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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) International Journal of Evaluation and Research in Education (IJERE) Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Nursuriati Jamil
Universiti Teknologi MARA
Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Evaluations of Internet of Things-based personal smart farming system for residential apartments Fatin Natasya Shuhaimi; Nursuriati Jamil; Raseeda Hamzah
Bulletin of Electrical Engineering and Informatics Vol 9, No 6: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v9i6.2496

Abstract

Urban farming is popularly accepted by communities living in cities as they are more health-conscious and to help support the high cost of living. Unfortunately, farming takes a considerable amount of time specially to monitor the plant’s growth. Therefore, smart farming using Internet of Things (IoT) should be adopted to realize urban farming. In this study, two IoT-based smart farming system designs for personal usages in a residential apartment were proposed and evaluated. As the design was meant for beginners, two utmost parameters for maintaining plant growth was evaluated, that are humidity and temperature. The humidity and temperature readings of design A using DHT 11 sensor and design B using DHT 22 sensor were recorded for 3 days and were compared against the actual humidity and temperature of the environment. After comparing the sum of absolute difference (SAD) of both designs, the implementation costs, and the consumption power, there is an inconclusive finding in terms of accuracy and costs. However, the basic design and cost of implementing a personal IoT-based smart farming system were proposed. The factors to be considered in constructing a personal smart farming system were also described.
Evaluation of Support Vector Machine and Decision Tree for Emotion Recognition of Malay Folklores Mastura Md Saad; Nursuriati Jamil; Raseeda Hamzah
Bulletin of Electrical Engineering and Informatics Vol 7, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (599.337 KB) | DOI: 10.11591/eei.v7i3.1279

Abstract

In this paper, the performance of Support Vector Machine (SVM) and Decision Tree (DT) in classifying emotions from Malay folklores is presented. This work is the continuation of our storytelling speech synthesis work to add emotions for a more natural storytelling. A total of 100 documents from children short stories are collected and used as the datasets of the text-based emotion recognition experiment. Term Frequency-Inverse Document Frequency (TF-IDF) is extracted from the text documents and classified using SVM and DT. Four types of common emotions, which are happy, angry, fearful and sad are classified using the two classifiers. Results showed that DT outperformed SVM by more than 22.2% accuracy rate. However, the overall emotion recognition is only at moderate rate suggesting an improvement is needed in future work. The accuracy of the emotion recognition should be improved in future studies by using semantic feature extractors or by incorporating deep learning for classification.
SiulMalaya: an annotated bird audio dataset of Malaysia lowland forest birds for passive acoustic monitoring Nursuriati Jamil; Ahmad Nazem Norali; Muhammad Izzad Ramli; Ahmad Khusaini Mohd Kharip Shah; Ismail Mamat
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.5243

Abstract

The laborious point count method of conducting bird surveys is still a common practice in Malaysia. An alternative method known as passive acoustic monitoring (PAM) is deployed in many countries by placing sound recorders at surveying sites to collect bird sounds. Studies revealed that the number of bird densities counted by human observers was agreeable with those obtained using PAM. However, one of the most essential gaps in conducting PAM is the lack of expert-verified bird-call databases. Therefore, the aim of this study is to construct the first annotated Malaysia lowland forest bird sounds called SiulMalaya to be used as ground-truth datasets for PAM-related activities. The raw bird sounds dataset was downloaded from Macaulay Library using the eBird platform. Data pre-processing was done to produce annotated audio tracks that can be used as training datasets for bird classification. SiulMalaya dataset was further validated by two bird experts from the Department of Wildlife and National Parks, Malaysia. A bird identification experiment was carried out to assess and validate SiulMalaya dataset using a convolutional neural network (CNN) learning model. Even though the accuracy of bird identification is slightly above 50%, the annotated dataset is shown to be viable for PAM-related operations.
Co-Authors Ahmad Khusaini Mohd Kharip Shah Ahmad Nazem Norali Ali Abd Almisreb Fatin Natasya Shuhaimi Gehad Saleh Ahmed Mohammed Ismail Mamat Izzad Ramli Mastura Md Saad Muhammad Izzad Ramli N. Md Din Norizah Ardi Norizan Mat Diah Raseeda Hamzah Syed Mohd Zahid Syed Zainal Ariffin Zaidah Ibrahim