Arm Robot 5-DOF using Matlab GUI Text Commands

This research explores the relationship between Matlab programming language and robot arm control through string or word commands. Experiments were conducted with various commands to move the robot arm in the desired direction. The implementation of this system was tested on a robot arm equipped with a unique gripper for grasping and moving test tubes. Experimental results show that the system can recognize GUI text commands with a high accuracy of 98% in providing direction/action to the test tube according to the instructions given. Thus, incorporating the robot arm control method through string or word commands in the development of this system contributes positively to the precision and responsiveness of the robot arm control system. This research opens up opportunities for further growth in robotics, especially in chemical laboratory applications, where manipulating sensitive materials requires highly accurate control. The successful use of string or word commands in controlling robots offers the potential for widespread implementation in various fields that need responsive and efficient human-machine interaction. This study contributes to the robotics and automation development literature, combining chemical robot technology and Matlab to improve robot controllability. The results show that Matlab with string/word commands can effectively control the robot ar

Efficient Real and Fake Face detection Using ResNet18

This study aims to develop a classification model for distinguishing between real and fake facial images using a lightweight Convolutional Neural Network architecture, specifically ResNet18. The research addresses the growing misuse of synthetic facial images in biometric security systems and identity verification processes. A combined dataset was used, consisting of secondary data from the 140K Real and Fake Faces dataset on Kaggle and primary images captured via a local camera. Preprocessing steps included resizing all images to 128×128 pixels, horizontal flipping, and normalization. The model was trained for five epochs using the FastAI framework with the one-cycle learning rate strategy. The experimental results show that the ResNet18 model achieved a test accuracy of 92.1%, with balanced precision, recall, and F1-score across both classes. Evaluation metrics were supported by a classification report and confusion matrix. The model contains 11.7 million parameters and completed training in approximately 9 minutes and 42 seconds, indicating its computational efficiency on a T4 GPU environment. While the study referenced deeper architectures such as ResNet34 and ResNet50 for context, no direct comparative experiments were conducted. Therefore, conclusions regarding relative performance are limited to the reported metrics of ResNet18 alone. The findings support the feasibility of deploying ResNet18-based models for real-time facial image classification in resource-constrained environments. Future research is encouraged to explore architecture comparisons, more advanced augmentation techniques, and evaluation using video-based inputs for improved generalizatio

Enchancing Brain Tumor Disease Classification via SqueezeNet Architecture Integrated with Group Convolution

Brain tumor classification using MRI images is a major challenge in medical image processing, particularly when facing imbalanced data between classes. This imbalance often leads to model bias toward the majority class and reduces sensitivity to the minority class—patients with tumors. This study aims to analyze the impact of applying Group Convolution techniques to the VGG19 and SqueezeNet architectures to enhance both computational efficiency and classification accuracy. A quantitative experimental approach was employed, implementing Convolutional Neural Networks (CNNs) using the PyTorch framework. The dataset includes two classes, “Yes” (with tumor) and “No” (without tumor), organized into Train, Validation, and Test folders. The models were evaluated by comparing the performance of standard architectures with modified versions integrating Group Convolution. Experimental results show that SqueezeNet with Group Convolution achieved up to 90% accuracy, outperforming the original model. Additionally, the model exhibited significantly improved sensitivity to the minority class, indicating better performance under imbalanced conditions. These findings suggest that Group Convolution enhances not only computational efficiency but also classification capability. Therefore, this technique is applicable in developing automated diagnostic systems. Future research is encouraged to combine Group Convolution with methods such as attention mechanisms to achieve more optimal and reliable classification results

Implementation of Transfer Learning on CNN using DenseNet121 and ResNet50 for Brain Tumor Classification

Brain tumors are conditions characterized by abnormal cell growth in the brain, which can disrupt brain function. Early detection and accurate classification are crucial to ensuring effective treatment. This study aims to improve the accuracy of brain tumor classification by implementing Convolutional Neural Networks (CNN) using Transfer Learning approaches on DenseNet121 and ResNet50 models. Transfer Learning leverages knowledge from pre-trained models on larger datasets, thereby accelerating the training process and enhancing performance on the brain tumor dataset. The dataset used consists of medical images, including images of brain tumors and images without tumors. The data was divided into two parts, with 80% for training and 20% for validation. This split ensures that the model learns optimally from the training data and is tested on unseen data to objectively evaluate its performance. Experimental results show that the ResNet50 model achieved an accuracy of 98.44% on the validation data, while the DenseNet121 model achieved an accuracy of 96.31%. In conclusion, the ResNet50 model outperformed DenseNet121 in brain tumor classification. The implications of this study demonstrate that the Transfer Learning approach with ResNet50 can serve as an effective tool for automated brain tumor diagnosis, potentially improving patient outcomes through more accurate detection and classificatio

Grouping of Tourism Locations in Indonesia Using Distance Variations in the K-Means Algorithm

Indonesia is home to a diverse range of tourist destinations, yet the classification and mapping of these locations remain a challenge in tourism management. This study aims to cluster tourist destinations in Indonesia by applying the K-Means algorithm with three distance metric variations: Euclidean Distance, Manhattan Distance, and Canberra Distance. The dataset was sourced from public data repositories and underwent preprocessing steps, including data normalization. The optimal number of clusters was determined using the Elbow Method, while the clustering results were evaluated using the Silhouette Score and Davies-Bouldin Index. The findings indicate that Manhattan Distance produced the highest Silhouette Score (0.321463), suggesting superior clustering performance compared to the other two metrics. The results of this study provide valuable insights for stakeholders in formulating strategic tourism promotion and infrastructure development efforts

CatBoost Algorithm Implementation for Classifying Women's Fashion Products

The rapid growth of the women's fashion industry in the digital era has intensified the need for data-driven approaches to understand customer preferences. This study aims to classify women’s clothing products based on customer reviews by applying CatBoost, a gradient boosting algorithm known for its strong performance with categorical features. The dataset, consisting of 23,486 entries and 11 attributes, was obtained from Kaggle and processed through data cleaning, normalization, exploratory analysis, and model training. Hyperparameter optimization was conducted using Grid Search. Model performance was evaluated using accuracy, precision, recall, and F1-score, and benchmarked against four traditional classifiers: Decision Tree (C4.5), Naïve Bayes, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). The results show that CatBoost achieved an accuracy of 93.70%, an F1-score of 0.9606, and an AUC of 0.9691, indicating excellent and balanced classification performance. This study demonstrates the effectiveness of CatBoost in handling customer review data and contributes to the development of intelligent product classification systems in the fashion industr

Analysis Of Mobile Banking User Activity Based On Transaction Time Clustering Using Self-Organizing Map (SOM) Method

The rapid growth of mobile banking services in Indonesia demands a deeper understanding of user behavior, especially in terms of time and transaction patterns. However, the challenge is how to effectively cluster users based on their time habits in making transactions, so that service strategies can be tailored accordingly. To address this issue, this study applies the Self-Organizing Maps (SOM) method to cluster users based on transaction time features, such as the number of transactions in the morning, afternoon, evening, night, and the division between weekdays and weekends. The dataset used includes 87,361 mobile banking users throughout 2023. The results showed that the SOM method was able to form nine different user behavior clusters, with the largest cluster being Early User (Weekday) consisting of 32,324 users (37.0%). Overall, the Early User (Weekday) segment covers about 60.3% of the user population. Meanwhile, there are also minority segments such as Night Owl (Weekday) (5.9%) and Early User (Weekend) (2.7%) that show unique behavior patterns. The model performance evaluation resulted in a Quantization Error (QE) value of 0.339 and Topographic Error (TE) of 0.066, both on validation data and test data, indicating that the clustering results are quite accurate and the data mapping topology is well maintained. This research contributes to the understanding of mobile banking user behavior segmentation and can be used as a basis for a more adaptive and personalized time-based service strategy

Classification Of Outstanding Students Using Support Vector Machine (SVM) Based on Data Mining

This research aims to classify outstanding students at the Pagar Alam Institute of Technology using the Support Vector Machine (SVM) algorithm based on data mining. Early identification of outstanding students is crucial for supporting potential development and institutional decision-making. Historical data from 245 students from the 2016 to 2018 cohorts were utilized, encompassing course grades and Cumulative Grade Point Average (CGPA). The research process included data preprocessing such as normalization and splitting the data into 80% training data and 20% testing data. The SVM model was implemented with a Radial Basis Function (RBF) kernel and parameters C=1.0 and gamma=0.1. Evaluation results show that the model achieved an overall accuracy of 89.80% on the testing data. The model's performance was further validated through a confusion matrix (9 True Positives, 1 False Negative) and a classification report indicating good precision and recall for both classes. Furthermore, an Area Under the Curve (AUC) value of 0.93 signifies the model's excellent discriminative ability. This study contributes by providing an effective classification tool for identifying outstanding students, which can serve as a basis for the institution to design more targeted development and recognition programs

Implementation of Random Forest Algorithm for Early Detection of Heart Health Using IoT and MAX30102 Sensors

Heart disease is one of the leading causes of death in the world, including in Indonesia, which ranks second after stroke. Early detection is essential to reduce the risk of serious complications and death from cardiovascular disorders. This study aims to design an Internet of Things (IoT)-based early detection system for heart health that is integrated with MAX30102 sensors and Random Forest algorithms to classify heart rate conditions. Biometric data in the form of heart rate (BPM), blood oxygen level (SpO₂), and activity condition features (rest, light exercise, stress) were collected from 150 respondents. This data collection was validated by comparing the results using ECG devices by medical personnel. Pre-processing is done through data cleansing, category variable encoding, and feature extraction (BPM variability, PPG amplitude). The classification model was developed with the Random Forest 100 decision tree and tested with 5-fold cross validation. The results showed that the system was able to achieve an average accuracy of 93% with a standard deviation of 0.03, as well as an accuracy per fold of 93%, 93%, 97%, 93%, and 87%. The classification results are in line with the ECG data of medical personnel, indicating that this system is reliable enough for the early detection of normal or abnormal heart conditions. The study concluded that the integration of IoT and Random Forest is effective as a real-time, cost-effective, and supporting early detection of heart health, especially in remote areas. Advanced development is suggested to expand activity data and add biometric features to improve classification accuracy

Coffee Quality Classification Based on Customer Reviews Using C4.5 Algorithm

Coffee is a very popular commodity throughout the world, and its quality is oken evaluated through customer reviews. This research aims to classify coffee quality based on reviews given by consumers using the C4.5 algorithm. C4.5 is a machine learning algorithm used to generate decision trees, which allows decision making based on relevant attributes. In this research, the data used consists of customer reviews taken from e-commerce plaVorms and coffee discussion forums. The data is then processed with natural language processing (NLP) techniques to extract important features such as sentiment, keywords and term frequency. These features are used as input for the C4.5 algorithm, which builds a classification model based on patterns contained in the data. The results of the research show that the C4.5 model is able to classify coffee quality with high accuracy, reaching up to 85%. The factors that most influence quality classification include taste, aroma, and packaging, which are frequently mentioned in reviews. In addition, the analysis also shows significant differences in the quality of coffee produced from different coffee producing regions, which can provide insight for producers to improve their products