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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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An iterative LQR control method for 6-DOF UAV quadcopter under disturbance and payload variation Vo Van An; Nguyen Van Binh
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.v14i1.1017

Abstract

This paper proposes an optimal control method based on the iterative LQR aimed at enhancing the trajectory tracking accuracy of a 6 DOF quadcopter UAV under external disturbances and varying payloads. The main distinguishing feature of the proposed method is the iterative optimization mechanism of the weighting matrices Q and R through the logarithmic normalization of error metrics, including ITAE, ISE, and RMSE, which automatically adjusts the controller based on the system's actual performance. The effectiveness of the method is evaluated through simulations in MATLAB across various flight trajectories and scenarios, including ideal conditions, disturbances, and combined disturbances with varying payloads. The simulation results show that the iterative LQR method significantly improves control quality compared to traditional PID and LQR controllers, with error reductions ranging from 6.6% to 41.8%, particularly evident in the x and y position axes and the yaw angle ψ. Furthermore, preliminary experimental results on quadcopter UAV hardware show that the proposed iterative LQR algorithm helps improve control quality and enhance flight attitude stability under low wind disturbance and varying payloads. These results demonstrate the adaptability and robustness of the proposed method under the complex operating conditions of UAVs.
Area-Efficient Concryption for Secure Data Transmission Thejaswini P; Gunasagari G S; Sunita Shirahatti; Vivekananda G
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.v14i1.1020

Abstract

As computer networks become more connected, it is more important than ever to protect users' data and privacy. Especially because modern cyberattacks are so advanced, cryptography is a key way to keep user data private, real, and safe. However, since cryptographic methods do not reduce file sizes, concryption techniques are employed to optimize storage and enhance bandwidth for secure and efficient data transmission. The concryption method is implemented using two of the most flexible and efficient symmetric block ciphers: The concryption method uses the Blowfish and Twofish symmetric block ciphers, along with compression techniques such as LZW and MTF. We propose an area-efficient encryption algorithm that is better than the Twofish algorithm. The proposed architecture is a suitable fit for IoT devices, embedded systems, and hardware platforms that don't need much power (0.135 mW) and don't need much space (1,197 gates). Therefore, the proposed encryption architecture is a good balance between the cost of hardware, the effectiveness, and the security of lightweight cryptographic applications. The proposed encryption technique is applied to the concryption process, where compression before encryption results in a better compression ratio compared to encrypting the data first, thereby reducing bandwidth usage and enhancing transmission speed for secure data transfers.
The Ontology Driven E-Commerce Chatbot for Fashion Industry Maryam Mairaj; Shakil Ahmed; Kiran Hidayat; Aamir Zeb Shaikh; Shabbar Naqvi
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.v14i1.1055

Abstract

Conventional chatbots in fashion e-commerce rely on keyword matching and lack the semantic structure needed to correctly handle multi-entity product queries. This leads to inaccurate, context-blind responses that frustrate users and reduce platform engagement. This paper proposes and evaluates an ontology-driven chatbot system for fashion e-commerce and compares it directly against a non-ontology baseline built on the same dataset. Both systems were developed using Google Dialogflow for natural language processing, with product data collected through Python web scraping from the Alkaram fashion website. The ontology knowledge base was engineered in OWL 2 using Protégé and queried via SPARQL, with a class hierarchy covering product categories, attributes, and brand entities. To evaluate both models, five analysts conducted structured 10-minute conversations using a fixed set of 50 product queries, scoring each response across seven criteria, including accuracy, context retention, and grammatical quality, on a 0 to 9 scale. The ontology-based system achieved 88% accuracy compared to 67% for the non-ontology model, a 21% improvement. It also outperformed across all other criteria, with inter-analyst agreement within 0.5 points confirming evaluation reliability. These results show that integrating an OWL/SPARQL ontology into a Dialogflow-based chatbot meaningfully improves product query handling in fashion e-commerce, and the approach is practical enough to scale to other retail domains.
Particle Swarm Optimization–Tuned Random Forest for Multi-Class Mental Health Sentiment Classification from Textual Data Supriady; Ode Andi Alamsyah; Nesya Salma Ramadhani; Syafrial Fachri Pane
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.v14i1.1056

Abstract

Mental health sentiment classification from textual data has attracted increasing attention as a computational approach to support large-scale psychological assessment; however, multi-class classification remains challenging due to noisy text, class imbalance, and semantic overlap among categories. This study proposes and evaluates a machine learning framework for seven-class mental health sentiment classification that integrates enhanced text preprocessing with lemmatization, data augmentation via back-translation, TF-IDF feature extraction, and systematic model evaluation across multiple classifiers, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, K-Nearest Neighbors, AdaBoost, and XGBoost, under three hyperparameter tuning strategies: Grid Search, Random Search, and Particle Swarm Optimization (PSO). Experimental results indicate that ensemble-based models consistently outperform single classifiers, with the PSO-optimized Random Forest achieving the best numerical performance, attaining an accuracy of 0.933, a macro F1-score of 0.923, and a ROC AUC of 0.989, demonstrating strong generalization and balanced class-level performance despite dataset imbalance. These findings confirm that the combination of robust preprocessing and metaheuristic-based hyperparameter optimization significantly enhances multi-class mental health sentiment classification and supports its potential use as a scalable decision-support tool for large-scale mental health screening, while not intended to replace clinical diagnosis.
Consistency of News Titles and Contents With Multi Level Classification Using Bidirectional Encoder Representations from Transformer (BERT) Afrida Helen; Akmal -; Muhammad Razzaaq Fadilah
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.v14i1.1097

Abstract

News is written in a standard format that consist of a title followed by the content. The title must be present a content of news. Sometimes found that news title did not represent the content, causing readers to be disappointed when they finish reading the whole news. This research proposes multi-class and multi level classification using BERT algorithm to detect the suitability of news titles and their contents. The first level performs classification for complete data and the second level performs classification for data that has been separated into 2 parts, Title dan Conten. The dataset pertains to the realm of Covid-19 news because its substantial volume and diverse range of conversation topics. It is sufficient to be used as a dataset. We select several news topic classes that are often discussed, that are health, politic, and economy. In order to determine suitability, we categorise both the news Title and the news Content into predefined topic classes. The dataset exhibits an inbalance for each topic class. This research proposed a model to determine the appropriateness of both the news Title and the news Content. We use Bidirectional Encoder Representations from Transformers for building model. BERT is one of the state-of-the-art in the field of Natural Language Processing (NLP). Multi-layer classification that separates dataset (title and content) determine the consistency between them is very effective in increasing accuracy, and the BERT model is very supportive in solving linguistic problems well. This research succeeded in providing an accuracy 15% greater compared to previous research.

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