Nanna Suryana
Universiti Teknikal Malaysia Melaka

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Basic principles of blind write protocol Khairul Anshar; Nanna Suryana; Noraswaliza Noraswaliza
Bulletin of Electrical Engineering and Informatics Vol 9, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (771.247 KB) | DOI: 10.11591/eei.v9i3.2032


The current approach to handle interleaved write operation and preserve consistency in relational database system still relies on the locking protocol. If any entity is locked by any transaction, then it becomes temporary unavailable to other transaction until the lock is released. The temporary unavailability can be more often if the number of write operation increases as happens in the application systems that utilize IoT technology or smartphone devices to collect the data. To solve this problem, this research is proposed blind write protocol which does not lock the entity while the transaction is performing a write operation. This paper presents the basic principles of blind write protocol implementation in a relational database system
New instances classification framework on Quran ontology applied to question answering system Fandy Setyo Utomo; Nanna Suryana; Mohd Sanusi Azmi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 1: February 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i1.9794


Instances classification with the small dataset for Quran ontology is the current research problem which appears in Quran ontology development. The existing classification approach used machine learning: Backpropagation Neural Network. However, this method has a drawback; if the training set amount is small, then the classifier accuracy could decline. Unfortunately, Holy Quran has a small corpus. Based on this problem, our study aims to formulate new instances classification framework for small training corpus applied to semantic question answering system. As a result, the instances classification framework consists of several essential components: pre-processing, morphology analysis, semantic analysis, feature extraction, instances classification with Radial Basis Function Networks algorithm, and the transformation module. This algorithm is chosen since it robustness to noisy data and has an excellent achievement to handle small dataset. Furthermore, document processing module on question answering system is used to access instances classification result in Quran ontology.
Voronoi diagram with fuzzy number and sensor data in an indoor navigation for emergency situation Nanna Suryana; Fandy Setyo Utomo; Mohd Fairuz Iskandar Othman; Mohd Nazrin Muhammad
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 4: August 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i4.14905


Finding shortest and safest path during emergency situation is critical. In this paper, an indoor navigation during an emergency time is investigated using the combination of Voronoi Diagram and fuzzy number. The challenge in indoor navigation is to analyses the network when the shortest path algorithm does not work as always expected. There are some existing methods to generate the network model. First, this paper will discuss the feasibility and accuracy of each method when it is implemented on building environment. Next, this paper will discuss selected algorithms that determine the selection of the best route during an emergency situation. The algorithm has to make sure that the selected route is the shortest and the safest route to the destination. During a disaster, there are many uncertainties to deal with in determining the shortest and safest route. Fuzzy logic can be hardly called for to deal with these uncertainties. Based on sensor data, this paper will also discuss how to solve shortest path problem using a fuzzy number.
Spatial analysis model for traffic accident-prone roads classification: a proposed framework Anik Vega Vitianingsih; Nanna Suryana; Zahriah Othman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 2: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i2.pp365-373


The classification method in the spatial analysis modeling based on the multi-criteria parameter is currently widely used to manage geographic information systems (GIS) software engineering. The accuracy of the proposed model will play an essential role in the successful software development of GIS. This is related to the nature of GIS used for mapping through spatial analysis. This paper aims to propose a framework of spatial analysis using a hybrid estimation model-based on a combination of multi-criteria decision-making (MCDM) and artificial neural networks (ANNs) (MCDM-ANNs) classification. The proposed framework is based on the comparison of existing frameworks through the concept of a literature review. The model in the proposed framework will be used for future work on the traffic accident-prone road classification through testing with a private or public spatial dataset. Model validation testing on the proposed framework uses metaheuristic optimization techniques. Policymakers can use the results of the model on the proposed framework for initial planning developing GIS software engineering through spatial analysis models.