sampel plasma dan tisu dianalisis menggunakan kaedah Elisa dan pewarnaan histologi. Kehilangan tulang alveolar pada kumpulan TS adalah paling tinggi berbanding dengan kumpulan kawalan, CS dan CH namun, tiada perbezaan yang signifikan berbanding dengan kumpulan TH. Berdasarkan ujian imunohistokimia, ekspresi IL-6 dan TNF-α pada tisu periodontium adalah tinggi secara signifikan pada kumpulan TS berbanding dengan kumpulan lain. Namun, tiada perubahan aras IL-6 dan TNF-α yang signifikan pada plasma ke semua tikus kajian

Hollow-core photonic crystal fiber refractive index sensor based on modal interference

A refractive index sensor based modal interference in hollow core photonic crystal fiber (HCPCF) is proposed and demonstrated. The sensor is realized by splicing both ends of a HCPCF section to single mode fiber (SMF). At both splicing points, the HCPCF air holes are fully collapsed by the arc discharge. The collapsed regions excite and recombine core and cladding modes which formed modal interference for sensing purpose. The HCPCF sensor is tested in sugar solution and the response is measured from the wavelength shift in the interference spectra. The achieved sensitivity and resolution are 36.184 nm/RIU and 5.53-10-4 RIU, respectively, in refractive index range between 1.3330 and 1.3775. Result also shows that the sensor has a small temperature sensitivity of 19 pm/°C in the range of 35.5°C to 60.5 °C. The propos sensor potentially can be applied in biomedical, biological and chemical applications

A kinetic study of a membrane anaerobic reactor (MAR) for treatment of sewage sludge

The application of kinetic models (Monod, Contois and Chen & Hashimoto) and overall microbial kinetic on the membrane anaerobic reactor (MAR) for treatment of sewage sludge was investigated. The system consists of a cross-flow ultrafiltration membrane and six steady states were attained over a range of mixed liquor suspended solids of 12,760-21,800 mg/l. The results of all six steady states were successfully fitted above 98% for three known kinetics. The growth yield coefficient, Y, was found to be 0.74 gVSS/gCOD while the specific microorganism decay rate was 0.20 d-1. The k values were in the range of 0.350-0.519 gCOD/gVSS.d and μmax values were between 0.259 and 0.384 d-1. The COD removal efficiency was 96.5-99% with HRT of 7.8 days. The methane gas yield was between 0.19 l/g COD/d to 0.54 l/g COD/d when the organic loading rate increased from 0.1 kg COD/m3/d to 10 kg COD/m3/d. The system efficiency was greatly influenced by SRT and OLRs. Membrane flux rate deterioration was observed from 62.1 l/m2/h to 6.9 l/m2/h due to membrane fouling