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INDONESIA
IJoICT (International Journal on Information and Communication Technology)
Published by Universitas Telkom
ISSN : -     EISSN : 23565462     DOI : https://doi.org/10.21108/ijoict
Core Subject : Science, Social,
Computer Science & IT Environmental Science
nternational Journal of Information Communication Technology (IJoICT) is a peer-reviewed Journal. This journal includes novel ideas on ICT, state of the art technique implementations, and study cases on developing countries. This journal fully acknowledges the articles that emphasize a balanced coverage between theory and practice. Subject areas that is suitable for publication to the following fields: Computer Networking and Communication Graphics & Multimedia Theoretical CS & Statistic Embeded System Software Engineering Information System Security & Cryptography Data Science Parallel and Distributed Systems Database Systems Intelligence System
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 25 Documents
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QURANIC KNOWLEDGE GRAPH: A MULTIDISCIPLINARY APPROACH TO MAPPING SEMANTIC NETWORKS IN THE QURAN Kemas Saleh Rahmat Wiharja; Muhammad Arif Bijaksana; Kemas Muslim Lhaksmana; Hafizh Putra Ardhana; Muhammad Aqil Ghazali Anhein
IJoICT (International Journal on Information and Communication Technology) Vol. 12 No. 1 (2026): Vol.12 No.1 Jun 2026
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v12i1.10653

Abstract

Understanding the Al-Quran is the eternal goal of every Muslim, wherever they are. Before the emergence of the Knowledge Graph, readers of the Al-Quran needed to move from the Al-Quran to Tafsir, Hadith, scholar opinions or other resources in order to grasp the full meaning of a verse or a chapter in the Al-Quran. To help the readers of the Al- Quran in pondering the meaning of the Al-Quran and linking the verses to the meaning of the word or the interpretation of the verse in a tafsir book, we propose the first multi-layer Quranic Knowledge Graph that uses the property graph format. We also add a chatbot on top of our Quranic Knowledge Graph. And to save cost in using a Large Language Model, we deploy a vector database to memorize the previously answered user queries and their corresponding Cypher translations. We evaluate our approach using three point of views: large language model, knowledge graph, and chatbot. The result of evaluation shows that our Quranic Knowledge Graph achieves 100% correctness and 90% accuracy, despite only covering 9 parts of the Al-Quran. For the interested readers, please access https://zentilax.github.io/quranic-chatbot-UI/ for exploring the quranic knowledge graph
Analysis of the Effect of QUIC and TCP on Quality of Experience (QoE) during Handover Muhammad Adlan Hafizha; Aji Gautama Putrada; Ryan Lingga Wicaksono
IJoICT (International Journal on Information and Communication Technology) Vol. 12 No. 1 (2026): Vol.12 No.1 Jun 2026
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v12i1.10893

Abstract

Video streaming on mobile devices experiences interruptions when the wireless connection switches to a new network. This study aims to evaluate Quick UDP Internet Connections (QUIC) protocol’s performance as an alternative to Transmission Control Protocol (TCP) that oftentimes encounters performance setbacks during handovers. The simulation was performed using Mininet-WiFi emulator with a topology of two Access Points with various bandwidth capacities (10 Mbps to 1000 Mbps) and one mobile station (client). The simulations were repeated 30 times to ensure statistical validity for packet loss, latency, throughput, and buffering time metrics. Simulation results show that QUIC outperforms TCP in network metrics performance. QUIC’s Connection ID and 0-RTT initiation features reduce packet loss by up to 6.39%, maintain latency below 0.14 ms, and instantly restore throughput. However, an anomaly was found at high bandwidth (>500 Mbps) where buffering time of QUIC was longer due to the computational load of TLS 1.3 decryption in user space. Although QUIC is strong in wireless mobility, the CPU processing of the receiver’s device must be optimized on high-speed network.
The Developing a CIS Framework-Based Training Syllabus for Venture Capital Firms: A Competency-Driven Approach Jandika Triindra Burhan; Farisya Setiadi; Rio Guntur Utomo
IJoICT (International Journal on Information and Communication Technology) Vol. 12 No. 1 (2026): Vol.12 No.1 Jun 2026
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v12i1.10605

Abstract

The purpose of this study is to develop and validate a role-based cybersecurity training syllabus forVenture Capital (VC) firms using the CIS Critical Security Controls v8.1 (Implementation Group1) and an Outcome-Based Education (OBE) approach grounded in a cybersecurity-adapted TrainingNeeds Assessment (TNA) framework. A mixed-method design was employed. In the qualitativephase, interviews with IT personnel and senior management were conducted to identify dominantcybersecurity risks in VC environments. In the quantitative phase, a structured survey wasdistributed to VC employees to assess the relevance, clarity, and applicability of the proposedsyllabus. Content validity was evaluated through expert judgment using the Content Validity Ratio(CVR), while employee acceptance and internal consistency were examined using Cronbach’sAlpha. The results show that the most critical risks in VC firms are low security awareness, phishingthreats, and compliance-related vulnerabilities. Three CIS IG1 controls—Security AwarenessTraining, Malware Defenses, and Incident Response Management—were prioritized as corecontent. The final syllabus integrates these controls into role-specific learning outcomes andassessment strategies aligned with OBE principles. Validation results indicate strong expertagreement and high employee acceptance, supporting the syllabus as relevant, understandable, andsuitable for implementation in VC firms.
Text Classification Using Large Language Models to Detect AI and Human-Generated Sentences Arfan Rahman Yudiantoro; Prati Hutari Gani; Donni Richasdy
IJoICT (International Journal on Information and Communication Technology) Vol. 12 No. 1 (2026): Vol.12 No.1 Jun 2026
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v12i1.11223

Abstract

With the rapid development of the technology of Artificial Intelligence (AI), especially Large Language Models (LLMs) like ChatGPT, GPT-4 and Gemini, systems have become able to generate texts very similar to human writing. This similarity has been a boon to various sectors, but also poses fresh challenges of content authenticity and data integrity. A significant challenge is finding a way to automatically and accurately distinguish AI-generated sentences from human-written ones. This research focuses on building a text classification model that uses Large Language Models to distinguish between AI-generated and human-written sentences. This approach of research is based on recent research models that combine deep learning and classification using LLMs . The research process involves gathering human and AI-generated text data, pre-processing the text to normalize and tokenize it, feature extraction using the embeddings of large language models like IndoBERT, training the binary classification model, and assessing the model's performance using the metrics accuracy, precision, recall, and F1-score. Keywords: Text clustering, Large language models, AI text identification, Text authenticity.
Classification of Extroverted, Introverted Personality based on response to “Peringatan Darurat” on social media X using IndoBERT method Aura sabina; Yuliant Sibaroni
IJoICT (International Journal on Information and Communication Technology) Vol. 12 No. 1 (2026): Vol.12 No.1 Jun 2026
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v12i1.11389

Abstract

The performance of the IndoBERT model was tested to classify introvert and extrovert personalities based on text data. The data consists of responses from users of social media platform X to the viral issue of “Peringantan Darurat”. The workflow includes data crawling, labelling according to personality categories, and text pre-processing prior to analysis. The IndoBERT model utilises training data to learn language patterns within each personality category in order to perform classification effectively. Test results show that the model is able to identify personality types quite well, with an F1-score of 90%. The use of a more balanced dataset, particularly regarding extrovert data, is recommended to improve classification performance whilst supporting the development of Indonesian natural language processing. Keywords: Personality classification, extrovert, introvert, IndoBERT, social media.

Page 3 of 3 | Total Record : 25

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