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Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Published by Ikatan Ahli Informatika Indonesia
ISSN : 25800760     EISSN : 25800760     DOI : https://doi.org/10.29207/resti.v2i3.606
Core Subject : Science, Engineering,
Computer Science & IT Engineering
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) dimaksudkan sebagai media kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai penelitian Rekayasa Sistem, Teknik Informatika/Teknologi Informasi, Manajemen Informatika dan Sistem Informasi. Sebagai bagian dari semangat menyebarluaskan ilmu pengetahuan hasil dari penelitian dan pemikiran untuk pengabdian pada Masyarakat luas dan sebagai sumber referensi akademisi di bidang Teknologi dan Informasi. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) menerima artikel ilmiah dengan lingkup penelitian pada: Rekayasa Perangkat Lunak Rekayasa Perangkat Keras Keamanan Informasi Rekayasa Sistem Sistem Pakar Sistem Penunjang Keputusan Data Mining Sistem Kecerdasan Buatan/Artificial Intelligent System Jaringan Komputer Teknik Komputer Pengolahan Citra Algoritma Genetik Sistem Informasi Business Intelligence and Knowledge Management Database System Big Data Internet of Things Enterprise Computing Machine Learning Topik kajian lainnya yang relevan
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,046 Documents
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Deep Learning-Based Waste Classification with Transfer Learning Using EfficientNet-B0 Model Risfendra, Risfendra; Ananda, Gheri Febri; Setyawan, Herlin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5875

Abstract

Recycling of waste is a significant challenge in modern waste management. Conventional techniques that use inductive and capacitive proximity sensors exhibit limitations in accuracy and flexibility for the detection of various types of waste. Indonesia generates approximately 175,000 tons of waste per day, highlighting the urgent need for efficient waste management solutions. This study develops a waste classification system based on deep learning, leveraging the powerful EfficientNet-B0 model through transfer learning. EfficientNet-B0 is designed with a compound scaling method, which uniformly scales network depth, width, and resolution, providing an optimal balance between accuracy and computational efficiency. The model was trained on a dataset containing six classes of waste—glass, cardboard, paper, metal, plastic, and residue—totalling 7014 images. The model was trained using data augmentation and fine-tuning techniques. The training results show a test accuracy of 91.94%, a precision of 92.10%, and a recall of 91.94%, resulting in an F1-score of 91.96%. Visualization of predictions demonstrates that the model effectively classifies waste in new test data. Implementing this model in the industry can automate the waste sorting process more efficiently and accurately than methods based on inductive and capacitive proximity sensors. This study underscores the significant potential of deep learning models, particularly EfficientNet-B0, in industrial waste classification applications and opens opportunities for further integration with sensor and robotic systems for more advanced waste management solutions.
Classification of Toraja Wood Carving Motif Images Using Convolutional Neural Network (CNN) Nurilmiyanti Wardhani; Asrul, Billy Eden William; Antonius Riman Tampang; Sitti Zuhriyah; Abdul Latief Arda
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5897

Abstract

Wood carving is a cultural heritage with deep meaning and significance for the Toraja ethnic group's culture. By understanding the meaning of each Toraja carving, both tourists and the local community can gain knowledge about Toraja culture, thereby preserving and maintaining the culture amidst modern developments. Image processing approaches, particularly the development of Convolutional Neural Networks (CNN), offer a solution for extracting information from the diverse and intricate patterns of Toraja wood carvings. This study is highly significant as it implements a deep learning model using the CNN algorithm optimized with the ResNet50 architecture. The methodology in this study involves adjusting the batch size during the model training phase and applying weak-to-strong pixel transformation during the double threshold hysteresis phase in the Canny Feature Extraction process on the edges of Toraja carving images, resulting in ResNet50 architecture accurately recognizing the patterns of Toraja wood carvings. The results demonstrate significant improvements in the performance of the ResNet50 architecture with the preprocessed dataset. average precision, recall, precision, and F1-Score improvements in each Toraja carving class. For the Pa' Lulun Pao class, it was found that the precision and recall values were 0.94, and the F1-Score was 0.94. The Pa’ Somba class also showed good results, with a precision value of 0.9697, a recall of 0.96, and an F1-Score of 0.9648. The Pa’ Tangke Lumu class showed even better results, with a precision value of 0.9898, a recall of 0.97, and an F1-Score of 0.9798. The Pa’ Tumuru class also demonstrated good performance, with a precision value of 0.9327, a recall of 0.97, and an F1-Score of 0.9500. This study not only underscores the effectiveness of processing in enhancing CNN capabilities but also opens opportunities for further research in applying these methods to various image types and exploring different CNN architectures.
Comparing Correlation-Based Feature Selection and Symmetrical Uncertainty for Student Dropout Prediction Haryono Setiadi; Larasati, Indah Paksi; Esti Suryani; Wardani, Dewi Wisnu; Wardani, Hasan Dwi Cahyono; Ardhi Wijayanto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5911

Abstract

Predicting student dropout is essential for universities dealing with high attrition rates. This study compares two feature selection (FS) methods—correlation-based feature selection (CFS) and symmetrical uncertainty (SU)—in educational data mining for dropout prediction. We evaluate these methods using three classification algorithms: decision tree (DT), support vector machine (SVM), and naive Bayes (NB). Results show that SU outperforms CFS overall, with SVM achieving the highest accuracy (98.16%) when combined with SU Moreover, this study identifies total credits in the fourth semester, cumulative GPA, gender, and student domicile as key predictors of student dropout. This study shows how using feature selection methods can improve the accuracy of predicting student dropout, helping educational institutions retain students better.
Optimizing Multilayer Perceptron with Cost-Sensitive Learning for Addressing Class Imbalance in Credit Card Fraud Detection Priatna, Wowon; Hindriyanto Dwi Purnomo; Ade Iriani; Irwan Sembiring; Theophilus Wellem
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5917

Abstract

The increasing use of credit cards in global financial transactions offers significant convenience for consumers and businesses. However, credit card fraud remains a major challenge due to its potential to cause substantial financial losses. Detecting credit card fraud is a top priority, but the primary challenge lies in class imbalance, where fraudulent transactions are significantly fewer than non-fraudulent ones. This imbalance often leads to machine learning algorithms overlooking fraudulent transactions, resulting in suboptimal performance. This study aims to enhance the performance of Multilayer Perceptron (MLP) in addressing class imbalance by employing cost-sensitive learning strategies. The research utilizes a credit card transaction dataset obtained from Kaggle, with additional validation using an e-commerce transaction dataset to strengthen the robustness of the findings. The dataset undergoes preprocessing with RUS and SMOTE techniques to balance the data before comparing the performance of baseline MLP models to those optimized with cost-sensitive learning. Evaluation metrics such as accuracy, recall, F1 score, and AUC indicate that the optimized MLP model significantly outperforms the baseline, achieving an AUC of 0.99 and a recall of 0.6. The model's superior performance is further validated through statistical tests, including Friedman and T-tests. These results underscore the practical implications of implementing cost-sensitive learning in MLPs, highlighting its potential to significantly enhance fraud detection accuracy and offer substantial benefits to financial institutions.
Advanced Earthquake Magnitude Prediction Using Regression and Convolutional Recurrent Neural Networks Id Hadiana, Asep; Muhammad Sukma, Rifaz; Krishna Putra, Eddie
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5922

Abstract

Earthquake magnitude prediction is critical in seismology, with significant implications for disaster risk management and mitigation. This study presents a novel earthquake magnitude prediction model by integrating regression analysis with Convolutional Recurrent Neural Networks (CRNNs). It utilises Convolutional Neural Networks (CNNs) for spatial feature extraction from 2-dimensional seismic signal images and Long Short-Term Memory (LSTM) networks to capture temporal dependencies. The innovative model architecture incorporates residual connections and specialised regression techniques for sequential data. Validated against a comprehensive seismic dataset, the model achieves a Mean Squared Error (MSE) of 0.1909 and a Root Mean Squared Error (RMSE) of 0.4369, with a coefficient of determination of 0.79772. These metrics, alongside a correlation coefficient of 0.8980, demonstrate the model's accuracy and consistency in predicting earthquake magnitudes, establishing its potential for enhancing seismic risk assessment and informing early warning systems.
Augmentation for Accuracy Improvement of YOLOv8 in Blind Navigation System Syahrudin, Erwin; Utami, Ema; Hartanto, Anggit Dwi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5931

Abstract

This study addresses the critical need for enhanced accuracy in YOLOv8 models designed for visually impaired navigation systems. Existing models often struggle with consistency in object detection and distance estimation under varying environmental conditions, leading to potential safety risks. To overcome these challenges, this research implements a rigorous approach combining data augmentation and meticulous model optimization techniques. The process begins with the meticulous collection of a diverse dataset, essential for training a robust model. Subsequent preprocessing of images in the HSV color space ensures standardized input features, crucial for consistency in model training. Augmentation techniques are then applied to enrich the dataset, enhancing model generalization and robustness. The YOLOv8 model is trained using this augmented dataset, leading to significant enhancements in key performance metrics. Specifically, mean average precision (mAP) improved by 13.3%, from 0.75 to 0.85, precision increased by 10%, from 0.80 to 0.88, and recall rose by 10.3%, from 0.78 to 0.86. Further optimization efforts, including parameter tuning and the strategic integration of a Kalman Filter, notably improved object tracking and distance estimation capabilities. Final validation in real-world scenarios confirms the efficacy of the optimized model, demonstrating its readiness for practical deployment. This comprehensive approach showcases tangible advances in navigational assistance technology, significantly improving safety and reliability for visually impaired users.
The Effect of Resampling Techniques on Model Performance Classification of Maternal Health Risks Nia Mauliza; Aisha Shakila Iedwan; Yoga Pristyanto; Anggit Dwi Hartanto; Arif Nur Rohman
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5934

Abstract

Indonesia's maternal mortality rate was the second highest in ASEAN, reflecting the problem of class imbalance in maternal health data. This research aimed to improve prediction accuracy in the classification of pregnant women's diseases through the application of various resampling methods. The methods used in this research included Synthetic Minority Over-sampling Technique (SMOTE), SMOTE-Edited Nearest Neighbor (SMOTE-ENN), Adaptive Synthetic Sampling (ADASYN), and ADASYN-ENN, using five classification algorithms: Decision Tree, K-Nearest Neighbor (KNN), Naïve Bayes, Random Forest, and Support Vector Machine (SVM). Performance evaluation was carried out using accuracy, precision, recall, and F1-score metrics to determine the best method and algorithm. The results showed that the SMOTE-ENN and ADASYN-ENN methods significantly improved the model's performance in predicting maternal disease. Random Forest and Decision Tree algorithms showed the best results in terms of accuracy and consistency. These findings provided practical guidance for the application of resampling techniques in the classification of pregnant women's health data, which could contribute to improving the quality of maternal health services in Indonesia.
An Optimized Hyperparameter Tuning for Improved Hate Speech Detection with Multilayer Perceptron Muhamad Ridwan; Ema Utami
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5949

Abstract

Hate speech classification is a critical task in the domain of natural language processing, aiming to mitigate the negative impacts of harmful content on digital platforms. This study explores the application of a Multilayer Perceptron (MLP) model for hate speech classification, utilizing Bag of Words (BoW) for feature extraction. The hypothesis posits that hyperparameter tuning through sophisticated optimization techniques will significantly improve model performance. To validate this hypothesis, we employed two distinct hyperparameter tuning approaches: Random Search and Optuna. Random Search provides a straightforward yet effective means of exploring the hyperparameter space, while Optuna offers a more sophisticated, optimization-based approach to hyperparameter selection. The study involved training the MLP model on a labeled dataset is based on crawling results on the Twitter platform of hate speech and non-hate speech overall total dataset is 13.169, followed by evaluation using standard metrics. Our experimental results demonstrate the comparative effectiveness of these two hyperparameter tuning methods. Notably, the MLP model tuned with Optuna achieved a higher F1-score of 81.49%, compared to 79.70% achieved with Random Search, indicating the superior performance of Optuna in optimizing the hyperparameters. These results were obtained through extensive cross-validation to ensure robustness and generalizability. The findings underscore the importance of optimized hyperparameters in developing robust hate speech classification systems. The superior perform ance of Optuna highlights its potential for broader application in other machine learning tasks requiring hyperparameter optimization. This improvement enables more reliable and efficient automated moderation, which is crucial for the integrity and security of digital communication platforms such as Twitter.
Optimizing Book Stocktaking Process: Integration of Mobile Robot QRCode Commands with SLiMS Mohammad Harry Khomas Saputra; Pipit Anggraeni; Wahyu Adhie Candra
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 5 (2024): October 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i5.5638

Abstract

This study presents a novel approach to enhance efficiency and precision in library management through the utilization of QR code technology. Integration of a mobile robot equipped with a QR code reader into the stocktake process, interfaced with the SliMS framework via an accessible API, lays the groundwork for an automated book inventory management system. This groundbreaking system enables the generation of dynamic QR code commands, facilitating seamless adjustments to bookshelf layouts. The autonomous and accurate movement of the mobile robot significantly reduces the time required for recording, allowing library staff to allocate more time to value-added tasks. The implementation of this method entailed configuring the mobile robot to navigate library aisles, scan QR codes on book spines, and transmit inventory data to the SliMS system in real time. Research findings indicate a notable decrease in inventory processing time, accompanied by an improvement in accuracy resulting from the eradication of manual data entry errors. Specifically, the calculated efficiency gain of approximately 66.81% highlights the substantial benefits of integrating the mobile robot scan QR code process compared to manual methods. In conclusion, the deployment of this automated book inventory management system, driven by QR code technology, marks a positive shift in library management practices, enhancing the efficiency of the book inventory process and overall operational effectiveness.
Quantum-Enhanced K-Medoids Clustering: Comparative Analysis of Stroke Medical Data Siahaan, Ricardo; Purba, Swingly; Siregar, Jeremia; Hutabarat, Marvin Frans Sakti; Sitohang, Rasmi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 5 (2024): October 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i5.5814

Abstract

Stroke is a severe medical condition that occurs when the blood supply to parts of the brain is interrupted or reduced, resulting in brain tissue that lacks oxygen and nutrients. This causes brain cells to start to die in minutes. Early prevention reduces the risk of stroke. In this study, a quantum computing approach is used to improve the performance of the K-Medoids method. A comparative analysis of these methods was carried out with a focus on their performance, especially on the accuracy of the test results. The investigation was carried out using a data set of stroke patient medical records. The data set was tested using the classical and K-Medoids methods with a quantum computing approach utilizing Manhattan distance calculations. The findings of this research reveal improvements in the K-Medoids algorithm with Manhattan distance calculation influenced by the integration of a quantum computing framework. In particular, the simulation test results show an increase in accuracy from the classical K-Medoids method to the K-Medoids method with a quantum computing approach, from 52% to 64%. These results highlight that the performance of the K-Medoids method with a quantum computing approach is superior to that of the classical K-Medoids method.

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