Evaluasi Evaluasi Material Waterway Terhadap Optimalisasi Daya PLTM Sion 2 X 5 MW

Mini-Hydro Power Plants (PLTM) are a renewable energy source with significant potential in Indonesia, yet their utilization remains under-utilized. This study aims to evaluate the effect of waterway materials on power efficiency at the Sion PLTM in North Sumatra. The goal is to optimize power efficiency by selecting waterway materials, as well as to provide knowledge and a template for future similar projects or studies. The evaluation was conducted by comparing three types of channel materials: Open Channel (open concrete channel), Glass Fiber Reinforced Plastic (GRP) pipe, and High-density Polyethylene (HDPE) pipe. This study measured and compared the head loss, power output, and power efficiency of each material. The calculations revealed the lowest head loss value for GRP pipe (1.17 m), followed by HDE pipe (2.34 m), and Open Channel (18.14 m). Meanwhile, the results of the comparison of the power produced, the highest power was obtained from GRP pipes (13,624.64 kW), followed by HDPE pipes (13,406.21 kW) and Open Channel (10,456.51 kW). In conclusion, GRP pipe material is the most optimal choice for improving power efficiency at PLTM Sion, supporting the enhanced utilization of renewable energy to replace fossil fuels.Pembangkit Listrik Tenaga Mini Hidro (PLTM) merupakan salah satu sumber energi terbarukan yang memiliki potensi besar di Indonesia, namun pemanfaatannya masih belum optimal. Penelitian ini bertujuan untuk mengevaluasi pengaruh material saluran pembawa (waterway) terhadap efisiensi daya pada PLTM Sion di Sumatera Utara. Evaluasi dilakukan dengan membandingkan tiga jenis material saluran, yaitu Open Channel (saluran terbuka beton), pipa GRP (Glass Fiber Reinforced Plastic), dan pipa HDPE (High-density Polyethylene). Penelitian ini mengukur dan membandingkan head loss, daya yang dihasilkan, dan efisiensi daya untuk masing-masing material. Hasil analisis menunjukkan bahwa pipa GRP memberikan hasil terbaik dengan efisiensi daya sebesar 36,24%, diikuti oleh pipa HDPE (34,06%) dan saluran terbuka beton (4,56%). Kesimpulannya, material pipa GRP merupakan pilihan terbaik dalam meningkatkan efisiensi daya pada PLTM Sion, yang dapat mendukung optimalisasi pemanfaatan energi terbarukan untuk menggantikan bahan bakar fosil

Prototipe Sistem Pemantauan Beban Jembatan menggunakan ESP32 dan Load Cell dengan Integrasi Blynk

Infrastruktur seperti jembatan memiliki peran penting dalam konektivitas sebuah wilayah. Pemantauan pada jembatan dengan cara meninjau dan menjamin keselamatan pengguna jembatan serta mengantisipasi kerusakan struktural secara real-time berbasis Internet of Think. Perancangan sistem ini menggunakan load cell (sensor beban) untuk pengukuran beban, ADXL345 (sensor akselerometer) untuk pengukuran getaran, algoritma Fast Fourier Transform (FFT) untuk analisis sinyal getaran dalam domain frekuensi, aplikasi Blynk sebagai device pemantauan real-time. Pengujian dilakukan pada 2 kondisi permukaan jalan, yaitu jalan rusak dan jalan normal dengan menggunakan load cell dan ADXL345 dan menghasilkan rata-rata error beban rendah yang sesuai dengan datasheet, yaitu 0,01% dan dapat mendeteksi kondisi permukaan jalan dengan baik. Sistem ini mempermudah untuk pemantauan kondisi jembatan dan meningkatkan efisien anggaran dan waktu.Infrastruktur seperti jembatan memiliki peran penting dalam konektivitas sebuah wilayah. Pemantauan pada jembatan dengan cara meninjau dan menjamin keselamatan pengguna jembatan serta mengantisipasi kerusakan struktural secara real-time berbasis Internet of Think. Perancangan sistem ini menggunakan load cell (sensor beban) untuk pengukuran beban, ADXL345 (sensor akselerometer) untuk pengukuran getaran, algoritma Fast Fourier Transform (FFT) untuk analisis sinyal getaran dalam domain frekuensi, aplikasi Blynk sebagai device pemantauan real-time. Pengujian dilakukan pada 2 kondisi permukaan jalan, yaitu jalan rusak dan jalan normal dengan menggunakan load cell dan ADXL345 dan menghasilkan rata-rata error beban rendah yang sesuai dengan datasheet, yaitu 0,01% dan dapat mendeteksi kondisi permukaan jalan dengan baik. Sistem ini mempermudah untuk pemantauan kondisi jembatan dan meningkatkan efisien anggaran dan waktu

Magnetic Tactile Display for Heartbeat Emulation

Recently, humanoid robots have developed rapidly and have the potential to become a medical innovation, enhancing human interaction. The ability to realistically mimic a heartbeat is a key feature under development. This research aims to design a ferrofluid-based magnetic tactile display to represent a heartbeat in a humanoid robot. This system utilizes an electromagnetic matrix, ferrofluid, and an Arduino Uno as a magnetic flield controller. Electrocardiogram (ECG) data is used to represent various heart rate patterns, such as normal, bradycardia, and tachycardia. The development results indicate that the magnetic tactile display is capable of providing tactile feedback that corresponds to the heart rate pattern. This device mimics heartbeats based on ECG data by controlling ferrofluid through magnetic field settings, allowing users to feel it tactilely. Testing using an accelerometer sensor shows that the system can replicate normal heartbeats and bradycardia with a small average time difference of 4.52% and 3.93%, respectively. However, in tachycardia, there were difficulties in handling rapid interval changes, resulting in a time error of 31.67%. The results of this study indicate that magnetic tactile displays have great potential in physiological emulation in humanoid robots

Performance Evaluation of Delay and Jitter in Optical Fiber Networks for Real-Time Multimedia Applications

The increasing reliance on real-time services such as VoIP and video streaming underscores the critical role of fiber optic networks as high-capacity communication backbones. While optical fibers offer substantial bandwidth, this alone does not ensure optimal Quality of Experience (QoE); network delay and jitter remain prominent challenges that can disrupt audio clarity and video continuity. This paper presents a detailed analysis of delay and jitter phenomena within fiber optic systems, particularly emphasizing Passive Optical Network (PON) topologies. It explores the underlying causes of performance degradation from physical-layer limitations to architectural inefficiencies and critically assesses current mitigation techniques implemented at both the network level (e.g., Quality of Service mechanisms) and application level (e.g., jitter buffering strategies). The main contribution of this work is the introduction of a novel "Cross-Layer Performance Optimization Framework," which addresses the disconnect between static QoS enforcement and reactive application adaptations. By fostering dynamic interaction between the network and application layers, this framework aims to enable predictive control mechanisms that better safeguard user experience. Ultimately, this approach offers a pathway to more robust and efficient delivery of real-time services over next-generation optical access networks

Image Steganography: Digital Information Embedding Using Singular Value Decomposition and Simulation Software

Singular Value Decomposition (SVD) is a matrix decomposition technique that is widely used in digital signal and image processing because of its ability to represent important information efficiently. This study aims to explore the use of the SVD method in the steganography and watermarking process of digital images as part of efforts to improve the security of multimedia information. The approach used involves inserting hidden data in the form of images, sentences, and paragraphs into the host image by modifying the singular value elements of the image matrix decomposition results. Various scenarios are simulated, including inserting RGB format watermarks into grayscale images and vice versa, by testing variations in the insertion parameter (α). Evaluation is carried out on the visual quality of the resulting image (imperceptibility), as well as the success rate of information extraction. The experimental results show that this method can insert information without causing significant visual distortion and still maintain high message extraction accuracy. This study confirms the effectiveness and flexibility of the SVD technique as an information insertion method that can be applied in digital copyright protection systems and visual data security. This method also has the potential to be integrated with other techniques in more complex and robust watermarking systems

Evaluation Of The C4.5 Decision Tree and Random Forest Classification Algorithms in Predicting Diabetes

This study investigates diabetes prediction as a binary classification task using the C4.5 Decision Tree and Random Forest algorithms on the Pima Indians Diabetes dataset. The objective of this study is to compare the performance of both algorithms under three reported experimental settings: without data balancing, with data balancing, and with hyperparameter tuning without balancing. The dataset consists of 768 records, including 500 non-diabetes cases and 268 diabetes cases. The preprocessing stage included data cleaning, Box-Cox transformation, min-max normalization, feature selection, and data splitting into 80% training data and 20% test data. Model performance was evaluated using accuracy, precision, recall, and F1-score through 3-fold, 5-fold, and 9-fold cross validation. The results show that Random Forest consistently outperformed the C4.5 Decision Tree across all reported settings. Under the non-balancing condition, Random Forest achieved the highest accuracy of 77.82%, while C4.5 achieved 69.65%. After applying data balancing, the performance of both models improved, with Random Forest achieving the best overall reported accuracy of 84.19%, compared with 75.68% for C4.5. Under hyperparameter tuning without balancing, Random Forest achieved 78.18%, while C4.5 achieved 74.18%. These findings indicate that Random Forest is more robust and effective than the C4.5 Decision Tree for diabetes prediction, and that data balancing contributes more significantly to performance improvement than hyperparameter tuning alone.

Deep Learning-Based Brain Tumor Classification Using Convolutional Neural Network

An essential noninvasive medical diagnostic technique is magnetic resonance imaging (MRI), which is particularly useful for identifying brain cancers. While earlier algorithms proved effective on smaller MRI datasets, their performance suffered on bigger datasets. This study addresses the need for a swift and reliable brain tumor classification system capable of sustaining optimal performance across comprehensive MRI datasets. The convolutional neural network is implemented using the Keras library, incorporating the ResNet50 architecture as a pre-trained model. The ResNet50 model is fine-tuned for the specific brain tumor classification task, with a Global Average Pooling layer, dropout, and a final dense layer with softmax activation. Data augmentation techniques are employed to enhance the model’s robustness, including rotation, width and height shifts, and horizontal flips. The training process involves optimizing the model using the Adam optimizer with a learning rate of 0.0001. Early stopping, learning rate reduction on plateau, and model checkpointing are implemented as callbacks to ensure efficient training and prevent overfitting. The proposed model achieves a remarkable accuracy of 99.28 percent after 15 epochs. The classification task involves distinguishing among four classes: glioma, meningioma, pituitary, and no tumor

Design and Implementation of a Vision-based Wheeled Mobile Robot Using HSV Color Segmentation and P-D Control

This study presents the design and implementation of a wheeled mobile robot capable of detecting and tracking a ping-pong ball using vision-based processing. The system integrates a Raspberry Pi 3 Model B+ as the main controller, a Raspberry Pi Camera Rev 1.3 for visual input, and DC motors driven by an L298N motor driver for actuation. Object detection is achieved through color segmentation in the HSV color space using the OpenCV library, followed by morphological filtering and contour analysis. A proportional-derivative (PD) control algorithm is employed to adjust motor speeds dynamically based on the ball's horizontal position in the frame. The experimental results demonstrate that the robot can successfully detect and follow a ping-pong ball, although it exhibits limitations in processing speed and motion stability. The average frame rate during operation was 5 FPS, which is sufficient for basic tracking tasks but suboptimal for high-speed applications. This project highlights the feasibility of vision based robotic systems for simple object tracking tasks

Classification Of Multi-Class Face Expression Using Modification Of VGG-16 Model

In the era of modern technology, facial recognition has become an important application in various fields, such as security, education and health. One method used to recognize faces is a Convolutional Neural Network (CNN), specifically the VGG-16 architecture which is known for its consistent performance. But even though CNN can recognize faces, its accuracy in recognizing faces is inadequate. This research aims to increase the accuracy of facial expression classification so that it is more optimal by modifying the CNN VGG-16 architecture. This research uses GridSearch techniques, K-Fold Cross Validation, and utilizes multiple datasets. The dataset used consists of two image datasets, namely SMIC and SAMM facial-micro expressions, each of which has been normalized and converted to a grayscale scale measuring 48x48 pixels. The GridSearch process is applied to optimize parameters such as the number of filters, learning rate, dropout rate, activation function, and batch size. The K-Fold Cross Validation technique with five folds was used to ensure the generalization of the model to new data. The research results show that this modification is able to achieve validation accuracy of up to 98.31% in the training process, showing a significant improvement compared to the standard method. And showed an increase in accuracy in testing of 98.04% in research.Di era teknologi modern, pengenalan wajah telah menjadi aplikasi penting di berbagai bidang, seperti keamanan, pendidikan, dan kesehatan. Salah satu metode yang digunakan untuk mengenali wajah adalah Convolutional Neural Network (CNN), khususnya arsitektur VGG-16 yang dikenal dengan kinerjanya yang konsisten. Namun meskipun CNN dapat mengenali wajah, akurasinya dalam mengenali wajah belum memadai. Penelitian ini bertujuan untuk meningkatkan akurasi klasifikasi ekspresi wajah agar lebih optimal dengan memodifikasi arsitektur CNN VGG-16. Penelitian ini menggunakan teknik GridSearch, K-Fold Cross Validation, dan memanfaatkan beberapa dataset. Dataset yang digunakan terdiri dari dua dataset citra, yaitu ekspresi wajah-mikro SMIC dan SAMM yang masing-masing telah dinormalisasi dan dikonversi ke skala grayscale berukuran 48x48 piksel. Proses GridSearch diterapkan untuk mengoptimalkan parameter seperti jumlah filter, learning rate, dropout rate, fungsi aktivasi, dan ukuran batch. Teknik K-Fold Cross Validation dengan lima kali lipat digunakan untuk memastikan generalisasi model ke data baru. Hasil penelitian menunjukkan bahwa modifikasi ini mampu mencapai akurasi validasi hingga 98,31% pada proses pelatihan, menunjukkan peningkatan yang signifikan dibandingkan dengan metode standar. Dan menunjukkan peningkatan akurasi pada pengujian sebesar 98,04% pada penelitian

Analisis Performansi Jaringan Saraf Dalam terhadap Dataset Digit Berderau

This work investigates the impact of noise on model performance by training a neural network on a digit dataset with varying Signal-to-Noise Ratios (SNR) to assess its resilience and generalization ability. The experimental setup involved training the model on datasets with noise levels ranging from clean images to highly distorted ones (SNR 5%–25%), analyzing accuracy, mini-batch loss, and training time. Results indicate that while the model achieves high accuracy (96.88%) at mild noise levels (SNR 5%), performance declines significantly at higher noise levels, with accuracy dropping to 78.91% at SNR 25%. The analysis of mini-batch loss and training time reveals that noise slows convergence and increases computational complexity. The confusion matrix further confirms that while the model effectively distinguishes between classes, noise-induced misclassifications become more frequent at lower SNRs. These findings emphasize the importance of noise reduction techniques and data preprocessing to improve model robustness in real-world applications.Jaringan saraf tiruan banyak digunakan untuk pengenalan gambar, tetapi kinerjanya dapat terpengaruh secara signifikan oleh keberadaan noise. Tulisan ini bertujuan menyelidiki bagaimana jaringan saraf yang dilatih pada dataset digit dengan berbagai tingkat noise dapat mengenali gambar, dengan tujuan mengevaluasi ketahanan dan kemampuan generalisasinya. Eksperimen dilakukan dengan melatih model pada dataset dengan Signal-to-Noise Ratio (SNR) yang bervariasi, mulai dari gambar normal hingga kondisi dengan berbagai Tingkat noise (SNR 5%–25%), kemudian menganalisis akurasi, mini-batch losses, dan waktu pelatihan. Hasil menunjukkan bahwa model mencapai akurasi tinggi (96,88%) pada SNR 5%, tetapi kinerjanya menurun seiring meningkatnya noise, dengan akurasi turun menjadi 78,91% pada SNR 25%. Analisis juga menunjukkan bahwa noise memperlambat konvergensi dan meningkatkan kompleksitas komputasi, yang terlihat dari waktu pelatihan yang lebih lama dan kehilangan yang lebih tinggi pada SNR rendah. Matriks kebingungan mengonfirmasi bahwa meskipun model dapat mengklasifikasikan sebagian besar sampel dengan baik, misklasifikasi lebih sering terjadi pada tingkat noise yang lebih tinggi. Temuan ini menekankan pentingnya teknik reduksi noise dan prapemrosesan data untuk meningkatkan ketahanan model dalam aplikasi dunia nyata