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Analysis of Inhibiting Factors in Shipyards in Clusterizing Shipyards on the Northern Coast of Aceh Indonesia Using the Fuzzy AHP Method – A Preliminary Study

Rizwan Thaib, Chaliluddin Makwiyah A., Nuvus Hayatun, Arif Muhammad, Muchlis Yusrizal, Akhyar Akhyar

Ecological Engineering & Environmental Technology

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2023

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Vol. 24, iss. 7

38--45

EN

The fishing shipyard in Banda Aceh City is a privately owned shipyard and is managed in a family manner. The shipyard here is active in carrying out maintenance, repair and construction of new ships when there is demand from consumers. The shipyards in Banda Aceh City generally make ships made of wood. The problem that is currently being faced is that there are many abandoned ships due to lack of finance, natural resources, human resources and environment, this is an obstacle to the progress and development of shipyards. The purpose of this study was to determine the inhibiting factors that exist in shipyards in the city of Banda Aceh and find alternative solutions to these problems. The method used in this study was a survey method used to look at existing symptoms and collect data on the factors related to research variables and then analyzed using the Fuzzy AHP method. The results of this study indicate that the financial inhibiting factor is the most influential factor in shipyards with a resulting value of 0.4635, the inhibiting factor of Natural Resources is worth 0.35675, the inhibiting factor of Human Resources is worth 0.2865 and the inhibiting factor from the environment is the inhibiting factor which is the lowest or less influential with a value of 0.14325. The alternative solutions to financial problems are capital loans and investments.An alternative for natural resources is the addition of a minimum stock to anticipate stock scarcity and delays in the delivery of materials and tools. The alternative for human resources is the existence of an office, organizational structure, and division of tasks as well as raising awareness of occupational health and safety.As for the alternatives for the environment, namely the need for buildings or installation of tarpaulins for the areas where ships are built, good land management and studies of other natural impacts.

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Classification of Seizure Types Using Random Forest Classifier

Basri Ashjan, Arif Muhammad

Advances in Science and Technology. Research Journal

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2021

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Vol. 15, no 3

167--178

EN

Epilepsy is one of the most common mental disorders in the world, affecting 65 million people. The prevalence in Arab countries of Epilepsy is estimated at 174 per 100,000 individuals, and in Saudi Arabia is 6.54 per 1,000 individuals. Epilepsy seizures have different types, and each patient needs to have a treatment plan according to the seizure type. Hence, accurate classification of seizure type is an essential part of diagnosing and treating epileptic patients. In this paper, features based on fast Fourier transform from EEG montages are used to classify different types of seizures. Since the distribution of classes is not uniform and the dataset suffers from severe imbalance. Various algorithms are used to under-sample the majority class and over-sample the minority classes. Random forest classifier produced classification accuracy of 96% to differentiate three types of seizures from the healthy EEG reading.

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Arabic and American Sign Languages Alphabet Recognition by Convolutional Neural Network

Alshomrani Shroog, Aljoudi Lina, Arif Muhammad

Advances in Science and Technology. Research Journal

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2021

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Vol. 15, no 4

136--148

EN

Hearing loss is a common disability that occurs in many people worldwide. Hearing loss can be mild to complete deafness. Sign language is used to communicate with the deaf community. Sign language comprises hand gestures and facial expressions. However, people find it challenging to communicate in sign language as not all know sign language. Every country has developed its sign language like spoken languages, and there is no standard syntax and grammatical structure. The main objective of this research is to facilitate the communication between deaf people and the community around them. Since sign language contains gestures for words, sentences, and letters, this research implemented a system to automatically recognize the gestures and signs using imaging devices like cameras. Two types of sign languages are considered, namely, American sign language and Arabic sign language. We have used the convolutional neural network (CNN) to classify the images into signs. Different settings of CNN are tried for Arabic and American sign datasets. CNN-2 consisting of two hidden layers produced the best results (accuracy of 96.4%) for the Arabic sign language dataset. CNN-3, composed of three hidden layers, achieved an accuracy of 99.6% for the American sign dataset.