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Compiler
Published by Institut Teknologi Dirgantara Adisutjipto
ISSN : 22523839     EISSN : 25492403     DOI : 10.28989/compiler
Core Subject : Science,
Computer Science & IT
Jurnal "COMPILER" dengan ISSN Cetak : 2252-3839 dan ISSN On Line 2549-2403 adalah jurnal yang diterbitkan oleh Departement Informatika Sekolah Tinggi Teknologi Adisutjipto Yogyakarta. Jurnal ini memuat artikel yang merupakan hasil-hasil penelitian dengan bidang kajian Struktur Diskrit, Ilmu Komputasi , Algoritma dan Kompleksitas, Bahasa Pemrograman, Sistem Cerdas, Rekayasa Perangkat Lunak, Manajemen Informasi, Dasar-dasar Pengembangan Perangkat Lunak, Interaksi Manusia-Komputer, Pengembangan Berbasis Platform, Arsitektur dan Organisasi Komputer, Sistem Operasi, Dasar-dasar Sistem,Penjaminan dan Keamanan Informasi, Grafis dan Visualisasi, Komputasi Paralel dan Terdistribusi, Jaringan dan Komunikasi, Desain, Animasi dan Simulasi Pesawat Terbang. Compiler terbit setiap bulan Mei dan November.
Arjuna Subject : -
Articles 423 Documents
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Multi-Label Opinion Mining Based on Random Forest with SMOTE and ADASYN Ardiansyah, Ricy; Yuliansyah, Herman; Yudhana, Anton
Compiler Vol 14, No 2 (2025)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v14i2.3185

Abstract

Multi-label classification is essential to categorize data into multiple labels simultaneously. However, data imbalance poses a challenge, where some labels have much less representation, thus reducing the model performance. This study aims to propose a candidate-based sentiment analysis model on the 2024 Jakarta Presidential and Gubernatorial Election review. The SMOTE and ADASYN oversampling methods are applied to handle class imbalance. Both oversampling methods are compared with the Random Forest machine learning method. The experimental results show that. The experimental results show that in the classification of Presidential candidates, Random Forest achieves an accuracy of 0.947 with SMOTE and 0.948 with ADASYN. For sentiment labels, the accuracy of Random Forest remains high with a result of 0.989 for both SMOTE and ADASYN. In the classification of Jakarta Gubernatorial candidates, Random Forest + SMOTE produces an accuracy of 0.975, while with ADASYN it decreases slightly to 0.973. For sentiment labels, both SMOTE and ADASYN have the highest accuracy of 0.993. The application of SMOTE and ADASYN helps to improve the distribution of the minority class without decreasing the overall accuracy, as well as improving the stability in recognizing various multi-label classes in a balanced manner.
Baseline Evaluation of Backpropagation Artificial Neural Network for Visual Image-Based Vehicle Type Classification Harman, Rika; Riadi, Imam; Fadlil, Abdul
Compiler Vol 14, No 2 (2025)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v14i2.3210

Abstract

The increasing number of vehicles in urban areas requires technology-based solutions for efficient transportation management. This study proposes a vehicle classification model using Artificial Neural Networks (ANN) with the backpropagation algorithm, based on digital image data. The model is a feedforward neural network comprising an input layer, a hidden layer with 64 sigmoid-activated neurons, and an output layer with 7 softmax-activated neurons. The dataset, sourced from Roboflow Inc., consists of 16,185 images across eight vehicle classes: Hummer, Toyota Innova, Hyundai Creta, Suzuki Swift, Audi, Mahindra Scorpio, Rolls Royce, and Tata Safari. The data is split 80:20 for training and testing. Input features include vehicle dimensions, dominant RGB color, number of axles, and license plate detection. The model is trained using gradient descent and categorical crossentropy loss. Evaluation results show 85% validation accuracy at epoch 28 and 100% test accuracy. Precision, recall, and F1-score indicate strong performance, though minor errors occur in visually similar classes. These findings demonstrate that backpropagation-based ANN is effective for vehicle classification and can be applied in systems such as automatic parking and traffic monitoring
Digital Forensic Analysis of Hybrid Scooter Motors using Smart Flow and Integrated Digital Forensics Standard Saputri, Yerly Ania; Fazal, Ahmad; Ningrat, Aditya Wahyu; Hariyadi, Dedy
Compiler Vol 14, No 2 (2025)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/compiler.v14i2.3211

Abstract

This research addresses the challenges of digital forensics for connected hybrid vehicles, focusing on the Yamaha Fazzio hybrid scooter. The study highlights how limited collection methods and mobile device encryption often compromise the integrity of electronic evidence. To address these issues, a five-stage framework was developed, combining guidelines from NIST SP 800-101 Rev.1 and ISO/IEC 27037:2012. This comprehensive framework includes data collection, evidence identification, forensic acquisition, examination and analysis, and final reporting. The framework's effectiveness is boosted by Smart Flow automation on Cellebrite UFED devices, which automates the identification of Android devices, extraction of Y-Connect App databases, GPS logs in JSON, and travel route thumbnails. This automation significantly enhances the efficiency of the acquisition and analysis processes while maintaining evidence integrity. Evaluations showed successful data acquisition from a Xiaomi Mi 5s Plus with the Y-Connect App. Details from the riding_log table were extracted, providing information on travel routes, distance, average and maximum speeds, and estimated fuel consumption during a Yogyakarta - Klaten travel test scenario. These results are crucial for developing digital forensics SOPs for other connected hybrid vehicles in future research.

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