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Abdul HAKIM,Rahmat MADJID,Endro SUKOTJO,Yusuf YUSUF 한국유통과학회 2022 The Journal of Asian Finance, Economics and Busine Vol.9 No.3
This research aims to determine and analyze: (a) the effects of digital marketing activity (DMAc), digital marketing capability (DMC), and Digital marketing asset (DMA), (b) the effects of DMAc, DMC, and DMA on marketing performance (MP), (c). the effects of entrepreneurial orientation (OE) on MP and (d). the mediating role of OA on the effects of DMAc, DMC, and DMA on OE. The research population is all the small enterprise actors in Kendari city applying digital marketing and having a permanent establishment in Kendari City. The analysis tool used is SEM Partial Least Square. Results of the research show that: (a). DMC and DMA have positive and significant effects on OE while DMAc is found to be insignificant on OE, (b). then, DMAc and DMA have positive and significant effects on MP, and DMC is found to be insignificantly increasing MP, (c). OE has positive and significant effects on MP and (d). DMC effects on MP and effects between DMA and MP are mediated by OE, whereas DMA effects on MP are not mediated by OE position. Based on research findings, DMC and DMA are superior at forming research opinions since they have a substantial influence on enhancing collaboration.
Abdul Hakim Mohamed Salleh,Mohd Saberi Mohamad,Safaai Deris,Sigeru Omatu,Florentino Fdez-Riverola,Juan Manuel Corchado 한국생물공학회 2015 Biotechnology and Bioprocess Engineering Vol.20 No.4
The increasing demand of biochemical supply for various industries has spurred the development of metabolic engineering to find the optimal design of the microbial cell factories. Traditional method of chemical synthesis using the natural producer leads to the production far below their theoretical maximums. Gene knockout strategy is then introduced to improve the metabolite production. To aid the process, many computational algorithms have been developed to design the optimal microbial strain as cell factories to increase the production of the desired metabolite. However, due to the size of the genome scale model of the microbial strain, finding the optimal combination of genes to be knocked out is not an easy task. In this paper, we propose a hybrid of Genetic Ant Colony Optimization (GACO) and Flux Balance Analysis (FBA) namely GACOFBA to find the optimal gene knockout that increase the production of the target metabolite. Using E. coli and S. cerevisiae genome scale model, we test our proposed hybrid algorithm to increase the production of four different metabolites. By comparing with the results from existing method OptKnock as well as the conventional Ant Colony Optimization (ACO), the results show that our proposed hybrid algorithm able to identify the best set of genes and increase the production while maintaining the optimal growth rate.
Modal parameters based structural damage detection using artificial neural networks - a review
Hakim, S.J.S.,Razak, H. Abdul Techno-Press 2014 Smart Structures and Systems, An International Jou Vol.14 No.2
One of the most important requirements in the evaluation of existing structural systems and ensuring a safe performance during their service life is damage assessment. Damage can be defined as a weakening of the structure that adversely affects its current or future performance which may cause undesirable displacements, stresses or vibrations to the structure. The mass and stiffness of a structure will change due to the damage, which in turn changes the measured dynamic response of the system. Damage detection can increase safety, reduce maintenance costs and increase serviceability of the structures. Artificial Neural Networks (ANNs) are simplified models of the human brain and evolved as one of the most useful mathematical concepts used in almost all branches of science and engineering. ANNs have been applied increasingly due to its powerful computational and excellent pattern recognition ability for detecting damage in structural engineering. This paper presents and reviews the technical literature for past two decades on structural damage detection using ANNs with modal parameters such as natural frequencies and mode shapes as inputs.
Hakim, S.J.S.,Razak, H. Abdul Techno-Press 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.45 No.6
In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs) techniques are developed and applied to identify damage in a model steel girder bridge using dynamic parameters. The required data in the form of natural frequencies are obtained from experimental modal analysis. A comparative study is made using the ANNs and ANFIS techniques and results showed that both ANFIS and ANN present good predictions. However the proposed ANFIS architecture using hybrid learning algorithm was found to perform better than the multilayer feedforward ANN which learns using the backpropagation algorithm. This paper also highlights the concept of ANNs and ANFIS followed by the detail presentation of the experimental modal analysis for natural frequencies extraction.
Community Development and Economic Welfare through the Village Fund Policy
Djoko UDJIANTO,Abdul HAKIM,Tjahjanulin DOMAI,Suryadi SURYADI,H. HAYAT 한국유통과학회 2021 The Journal of Asian Finance, Economics and Busine Vol.8 No.1
This study aims to investigate the implementation of village fund (VF) policy in Indonesia by addressing the following issues: (1) what is the VF policy; (2) factors that support and hinder policy implementation; (3) impact of policy implementation; and (4) model for implementing village fund policies that can improve community welfare. Through a descriptive qualitative-based approach, several indicators are measured, namely, the substance of implementing rules, the results of project implementation, supporting and inhibiting factors for policies, participation factors, and the impact generated by village fund policies, which include social and economic effects. The extraction of this information and indicators will lead this study to produce ideal models and propositions for quantitative confirmatory research as a future research agenda. This study was conducted in two villages (Mojomulyo and Tambakromo) in Pati District, Central Java, Indonesia. Data collection model using interviews and observations from all actors who play a role (e.g., village government, village supervisory agency, and community). The study results show that policies have been implemented by normative rules; there are several supporting and inhibiting factors both internal and external. The study results also confirm the relevance of the articulated theory and some comprehensive input to our study.
Pooi San Chua,Abdul Hakim Mohamed Salleh,Mohd Saberi Mohamad,Safaai Deris,Sigeru Omatu,Michifumi Yoshioka 한국생물공학회 2015 Biotechnology and Bioprocess Engineering Vol.20 No.2
The current problem for metabolic engineering is how to identify a suitable set of genes for knockout that can improve the production of certain metabolites and sustain the growth rate from the thousands of metabolic networks which are complex and combinatorial. Some approaches, such as OptKnock and OptGene, are developed to enhance the production of desired metabolites. However, the performances of these approaches are suboptimal and the obtained results are unsatisfactory because of computational limitations such as local minima. In this paper, we propose a hybrid of Bat Algorithm and Flux Balance Analysis (BATFBA) to enhance succinate and lactate production by identifying a set of genes for knock out. The Bat Algorithm is an optimisation algorithm, whereas Flux Balance Analysis (FBA) is a mathematical approach to analyse the flow of metabolites through a metabolic network. The Escherichia coli iJR904 dataset was used to determine optimal knockout genes, production rate, and growth rate. By applying this hybrid method to the iJR904 dataset, we found that BATFBA yielded better results than existing methods, such as OptKnock and a hybrid of Artificial Bee Colony algorithms and Flux Balance Analysis (ABCFBA), at predicting succinate and lactate production.
S.J.S. Hakim,H. Abdul Razak 국제구조공학회 2013 Steel and Composite Structures, An International J Vol.14 No.4
Damage in structures often leads to failure. Thus it is very important to monitor structures for the occurrence of damage. When damage happens in a structure the consequence is a change in its modal parameters such as natural frequencies and mode shapes. Artificial Neural Networks (ANNs) are inspired by human biological neurons and have been applied for damage identification with varied success. Natural frequencies of a structure have a strong effect on damage and are applied as effective input parameters used to train the ANN in this study. The applicability of ANNs as a powerful tool for predicting the severity of damage in a model steel girder bridge is examined in this study. The data required for the ANNs which are in the form of natural frequencies were obtained from numerical modal analysis. By incorporating the training data, ANNs are capable of producing outputs in terms of damage severity using the first five natural frequencies. It has been demonstrated that an ANN trained only with natural frequency data can determine the severity of damage with a 6.8% error. The results shows that ANNs trained with numerically obtained samples have a strong potential for structural damage identification.
S.J.S. Hakim,H. Abdul Razak 국제구조공학회 2013 Structural Engineering and Mechanics, An Int'l Jou Vol.45 No.6
In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural networks (ANNs) techniques are developed and applied to identify damage in a model steel girder bridge using dynamic parameters. The required data in the form of natural frequencies are obtained from experimental modal analysis. A comparative study is made using the ANNs and ANFIS techniques and results showed that both ANFIS and ANN present good predictions. However the proposed ANFIS architecture using hybrid learning algorithm was found to perform better than the multilayer feedforward ANN which learns using the backpropagation algorithm. This paper also highlights the concept of ANNs and ANFIS followed by the detail presentation of the experimental modal analysis for natural frequencies extraction.