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All Journal Journal of Applied Data Sciences INCODING: Journal of Informatics and Computer Science Engineering Jurnal Informatika Teknologi dan Sains (Jinteks)
Arnes Sembiring
Universitas Medan Area
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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KLASIFIKASI JENIS KENDARAAN PADA JALAN RAYA MENGGUNAKAN YOLOV7 Bayu Aditya Pratama; Sayuti Rahman; Arnes Sembiring
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 5 No 4 (2023): EDISI 18
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v5i4.3493

Abstract

This research aims to develop a classification system capable of identifying types of vehicles on the highway using YOLOv7 (You Only Look Once version 7), a deep learning-based object detection model that can be used for real-time object detection. With the rapid growth of traffic conditions, monitoring and managing traffic become increasingly important to reduce congestion and improve road safety. The research involves collecting image data and labeling the types of vehicles found on the highway. Subsequently, training the YOLOv7 model using the obtained dataset to classify various types of vehicles such as cars, motorcycles, trucks, and buses. The results of this study indicate that YOLOv7 can be efficiently used to classify types of vehicles on the highway with a fairly good level of accuracy, reaching a maximum of 86% for video and 91% for image detection.
Analisis Pengaruh Fungsi Aktivasi CNN terhadap Performa Klasifikasi Hewan Raja Pahlefi Ray; Arnes Sembiring
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.847

Abstract

This study aims to analyze the impact of five activation functions ReLU, LeakyReLU, ELU, Sigmoid, and Tanh—on the performance of a Convolutional Neural Network (CNN) model for image classification into three categories: cats, dogs, and wild animals. The evaluation was conducted using validation accuracy metrics, accuracy trends across training epochs, and confusion matrix analysis. The results show that modern activation functions such as LeakyReLU, ELU, and ReLU yield high accuracy and balanced predictions, demonstrating their effectiveness in mitigating vanishing gradient issues and enhancing the model's generalization capability. In contrast, classical functions like Sigmoid and Tanh performed poorly, producing imbalanced predictions and stagnant accuracy Therefore, the choice of activation function plays a critical role in building an optimal CNN model for image classification tasks. This study recommends ReLU-based activation functions, particularly LeakyReLU, as the primary choice for developing multi-class image classification models.
Analisis Performa Convolution Neural Network untuk Klasifikasi Hewan Berdasarkan Perbedaan Ukuran Kernels Ilham Maratua Pane; Arnes Sembiring
INCODING: Journal of Informatics and Computer Science Engineering Vol 5, No 2 (2025): INCODING OKTOBER
Publisher : Mahesa Research Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34007/incoding.v5i2.849

Abstract

This study aims to analyze the impact of kernel size variation in Convolutional Neural Network (CNN) architectures on the performance of animal image classification. The kernel sizes evaluated include 3x3, 5x5, 7x7, and 9x9. Performance was assessed using accuracy metrics and confusion matrix analysis to determine the effectiveness of each model. The results indicate that the 5x5 kernel achieved the highest accuracy and the most balanced classification distribution, while the 9x9 kernel resulted in a significant decline in performance. Excessively large kernels led to the model’s inability to capture local features, causing a high rate of misclassification. In contrast, moderately sized kernels maintained a balance between capturing global context and preserving local detail. These findings highlight the importance of selecting an appropriate kernel size in CNN architecture design to achieve optimal classification results.
An Integrated Linguistic and Metaheuristic-Optimized Elman Neural Network Framework for Cyberbullying Detection Siti Aisyah; Arnes Sembiring; Faadhil Faadhil; Hartono Hartono; Rahmad Syah; M. Khahfi Zuhanda
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1337

Abstract

The rapid growth of social media platforms has intensified the need for accurate cyberbullying detection systems capable of understanding contextual and linguistically complex expressions. Existing machine learning and deep learning approaches often suffer from limited interpretability, insufficient contextual understanding, and suboptimal parameter optimization, reducing their effectiveness in identifying harmful online content. This study proposes a novel cyberbullying detection framework that integrates Linguistic Rule-Based Feature Extraction, an Elman Neural Network (ENN), and the Local Search-Based Improved Bat Algorithm (LSBIA). The main contribution of this research lies in the synergistic combination of interpretable linguistic knowledge, contextual sequence modeling, and metaheuristic optimization within a unified classification framework. Linguistic rules are employed to capture negation patterns, intensifiers, and adjective–noun relationships, while ENN models contextual dependencies through recurrent memory structures. LSBIA is utilized to optimize network parameters and improve convergence stability. Experiments were conducted using textual data collected from Instagram, Twitter, and Facebook and evaluated using stratified 10-fold cross-validation. The proposed method achieved an accuracy of 99.12%, precision of 94.73%, recall of 97.45%, and F1-score of 93.91%, outperforming Support Vector Machine (91.20% accuracy), Naïve Bayes (89.75%), and Decision Tree (90.10%). Ablation experiments further demonstrated the importance of each component, where removing linguistic rules reduced accuracy to 94.90%, removing sentiment scoring reduced accuracy to 96.30%, and replacing ENN with LSTM, GRU, or Transformer architectures resulted in lower accuracies of 92.50%, 91.90%, and 93.20%, respectively. These findings confirm that integrating linguistic feature engineering, contextual neural modeling, and metaheuristic optimization significantly enhances cyberbullying detection performance while maintaining interpretability. The novelty of this study resides in the integration of linguistic rule-based representation with LSBIA-optimized ENN for context-aware cyberbullying classification.
Co-Authors Bayu Aditya Pratama Faadhil, Faadhil Hartono Hartono Ilham Maratua Pane M. Khahfi Zuhanda Rahmad B.Y Syah Rahman, Sayuti Raja Pahlefi Ray Siti Aisyah