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INDONESIA
Jiko (Jurnal Informatika dan komputer)
Published by Universitas Khairun
ISSN : 26148897     EISSN : 26561948     DOI : -
Core Subject : Science,
Computer Science & IT
Jiko (Jurnal Informatika dan Komputer) Ternate adalah jurnal ilmiah diterbitkan oleh Program Studi Teknik Informatika Universitas Khairun sebagai wadah untuk publikasi atau menyebarluaskan hasil - hasil penelitian dan kajian analisis yang berkaitan dengan bidang Informatika, Ilmu Komputer, Teknologi Informasi, Sistem Informasi dan Sistem Komputer. Jurnal Informatika dan Komputer (JIKO) Ternate terbit 2 (dua) kali dalam setahun pada bulan April dan Oktober
Arjuna Subject : -
Articles 13 Documents
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Search results for , issue "Vol 8, No 2 (2025)" : 13 Documents clear
NAÏVE BAYES AND SUPPORT VECTOR MACHINE BASED ON OPTIMIZATION FOR PUBLIC SENTIMENT ANALYSIS POST-2024 ELECTION Fikriah, Fari Katul; Ariyanto, Amelia Devi Putri
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 2 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i2.10147

Abstract

The 2024 election has sparked an explosion of public opinion across various digital platforms, but the complexity and large volume of data make it difficult for policymakers to understand public sentiment in a timely manner. Therefore, an accurate and efficient sentiment analysis method is needed to automatically classify public opinion. This study aims to analyze and compare the performance of the Naïve Bayes algorithm and an optimized Support Vector Machine (SVM) in classifying post-election public sentiment. The research method includes collecting 10,000 text data entries from various data sources, conducting text preprocessing, extracting features using the TF-IDF method, applying both algorithms with parameter tuning, and generating their performance using accuracy, precision, recall, and F1 score metrics. The results show that the optimized SVM algorithm delivers superior performance, achieving 88.24% accuracy, compared to 82.35% for Naïve Bayes. These findings indicate that SVM is more effective in handling complex public opinion sentiment classification, thus serving as a valuable reference for post-election policymaking
EVALUATION OF INDOBERT AND ROBERTA: PERFORMANCE OF INDONESIAN LANGUAGE TRANSFORMER MODELS IN SENTIMENT CLASSIFICATION Nur, M. Adnan; Umar, Najirah; Feng, Zhipeng; Gani, Hamdan
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 2 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i2.9988

Abstract

The development of Natural Language Processing (NLP) technology has had a significant impact on various fields, especially in sentiment analysis. This analysis becomes important in understanding public perception, especially on social media which has a lot of opinions. Indonesian, with its morphological complexity, dialectal variations, and dynamic everyday vocabulary usage, presents unique challenges in the development of NLP models. This study aims to evaluate and compare the performance of two Indonesian language transformer models, namely IndoBERT (Indonesia Bidirectional Encoder Representations from Transformers) and RoBERTa Indonesia (Robustly Optimized BERT Pretraining Approach) in applying sentiment classification using the Indonesian General Sentiment Analysis Dataset. Both models were fine-tuned using consistent hyperparameter configurations to ensure the validity of the comparison. Evaluation was conducted based on classification metrics, namely precision, recall, F1-score, and accuracy. The results show that the IndoBERT model excels in all aspects of evaluation. IndoBERT achieved an accuracy of 70%, while RoBERTa Indonesia only reached 67%. Additionally, the average F1-score of IndoBERT at 0.69 is higher compared to RoBERTa, which only reached 0.65. The performance of IndoBERT is also more balanced in classifying the three sentiment categories (negative, neutral, and positive), whereas RoBERTa shows less consistent performance, especially in negative and positive sentiments. In the loss analysis, IndoBERT produced a lower evaluation loss value, indicating better generalization capability. Additionally, IndoBERT also shows faster and more stable training times compared to RoBERTa. This performance difference shows that the architecture and pre-trained data used by each model affect their ability to understand Indonesian contextually. This research provides a comprehensive comparative overview of the effectiveness of two transformer models in the task of Indonesian language sentiment analysis, as well as lays the groundwork for selecting a more optimal model in the development of NLP systems for social media.
ENHANCED NETWORK SECURITY USING ZERO TRUST IN SMART HOME NETWORKS AGAINST MAN-IN-THE-MIDDLE ATTACKS SINGH, BEWIT RAJ; Yusuf, Raka
JIKO (Jurnal Informatika dan Komputer) Vol 8, No 2 (2025)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i2.10329

Abstract

The rapid adoption of Internet of Things (IoT) devices in Smart Home environments has increased network vulnerability to internal threats, such as Man-in-the-Middle (MitM) attacks, which traditional security models often fail to address. This study aims to design, simulate, and comparatively analyze the effectiveness of a Zero Trust architecture against a traditional security model in protecting a smart home network from MitM attacks. A comparative experiment was conducted in a GNS3 simulation environment featuring two topologies: a conventional flat network using HTTP and a Zero Trust network implementing microsegmentation via VLANs, Access Control Lists (ACLs), and encrypted HTTPS communication. MitM attacks, specifically ARP Spoofing and packet sniffing, were launched against both scenarios. The results unequivocally show that the traditional network was highly vulnerable, allowing attackers to successfully intercept user credentials in plaintext. In contrast, the Zero Trust architecture completely thwarted the attack; its layered defenses blocked unauthorized traffic and encrypted sensitive data, preventing any credential theft. This research concludes that the Zero Trust model is a significantly more effective and robust security strategy for IoT-based smart homes, providing superior protection against internal threats with minimal performance trade-offs compared to conventional approaches

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