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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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XGBoost Algorithm for Cervical Cancer Risk Prediction: Multi-dimensional Feature Analysis Sudi Suryadi; Masrizal
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6587

Abstract

Cervical cancer continues to pose a significant global health challenge, with early detection remaining the cornerstone for effective intervention. This study is situated at the intersection of clinical oncology and computational intelligence, exploring the potential of gradient-boosting algorithms to overcome the limitations of conventional screening methodologies. An XGBoost model was developed to predict cervical cancer risk. This model incorporates demographic, behavioral, and clinical parameters. The model was developed using data from 858 patients at the Hospital Universitario de Caracas. The preprocessing pipeline was designed to address the complexities inherent in medical data, including strategic management of missing values and standardizing heterogeneous features. The model demonstrated an overall accuracy of 96.3%, with a sensitivity of 66.7% and a specificity of 97.6%. This performance profile indicates adept navigation of the delicate balance between missed diagnoses and unnecessary interventions. Feature importance analysis revealed a multifaceted risk landscape, where screening test results contributed substantial predictive power (approximately 60%), complemented by demographic and behavioral factors, including age, reproductive history, and contraceptive usage patterns. The confusion matrix analysis revealed the clinical implications of the model predictions, demonstrating a promising positive predictive value of 55.0% despite the pronounced class imbalance. These findings suggest that ensemble learning approaches can effectively synthesize diverse patient data into meaningful risk assessments, potentially enhancing screening efficiency through personalized stratification. Future research directions include prospective validation across diverse populations, integration of longitudinal data, and further exploration of explainable AI techniques to bridge the gap between algorithmic predictions and clinical implementation.
Optimizing Sensitivity in Machine Learning Models for Pediatric Post-operative Kyphosis Prediction Raja Ayu Mahessya; Dian Eka Putra; Rostam Ahmad Efendi; Rayendra; Rozi Meri; Riyan Ikhbal Salam; Dedi Mardianto; Ikhsan; Ismael; Arif Rizki Marsa
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6606

Abstract

Post-operative kyphosis represents a significant complication following pediatric spinal corrective surgery, necessitating sophisticated prediction methods to identify high-risk patients. This study developed and evaluated machine learning models for kyphosis prediction using a dataset of 81 pediatric patients by comparing the logistic regression and decision tree approaches. Despite achieving a higher overall accuracy (82%), the logistic regression model failed to identify any kyphosis cases, rendering it clinically ineffective. Conversely, the decision tree model demonstrated superior clinical utility by successfully identifying 33% of kyphosis cases while maintaining 71% accuracy. Feature importance analysis established starting vertebral position as the dominant predictor (importance=0.554), followed by patient age (0.416), with vertebrae count contributing minimally (0.030). The decision tree identified critical thresholds for risk stratification: operations beginning at or above T8-T9, particularly in children aged 5-9 years, carried a substantially elevated kyphosis risk. Our methodological approach emphasizes sensitivity over conventional accuracy metrics, recognizing that missing high-risk patients have greater clinical consequences than unnecessary monitoring. This study demonstrates the capacity of decision tree models to extract clinically meaningful patterns from small, imbalanced surgical datasets that elude conventional statistical approaches.
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.
Breast Cancer Histopathological Image Classification with Convolutional Neural Networks Models Unaldi, Isil; Tomak, Leman
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6420

Abstract

Early diagnosis and treatment can reduce mortality rates by preventing the progression of breast cancer. Owing to convolutional neural networks (CNN), breast cancer diagnosis can be performed faster and more objectively than humans using thousands of histopathological images. This study aimed to evaluate and compare the rapid and effective diagnostic performance of CNN models on breast tumor images, utilizing transfer learning through pre-training and fine-tuning on novel datasets. The study was performed in two ways on BreakHis and BACH datasets. First, fine-tuned VGG16, VGG19, Xception, InceptionV3, ResNet50, and InceptionResNetV2 models were used for classification. Second, these CNN models were used as feature extractors and support vector machines (SVMs) as classifiers. The success of all models in tumor classification was interpreted using performance metrics, such as accuracy, precision, recall, F1 score, and AUC. The models showing the best performance as a result of the analyses were as follows: InceptionResNetV2+SVM model with an accuracy of 99.3%, precision of 99.0%, recall of 100.0%, F1 score of 99.5%, AUC of 98.9% for BreakHis dataset; and InceptionResNetV2 model with accuracy of 96.7%, precision of 93.8%, recall of 100.0%, F1 score of 96.8%, AUC of 96.7% for the BACH dataset. As a conclusion, it has been seen that the CNN methods have good generalization abilities and can respond to clinical needs.
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.

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