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Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi
Published by Universitas Dr. Soetomo Surabaya
ISSN : 25023470     EISSN : 25810367     DOI : 10.25139
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Library & Information Science
Inform: Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi is One of the journals published by the Informatics Engineering Department Dr. Soetomo University, was established in January 2016. Inform a double-blind peer-reviewed journal, the aim of this journal is to publish high-quality articles dedicated to the field of information and communication technology, Published 2 times a year in January and July. Inform with p-ISSN:2502-3470 and e-ISSN:2581-0367 has been accredited by the Ministry of Research and Technology of the National Research and Innovation Agency of the Republic of Indonesia Number 85/M/KPT/2020 dated April 1, 2020. Accreditation is valid for 5 years Vol.3 No.2 2018 to Vol.8 No.1 2023. Focus and Scope that is Scientific research related to information and communication technology fields, including Software Engineering, Information Systems, Human-Computer Interaction, Architecture and Hardware, Computer Vision, Pattern Recognition, Computer Application and Artificial intelligence, Game Technology, and Computer Graphics, but not limited to informatics scope.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 6 Documents
 1 
Entity Extraction and Annotation for Job Title and Job Descriptions Using Bert-Based Model Fitri Ana Wati, Seftin; Fitri, Anindo Saka; Putra, Herlambang Haryo; Widodo, Suryo; Aziiza, Arizia Aulia
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 10 No. 1 (2025)
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/inform.v10i1.7367

Abstract

This research paper investigates Named Entity Recognition (NER) within Indonesia’s job vacancy domain, employing state-of-the-art Bert-based models. The study presents a detailed data collection and preprocessing methodology, followed by the Bert-based model’s fine-tuning for enhanced NER. The dataset comprises 48,673 job vacancies collected from the JobStreet website in July 2023, specifically focusing on multi-entity recognition, including job titles and job descriptions. An original annotation algorithm was developed using Python and Laravel for precise entity recognition. In addition, this paper provides an extensive literature review of NER and Bert-based models and discusses their relevance in the context of the Indonesian job market. The outcomes highlight the efficacy of our BERT-based model, attaining an average accuracy of 78.5%, a precision of 79.7%, a recall of 81.1%, and an F1 score of 80.8% in the Named Entity Recognition (NER) task. The study concludes by discussing the implications, limitations, and future directions, underscoring the model’s potential applicability in streamlining job matching and recruitment processes in Indonesia and beyond. This research contributes to the field by providing a robust framework for NER in job vacancies, highlighting the potential for improved job matching, and proposing enhancements for future model development and application in other languages and regions.
Secure Authentication in Vehicular Networks: Integrating Zero-Knowledge Protocols with RSS Key Generation Kriswantoro, M. Cahyo; Handoyo, Eko; Sa Yu Zakka, Mutsna
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 10 No. 2 (2025): In Press: July, 2025
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/inform.v10i2.9488

Abstract

Zero Knowledge Authentication protocol is a cryptographic method used to identify users through interactive communications without exposing confidential. The identification scheme is an example of a real-world application of the Zero-Knowledge protocol, which provides a mechanism for actors in possession of a secret key to verify their identity using the corresponding public key. Several prominent identification schemes have been proposed in the literature, including the Feige-Fiat-Shamir (FFS), Guillou-Quisquater (GQ), and Schnorr protocols. In conjunction with these schemes, mechanisms for key generation and key updates have been developed to enhance privacy in zero-knowledge cloud-based file storage systems. To ensure data integrity within cloud environments, the Shacham-Waters auditing protocol has been employed. The FFS identification scheme, in particular, utilizes a public-private key pair in a parallel verification structure. To improve computational efficiency, this scheme has been enhanced by incorporating parallel constructions. Researchers utilize the Zero-Knowledge Authentication Feige-Fiat-Shamir protocol by combining the key generator obtained from Received Signal Strength (RSS) in vehicle communications, thereby replacing the channel in the Feige-Fiat-Shamir protocol with the key generator derived from RSS. The existing combination combines stages 1 and 4 in FFS. The change is that the channel sent is replaced with a key generator obtained from the RSS key generator. The results of this study are expected to serve as a reference for the implementation of vehicle communication technology, which is anticipated to experience rapid growth in the future.
Analysis and Design of AI and AR-Based Applications with a UIUX Approach to support Inclusive Learning for students with Disabilities Swalaganata, Galandaru; Andarwati, Mardiana; Al-Islama Achyunda Putra, Firnanda; Assih, Prihat; Sudarwati, Ririn; Bramasta, Yara
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 10 No. 2 (2025): In Press: July, 2025
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/inform.v10i2.9996

Abstract

The integration of Artificial Intelligence (AI) and Augmented Reality (AR) in education holds transformative potential, particularly in fostering inclusive learning environments for students with disabilities. This study investigates the integration of Artificial Intelligence and Augmented Reality to advance inclusive education. This research examines the analysis and design of AI and AR-based applications, employing a User Interface and User Experience (UI/UX) approach to cater to the diverse needs of students with physical, sensory, or cognitive impairments. By emphasizing inclusive design principles, the study aims to develop adaptive and accessible educational tools that promote engagement, deepen understanding, and improve learning outcomes. The research adopts a multidisciplinary methodology, combining insights from accessibility standards, UI/UX design frameworks, and educational technologies. Key objectives include identifying accessibility barriers, designing user-centric interfaces, and evaluating the effectiveness of AI-driven personalization and AR-enhanced interactivity in real-world learning scenarios. Preliminary findings highlight the importance of responsive and adaptive interfaces in facilitating equitable access and promoting active participation among students with disabilities. The study underscores the role of innovative technologies in bridging gaps in traditional education systems, ultimately promoting an inclusive and empowering learning experience. This research contributes to the broader discourse on leveraging technology to ensure educational equity and inclusion in the digital age.
Quantum-Assisted Architectures for 6G and IoT: A Framework for Secure and Efficient Wireless Networks Zangana, Hewa; Bibo Sallow, Amira; Mahmood Mustafa, Firas
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 10 No. 2 (2025): In Press: July, 2025
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/inform.v10i2.10436

Abstract

The convergence of Sixth-Generation (6G) wireless networks and the Internet of Things (IoT) demands unprecedented levels of performance, scalability, and security. Traditional architectures are increasingly inadequate in addressing the computational and security challenges posed by massive IoT connectivity, ultra-low latency, and high data throughput. This paper proposes a novel quantum-assisted architecture that integrates quantum computing and quantum communication principles to enhance the efficiency and security of 6G-enabled IoT systems. The framework leverages quantum key distribution (QKD), quantum machine learning (QML), and entanglement-assisted routing to provide end-to-end encryption, intelligent resource allocation, and resilient data transmission. Our simulation results and comparative analysis demonstrate significant improvements in network throughput, latency, and security resilience compared to classical 6G-IoT architectures. This research establishes a foundational step toward realising secure and intelligent next-generation wireless networks through the integration of quantum technology.
Optimized Hybrid CNN-Residual BiLSTM with Adaptive Prediction System for Enhanced Gas Turbine Performance Forecasting Pratama, Andika; Fatichah, Chastine
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 10 No. 2 (2025): In Press: July, 2025
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/inform.v10i2.10226

Abstract

Accurately forecasting critical performance parameters, such as Compressor Discharge Pressure (PCD), in gas turbines is a strategic imperative for ensuring operational reliability and energy efficiency, particularly in vital facilities like Central Processing Plants (CPPs). However, achieving reliable forecasts presents significant analytical challenges due to the complex multivariate, non-linear, and noisy nature of industrial sensor data, compounded by dynamic operational loads. This study introduces and validates an integrated analytical framework centered on a systematically optimized Hybrid Convolutional Neural Network-Residual Bi-Directional Long Short-Term Memory (CNN-Residual BiLSTM) architecture. This hybrid design synergistically leverages CNN layers for multi-scale temporal pattern extraction and Residual BiLSTM blocks for robust long-range dependency modelling, enhanced by residual connections for training stability. The framework emphasizes rigorous data pre-processing and the selection of a comprehensive feature set, incorporating thermodynamic, electrical, and operational control signals to provide a holistic view of the turbine's state. Automated hyperparameter optimization via the Optuna framework is employed to maximize the model's predictive potential. Empirical validation demonstrates that the optimized configuration's performance is superior to that of baseline models (RMSE = 0.0611, MAE = 0.0298, R² = 0.9601), confirming the framework's contribution to advancing data-driven performance diagnostics and predictive maintenance (PdM) strategies for gas turbines.
A Compliance Evaluation System for Broadcasting Institutions in the Post-Analog Transition Era: Applying AHP and SAW to Administrative, Technical, and Legal Criteria Trianthono, Indra; Salim, Ravi Ahmad
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 10 No. 2 (2025): In Press: July, 2025
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/inform.v10i2.10496

Abstract

This study addresses the pressing need for systematic compliance evaluation of radio broadcasting institutions in Indonesia amid the country's transition to digital broadcasting. As the Ministry of Communication and Information Technology mandates adherence to new regulatory frameworks, ensuring broadcaster compliance has become critical for maintaining media accountability, quality, and governance. To evaluate compliance across administrative, technical, and legal dimensions, this study integrates the Analytic Hierarchy Process (AHP) to determine priority weights and the Simple Additive Weighting (SAW) method to rank 527 radio broadcasters. The AHP method achieved a consistency ratio (CR) of less than 0.1, validating expert-based weight assignments for eight regulatory criteria, including licensing, permit payment, technical equipment standards, and ownership reporting. The SAW method then translated these weights into performance scores, categorizing stations into three compliance levels: High (382 providers, with an average score of 100%), Moderate (120 providers, with an average score of 83.65%), and Low (25 providers, with an average score of 50.85%). These results indicate a relatively high regulatory compliance among radio broadcasters, suggesting improved institutional awareness and the effectiveness of current oversight mechanisms. A data-driven Decision Support System (DSS) developed from this framework offers regulators a scalable tool for formulating targeted policies and interventions based on measurable compliance outcomes.

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