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Heskyel Pranata Tarigan
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INDONESIA
Jurnal Komputer
Published by Gein Rafflesia
ISSN : -     EISSN : 29620651     DOI : -
Core Subject : Science,
Computer Science & IT
Domain Specific Frameworks and Applications IT Management dan IT Governance e-Government e-Healthcare, e-Learning, e-Manufacturing, e-Commerce ERP dan Supply Chain Management Business Process Management Smart Systems Smart City Smart Cloud Technology Smart Appliances & Wearable Computing Devices Robotic Systems Smart Sensor Networks Information Infrastructure for Smart Living Spaces Intelligent Transportation Systems Models, Methods and Techniques Conceptual Modeling, Languages and design Software Engineering Information-centric Networking Human Computer Interaction Media, Game and Mobile Technologies Data Mining Information Retrievel Information Security Image Processing and Pattern Recognition Remote Sensing Natural Language Processing
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol 2 No 2 (2024): Januari-Juni" : 5 Documents clear
Penerapan Model Transformer Untuk Deteksi Sentimen Pada Data Twitter Berbahasa Indonesia Alfatah, Dhika
Jurnal Komputer Vol 2 No 2 (2024): Januari-Juni
Publisher : CV. Generasi Insan Rafflesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70963/jk.v2i2.99

Abstract

Social media has become an important platform for people to voice their opinions, aspirations and feelings on various social, economic and political issues. Twitter, as one of the most popular social media platforms, presents a wealth of data for research, especially in the field of sentiment analysis. This research explores the application of the Transformer model, specifically IndoBERT, in detecting sentiment from Indonesian tweets. The dataset used was collected from the Twitter API, processed, and manually labelled into three categories: positive, negative, and neutral. Model evaluation was conducted by comparing IndoBERT's performance with traditional classification methods such as Naïve Bayes and Support Vector Machine (SVM). The results show that IndoBERT significantly outperforms conventional models in terms of accuracy, recall, precision, and F1-score, signalling that the Transformer model is highly effective for sentiment analysis in Indonesian.
Hybrid Learning Approach For Intrusion Detection In Network Security Using Ensemble Methods Tarigan, Heskyel Pranata
Jurnal Komputer Vol 2 No 2 (2024): Januari-Juni
Publisher : CV. Generasi Insan Rafflesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70963/jk.v2i2.100

Abstract

The increasing frequency and sophistication of cyberattacks have led to a pressing need for advanced network security systems, particularly Intrusion Detection Systems (IDS). While traditional IDS models provide a baseline of protection, they often fall short in detecting novel and complex threats. This research proposes a hybrid learning approach for IDS, leveraging the strengths of ensemble machine learning methods such as Random Forest, Gradient Boosting, and Voting Classifier. The proposed system aims to enhance detection accuracy and reduce false positives by combining multiple classifiers into a cohesive model. Using the NSL-KDD dataset, the model was trained and tested, showing superior performance compared to individual learning algorithms. This paper discusses the design, implementation, and performance evaluation of the hybrid IDS model.
Desain dan Implementasi Sistem Pakar Diagnosa Penyakit Menggunakan Forward Chaining Saputri, Vettyca Diana
Jurnal Komputer Vol 2 No 2 (2024): Januari-Juni
Publisher : CV. Generasi Insan Rafflesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70963/jk.v2i2.109

Abstract

The development of information technology has encouraged the utilisation of expert systems in various fields, including health. An expert system is a computer system designed to mimic the ability of an expert to make decisions. This article discusses the design and implementation of an expert system to diagnose diseases based on the forward chaining method. This method works by tracing the facts provided by the user to the right conclusion. The purpose of this research is to assist the community in making an initial diagnosis of common diseases. The implementation results show that the system is able to provide accurate diagnosis results based on the symptoms entered by the user.
Pengembangan Sistem Rekomendasi Buku Menggunakan Collaborative Filtering Pratama, Sutan Abeng
Jurnal Komputer Vol 2 No 2 (2024): Januari-Juni
Publisher : CV. Generasi Insan Rafflesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70963/jk.v2i2.112

Abstract

In the rapidly evolving digital era, the need for accurate and personalized recommendation systems is increasingly important, particularly in digital libraries and online bookstores. This study aims to develop a book recommendation system using a collaborative filtering approach, which leverages user interaction data to suggest books that align with individual preferences. The system utilizes a user-based collaborative filtering method by calculating similarities between users based on their historical book ratings. The dataset used in this research is a simulated, anonymized dataset from a school library. Testing results indicate that the system is capable of delivering relevant recommendations with good accuracy, demonstrated by a low Mean Absolute Error (MAE) score and positive user feedback. This system allows users to discover books aligned with their interests more efficiently, thereby enhancing the overall reading experience.
Analisis Performa Algoritma CNN dalam Klasifikasi Citra Medis Berbasis Deep Learning Sari, Nely Puspita
Jurnal Komputer Vol 2 No 2 (2024): Januari-Juni
Publisher : CV. Generasi Insan Rafflesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70963/jk.v2i2.113

Abstract

The advancement of artificial intelligence technologies, particularly in the field of deep learning, has driven the application of Convolutional Neural Network (CNN) algorithms in various domains, including medical image classification. This study aims to analyze the performance of CNN in classifying medical images associated with different diseases using a standard CNN architecture. The dataset utilized consists of labeled X-ray and MRI images based on medical diagnoses. Evaluation metrics such as accuracy, precision, recall, and F1-score were used to assess how effectively the model recognizes complex visual patterns. The results demonstrate that CNN achieves high accuracy in identifying objects within medical images, with an average F1-score exceeding 90% on selected datasets. These findings suggest that CNN has significant potential to support automated and efficient medical diagnosis, although further clinical validation is necessary for real-world implementation.

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