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EMITTER International Journal of Engineering Technology
Published by Politeknik Elektronika Negeri Surabaya
ISSN : 2355391x     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
EMITTER International Journal of Engineering Technology is a BI-ANNUAL journal published by Politeknik Elektronika Negeri Surabaya (PENS). It aims to encourage initiatives, to share new ideas, and to publish high-quality articles in the field of engineering technology and available to everybody at no cost. It stimulates researchers to explore their ideas and enhance their innovations in the scientific publication on engineering technology. EMITTER International Journal of Engineering Technology primarily focuses on analyzing, applying, implementing and improving existing and emerging technologies and is aimed to the application of engineering principles and the implementation of technological advances for the benefit of humanity.
Arjuna Subject : -
Articles 455 Documents
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Towards Robust Recognition of Handwritten Arabic Characters with Diacritics Using an Incremental Learning Approach Based on CNNs Fatima Aliyu Shugaba; Usman Ullah Sheikh; Mohd Afzan Othman; Nurulaqilla Khamis; Muhammad Habibullah Abdulfattah
EMITTER International Journal of Engineering Technology Vol 13 No 2 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i2.982

Abstract

Handwritten Arabic text recognition (HATR) presents unique challenges due to complex character shapes, contextual variations, cursive connections, and the presence of diacritical marks. This study introduces AHAD (Arabic Handwritten Alphabet with Diacritics), a novel benchmark dataset of 71,061 handwritten Arabic character images annotated with five primary vowel diacritics; Fathah, Kasrah, Dammah, Shaddah, and Sukoon, covering 492 distinct classes that combine character identity, contextual form, and diacritic. Leveraging this dataset, we propose an incremental learning framework based on Convolutional Neural Networks (CNNs) to address fine-grained recognition of handwritten Arabic characters with its corresponding diacritics. The model was initially trained on a 114-class dataset of handwritten Arabic characters (in all contextual forms) of non-diacritic characters and fine-tuned in two phases using the AHAD dataset. The two-phase strategy includes output layer expansion, learning rate adjustment, and gradual unfreezing of deeper layers to enhance knowledge retention and prevent catastrophic forgetting. The proposed method achieved a validation accuracy of 92.96% and a test accuracy of 93.26%. Our findings demonstrate the effectiveness of incremental learning for diacritic-aware Arabic handwriting recognition and establish AHAD as a strong baseline for future research in this field.
Design of Optimized Hardware Architecture for Discrete Cosine Transform using Loeffler’s Algorithm CHINMAYI S V; VEENA M B
EMITTER International Journal of Engineering Technology Vol 13 No 2 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i2.819

Abstract

The Discrete Cosine Transform (DCT) is the most commonly used transformation technique in image processing applications for data compression. It transforms a finite set of data points or pixels into frequency domain in terms of sum of cosine coefficients of various frequencies. This paper proposes an optimized hardware architecture for 2D 8x8 Discrete and Inverse discrete cosine transforms (DCT and IDCT) using Loeffler’s algorithm. The hardware architecture is optimized using efficient adders and multipliers. Modified carry select adders (CSLA) are used to boost the speed, wherein Booth multipliers improve overall performance of the design. The Loeffler’s algorithm consisting of 8-stage pipelined architecture reduces the arithmetic operations per cycle and improves processor efficiency. The front-end RTL design of the proposed architecture is implemented on Virtex-7 FPGA, while the backend design is implemented in Cadence using 45nm CMOS technology. The proposed design possesses 24% lesser area, 25% lesser leakage power and 8.8% lesser delay than the existing designs.
Sentiment Analysis Design and Development for Low Resource Languages in the Case of Telugu Srinivasu Badugu; Suneetha Chittineni; G.L. Anand Babu; G. Sekhar Reddy; S. Vijaykumar; N. Nagalakshmi
EMITTER International Journal of Engineering Technology Vol 13 No 2 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i2.861

Abstract

The use of sentiment analysis has become more widespread because it is necessary to filter and analyze information on the internet. It has a wide range of applications, including monitoring social media market research and opinion mining. Still, this development is restricted to few languages with enough resources. The Telugu language lags behind in this field of study, even though it is the fourth most spoken language in India and generates a vast quantity of data every day. In this research paper, we develop a trustworthy source for sentiment analysis in Telugu. To use in sentiment analysis, the data is annotated with Telugu movie reviews. We extracted 1844 sentences from 100 film reviews. We annotated the data with two annotators and calculated the kappa coefficient to determine the annotators' inter-rater reliability. We obtained a kappa value of 0.90 for 1844 sentences, indicating nearly perfect agreement. After the annotators' disagreements and discrepancies were resolved, 1807 sentences were chosen. For feature extraction, we used two vectorization methods: TF-IDF and Count vectorization. Using the two vectorization methods, we used SVM and Logistic regression. We used two vectorization approaches to test different split ratios such as 80-20%, 70-30%, and 60-40% on SVM and Logistic regression. The outcomes of the various combinations are compared. We discovered that combining TF-IDF with SVM for a 70-30% ratio yields the highest accuracy among the combinations tested on our dataset.
Machine Learning Approaches for Subcluster in IoT Sensor Networks with Hierarchical Clustering and Dendrograms Fuad Bajaber
EMITTER International Journal of Engineering Technology Vol 13 No 2 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i2.906

Abstract

This research focuses on optimizing IoT Sensor Networks (ISNs) by implementing hierarchical clustering algorithms. Traditional clustering methods often lead to imbalanced energy consumption, impacting network lifetime and performance. Our approach leverages hierarchical clustering to partition the network into a set of clusters. Each cluster has a cluster head and a set of sensor nodes. To enhance data aggregation and energy efficiency, we introduce subclustering within clusters using dendrograms. We assessed performance metrics using simulation, including energy consumption and scalability. The proposed hierarchical clustering methodology significantly improves network lifetime, energy efficiency, and data aggregation.
The Impact of Social Force Model Parameters On Frontier-Based Exploration Performance Asyam Irsyad; Bima Sena Bayu Dewantara Dewantara; Setiawardhana
EMITTER International Journal of Engineering Technology Vol 13 No 2 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i2.994

Abstract

Autonomous exploration is one of the most challenging tasks in mobile robotics, particularly in environments that contain dynamic obstacles and require fully autonomous mapping without human intervention. This study addresses the dual problem of enabling navigation in the presence of potential static obstacles and achieving autonomous map building. To solve this, we utilize the Social Force Model (SFM), which offers a behavior-based approach suitable for dynamic and uncertain environments. The objective of this research is to investigate how different SFM parameters—Gain (ks), Radius (rR), and Effective range (ψs)—influence the effectiveness of autonomous exploration. Experiments were conducted using a TurtleBot3 robot in a simulated 155 m² environment, where various parameter combinations were tested. Evaluation metrics included mapping completion, failure types, travel distance, and exploration duration. Results indicate that tuning the SFM parameters significantly affects the robot's ability to explore autonomously and avoid obstacles. Extremely low parameter values led to collisions, while excessively high values caused unstable or inefficient behavior. The Radius parameter had a major impact on spatial awareness, and moderate effective range values contributed to stable tracking. Furthermore, higher frontier sensing latency resulted in longer exploration times. This study provides practical insights into the sensitivity of SFM parameters and offers guidance for optimizing navigation systems for fully autonomous exploration in both simulated and real-world settings.
An Attention based Vision Transformer for the Detection of Insect Pests in Castor Crop Nitin; Satinder Bal Gupta; Pankaj Kumar Tyagi; Ravi Yadav; Amit Kumar Singh; Ajay Kumar; Shiv Kant
EMITTER International Journal of Engineering Technology Vol 14 No 1 (2026)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v14i1.984

Abstract

Castor (Ricinus communis L.) is a significant crop valued for its non-edible oil, yet its economic importance is compromised by insect pests causing substantial yield losses of 35-40%. This paper explores the efficiency of utilization of vision transformers for efficient pest classification. We propose CASTIPestViT, a Vision Transformer-based model specifically designed for insect pest detection in castor crops. The model integrates transfer learning and fine-tuning mechanisms, leveraging a pre-trained Vision Transformer (ViT) initially trained on ImageNet1k, and is fine-tuned on a custom dataset of castor insect pests. CASTIPestViT uses the self-attention mechanism of ViTs to capture global and local features of insect pests. The performance of CASTIPestViT is compared with six different pre-trained CNN models. The results obtained by the proposed model achieve a validation accuracy of 97.60% in insect pest detection and outperforming other state-of-the-art models in terms of precision, accuracy, and f1-score. The model offers a robust solution in early-stage insect pest detection to reduce yield losses. The efficiency and accuracy of the model make it suitable in sustainable crop management and smart agriculture systems for yield optimization.
Accurate Automatic Recognition of Iraqi License Plates Using YOLOv11n and Enhanced OCR Techniques Aymen saad; Usman Ullah Sheikh; Zaid Abdi Alkareem
EMITTER International Journal of Engineering Technology Vol 14 No 1 (2026)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v14i1.999

Abstract

Automatic License Plate Recognition (ALPR) systems are widely utilized for traffic monitoring, law enforcement, and security applications. While previous ALPR techniques have demonstrated high accuracy on uniform license plate formats, recognizing license plates in many countries particularly in Arabic-speaking regions remains a significant challenge due to the complex structure and diverse designs of the plates. Moreover, visual similarities between certain English letters and Arabic numerals often lead to misinterpretations by Optical Character Recognition (OCR) systems. This study proposes an efficient ALPR framework tailored for the new Iraqi license plates, employing image processing and deep learning (DL) techniques. The system integrates the YOLOv11n object detection model for accurate license plate localization, and OCR for character recognition. To address the OCR misclassification issue, particularly those caused by Arabic-English character similarities, the OCR component is enhanced using Character Index Checking (CCI) and Regular Expression Patterns (REP), resulting in more stable and accurate recognition outputs. The proposed method was evaluated on a real dataset of Iraqi license plates under varying illumination, viewpoints, and background complexity. Experimental results demonstrate strong end-to-end ALPR performance, achieving 100% plate detection accuracy, 99.8% recognition recall, and 99.5% mAP@0.5, confirming the robustness and practical effectiveness of the proposed system.
Exploring YOLO-Based Deep Learning Approaches for Fish Detection in Intelligent Aquatic Monitoring Systems Tresna Dewi; Riyo Irawan; Agum Try Wardhana; Muhammad Amri Yahya; Lukman Nul Hakim; Dini Septiyani AR
EMITTER International Journal of Engineering Technology Vol 14 No 1 (2026)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v14i1.1007

Abstract

The Advancements in precision aquaculture demand robust visual monitoring systems capable of accurate, real-time fish detection in complex underwater environments characterized by turbidity, occlusion, and dynamic illumination. While YOLO (You Only Look Once) architectures have demonstrated high efficiency in object detection tasks, their comparative performance for underwater fish detection remains underexplored, particularly across recent variants such as YOLOv5, YOLOv8, and YOLOv11. This study presents a systematic evaluation of three state-of-the-art YOLO models using a curated GlowFish dataset consisting of 533 annotated images across three fluorescent species. Data were acquired under controlled but visually diverse conditions using multi-angle imaging and standardized illumination. A uniform training pipeline, consistent annotation using the COCO format, and identical hyperparameters were applied across models to ensure fair benchmarking. Key evaluation metrics include precision, recall, mAP@0.5, and mAP@0.5:0.95. Experimental results reveal that YOLOv5 achieved the highest precision (0.963) and mAP@0.5 (0.967), while YOLOv8 delivered superior recall (0.930) and more balanced detection across species classes. YOLOv11 demonstrated architectural potential but showed greater sensitivity to class imbalance and reduced confidence stability. Visual analysis and confusion matrices further confirmed model-specific trade-offs in classification reliability and localization precision. This work contributes critical empirical insights into the selection of YOLO architectures for intelligent aquaculture systems, offering practical guidance for real-time aquatic monitoring deployments. Future research will extend this framework to multi-species, multi-environment datasets, integrate spatiotemporal behavioral tracking, and investigate deployment on resource-constrained edge-AI platforms, advancing the field toward interpretable and autonomous aquatic monitoring solutions.
Supporting Independent Prayer in Alzheimer’s Patients with an EEG-Enabled BCI System Huda Almuzaini; Sara AlRahili; Maha Al-sharikh; Samia Al-faifi
EMITTER International Journal of Engineering Technology Vol 14 No 1 (2026)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v14i1.1030

Abstract

Alzheimer's disease (AD) significantly impairs cognitive functions, making independent activities, including Muslim prayers, challenging for patients. This study introduces an innovative Brain-Computer Interface (BCI) system leveraging Electroencephalography (EEG) signals to facilitate prayer practices for individuals with AD. Utilizing a 5-channel EEG headset to monitor attention levels, our system detects alpha and beta wave patterns to assess user focus. When attention diminishes, the system provides guidance to the next prayer step, ensuring continuity and support. Additionally, motion detection technology captures physical movements associated with prayer, enabling the classifier to learn and recognize different prayer postures from a dataset of two individuals. This approach aids AD patients in maintaining religious practices independently while significantly enhancing their psychological well-being by fostering autonomy and spiritual fulfillment. Our findings suggest that integrating EEG-based BCI systems with motion detection offers a promising avenue for supporting daily activities and improving quality of life for individuals with cognitive impairments.
Modeling and Forecasting Piezoelectric Energy Harvesting Using Deep LSTM–ANN Architectures Yurni Oktarina; Tresna Dewi; Muhammad Amri Yahya; Assyifa Mourlina Faraquinnsha; Denny Juraijin
EMITTER International Journal of Engineering Technology Vol 14 No 1 (2026)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v14i1.1002

Abstract

Piezoelectric energy harvesting (PEH) enables maintenance-free micro-power generation for autonomous sensing and ultra-low-power electronics by converting ambient mechanical excitation into electrical energy. Despite substantial progress in piezoelectric materials and device structures, forecasting PEH electrical outputs remains difficult because the response is nonlinear, excitation is stochastic, and performance can drift under repeated loading. This paper proposes a hybrid deep learning architecture that integrates Long Short-Term Memory (LSTM) and Artificial Neural Network (ANN) components to forecast voltage, current, and power from footstep-driven PEH time-series data. The dataset is constructed by sampling the harvester voltage under controlled walking-induced excitation and organizing the continuous signal into supervised samples using a sliding-window scheme; features are normalized and paired with future targets for multi-output regression. The model is trained and evaluated against standalone LSTM, standalone ANN, and classical forecasting baselines using RMSE, MAE, MSE, and R2. Experimental results show high voltage prediction accuracy (R2=0.9896, RMSE = 0.0035, MAE = 0.0022), while current and power are predicted with acceptable performance consistent with their higher noise sensitivity and nonlinear coupling. These findings indicate that combining temporal memory with nonlinear regression improves forecasting stability for PEH outputs within the defined experimental setting and provides a practical basis for energy-aware scheduling and monitoring in self-powered sensing applications. Future work will extend the dataset to broader excitation conditions and incorporate uncertainty-aware modeling for robust edge deployment.

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