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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Markom, Arni Munira
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Computer Science & IT Electrical & Electronics Engineering

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Digital technologies evolution in swiftlet farming: a systematic literature review Markom, Arni Munira; Yusof, Yusrina; Markom, Marni Azira; Haris, Hazlihan; Muhammad, Ahmad Razif
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4456-4470

Abstract

The integration of cutting-edge technologies into swiftlet farming has greatly enhanced efficiency, productivity, and sustainability. The internet of things (IoT) provides farmers with up-to-date environmental data, enabling them to create and sustain ideal circumstances for swiftlets. Artificial intelligence (AI) enhances this process by analysing vast databases and providing farmers with well-informed choices to optimize yield. Biotechnology, by combining genetic selection and breeding programs, effectively connects with the IoT, enabling constant monitoring and control of the health and genetic traits of swiftlets. The integration of renewable energy technology seeks to diminish dependence on conventional energy sources, promoting sustainability. In this paper, a systematic review of the literature is examined the utilization of digital technology in the swiftlet farmhouse. The findings were classified into three main themes: smart monitoring and control systems, advanced bird detection techniques, and sustainable practices and innovative approaches, specifically in the manufacture of edible bird nest. This systematic literature review emphasizes the multidisciplinary nature of swiftlet farming's technological evolution, technology developers, challenges and recommendations that farmers and the industry face in their pursuit of sustainable growth.
Dissipative soliton generation with sidebands using Bismuth Telluride (Bi2Te3) in erbium doped fiber laser Haris, Hazlihan; Awalin, Lilik Jamilatul; Muhammad, Ahmad Razif; Mustaffa, Siti Nasuha; Markom, Arni Munira; Hasnan, Megat Muhammad Ikhsan Megat; Harun, Sulaiman Wadi; Tan, Sin Jin; Saad, Ismail
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8175

Abstract

In this work, the demonstration of dissipative soliton (DS) was observed in erbium doped fiber laser (EDFL) using of Bismuth Telluride (Bi2Te3) nanosheets saturable absorber (SA). The prepared SA was deposited on a fiber ferrule using optical deposition method. Interestingly, the DS generatered was accompanied with sidebands and the number of sidebands grew with laser diode pump power. Sidebands were observed as a result of modulation instability (MI) process, which arises from the interaction between DS and nonlinear gain in the fiber laser cavity. Signal to noise ratio (SNR) of 58 dB was attained, confirming the stability of the generated pulse. This work proved the capability of Bi2Te3 as SA for generating DS with sidebands in an EDFL.
Integrating time-frequency features with deep learning for lung sound classification Chang, Su Yuan; Markom, Marni Azira; Choong, Zhi Sheng; Markom, Arni Munira; Kamaruddin, Latifah Munirah; Tan, Erdy Sulino Mohd Muslim
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3737-3747

Abstract

Deep learning has transformed medical diagnostics, especially in analyzing lung sounds to assess respiratory conditions. Traditional methods like CT scans and X-rays are impractical in resource-limited settings due to radiation exposure and time consumption, while conventional stethoscopes often lead to misdiagnosis due to subjective interpretation and environmental noise. This study evaluates deep learning models for lung sound classification using the International Conference on Biomedical Health Informatics 2017 dataset, comprising 920 annotated samples from 126 subjects. Pre-processing includes down sampling, segmentation, normalization, and audio clipping, with feature extraction techniques like spectrogram and Mel-frequency cepstral coefficients (MFCC). The adopted automatic lung sound diagnosis network (ASLD-Net) model with triple feature input (time domain, spectrogram, and MFCC) achieved the highest accuracy at 97.25%, followed by the dual feature model (spectrogram and MFCC) at 95.65%. Single-input models with spectrogram and MFCC performed well, while the time domain input alone had the lowest accuracy.
IoT-based real-time monitoring of river water quality: a case study of the Selangor River Ahmad Jafri, Nur Aqilah; Markom, Arni Munira; Yusof, Yusrina; Burham, Norhafizah; Markom, Marni Azira
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i3.pp1541-1552

Abstract

Monitoring river water quality is crucial for preserving freshwater ecosystems, ensuring public health, and supporting resource management. Traditional methods, while accurate, lack the scalability and real-time capabilities needed for proactive intervention. This study introduces an IoT based water quality monitoring system for the Selangor River, integrating sensors for pH, temperature, turbidity, and total dissolved solids (TDS) with a NodeMCU ESP32 microcontroller. To complement the IoT system, a handheld test pen was used to measure salinity and electrical conductivity (EC), offering additional insights into water quality. Field tests at four stations along the river revealed significant spatial variations. Station 1, near the river mouth, showed high salinity, EC, and TDS, indicating saltwater intrusion, with relatively low turbidity. Stations 2 and 3 recorded the highest turbidity levels, suggesting sedimentation and upstream activities, with moderate salinity and EC. Station 4, upstream, demonstrated stable freshwater characteristics, with low salinity, EC, and turbidity levels. The IoT system reliably monitored real-time parameters, and its measurements were validated against those from the handheld test pen. Minor discrepancies in TDS and temperature readings highlighted the importance of calibration.
Co-Authors Ahmad Jafri, Nur Aqilah Awalin, Lilik Jamilatul Burham, Norhafizah Chang, Su Yuan Choong, Zhi Sheng Haris, Hazlihan Harun, Sulaiman Wadi Hasnan, Megat Muhammad Ikhsan Megat Kamaruddin, Latifah Munirah Markom, Marni Azira Muhammad, Ahmad Razif Mustaffa, Siti Nasuha Saad, Ismail Tan, Erdy Sulino Mohd Muslim Tan, Sin Jin Yusof, Yusrina