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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 28 Documents
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Search results for , issue "Vol 9 No 4 (2025): August 2025" : 28 Documents clear
Comparative Performance of ResNet Architectures for Toraja Carving Image Classification with Data Augmentation Herman; Akbar, Muhammand; Nasir, Haidawati; Herdianti; Azis, Huzain; Hayati, Lilis Nur
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6181

Abstract

The complexity of the motifs and large number of different patterns make the classification of Toraja carvings challenging. The objective of this study is to develop a Convolutional Neural Network automatic classification model using a comparative analysis of the performance of three ResNet architectures. Data augmentation techniques were used to enrich the diversity of the training samples and improve the robustness of the model. The experimental results showed that ResNet101V2 had the highest validation accuracy, which was greater than 97%, followed by ResNet50V2 with more than 96%, and finally, ResNet152V2 with more than 94.74%. These test results indicate that the ResNet101V2 architecture has a better classification performance for complex motifs, with a good balance between precision and recall. However, the confusion matrix and per-class performance metrics indicated that motifs with high similarity, such as Paqdon-Bolu and Paqtedong, remained challenging. This study demonstrated that deeper CNN architectures and data augmentation techniques are effective in improving the classification accuracy of complex carving patterns. Further research should explore hybrid or advanced augmentation methods to improve the overall robustness and accuracy of the model.
Real-time Emotion Recognition Using the MobileNetV2 Architecture Hendrawati, Triyani; Apriliyanti Pravitasari, Anindya
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6158

Abstract

Facial recognition technology is now advancing quickly and is being used extensively in a number of industries, including banking, business, security systems, and human-computer interface. However, existing facial recognition models face significant challenges in real-time emotion classification, particularly in terms of computational efficiency and adaptability to varying environmental conditions such as lighting and occlusion. Addressing these challenges, this research proposes a lightweight, yet effective deep learning model based on MobileNetV2 to predict human facial emotions using a camera in real time. The model is trained on the FER-2013 dataset, which consists of seven emotion classes: anger, disgust, fear, joy, sadness, surprise, and neutral. The methodology includes deep learning-based feature extraction, convolutional neural networks (CNN), and optimization techniques to enhance real-time performance on resource-constrained devices. Experimental results demonstrate that the proposed model achieves a high accuracy of 94.23%, ensuring robust real-time emotion classification with a significantly reduced computational cost. Additionally, the model is validated using real-world camera data, confirming its effectiveness beyond static datasets and its applicability in practical real-time scenarios. The findings of this study contribute to advancing efficient emotion recognition systems, enabling their deployment in interactive AI applications, mental health monitoring, and smart environments. Real-world camera data is also used to evaluate the model, demonstrating its usefulness in real-time applications and its efficacy beyond static datasets. The results of this work advance effective emotion identification systems, making it possible to use them in smart settings, interactive AI applications, and mental health monitoring.
Face Dermatological Disorder Identification with YoloV5 Algorithm Ayu Wirdiani; Lennia Savitri Azzahra Lofiana; I Putu Arya Dharmadi; Oka Sudana
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6237

Abstract

Dermatological disorders are common in humans. The accurate identification of skin diseases is paramount for determining the most efficacious treatment. This system can screen images of skin diseases on the face and provide analysis results in the form of object detection. Dermatological disorders of the face are classified into six categories: acne nodules, melasma, filiform warts, milia, papules, and pustules. The YoloV5 algorithm was selected because of its effectiveness in live-detection tasks. The image-enhancement process involves the implementation of two methodologies: sharpening and histogram equalization. The former adjusts the brightness values whereas the latter adjusts the contrast values. The dataset comprised 1,223 images of skin diseases, with 947 images allocated for training and 276 for validation. The optimal mAP of the filiform wart class was determined to be 87.6%, with values of 76.7% for pustules, 72% for papules, 71% for milia, 68% for nodules, and 38.2% for melasma, representing the lowest value. The low mAP of melasma was attributed to the abstract image data type and complexity of localization. The congruence of object features and disparity in data variance has the potential to influence outcomes.
Optimizing Sentiment Analysis for Lombok Tourism Using SMOTE and Chi-Square with Machine Learning Hairani; Anggrawan, Anthony; Muhammad Ridho Akbar; Khasnur Hidjah; Muhammad Innuddin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6623

Abstract

Tourism is a vital economic sector for Lombok Island, which is renowned for its natural beauty and cultural richness as a top destination. The rapid growth of tourism in Lombok requires a deep understanding of tourists' perceptions and sentiments to ensure an optimal service quality. The sentiment analysis of online reviews is valuable for identifying service strengths and weaknesses and addressing tourists' needs more effectively. This not only enhances tourist satisfaction, but also aids in the design of more effective marketing strategies. However, text data analysis from online reviews presents unique challenges such as noise, class imbalance, and numerous features that may affect classification results. Therefore, this study aims to classify tourist sentiment toward Lombok tourism using machine learning methods combined with feature selection and oversampling techniques. This study focuses on optimizing sentiment analysis of tourism-related tweets using a combination of SMOTE oversampling and Chi-Square feature selection on improving classification performance without hyperparameter tuning. The study applies machine learning methods, such as SVM and Naïve Bayes, with feature selection and oversampling using Chi-Square and SMOTE. The dataset used was sentiment data regarding Lombok tourism obtained from Twitter in 2023, consisting of 940 instances divided into three classes: Negative, Neutral, and Positive. The research findings show that the use of SMOTE and Chi-Square can improve the accuracy of the SVM and Naive Bayes methods. Without optimization, the SVM method achieved an accuracy of 73.93% and a Naive Bayes of 67.02%. After optimization with SMOTE and Chi-Square, the accuracy increased for SVM by 90% and Naive Bayes by 84% to classify tourist sentiment towards Lombok tourism. The implications indicate that combining data balancing using SMOTE with feature selection via Chi-Square effectively improves the performance of sentiment classification models for tourist opinions on Lombok's tourism.
DiG-MFV: Dual-integrated Graph for Multilingual Fact Verification Agustina, Nova; Kusrini; Utami, Ema; Hidayat, Tonny
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6695

Abstract

The proliferation of misinformation in political domains, especially across multilingual platforms, presents a major challenge to maintaining public information integrity. Existing models often fail to effectively verify claims when the evidence spans multiple languages and lacks a structured format. To address this issue, this study proposes a novel architecture called Dual-integrated Graph for Multilingual Fact Verification (DiG-MFV), which combines semantic representations from multilingual language models (i.e., mBERT, XLM-R, and LaBSE) with two graph-based components: an evidence graph and a semantic fusion graph. These components are processed through a dual-path architecture that integrates the outputs from a text encoder and a graph encoder, enabling deeper semantic alignment and cross-evidence reasoning. The PolitiFact dataset was used as the source of claims and evidence. The model was evaluated by using a data split of 70% for training, 20% for validation, and 10% for testing. The training process employed the AdamW optimizer, cross-entropy loss, and regularization techniques, including dropout and early stopping based on the F1-score. The evaluation results show that DiG-MFV with LaBSE achieved an accuracy of 85.80% and an F1-score of 85.70%, outperforming the mBERT and XLM-R variants, and proved to be more effective than the DGMFP baseline model (76.1% accuracy). The model also demonstrated stable convergence during training, indicating its robustness in cross-lingual political fact verification tasks. These findings encourage further exploration in graph-based multilingual fact verification systems.
Advancing Vehicle Logo Detection with DETR to Handle Small Logos and Low-Quality Images Ubaidillah, Rifky Fahrizal; Sulistiyo, Mahmud Dwi; Kosala, Gamma; Rachmawati, Ema; Haryadi, Deny
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6236

Abstract

Image-based vehicle logo detection is an important component in the implementation of vehicle information recognition technology, which supports the development of intelligent transportation systems. Vehicle logos, as elements that represent the identities of vehicle brands and models, play a significant role in completing vehicle identity data. The information obtained from this logo can be utilized to solve various traffic problems, such as vehicle document counterfeiting and theft, and for better traffic planning and management purposes. However, the main challenge in developing an accurate logo detection system lies in the wide variety of shapes, sizes, and positions of logos in different types of vehicles. In addition, the generally small size of logos, especially on certain vehicles, often makes it difficult for computer-based detection systems to recognize logos consistently, thus affecting the overall performance of the detection model. In this research, the Detection Transformers (DETR) method is used to build a vehicle logo detection system that focuses on small-scale logo. The testing process was conducted using the VL-10 dataset, which was specifically designed for vehicle logo detection evaluation. The results show that the DETR model can detect vehicle logos very well, even for small-scale logos. The model achieved an AP50 value of 0.952, which indicates a high level of accuracy and reliability in detecting the vehicle logo in the dataset used.
Enhancing Lung Cancer Detection: Optimizing CNN Architectures through Hyperparameter Tuning Sundari Retno Andani; Poningsih; Abdul Karim
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6357

Abstract

TThis study aimed to compare the performance of various Convolutional Neural Network (CNN) architectures, including LeNet, ResNet, AlexNet, GoogleNet, VGGNet, and the proposed model, in medical image classification for disease detection. The proposed model was developed by adding additional layers and fine-tuning the hyperparameters in the ResNet architecture to enhance its ability to extract complex features. The training and testing processes were conducted using an augmented X-ray image dataset to increase the data diversity. The results indicate that the proposed model achieved the highest testing accuracy of 76.33%, surpassing other models in terms of accuracy, precision, recall, and F1-score. Although there are some limitations in specificity and the Matthews Correlation Coefficient (MCC), the proposed model still demonstrates better generalization ability, with an AUC-ROC score approaching an optimal value. These findings suggest that the proposed model has advantages in medical image classification and holds potential for further development to enhance disease classification accuracy.
A New Triple-Weighted K-Nearest Neighbor Algorithm for Tomato Maturity Classification Lidya, Lidya Ningsih; Arif, Arif Mudi Priyatno; dini, Addini Yusmar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6441

Abstract

As climatic products, tomatoes are highly sensitive to harvesting and processing. The sorting of tomatoes can be significantly improved by utilizing Hue Saturation Value (HSV) color features that are classified using neighboring algorithms, such as K-Nearest Neighbor (KNN), Weighted K-Nearest Neighbor (W-KNN), and DW-KNN. However, the DW-KNN algorithm does not consider the relative relationship between the farthest, nearest, and surrounding neighbors, which may impact the classification accuracy, particularly in datasets with uneven distributions. This study proposes a Triple Weighted K-Nearest Neighbor (TW-KNN) algorithm for tomato image classification. This algorithm effectively handles the problem of sensitivity and outliers in the data distribution and considers the relationship between neighboring distances. The classification data consisted of 400 tomato images with five maturity levels divided into training and testing sets using k-fold cross-validation. Tests were conducted using several variations of parameter k, namely 4, 6, 9, and 15, to evaluate the classification performance. The results show that the proposed TW-KNN algorithm consistently outperforms other methods by producing better classification results. This is demonstrated by an accuracy rate of 95.52% across different values of k. The superior performance of the TW-KNN highlights its ability to provide robust and stable classification results compared to conventional KNN variants. This finding indicates that the TW-KNN is more effective in consistently classifying tomato fruits, making it a promising approach for automated fruit sorting applications.
Enhanced Predictive Modeling for Non-Invasive Liver Disease Diagnosis Prabowo, Donni; Bety Wulan Sari; Yoga Pristyanto; Afrig Aminuddin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6449

Abstract

Liver diseases (e.g. cirrhosis, hepatitis, and fatty liver disease) are globally one of the leading causes of mortality and are typically diagnosed in advanced stages due to vague symptoms and the difficulty involved in existing diagnostic techniques (e.g. biopsies). To optimize the early diagnosis of liver disease, this study proposes an enhanced, non-invasive approach using machine learning techniques. The research is enriched with a full pipeline, from exploratory data analysis and imputation of the dataset, treatment of the outlier, encoding of labels and scaling using ILPD (Indian Liver Patient Dataset). The classification models compared were RandomForest, XGBoost, LGBM, and CatBoost. The CatBoost algorithm fine-tuned with RandomizedSearchCV showed the highest performance with a test accuracy of 93%. The performance was again better than any already published methods showing that advanced ensembling and hyperparameter optimization worked. The proposed model is suitable for incorporation into clinical decision support systems and provides reliable and accurate diagnostic assistance. In addition to its high accuracy, the model is robust for missing and categorical data, which is a challenge in any real-world clinical scenario. These findings add to the growing body of evidence supporting AI-based medical diagnostics and suggest that CatBoost is a highly promising tool for facilitating timely screening and diagnosis of liver disease. Furthermore, the study stresses the need for thorough preprocessing and cross-validation, which serve to reduce biases that are present in widely applied datasets. Ongoing future efforts may involve the integration of multi-source data and implementation of explainable AI techniques to allow for wider clinical trust and use.
Analysis of the Impact of Backpropagation Hyperparameter Optimization on Heart Disease Prediction Models Nita Syahputri; Putrama Alkhairi; Enok Tuti Alawiah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6473

Abstract

Heart disease is a major global health issue, highlighting the need for early and accurate prediction to reduce complications and improve patient outcomes. The Backpropagation Neural Network (BPNN) is a widely used method for heart disease prediction, but its performance relies heavily on proper hyperparameter selection, including neuron count, activation function, optimizer, and batch size. This study analyzed the impact of hyperparameter optimization on BPNN performance. A standard BPNN model was compared with an optimized version, where key hyperparameters were fine-tuned to enhance predictive accuracy and stability. Both models were trained and tested on the same dataset, and their performance was evaluated using Accuracy, Precision, Recall, Mean Squared Error (MSE), and Mean Absolute Error (MAE). The results show that the optimized model achieves a slightly better accuracy (99.11% vs. 99.09%) and lower error rates (MSE and MAE of 0.0089 vs. 0.0091). It also demonstrates higher precision, reflecting an improved capability in correctly identifying heart disease cases. Although the performance gap was small, the optimized model showed a more balanced and consistent outcome. These findings highlight the importance of hyperparameter tuning for improving neural network models for medical prediction. This study contributes to the development of more accurate and reliable AI tools for the early diagnosis of heart disease. Future studies may apply advanced optimization techniques, such as Bayesian Optimization or Genetic Algorithms, and use larger and more diverse datasets to enhance model generalization.

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