Journal title Jurnal Elektro Initials jurnalelektro Abbreviation j. elektro Frequency Two issues per year DOI prefix 10.25170/jurnalelektro Print ISSN 1979-9780 Online ISSN 2746-4288 Editor-in-chief Nova Eka Budiyanta Publisher Universitas Katolik Indonesia Atma Jaya Jurnal Elektro (p-ISSN: 1979-9780; e-ISSN: 2746-4288) is a double-blind peer-reviewed, by a minimum of two independent reviewers, open-access scientific journal published by the Department of Electrical Engineering, School of Bioscience Technology and Innovation, Universitas Katolik Indonesia Atma Jaya. The journal aims to provide a platform for the dissemination of high-quality research and advances in the field of electrical engineering. Jurnal Elektro welcomes original research articles and reviews covering a broad spectrum of topics, including but not limited to power engineering, electronics, control systems, robotics, telecommunications, computer engineering, operations research, information systems, human-machine interaction, service quality, algorithms, artificial intelligence, internet of things, statistics, and various other relevant subfields contributing to the advancement and practical implementation of electrical engineering. By fostering academic and industrial collaborations, Jurnal Elektro strives to showcase cutting-edge research and technological innovations, making significant contributions to the global scientific community. The journal is published twice a year, in April and October. The editorial team can be contacted at: Dept. of Electrical Engineering, School of Bioscience Technology and Innovation, Atma Jaya Catholic University of Indonesia, BSD City, Jl. Cisauk, Sampora, Cisauk Tangerang, Banten 15345 Tel. : +62 21 570 8826 Fax : +62 21 579 00573 Email : jurnal.elektro@atmajaya.ac.id p-ISSN: 1979-9780 e-ISSN: 2746-4288
Prodi Teknik Elektro, Fakultas Teknik Unika Atma Jaya Jakarta
Implementation of Ultrasonic Sensor in IoT-Based Water Volume Control and Flow Rate Reading System
This system designs a non-intrusive water flow rate reader and volume controller based on a clamp-on transit-time ultrasonic flow meter (V-mount, 4-inch pipe) using the TUF-2000M, integrated with an ESP32. The time-of-flight difference is converted into velocity, flow rate is calculated (Q = v·A), and volume is estimated through integration. The volume controller stops the flow precisely at set points of 2, 4, and 6 liters to minimize overshoot. The Blynk IoT platform provides a remote interface for set point selection and real-time monitoring (flow rate, velocity, fluid sound). Performance is evaluated based on volume error, set point achievement time, and repeatability, with quick installation and minimal pressure loss. The ESP32 microcontroller serves as the controller and data processor, supported by a relay module to control the solenoid valve's open or closed state. Testing results show a 4% error for 2 liters (within a 10% error limit), a 0.10% error for 4 liters (within a 10% error limit), and a 2.5% error for 6 liters (within a 10% error limit).
Enhancing Stock Price Prediction Using Temporal Convolutional Network with Moving Average Features
Stock price prediction is a complex problem in the financial domain due to the non-linear, dynamic nature of the data and its dependence on various external factors. This study proposes a deep learning–based approach using a Temporal Convolutional Network (TCN) to predict the stock price of Bank Central Asia Tbk. The model is evaluated under two scenarios: using a single feature (close price) and using three features MA5, and MA10. The dataset consists of historical BBCA stock data from 2010 to 2018, split into 80% training data and 20% testing data. The model is trained with 100 epochs, a window size of 60, batch size of 32, the Adam optimizer, and a learning rate of 0.0005. Experimental results show that the TCN model using a single feature achieves an RMSE of 227.1920, MAE of 192.8089, and MAPE of 4.3411%. Meanwhile, the TCN model with additional features (MA5 and MA10) demonstrates improved performance, achieving an RMSE of 176.4599, MAE of 145.7689, and MAPE of 3.2750%, indicating an accuracy improvement of more than 20%.These findings indicate that TCN is effective in capturing temporal patterns in financial time series data, and that incorporating simple technical indicators such as Moving Averages can significantly enhance model performance. This study contributes to the development of efficient and practical stock price prediction methods, particularly in the Indonesian stock market context.
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.
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.
Perencanaan dan Implementasi Sistem Pembangkit Listrik Tenaga Surya On-Grid pada Instalasi Pengolahan Air
This study aims to design and evaluate an on-grid Solar Power Generation System (PLTS) for the Water Treatment Plant (IPA) Taban III in Lubuk Minturun, Padang, addressing the lack of research on on-grid PLTS in government-operated water treatment facilities. Using an engineering design approach, the study covers energy load analysis, Helioscope simulation, and economic feasibility assessment. The site has an average daily solar irradiance of 4.91 kWh/m²/day. The total facility load is 34.32 kW (40,376.47 VA). The PLTS is designed to supply 20% of this, prioritizing a 2.3 kW active load with daily energy demand of 13.8 kWh. The proposed system consists of 27 monocrystalline solar panels (550 Wp) and a 20 kW three-phase grid-tied inverter. Simulation results show an annual energy output of 19,287.9 kWh and a Performance Ratio of 78.06%. The PLTS supplies approximately 59% of daily load demand, with the remaining 41% from the PLN grid via net metering. Deviation between manual calculations and simulation is below 0.5%. Total investment is IDR 239,250,476 with annual savings of IDR 23,419,400, yielding a payback period of approximately 10 years and 3 months. The system is feasible and supports national renewable energy targets, and is recommended for similar public utility facilities.
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.
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.
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.
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.
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.
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.
Performance Analysis of Deep Neural Network to Noisy Digit Dataset
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.
Pengukuran Backup Time Uninterruptible Power Supply Untuk Penentuan Kapasitas Baterai VRLA
A battery is a device that stores power in an Uninterruptible Power Supply (UPS) system. The most common type of UPS battery found today is Valve Regulated Lead Acid (VRLA). So that the UPS can work optimally according to its capacity and backup time, it is necessary to determine the battery capacity. In this research, the capacity of the VRLA battery on a particular UPS was determined based on direct testing and based on calculations. VRLA battery capacity calculations refer to the UPS and battery datasheet. The calculation stage starts from calculating the current and battery power used according to the load power absorbed by the UPS and referring to the table of constant current discharge and constant power discharge characteristics. Determining battery capacity based on testing is done by measuring the backup time of the UPS. The calculation results and test results directly show that there is a difference in backup time because the battery used is an old stock battery. The research results also prove that determining battery capacity by calculation can ensure that the battery capacity is more appropriate or not too large so it is more economical.
Evaluasi Pengaruh 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.
Frequency Extraction of Phonocardiogram Signal using Fourier Transform
This article presents Fourier Transform application to extract features of Phonocardiogram Signals into its frequency components. Data was taken from Physionet Dataset of Phonocardiogram which comprises of normal and abnormal heart condition. Raw data was preprocessed using time clipping of 2 seconds at certain area that contains less noise. A lowpass filter was applied to denoise the raw signals. Experiments show the PCG of normal hearts has a dominant frequency of 50Hz to 150Hz, with the subdominant frequencies of 450 Hz to 650 Hz. The subdominant frequency of the normal hearts sometimes show anomaly with more amplitude compared to the dominant frequency.
Pengembangan Perangkat Lunak Untuk Akuisisi Data Konsumsi Daya Listrik Dari Power Meter
Technological developments make it possible to monitor electricity consumption in real-time via the internet. The system developed uses a power meter to collect data, Python to process data and IoT connectivity via NodeMCU ESP8266 to send data to the internet. Python is used so that measurement data can be processed and presented in an easy-to-understand format. The test results show that electrical data measurements can be displayed with an error ranging from 0.04% -11.56%. This is because there is a delay from the time the power meter is measured until the data is displayed on ThingSpeak.
Studi Performa Panel Surya 100 WP Menggunakan Software Homer Di Universitas Pamulang
Studies on designs and systems for solar panels are currently continuing to be developed. One application is to design a Homer-based solar power generation system (PLTS) with a solar panel capacity of 100 Wp and analyze the system performance at different loads. This research was conducted to determine the amount of power that solar panels can produce, load consumption, and determine a suitable system configuration to meet electricity needs at different loads. The method used is design, determining parameters on the homer and creating a simulation with loading. The research results show that the amount of power that can be produced by solar panels is 75,2kWh/year and can meet electricity needs at different loads. It is hoped that this research can be a solution to optimize the use of solar energy on a small scale and support renewable energy programs in Indonesia.
Implementasi dan Analisis Alat Kompres Demam Suhu Hangat dengan Sensor DS18B20 dan Pengendali Arduino Uno
Fever is a condition where the body temperature rises above normal levels, often caused by an infection. Non-medical management of fever can be done by using cold or warm compresses. Warm compresses are more effective but require a complex and repetitive process. This study is the design of a Digital Fever Compress Device using warm temperature based on Arduino Uno. The Digital Fever Compress Device is designed for non-medical fever management. The Digital Fever Compress Device is equipped with a DS18B20 sensor, Liquid Crystal Display (LCD), Relay, cartridge heater as a heating element, three pushbuttons, XL4005 stepdown module, and one On-Off switch. The Digital Fever Compress Device can produce warm temperatures and adjust them according to the user's preferences with a minimum limit of 37.5°C and a maximum of 42°C with a timer for 20 minutes. Environmental temperature affects the temperature setting processing carried out by the microcontroller. Based on the tests conducted in an air-conditioned room, the average time from the initial temperature to the setpoint is 6 minutes and 15 seconds, and the temperature increase from the setpoint ranges from 45.6°C or 15.079%. Meanwhile, testing in a non-air-conditioned room, the average time from the initial temperature to the setpoint is 2 minutes and 26 seconds, and the temperature increase from the setpoint ranges from 44.64°C or 12.37%.
Perancangan Aplikasi Koperasi Berbasis Android
The development of information technology cannot be separated from the use of mobile applications, which are increasingly varied. Mobile applications allow users to easily connect to internet services anywhere. Cooperatives are joint business entities that generally still use the manual method for recording members, transactions and so on, which can lead to human errors and time inefficiencies. The existence of a website-based cooperative mobile application that is implemented on smartphones using the Xamarin.Forms framework can reduce these problems. Smartphones will make online cooperatives more effective because they are portable, easy to use and more affordable.
Perbaikan Kurva Beban Harian pada Industri Kecil: Studi Kasus PT. X
This paper presents the improvement of daily load and load balance curve of home-scale garment industry with the study case in PT. X. Research is started with load measurement on every machine, proceeded by plotting schedule of each machine. The next step is rescheduling daily load and per-phase load balancing. Initial data shows unbalance load sharing between phase with the average load of 4916 kW and standard deviation of 2077. After being rescheduled and phase rearrangement, the interphase load sharing is more balance, with the average load of 4903 kW and standard deviation of 952.
Perbandingan Algoritma Machine Learning menggunakan Orange Data Mining untuk Klasifikasi Jenis Kendaraan pada Sistem Tilang Digital
This paper discusses the application of the Orange Data Mining application to compare several machine learning algorithms for classifying vehicle types in digital ticket systems. This research compares and analyzes the logistic regression algorithm, Support Vector Machine (SVM) and Neural Network (NN) to solve vehicle classification problems in digital traffic tickets. The research results show that in the training process and based on the dataset used, the algorithms that have the highest level of accuracy are Logistic Regression, Neural Network and Support Vector Machine. Meanwhile, during the testing process, all algorithms in the model were able to carry out classification with 100% accuracy
Comparison of Actual Results and PVSyst Simulation in the Design of Off-Grid Solar Power Generation System (PLTS) in Karuni Village, Southwest Sumba
This research aims to compare the actual production with the simulations using the PVSyst software for the Off-Grid Solar Power Plant (PLTS) in Karuni Village, Southwest Sumba. The Off-Grid PLTS in Karuni Village is a vital solution for improving remote areas' electricity access. Actual energy production data from the PLTS were obtained from monitoring systems, while simulation results were obtained through PVSyst. The analysis results indicate a difference of approximately 10% between the actual and simulated results. It observed that it is influenced by variability in local weather conditions, maintenance, system management levels, and limitations of the simulation model. The implications of this research emphasize the importance of using accurate data in simulations, improving PLTS system maintenance, and developing more sophisticated simulation models. Recommendations for further research include further analysis of factors influencing the differences in results. This study provides valuable insights into the planning and management of Off-Grid PLTS. It offers perspectives on enhancing the accuracy of future PLTS system planning and management.
Sistem Pemilahan Barang Berdasarkan Deteksi Label Menggunakan Vision Sensor
Sorting goods based on the results of checking packaging labels is an important process in controlling production quality in industry. Many industries still carry out manual sorting and label checking processes, which results in low productivity levels and is susceptible to human error. This research develops an automation system for sorting goods based on label inspection using vision sensors, programmable logic controller (PLC), and robot arm. The system controlled by a PLC will read and detect damage to packaging labels by using VeriSens vision sensor and sort them using a robot arm according to predetermined stock keeping unit (SKU) categories, namely SKU 1, SKU 2, SKU 3, SKU 4, and rejected goods. The pneumatic system is used as an actuator to push goods onto the conveyor, moving the robot arm with three degrees of freedom and vacuum. Detection is carried out by applying the edge detection concept to read text, images and code that are reprocessed with the VeriSens Application Suite software. The success rate of the goods sorting system reached 90% with a reading speed of 0.389 seconds and a work process duration ranging from 21.54 seconds to 28.99 seconds.
Effect of Combining of Shaping at Magnet Edge and Dummy Slotting at Stator Core on the Cogging Torque Reduction in Permanent Magnet Generator
Effect of combining the shaping at magnet edge and dummy slotting at stator on the cogging torque reduction in permanent magnet generator was studied. The cogging torque is the most issues in permanent magnet generator application, since it effects to reduce the generator performance. A permanent magnet generator with 24 slots and 20 poles structure is presented. For purpose of study, in the beginning the permanent magnet generator with conventional structure has been investigated as the basic reference of initial structure. In order to improve the performance of the permanent magnet generator, the magnet edge was shaped but the stator core is kept remain conventional structure. It can be found that the magnet edge effects to inprove a little bit of the performance of the permanent magnet generator. In the next investigation, the magnet edge slotting and dummy slotting in stator core of the permanent magnet generator was combined. Using the FEMM 4.2, the cogging torque value of the proposed of permanent magnet generator were analysed and compared. It was found that cogging torque reduction of the permanent magnet generator with combining the dummy slotting at stator core and magnet edge shaping can reduce the cogging torque from 0.0028554 N.m (intial base line) to 0.0000165 N.m (proposed model). In other word, the cogging torque can be to around 99.42 % compared with the base line. The magnet flux density at core of the permanent magnet generator proposed can be reduced to 35 %.
Sistem Pemantauan Cerdas Berbasis Internet of Things pada Pusat Perbelanjaan
Shopping centers in Indonesia are faced with the problem of corona virus pandemic. Shopping centers were temporarily closed by the government and experienced a decline in visitors and even income during the pandemic. In order for shopping centers to earn sufficient income, the government provides a policy for shopping centers by limiting the number of visitors, checking temperature using a thermo scanner or thermo gun, and using masks according to health protocol rules. For this reason, an intelligent monitoring system is needed to monitor the density of the number of visitors and check the temperature automatically to prevent transmission of the corona virus from crowded shopping centers. The intelligent monitoring system is realized in miniature using NodeMCU as a microcontroller, web pages as interface pages for shopping center managers in system monitoring, applications as interface pages for managers in monitoring systems and applications as interface pages for the public so they can see the number of visitors and remaining capacity. at a shopping center. The overall test results percentage obtained is 100% and it can be concluded that the intelligent monitoring system can work well.
Implementasi IoT dengan ESP 32 Untuk Pemantauan Kondisi Suhu Secara Jarak Jauh Menggunakan MQTT Pada AWS
The development of the internet of things (IoT) creates many new innovations in the industrial sector aimed at increasing effectiveness. One of them is to monitor the condition of industrial process machines remotely as discussed in this study. This monitoring can be done using a device connected to the internet. The design of this system requires a microcontroller with an ESP32 wifi module as a data receiver and sender. The data sent is data from the temperature sensor. The data is in the form of simulation data generated from the program and has a random number. Machine-to- server data communication uses the Message Queuing Telemetry Transport (MQTT) protocol. The entire machine-to-server, server-to-server communication system will be carried out in the cloud using a cloud computing platform, namely Amazon Web Services (AWS). The test results show that the payload and temperature data sent from the microcontroller can be stored in the database. To see the reliability of the system, two Normal and Stress Tests were carried out, with a success percentage of 100% fo r data storage to the database on two tests and 16.67% failure in sending data on the Stress Test. The two tests were arranged under different conditions.
Designing a Website-Based Cooperative Application
The development of the times in the field of technology allows information to be obtained quickly and easily. We often encounter exchange of information in the world of work, one of the business entities that is very dependent on the exchange of information is cooperatives. In traditional cooperatives, the use of services requires users to come directly to the physical location of the cooperative. The process of recording user data and transaction history is also still done manually, namely by writing it on a book, so it takes a long time. The cooperative system created is a web-based application with the Laravel framework and cooperative data managed using MySQL. Designing a web-based application will provide easy access to cooperative services and decrease the time required for transaction processing for users and increase the efficiency of cooperative management for cooperative managers.
A Preliminary Survey of Color Discrimination Among Indonesia Female Subjects Using Farnsworth-Munsell Hue Color Test
This study aimed to conduct a preliminary survey of the color discrimination test among Indonesian samples. The study involved 26 participants of university students and office workers. To test the ability of color discrimination, the Farnsworth-Munsell (FM) 100 Hue Color Vision Test was used. Farnsworth-Munsell test scoring software was also used to obtain the total score of each participant. Using the FM 100-hue test, the participants were classified into superior, average and low color discrimination ability. The results showed that that 19.2% female participants were classified as superior for color discrimination ability. Majority of participants were classified as average. Meanwhile for low classification, only 3.9% were classified as low color discrimination ability. Currently, many companies and industries have great need for workers with good characterized and accurate color vision. However, the study of color discrimination is still limited and unknown in Indonesia. Through this study, hopefully attention for a broader study with a larger scale could be obtained.
Simulasi Pengendali Suhu Menggunakan Algoritma Proportional, Integral, Derivative Berbasis Programmable Logic Controller Pada Modul Plant Honeywell
The process of heating in the food industry is often can be found, start from inserting water and the heating process begin to reach a certain temperature, so the process of controlling temperature is one of the important things in the food industry. Temperature controller is an important thing, to maintain certain temperature, so that it did not exceed or lower than needed. This system uses Honeywell programmable logic controller (PLC) as the automation based system. In this research, Honeywell PLC controller will be using proportional, integral, derivative (PID) algorithm to maintain the required temperature. The research will be using the Honeywell module in Automation Laboratorium, Engineering Faculty, Atma Jaya Catholic University of Indonesia. The heating process will begin if the water level in the tank is filled to the required level. System will turn on the heater based on the PID output in the PLC program, so the temperature is maintained to a required setpoint 35oC. The simulation system can be controlled through a human machine interface (HMI) HCix08 that was made with XDesigner Plus software. The maintained temperature has ±5% tolerance or ±1,75˚C. The result of the research shows that the best parameter for setpoint 35˚C is using a PD controller with the values for Kp is 8 and Kd is 2.
Sistem Pemberi Makan dan Minum Pintar Pada Hewan Peliharaan Berbasis Website dan Aplikasi Android
Based on a survey by “Mars”, about 59% of dogs and 52% of cats weighed more than they should. This is the motivation of this research in designing a feeding and drinking automation for pets so that it is easier for owners to take care their pet's health diet, and so that owners don't have to worry about forgetting to feed their pets. To connect to the internet, ESP32 is used. The supporting components consist of load cell (weight sensor), three ultrasonic sensors (proximity sensor), servo motor and pump motor. These components are combined to carry out the function of feeding and drinking automatically. The device will run after being given an order via website or application. For the feeding system, the animal owner can provide input on the schedule, frequency and portion of feeding. While the drinking system, the owner can press the calibration button and the pause button. Tests are carried out on each sensor, to measure its accuracy and testing of the entire system is carried out to ensure all components work together to carry out their functions. The test results show that every hardware and software component has worked in accordance with the designed objectives.