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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 5 Documents
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Search results for , issue "Vol 14, No 2 (2025)" : 5 Documents clear
K-Means Clustering On Rice Harvest Data For Planting Season Recommendation In Subak Cepaka, Tabanan Dewi, Ni Made Cahyani; Hidayat, Ahmad Tri
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.v14i2.3504

Abstract

The Subak farming system in Tabanan Regency, Bali, is vital as a primary rice granary but faces challenges in determining optimal planting patterns. Planting decisions based only on inherited experience often do not match climate conditions, reducing productivity and increasing crop failure risks. This study implements the K-Means Clustering algorithm on five years of historical rice harvest data (2020–2024) to generate accurate planting season recommendations. Monthly data were analyzed and grouped into three categories: rainy, dry, and transitional seasons. The clustering results were integrated into a mobile application that provides farmers with accessible recommendations through an interactive interface and visualization. The effectiveness of the clustering model was evaluated using the Silhouette Score, which indicated good separation and cohesion among clusters, while efficiency was assessed through processing time and algorithm simplicity, confirming that K-Means performed the task with minimal computational cost. This system enables farmers to make data-driven planting decisions, optimize productivity, and support sustainable food security in Bali.
Sentiment Analysis of Opinions on the Performance of the Governor of West Java in 2025 on Social Media X Using LSTM Safaat, Muhammad Apipudin; Ekawati, Nia
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.v14i2.3152

Abstract

The rise of political discourse on Indonesian social media platforms such as X (formerly Twitter) creates opportunities and challenges for policymakers. Existing sentiment analysis methods often fail to handle informal language, slang, and sarcasm, leading to frequent misclassification that may misguide governance decisions. This study aims to establish the first benchmark for three-class sentiment analysis (positive, neutral, negative) in Indonesian political discourse using a Long Short-Term Memory (LSTM) model with culture-specific preprocessing. A dataset of 1,002 tweets on the performance of the Governor of West Java (Feb–May 2025) was collected, normalized for slang and typos, and enriched with a political lexicon. Manual annotation achieved high agreement (κ = 0.82). An LSTM model with 128 units and 30% dropout was trained and evaluated. Results show 95.88% training accuracy but only 36.32% validation accuracy, indicating severe overfitting. Misclassifications (42%) mainly stemmed from sarcasm and contextual ambiguity, with the lowest precision in the positive class (31%). The study contributes by (1) providing the first benchmark for Indonesian political sentiment, (2) demonstrating the value of culture-specific preprocessing, and (3) offering policy insights into latent dissatisfaction hidden in neutral tweets. Limitations include small dataset size and lack of sarcasm-aware mechanisms, suggesting future exploration of hybrid and transformer-based models.
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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