Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

The Effect of Hyperparameters on Faster R-CNN in Face Recognition Systems

Jasman Pardede, Khairul Rijal — Wed, 28 May 2025 16:41:09 +0000

Face recognition is one of the main challenges in the development of computer vision technology. This study aims to develop a face recognition system using a Faster R-CNN architecture, optimized through hyperparameter tuning. This research utilizes the "Face Recognition Dataset" from Kaggle, which comprises 2,564 face images across 31 classes. The development process involves creating bounding boxes using the LabelImg application and implementing the Grid Search method. The Grid Search is applied with predefined hyperparameter combinations (3 epochs [10, 25, and 50] × 3 learning rates [0.001, 0.0001, and 0.00001] × 3 optimizers [SGD, Adam, and RMS], resulting in 27 models). The evaluation metrics used were accuracy, precision, recall, and F1-score. The experimental results show that the selection of hyperparameters significantly affects the model performance. Based on the experimental results, the combination of the learning rate 0.00001, 50 epochs, and Adam optimizer yielded the highest accuracy and improvement of 8.33% compared to the baseline model. The results indicate that hyperparameter optimization enhances the ability of the model to recognize faces. Compared to conventional models, a Faster R-CNN performs better in detecting faces more accurately. Future research could further enhance the face recognition efficiency and accuracy by exploring other deep learning architectures and more advanced hyperparameter optimization techniques.

Expertise Retrieval Using Adjusted TF-IDF and Keyword Mapping to ACM Classification Terms

Lyla Ruslana Aini, Evi Yulianti — Sun, 25 May 2025 07:12:47 +0000

In an era of collaboration, knowing someone's expertise is becoming increasingly necessary. Recognizing individuals' proficiency can be challenging because it requires considerable manual time. This study explores the expertise of lecturers from the Computer Science Department, Universitas Indonesia (Fasilkom UI), based on scientific publications. The data were obtained from the Sinta journal website’s scrapping process, which includes Scopus, Garuda, and Google Scholar data sources. The approach used was keyword extraction using the adjusted TF-IDF. The resulting keywords were then mapped to the ACM classification class using cosine similarity calculations with various embedding models, including BERT, BERT multilingual, FastText, XLM Roberta, and SBERT. The experimental results highlighted that combining the adjusted TF-IDF with mapping to the ACM classes using SBERT is a promising approach for gaining the best expertise. The use of abstract data has proved to be better than that of full-text data. Using title-abstract-EN data achieved a score of 0.49 for both the P@1 and NDCG@1 metrics, whereas the title-abstract-ENID data attained a score of 0.75 for both metrics P@1 and NDCG@1.

The Impact of Cancer on Poverty: An Analytical Study Using Big Data and OLS Regression

Heny Pratiwi, Muhammad Ibnu Sa’ad, Wahyuni Wahyuni, Syamsuddin Mallala — Sat, 24 May 2025 16:55:40 +0000

Cancer is one of the leading causes of death worldwide and has a significant impact on the economic condition of families, especially in developing countries. High medical costs and loss of work productivity often push families of patients with cancer into poverty. This study aimed to analyze the relationship between cancer mortality rates and poverty levels using the Ordinary Least Squares (OLS) regression method and big data covering various socio-economic indicators. The data in this study include cancer mortality rates and other socioeconomic indicators, which were then analyzed using the OLS regression method to understand the quantitative relationship between the two variables. The results of the analysis show a positive correlation between cancer mortality rates and increasing poverty, with the regression model explaining 73.8% of the variation in the target variable. The regression model demonstrated strong explanatory power and minimal error, with an R-squared value of 0.738, indicating that 73.8% of the data variability was explained by the model. Model quality was supported by low AIC (19070.4) and BIC (19110.4) values. Linearity was confirmed by a significant F-statistic of 1314.0 (p < 0.01), suggesting a robust linear relationship between independent and dependent variables. All parameters exhibited statistical significance (p < 0.05) at the 95% confidence level, with mean residuals close to zero, satisfying the unbiased expectation assumption. Although the model results show good performance, the model's estimators show low variance, as evidenced by small standard errors (e.g., Incidence_Rate: 0.009, Med_Income: 1.89e-05) and a Durbin-Watson statistic of 1.725, indicating no autocorrelation. These metrics collectively confirmed the reliability and stability of the regression model.

Classification of Retinoblastoma Eye Disease on Digital Fundus Images Using Geometric Features and Machine Learning

Arif Setiawan — Sat, 24 May 2025 16:37:37 +0000

Medical image analysis is essential for detecting retinoblastoma tumors due to the ability of this method to assist doctors in examining the morphology, density, and distribution of blood vessels. The classification of normal and retinoblastoma-affected retinas is a preliminary step in treating retinoblastoma tumors. Therefore, this study aimed to propose a new method for classifying normal and retinoblastoma-affected retinas using geometric feature extraction and machine learning. The workflow consisted of (1) fundus image data collection for retinoblastomas, (2) image segmentation, (3) feature extraction process, (4) building a classification model using machine learning, (5) splitting testing and training data, (6) classification process using machine learning methods, and (7) evaluation of classification results using a confusion matrix. The results showed that the segmentation method could detect retinoblastoma areas and extract their geometric features. The SVM method achieved an accuracy of 0.96 while the RF and DT had 0.55 and 0.63, respectively. Moreover, a comparison with previous research showed that the proposed method achieved a 4% improvement in the classification performance. This led to the conclusion that classification using geometric features combined with the SVM on digital fundus images of retinoblastoma eye disease produced the best results.

University Students Stress Detection During Final Report Subject by Using NASA TLX Method and Logistic Regression

Sat, 24 May 2025 11:54:37 +0000

Stress is a psychological response that occurs when someone faces pressure or demands that exceed their ability to adapt. In the context of a final-year student, stress is often a significant problem due to academic pressure, such as completing final assignments, as well as demands to immediately prepare to enter the workforce and demands to immediately prepare to enter the workforce. Research shows that stress that is not managed properly can cause various negative effects, such as sleep disorders and decreased cognitive function. This study aimed to identify and analyze stress levels among final-year students who completed a final report by integrating physiological and psychological data. In this study, 30 students were assessed using a wearable system to obtain physiological data, such as heart rate and body temperature, while subjective assessments were carried out using the NASA-TLX method to measure mental workload. The results showed that 19 out of 30 respondents experienced significant levels of stress and 11 respondents were in normal conditions, with the main causal factors including high academic pressure and distance regarding the future. In addition, the logistic regression analysis applied in this study succeeded in developing a predictive model with an accuracy of 94% in identifying students' stress conditions. This shows that this method is sufficiently accurate for detecting stress symptoms in final-year students.