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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 241 Documents
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SEGMENTATION OF SUBARACHNOID HEMORRHAGE ON BRAIN CT IMAGES USING U-NET AND ATTENTION U-NET: A COMPARATIVE ANALYSIS Saputra, Ilham Tristadika; Pradana, Afu Ichsan; Hartanti, Dwi
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.v8i2.9958

Abstract

Subarachnoid Hemorrhage (SAH) represents a critical medical condition resulting from bleeding in the subarachnoid space, typically due to the rupture of an aneurysm or trauma. Timely identification is vital to avoid long-term neurological impairment. This research assesses the efficacy of U-Net compared to Attention U-Net for the segmentation of SAH in brain CT images, aiming to determine if attention mechanisms enhance segmentation precision. The motivation for this comparison stems from the clinical difficulty in identifying subtle or low-contrast hemorrhagic areas that traditional architectures like U-Net might miss; in contrast, attention-based models are constructed to capture spatial details more proficiently. Both architectures were evaluated using a publicly available SAH CT dataset and assessed on metrics including Dice Score, Intersection over Union (IoU), Precision, Recall, and F1 Score. Attention U-Net outperformed U-Net with higher scores of Dice (0.896) and IoU (0.877), whereas U-Net excelled in precision. Visual assessments also indicated that Attention U-Net was superior in delineating diffuse hemorrhagic regions. These findings advocate for the incorporation of attention mechanisms to enhance segmentation accuracy and clinical relevance in neuroimaging
THE ROLE OF ANONYMITY IN ARTIFICIAL INTELLIGENCE BASED CHATBOT USAGE BY UNIVERSITY STUDENT AT MEDAN Chandra, Randy Brilliant; Panjaitan, Erwin Setiawan; Pasha, Muhammad Fermi; Thamrin, Thamrin; Robin, Robin
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.v8i2.9768

Abstract

AI Chatbot is a computer program integrated with artificial intelligence designed to interact with humans and provide useful information. AI Chatbot offers anonymity, one of the factors motivating someone to use it since they feel safe. Indonesian students nowadays inhibit shyness to ask questions and participate in learning due to anxiety factors and fear of negative judgment. Therefore, it is necessary to conduct a test to understand the extent of acceptance factors of AI Chatbots. This research aims to examine the effect of anonymity variable on the use of AI Chatbots among university students at Medan using the UTAUT2 model. A survey was conducted on 421 students. Using the Structural Equation Modeling (SEM) model with the help of SmartPLS software, this study introduces the anonymity variable in the UTAUT2 model, which has a positive and significant effect on students’ behavioral intention to use AI Chatbots. These findings also show that price value and habit have a positive and significant effect on behavioral intention, also habit and behavioral intention effect students’ behavior towards AI Chatbots at Medan. However, performance expectancy, effort expectancy, social influence, facilitating conditions, and hedonic motivation do not affect the students’ behavioral intention and behavior towards AI Chatbots
AUTOMATIC ABSTRACTIVE SUMMARIZATION OF CURRICULUM VITAE USING S-BERT AND T5 Herdiyanto, Reza Fahlevi; Maylawati, Dian Sa'adillah; Lukman, Nur
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.v8i2.10019

Abstract

The rapid advancement of technological disruption has catalyzed significant innovations in human resource management, particularly through the widespread adoption of automated applicant screening systems such as Applicant Tracking Systems (ATS). However, these systems often fail to identify potential candidates due to poorly formatted Curriculum Vitae (CV) or missing important keywords, resulting in many applicants being eliminated in the early stages of selection. This research aims to develop an automatic CV summarization system by utilizing Natural Language Processing (NLP) technology. This research uses a combination of Sentence-BERT (SBERT) algorithm for information extraction and Text-to-Text Transfer Transformer (T5) for text generation. The K-Fold Cross Validation method with k = 3 was used in the model performance evaluation, in accordance with the limited computing resources. Experimental results show that the SBERT model is able to extract important information with high accuracy (F1-score of 0.8866), while the T5 model is able to generate informative summaries with a ROUGE-1 score of 0.8680. The combination of SBERT in producing important information extraction from CV and T5 that produces an abstractive summary shows good results with ROUGE-1 scores of 0.5497, ROUGE-2 of 0.3537, and ROUGE-L of 0.4334. This system is able to produce CV summaries that make it easier for companies to select job applicants according to the criteria and increase the chances of applicants to pass the initial selection stage
COMBINATION OF MULTI-VIEW LEARNING AND DEEP REINFORCEMENT LEARNING TO IMPROVE WEBSITE PHISING DETECTION Hasbia, Muhamad; Witanti, Wina; Abdillah, Gunawan
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.v8i2.9811

Abstract

Phishing is one of the most common and dangerous forms of cyberattacks, where perpetrators attempt to obtain sensitive information by masquerading as trustworthy entities. Traditional detection methods often fail to anticipate new attacks due to the dynamic nature of phishing. This research proposes an adaptive phishing detection system that combines Multi-Kernel Learning (MKL) and Deep Q-Network (DQN) approaches. MKL is utilized to integrate features from URL structure, domain metadata, and webpage content into a rich multi-view representation, while DQN enhances the model's adaptability through a reward-based learning mechanism. This combination was chosen because MKL effectively captures feature variations from different sources, while DQN excels at handling rapidly changing attack patterns. The dataset consists of 11,056 entries with 32 features, divided in an 80:20 ratio for training and testing. Moreover, evaluation is performed using a 5-Fold Cross Validation method to ensure result stability, and hyperparameter exploration is conducted to obtain the best configuration. Evaluation results show that the system achieves an accuracy of 96.34%, precision of 95.8%, recall of 97.85%, F1-score of 96.73%, and AUC of 0.98. These results demonstrate that the MKL-DQN approach is highly effective in accurately and adaptively detecting phishing
OPTIMIZING GPT AND INDOBERT FOR SENTIMENT ANALYSIS AND CONSUMER TREND PREDICTION ON LAZADA PRODUCT REVIEWS Setyawan, Arif Fitra; Nugraha, Rozaq Isnaini
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.v8i2.10066

Abstract

Sentiment analysis has become a vital approach in understanding customer opinions through textual reviews. One of the primary challenges in sentiment classification lies in class imbalance, where positive reviews often dominate the dataset. This imbalance causes machine learning models to be biased toward the majority class and underperform in detecting minority sentiments. To address this issue, this study applies the Synthetic Minority Oversampling Technique (SMOTE) and evaluates the performance of two Transformer-based models: Generative Pre-trained Transformer (GPT) as a baseline and IndoBERT as the primary model. The dataset consists of 12,704 product reviews from Lazada, obtained from the Kaggle platform, and is categorized into three sentiment classes (positive, neutral, negative). The data was split into 80% for training and 20% for testing. After preprocessing and applying SMOTE for data balancing, the fine-tuned IndoBERT model achieved the best performance with an accuracy of 88%, significantly outperforming GPT, which yielded only 47% accuracy in a zero-shot setting. These findings highlight the critical role of addressing data imbalance and selecting context-aware models for improving sentiment classification accuracy in Indonesian language texts
COMPARISON OF NAÏVE BAYES CLASSIFIER AND K-NEAREST NEIGHBOR ALGORITHMS IN SENTIMENT ANALYSIS ON SOCIAL MEDIA X WITH VADER LEXICON Tiang, Steven; Chandra, Wenripin; Ferawaty, Ferawaty; Manulang, Mangasa A. S.
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.v8i2.9865

Abstract

The increasing use of social media as a platform for expressing public opinion has established platform X (formerly Twitter) an important data source for sentiment analysis. However, the ever-growing volume of data and the lack of sentiment labels present significant challenges for manual analysis, which is inefficient and time-consuming. This research addresses the problem of selecting effective algorithms for accurate and efficient sentiment classification on large-scale unlabeled data. The study aims to compare the performance of the Naïve Bayes Classifier and K-Nearest Neighbor (KNN) algorithms in sentiment classification related to the Value Added Tax (VAT) increase on platform X. To support classification accuracy, sentiment labeling is performed automatically using the VADER Lexicon. The research methodology involves data scraping, automatic sentiment labeling, implementation and training of classification models, and performance evaluation using a Confusion Matrix and ROC curve. The results show that the KNN algorithm with k = 1 achieved the best performance with an accuracy of 93.19%, precision of 94.07%, recall of 92.96%, a misclassification error of 6.81%, and an AUC of 0.95. In contrast, the Naïve Bayes Classifier achieved an accuracy of 88.29%, precision of 87.43%, recall of 86.67%, misclassification error of 11.71%, and an AUC of 0.93. Therefore, KNN is proven to be superior in classifying sentiment more accurately and efficiently than the Naïve Bayes Classifier.
UNDERSTANDING PUBLIC OPINION ON POLITICAL CANDIDATES THROUGH TWITTER SENTIMENT ANALYSIS: A COMPARATIVE STUDY OF FEATURE EXTRACTION Ariyanto, Amelia Devi Putri; Fikriah, Fari Katul
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.v8i2.9993

Abstract

Presidential elections are crucial in a country's political dynamics and are increasingly discussed on social media platforms like Twitter. However, sentiment analysis of public opinion on these platforms faces significant challenges, such as large data volumes, diverse formats, and the complexity of informal language. The key challenge is choosing the most appropriate feature extraction technique and classification algorithm to address the unique characteristics of Indonesian-language tweets in the context of presidential elections. This study aims to compare the effectiveness of two feature extraction approaches—semantic based on BERT (Bidirectional Encoder Representations from Transformers) and statistical based on TF-IDF (Term Frequency-Inverse Document Frequency)—in sentiment analysis of Indonesian-language tweets related to the presidential election, using four classification algorithms: Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors, and Decision Tree. The experimental results demonstrate that the combination of TF-IDF with SVM provides the best performance, with an accuracy of 85.1% and a macro f1-score of 0.81, outperforming the BERT approach used statically. These findings indicate that statistical approaches such as TF-IDF remain relevant and practical for short social media texts and emphasize the importance of choosing a method that suits the characteristics of the data and the context of the analysis.
ANALYSIS OF SECURITY CHALLENGES IN REST API IN EDGE COMPUTING-BASED IOT ECOSYSTEM: A REVIEW Sinaga, Rudolf; Samsinar, Samsinar; Fatima, Soomal; Frangky, Frangky
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.v8i2.10097

Abstract

REST APIs are the backbone of data communication in the Internet of Things (IoT)-based edge computing ecosystem because they are lightweight and flexible. However, the REST architecture's openness and the edge devices' limited resources give rise to security challenges such as MITM, spoofing, and replay attacks. This study aims to identify the key challenges of REST API security in IoT edge environments, evaluate the limitations of conventional solutions such as TLS and RSA/ECDSA algorithms, and explore the potential of Post-Quantum Signature-based digital authentication approaches (PQS). Through a comprehensive narrative literature review of 43 peer-reviewed publications (2020-2025), this research reveals two key findings: the results show that TLS generates significant overhead in memory and energy, while classical algorithms do not resist quantum threats. PQS schemes such as Falcon and Dilithium have proven more efficient and secure in limited devices. The study concludes that PQS-based lightweight authentication approaches have strong prospects for implementation in future REST API gateway architectures, particularly in supporting electronic-based governance systems (SPBEs).
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.

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