Jurnal Politeknik Negeri Batam (PoliBatam)
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Comparative Study of Web Server Performance Testing with and without Docker Based on Virtual Machines
Get PDFWeb server development is often hindered by the cost and resources required, as developing a web server typically requires a bare-metal server. Container technology, which allows for the development of multiple web servers on a single bare-metal server, has become popular. One of the most widely used containers is Docker. Docker reduces the need for costs and resources. Beyond the issues of cost and resource requirements, the performance of web servers also needs to be considered. The performance of web servers with and without Docker needs to be verified. This research aims to test the performance of two web servers, one using Docker and one not using Docker, utilizing the native hypervisor VMware ESXi. The web server performance test items in this study include CPU and RAM resource usage. The method for developing infrastructure systems uses SIDLC (System Infrastructure Development Life Cycle). Performance testing (Load Test) was conducted using Apache JMeter as a tool, with the manipulation of the number of threads predetermined. Resource usage information was monitored using Prometheus and Grafana. The research results show that with the same resources for each virtual machine, the CPU resource usage of Virtual Machine 2 (Undockerized) is less than that of Virtual Machine 1 (Dockerized). Meanwhile, RAM resource usage is not affected by the number of users on both virtual machines. Virtual Machine 2 (Undockerized) is better at handling HTTP requests. Virtual Machine 1 (Dockerized) can handle only 2,790 users, while Virtual Machine 2 (Undockerized) can handle more than 2,790 users without errors.Web server development is often hindered by the cost and resources required, as developing a web server typically requires a bare-metal server. Container technology, which allows for the development of multiple web servers on a single bare-metal server, has become popular. One of the most widely used containers is Docker. Docker reduces the need for costs and resources. Beyond the issues of cost and resource requirements, the performance of web servers also needs to be considered. The performance of web servers with and without Docker needs to be verified. This research aims to test the performance of two web servers, one using Docker and one not using Docker, utilizing the native hypervisor VMware ESXi. The web server performance test items in this study include CPU and RAM resource usage. The method for developing infrastructure systems uses SIDLC (System Infrastructure Development Life Cycle). Performance testing (Load Test) was conducted using Apache JMeter as a tool, with the manipulation of the number of threads predetermined. Resource usage information was monitored using Prometheus and Grafana. The research results show that with the same resources for each virtual machine, the CPU resource usage of Virtual Machine 2 (Undockerized) is less than that of Virtual Machine 1 (Dockerized). Meanwhile, RAM resource usage is not affected by the number of users on both virtual machines. Virtual Machine 2 (Undockerized) is better at handling HTTP requests. Virtual Machine 1 (Dockerized) can handle only 2,790 users, while Virtual Machine 2 (Undockerized) can handle more than 2,790 users without errors
Web-Based Mapping of Crime-Prone Areas in Samarinda Seberang and Loa Janan Ilir Districts, Samarinda Citys
Get PDFThe development of Geographic Information System (GIS) technology has provided significant benefits in various fields, including the monitoring of crime-prone areas. GIS is used to minimize the traces of these crimes. This study aims to map crime-prone areas in the Samarinda Seberang and Loa Janan Ilir Districts to identify which areas are potentially vulnerable, allowing for analysis for prevention and handling. The data used were collected from theft cases that occurred in these districts in 2019 and 2020. The research employs a scoring technique where each parameter is rated according to its classification. The results of the scoring process are then analyzed to determine the level of crime-prone areas, categorizing them as very vulnerable, vulnerable, or not vulnerable. Based on respondents\u27 feedback, the application facilitates users in locating crime-prone areas, with 94.34% of responses indicating agreement or strong agreement. These results suggest that the application is feasible for implementation.The development of Geographic Information System (GIS) technology has provided significant benefits in various fields, including the monitoring of crime-prone areas. GIS is used to minimize the traces of these crimes. This study aims to map crime-prone areas in the Samarinda Seberang and Loa Janan Ilir Districts to identify which areas are potentially vulnerable, allowing for analysis for prevention and handling. The data used were collected from theft cases that occurred in these districts in 2019 and 2020. The research employs a scoring technique where each parameter is rated according to its classification. The results of the scoring process are then analyzed to determine the level of crime-prone areas, categorizing them as very vulnerable, vulnerable, or not vulnerable. Based on respondents\u27 feedback, the application facilitates users in locating crime-prone areas, with 94.34% of responses indicating agreement or strong agreement. These results suggest that the application is feasible for implementation
Implementation of Identity Loss Function on Face Recognition of Low-Resolution Faces With Light CNN Architecture
Get PDFFace recognition in low-resolution images has seen significant advancements over the past few decades. Although extensive research has been conducted to improve accuracy in these conditions, one of the main challenges remains the difficulty in identifying unique facial features in low-resolution images, leading to high error rates in identification. The use of Deep Convolutional Neural Networks (DCNN) for low-resolution face recognition is still limited. However, employing super-resolution models like REAL-ESRGAN can enhance recognition accuracy in low-resolution images. This study utilizes the Light CNN architecture and applies the margin-based identity loss function AdaFace on low-resolution datasets. The model is trained using the Casia-WebFace dataset and evaluated using the LFW and TinyFace test datasets. Based on the evaluation results on the LFW test data, the best model is Light CNN9-AdaFace, achieving the highest accuracy of 97.78% at 128x128 resolution. For images with the lowest resolution of 16x16, an accuracy of 83.37% was achieved using super-resolution techniques. On the TinyFace test data, the use of super-resolution resulted in performance metrics with a Rank-1 accuracy of 47.26%, Rank-5 accuracy of 55.25%, Rank-10 accuracy of 58.61%, and Rank-20 accuracy of 61.90% using the Light CNN9-AdaFace architecture.Face recognition in low-resolution images has seen significant advancements over the past few decades. Although extensive research has been conducted to improve accuracy in these conditions, one of the main challenges remains the difficulty in identifying unique facial features in low-resolution images, leading to high error rates in identification. The use of Deep Convolutional Neural Networks (DCNN) for low-resolution face recognition is still limited. However, employing super-resolution models like REAL-ESRGAN can enhance recognition accuracy in low-resolution images. This study utilizes the Light CNN architecture and applies the margin-based identity loss function AdaFace on low-resolution datasets. The model is trained using the Casia-WebFace dataset and evaluated using the LFW and TinyFace test datasets. Based on the evaluation results on the LFW test data, the best model is Light CNN9-AdaFace, achieving the highest accuracy of 97.78% at 128x128 resolution. For images with the lowest resolution of 16x16, an accuracy of 83.37% was achieved using super-resolution techniques. On the TinyFace test data, the use of super-resolution resulted in performance metrics with a Rank-1 accuracy of 47.26%, Rank-5 accuracy of 55.25%, Rank-10 accuracy of 58.61%, and Rank-20 accuracy of 61.90% using the Light CNN9-AdaFace architecture
Forecasting Air Quality Indeks Using Long Short Term Memory
Get PDFExercise offers significant physical and mental health benefits. However, undetected air pollution can have a negative impact on individual health, especially lung health when doing physical activity in crowded sports venues. This study addresses the need for accurate air quality predictions in such environments. Using the Long Short-Term Memory (LSTM) method or what is known as high performance time series prediction, this research focuses on forecasting the Air Quality Index (AQI) around crowded sports venues and its supporting parameters such as ozone gas, carbon dioxide, etc. -others as internal factors, without involving external factors causing the increase in AQI. Preprocessing of the data involves removing zero values "‹"‹and calculating correlations with AQI and the final step performs calculations with the LSTM model. The LSTM model which adds tuning parameters, namely with epoch 100, learning rate with a value of 0.001, and batch size with a value of 64, consistently shows a reduction in losses. The best results from the AQI, PM2.5, and PM10 features based on performance are MSE with the smallest value of 6.045, RMSE with the smallest value of 4.283, and MAE with a value of 2.757
Analisis Nilai Tahanan Isolasi dan Tegangan Tembus pada Minyak Transformator 150 kV Sebelum dan Sesudah Purifikasi di PT Energi Listrik Batam
Get PDFTransformers are critical components in electricity transmission, and system failures can be catastrophic. PT Energi Listrik Batam has a 150 kV power transformer that experienced an oil leak in the Low Voltage section in September 2023. Transformer oil serves as an insulating material. Insulation Resistance and Breakdown Voltage tests showed a decline in oil quality, with an insulator value of 50 kV. PT Energi Listrik Batam conducted oil purification to restore its quality. Purification removes gases and particles that degrade oil quality. As a result, after purification, the insulation resistance and breakdown voltage of the transformer oil increased to 80 kV.
Transformator adalah komponen penting dalam penyaluran energi listrik, dan kegagalan sistem dapat berakibat fatal. PT Energi Listrik Batam memiliki Transformator Daya 150 kV yang mengalami kebocoran minyak pada bagian Low Voltage pada September 2023. Minyak transformator berfungsi sebagai bahan isolasi. Pengujian Tahanan Isolasi dan Tegangan Tembus menunjukkan penurunan kualitas minyak transformator, dengan nilai isolator sebesar 50 kV. PT Energi Listrik Batam melakukan purifikasi minyak untuk mengembalikan kualitasnya. Purifikasi menghilangkan gas dan partikel yang menurunkan kualitas minyak. Hasilnya, setelah purifikasi, nilai tahanan dan tegangan tembus minyak transformator meningkat hingga 80 kV.
 
Rancangan Sistem Kontrol dan Monitoring pada Wire Selection Boxes
Get PDFThe issue of incorrect material usage in the industry\u27s production process is a primary focus for quality control, as it directly impacts customers. This research was conducted due to challenges in maintaining the continuity of the manual cable selection process. Currently, cable selection involves choosing cables stored in various boxes with different specifications. Therefore, a system is needed to enhance the effectiveness and efficiency of the cable selection process. The approach used to address this issue involves developing a user interface for input, which is processed using a Raspberry Pi 4 connected to a servo motor that functions as the opener and closer of the cable storage boxes. The interface includes a login menu, a control menu, and a menu that allows users to add their own data. Additionally, the PCA9685 driver module is connected to the Raspberry Pi 4 to control 66 servo motors. The results of this research enhance the cable selection process by increasing its effectiveness and efficiency, as shown by a tool utilization effectiveness rate of 80.4%.Kasus kesalahan penggunaan material pada proses produksi di industri menjadi fokus utama bagi quality untuk diselesaikan kerena berdampak secara langsung pada customer. Penelitian ini dilakukan karena adanya hambatan dalam keberlangsungan proses pemilihan kabel secara manual. Pemilihan kabel dilakukan dengan mengambil kabel yang disimpan di dalam box yang berbeda dengan spesifikasi yang berbeda. Maka dari itu dibutuhkan sistem yang mampu untuk meningkatkan efektivitas dan efisiensi dalam proses pemilihan kabel. Metode yang digunakan untuk menyelesaikan permasalahan tersebut adalah dengan membuat user interface sebagai input yang akan diproses menggunakan Raspberry Pi 4 dan terhubung ke motor servo sebagai pembuka dan penutup kotak penyimpanan kabel. Interface yang dibuat berupa menu untuk login, menu kontrol, dan juga menu untuk user dapat menambahkan data sendiri. Modul driver PCA9685 dihubungkan dengan Raspberry Pi 4 untuk menjalankan 66 motor servo. Hasil dari penelitian ini memberikan kontribusi dalam proses pemilihan kabel yang dapat meningkatkan efektivitas dan efisiensi dalam proses pemilihan kabel yang ditunjukkan melalui hasil efektifitas penggunaan alat sebesar 80,4%
Automatic Vegetable Watering System Using Fuzzy Logic with Integration of Soil Moisture, Rain Sensors, and RTC
Get PDFConventional vegetable watering often presents challenges, particularly in ensuring that plants receive adequate water without excessive manual intervention. This research proposes a solution in the form of an automatic watering system using fuzzy logic, which integrates soil moisture sensors, rain sensors, and an RTC (Real-Time Clock) for scheduling. The system is designed to replace manual watering methods with an automated process, thus improving the efficiency and effectiveness of vegetable cultivation. The developed device uses a soil moisture sensor to monitor soil conditions, a rain sensor to detect rainfall, and an RTC to determine the optimal watering times. The Arduino Uno acts as the main controller that activates the water pump via a relay driver based on data received from the sensors. Test results show that the system operates according to the established criteria, with a satisfactory accuracy level. The system successfully waters the plants at 07:00 WIB and 15:00 WIB, based on dry soil conditions and no rain. The trials showed that the device has an average soil moisture measurement error of 5%, and a time discrepancy of about 22 seconds on the RTC module. Each 1% increase in soil moisture requires approximately 1 second of watering duration. Watering times are adjusted to prevent the plants from drying out or dying, with a soil moisture threshold of below 40% set as the condition for requiring watering
Evaluasi Pemilihan Supplier Komponen Sistem Pneumatik dalam Manajemen Pemeliharaan dengan Menggunakan Analytical Hierarchy Process pada Pabrik Kemasan Kertas
No full textTo ensure the continuity of the production process, it is essential for the company to focus on maintenance management, particularly in selecting high-quality pneumatic system components with a long lifespan. This study aims to determine the criteria and weights for choosing suppliers of these components in a paper packaging manufacturing. The supplier alternatives evaluated are PT. SMCAI, PT. FI, and PT. RMW, using the Analytical Hierarchy Process (AHP). The analysis of the four criteria reveals the following weights: cost (0.46), quality (0.23), service (0.16), and delivery (0.15). Based on these evaluations, PT. SMCAI ranks highest with a weight of 0.41, followed by PT. FI at 0.40, and PT. RMW with the lowest weight of 0.19. These results guide the company in selecting the most suitable supplier to maintain smooth production operations.Berdasarkan permasalahan yang dihadapi oleh perusahaan, maka sangat penting untuk menjaga dan menjamin kelangsungan proses produksi tanpa adanya kendala. Salah satu kegiatan yang sangat penting bagi perusahaan dalam menjaga keberlangsungan proses tersebut adalah manajemen pemeliharaan, khususnya dalam pemilihan pemasok komponen sistem pneumatik yang berkualitas dan memiliki umur pakai yang panjang. Penelitian ini bertujuan untuk mengidentifikasi kriteria dan bobot pemilihan pemasok komponen sistem pneumatik pada perusahaan pengemasan kertas. Alternatif pemasok yang dipertimbangkan adalah PT. SMCAI, PT. FI, dan PT. RMW yang dievaluasi menggunakan metode Analytical Hierarchy Process (AHP). Hasil pembahasan mengenai keempat kriteria tersebut menunjukkan bobot yang diberikan pada masing-masing kriteria yang menjadi pertimbangan perusahaan dalam melakukan evaluasi pemilihan pemasok komponen sistem pneumatik yaitu biaya memiliki bobot tertinggi (0,46), diikuti oleh kualitas (0,23), layanan (0,16), dan pengiriman memiliki bobot terendah (0,15). Berdasarkan evaluasi kriteria tersebut, alternatif dengan peringkat tertinggi adalah PT. SMCAI dengan bobot (0,41), diikuti oleh PT. FI dengan bobot (0,40), dan terakhir PT. RMW dengan bobot (0,19)
Betta Fish Identification System Based On Convolutional Neural Network
Get PDFThis study developed an automated identification system based on the Convolutional Neural Network (CNN) to classify Betta Splendens, a fish species with high economic value in Indonesia. The system aims to improve accuracy and efficiency in the identification process. The research was divided into several experiments, where the data was split into 320 images for training, 80 for validation, and 100 for testing. We used two optimizers, Adam and RMSprop. The Adam optimizer experiments conducted two stages with learning rates of 0.0001 and 0.001, each with 100 and 200 epochs. The results showed that a lower learning rate (0.0001) with 200 epochs yielded the best test accuracy of 71%, while a learning rate of 0.001 caused accuracy to stagnate at 66%, indicating potential overfitting. The RMSprop optimizer with a learning rate of 0.00001 demonstrated good stability, though with slightly lower accuracy than Adam. This study highlights the importance of selecting the appropriate learning rate and number of epochs to achieve an optimal balance between training, validation, and testing accuracy, ensuring the model generalizes well to new data.This study developed an automated identification system based on the Convolutional Neural Network (CNN) to classify Betta Splendens, a fish species with high economic value in Indonesia. The system aims to improve accuracy and efficiency in the identification process. The research was divided into several experiments, where the data was split into 320 images for training, 80 for validation, and 100 for testing. We used two optimizers, Adam and RMSprop. The Adam optimizer experiments conducted two stages with learning rates of 0.0001 and 0.001, each with 100 and 200 epochs. The results showed that a lower learning rate (0.0001) with 200 epochs yielded the best test accuracy of 71%, while a learning rate of 0.001 caused accuracy to stagnate at 66%, indicating potential overfitting. The RMSprop optimizer with a learning rate of 0.00001 demonstrated good stability, though with slightly lower accuracy than Adam. This study highlights the importance of selecting the appropriate learning rate and number of epochs to achieve an optimal balance between training, validation, and testing accuracy, ensuring the model generalizes well to new data
Prediction of Air Quality Index Using Ensemble Models
Get PDFThe impact of air pollution on health is measured by the Air Quality Index (AQI). Accurate AQI prediction is essential for pollution reduction and public health recommendations. Traditional methods of monitoring air quality are inaccurate and time-consuming. This study uses IoT-based air quality data from Kampung Kalipaten, Tangerang to build an AQI prediction model with machine learning, specifically an ensemble model. Ensemble techniques such as bagging and boosting, which increase the reliability of predictions by reducing model bias and inconsistency, improve AQI prediction. Four ensemble models used in this study, they are Random Forest Regressor, Gradient Boosting Regressor, Adaboosting Regressor, and Bagging Regressor. As the evaluation, RMSE and R2 metrics used. Random Forest Regressor perform the best with RMSE value of 0.6054 and R2 value of 0.6271, although no significant differences of RMSE and R2 value of the rest models