Search

Background: The hepatitis B test can be conducted using the real-time PCR method. In chronic cases, the test is performed by detecting one of the Hepatitis B Virus (HBV) specific genes, HBcAg. The real-time PCR method requires optimization to obtain optimal results. Purpose: This research aims to determine the optimal temperature and time for denaturation in the hepatitis B test using the real-time PCR method. Method: The research method used was a quasi-experiment involving variations in temperature (94 °C, 95 °C, and 96 °C) and time (10 seconds, 15 seconds, and 20 seconds) for denaturation. Data processing resulted in static group comparisons based on 27 primary data from the Cycle Threshold (CT) value. Result: Variations in temperature conditions, specifically at 94 °C, 95 °C, and 96 °C, combined with a denaturation time of 10 seconds, yielded the mean CT values of 26.495, 26.355, and 26.003, respectively. When the temperature conditions were maintained at 94 °C, 95 °C, and 96 °C, with a denaturation time of 15 seconds, yielded the mean CT values of 25.962, 25.641, and 25.396. Similarly, under temperature conditions of 94 °C, 95 °C, and 96 °C with a denaturation time of 20 seconds, yielded the mean CT values of 26.544, 26.505, and 25.830 were obtained. Conclusion: Optimal results in this study are obtained through the acquisition of the smallest CT value, namely at temperature conditions of 96 °C and 15 seconds.

Presently, e-commerce platforms incorporate image search functionalities. Nevertheless, these systems possess constraints; input images necessitate static and manual cropping since the system does not automatically generate bounding boxes. Addressing this concern requires the implementation of an object detection algorithm to ascertain the quantity, location, and type of desired objects within real-time bounding boxes before users finalize their selection. This capability empowers users to readily discern their desired items, thereby augmenting the precision and efficiency of visual searches. Despite the availability of swifter object detection algorithms such as R-CNN and Mask R-CNN, which prioritize accuracy over speed, rendering them less suited for real-time detection, we opted to employ the YOLOv4 algorithm as an alternative, renowned for its efficacy in real-time object detection. Furthermore, we adopted the Color, Texture, and Edge-Based Image Retrieval (CTEBIR) technique for image matching. The results of our experimentation demonstrate that the utilization of the YOLOv4 algorithm can enhance the accuracy and speed of visual searches by streamlining the search process based on the identified classes. Additionally, our precision assessment yielded a score of 95%, with individual scores for camera objects reaching 90%, keyboards achieving 85%, and laptops attaining 71%. These findings corroborate the dependability of the CTEBIR algorithm in image matching and contribute to a deeper comprehension of the system's efficacy in accurately detecting and distinguishing objects.

Indonesian Sign Language (BISINDO) is one of the sign languages used in Indonesia. The process of classifying BISINDO can be done by utilizing advances in computer technology such as deep learning. The use of the BISINDO letter classification system with the application of the MobileNet V2 FPNLite SSD model using the TensorFlow object detection API. The purpose of this study is to classify BISINDO letters A-Z and measure the accuracy, precision, recall, and cross-validation performance of the model. The dataset used was 4054 images with a size of consisting of 26 letter classes, which were taken by researchers by applying several research scenarios and limitations. The steps carried out are: dividing the ratio of the simulation dataset 80:20, and applying cross-validation (k-fold = 5). In this study, a real time testing using 2 scenarios was conducted, namely testing with bright light conditions of 500 lux and dim light of 50 lux with an average processing time of 30 frames per second (fps). With a simulation data set ratio of 80:20, 5 iterations were performed, the first iteration yielded a precision result of 0.758 and a recall result of 0.790, and the second iteration yielded a precision result of 0.635 and a recall result of 0.77, then obtained an accuracy score of 0.712, the third iteration provides a recall score of 0.746, the fourth iteration obtains a precision score of 0.713 and a recall score of 0.751, the fifth iteration gives a precision score of 0.742 for a fit score case and the recall score is 0.773. So, the overall average precision score is 0.712 and the overall average recall score is 0.747, indicating that the model built performs very well.

The SOLAP system for Indonesian Agricultural Commodities is a successful development based on previous studies. Agricultural commodity data are managed in a data warehouse with a galactic schema, which has 7 fact tables, namely cut flower horticulture, ornamental plant horticulture, horticulture, food crops, plantation, livestock population, and livestock production, as well as 3 dimensional tables, namely location, time, and commodity. The results of SOLAP operations on the system can be visualized in the form of crosstabs, graphs and maps. The system uses a web platform so that it can be accessed by the public. However, the SOLAP system cannot update data in real time. This study aims to develop a data warehouse for Indonesian Agricultural Commodities SOLAP in real time by creating a scraping system. This study has succeeded in developing a data warehouse in real time on the indonesian agricultural commodity SOLAP system by creating a real time scraping system that is applied to the SOLAP server and has succeeded in making the ETL process run in real time on the SOLAP server and optimizing polygon-based spatial data visualization using the Douglas-Peucker. This study has also carried out functional testing of OLAP features and functions on the Indonesian Agricultural Commodity SOLAP system using the black box testing method. The results of this study provide accurate and real-time data on the SOLAP of Indonesian Agricultural Commodities, with the results of SOLAP feature testing achieving 100 percent pass and the data conformity test results of OLAP function as expected. In addition, the results of this study make it possible to automatically update the data according to a predetermined schedule to provide real-time information.

Copyright of Formosa Journal of Computer & Information Science (FJCIS) is the property of Formosa Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Facial recognition has become a growing topic among Computer Vision researchers because it can solve real-life problems, including during the COVID-19 pandemic. The pandemic is why the Indonesian government has imposed social restrictions and physical contact in public places. Before the pandemic, most touch-based attendance systems used fingerprints or Radio Frequency Identification (RFID) cards. The solution proposed in this study is to identify real-time facial recognition of the Civil Service presence system using a Deep Neural Network. The goal is to minimize physical contact. The research stages include data collection, augmentation and preprocessing, CNN modeling and training, model evaluation, converting to OpenCV DNN, implementation of transfer learning, and identification of test data. This research contributes to testing variations in distance and position so it can recognize a person's face even when wearing a mask and glasses. This DNN model produces a validation accuracy value of 99.48% and a validation loss of 0.0273 with a data training process of 10 times. Tests for variations in distance, position, use of masks, and glasses on MTCNN detection provide an average accuracy for each trial of 100%, 96%, and 100%, respectively. Therefore, the average accuracy of the Haar Cascades detection test is 100%, 85%, and 100%.

CCTV surveillance is the most extensively used intelligent latest innovation. The use of surveillance cameras has risen dramatically because of the convenience of monitoring from anywhere and the reduction of crime rates in public areas. In this paper, we introduce the idea of bad vibe activity detection from live videos to enhance the security and safety of pilgrims. The proposed bad vibes activity recognition model is intended to be addressed in the most efficient manner possible using cutting-edge technologies such as TensorFlow and Keras. TensorFlow was chosen because the project could be deployed to a mobile environment in the future with the possibility of extension of other areas such as airport security, bus stain, and public areas that may deserve special attention for security checks. We choose MediaPipe Holistic for employee bad vibe recognition in the model.

ABSTRAK Computer vision merupakan teknologi yang dapat mendeteksi objek yang ada disekitarnya pada penelitian ini membahas optimasi teknologi computer vison pada robot sebagai pemindah objek berdasarkan warna. Sistem pada robot terdiri dari pengenalan bola berwarna dan memindahkan bola berwarna sesuai dengan warna yang dideteksi. Teknologi computer vision pada pixy 2 camera dapat mendeteksi objek berwarna menggunakan metode deteksi real-time dengan hasil optimasi yang tinggi yaitu 0,2 detik ketika mendeteksi objek berwarna. Pengujian pengenalan objek berwarna dilakukan sebanyak tiga kali pada setiap objek berwarna dengan tingkat akurasi sebesar 100%. Optimasi computer vision dapat membantu robot mengenali objek berwarna. Kata kunci: Computer Vision, Deteksi Objek Berwarna, Pixy2 Camera, Real-Time  ABSTRACT Computer vision is a technology that can detect objects that are around it. This study discusses the optimization of computer vision technology on robots as object transfers based on color. The system on the robot consists of recognizing colored balls and moving colored balls according to the detected color. Computer vision technology on the pixy 2 camera can detect colored objects using a real-time detection method with a high optimization result of 0.2 seconds when detecting colored objects. The color object recognition test was carried out three times on each colored object with an accuracy rate of 100%. Computer vision optimization can help robots recognize colored objects. Keywords: Computer Vision, Color Object Detection, Pixy2 Camera, Real-Time

REPORT OF OBSERVATION OF BED AVAILABILITY FOR COVID-19 PATIENTS AT HOSPITAL IN BANDUNG CITY REGION BASED ON SIRANAP WEB. This observation aims to analyze the level of accuracy and the real time nature of the data on the SIRANAP web conducted by hospitals in the city of Bandung. We did this observation for 16 days by taking screenshots 3 times every day with a random screenshot schedule. This observed update time is then rounded to the nearest 30 minutes to obtain a data resolution. From these observations, it is suspected that the data from seven hospitals in Bandung on SIRANAP website is inaccurate. Of the seven hospitals in the city of Bandung, only one hospital is categorized as real-time, which are Hasan Sadikin General Hospital, the other six hospitals are categorized as not real-time. Based on the standard deviation of SIRANAP data, seven hospitals in Bandung City have different update times. Therefore, it can also be concluded that the data on SIRANAP has not been able to help the people of Bandung City optimally.

Fuel is an important aspect in the operation of ships that require high costs. The high cost of fuel is not followed by an automatic fuel monitoring process. By not using the fuel consumption monitoring method that works automatically, the shipping management does not know for sure the ship's fuel consumption is in accordance with the shipping mileage, thus triggering fraud committed by the ship's crew against the ship's fuel. Fuel consumption monitoring is carried out primarily to identify opportunities to improve energy efficiency and reduce costs. By following technological developments, Internet of Things (IoT) technology has begun to be applied in various industrial fields because it can transmit data in real-time via the internet network without human-to-human or human-to-computer interaction. In this research, the design of models and experiments related to a monitoring system for fuel consumption was carried out using sensors and microcontrollers integrated with the internet to obtain accurate and real-time data. The test results show that the volume of fuel available in the tank, the volume of fuel discharged, the flow rate of fuel, and the location of the system can be known by the user in real-time via the IoT website. Based on the results of measurements using an ultrasonic level sensor, it is known that the measurement results are quite accurate with a deviation of ± 0.5 cm. Meanwhile, the measurement results by the flow sensor are less accurate because the fuel flow only relies on the force of gravity.

The voice of each speaker has a unique specific character, influenced by gender, age, emotion, dialect, etc. The use of voice-based gender identification is growing rapidly, such as in the fields of security systems, speech recognition, artificial intelligence, etc. However, in speech processing, there are difficulties where the characteristics of the speech signal based on increasing age are difficult to determine accuracy, and there are overlapping fundamental frequency values between males and females. In this research, modeling of a gender identification system based on voice in real-time has been carried out on a Raspberry Pi device. This system is implemented by 2 methods, namely the YIN algorithm and feature extraction of Mel-Frequency Cepstral Coefficient (MFCC). The test results showed that the success of identification in the tuning parameters of scheme two is better than the first scheme by narrowing the overlapping frequency parameters. In the female test data in the closed test, the accuracy is from 98% to 100%, then in the open test starts from 92% to 96%. Meanwhile, the test data for the male closed test increased from 92% to 98%, and the open test started at 90% and rose to 94%. It indicates that the data used in this research is more suitable to use the second scheme parameter tuning to increase the accuracy of the results.

ABSTRAK Kebakaran rumah seringkali disebabkan oleh kelalaian manusia. Oleh karena itu diperlukan sebuah sistem yang dapat mendeteksi kebakaran secara online realtime. Pada studi ini, dirancang dan diimplementasikan sebuah sistem pendeteksi kebakaran dengan sejumlah sensor untuk mengukur beberapa parameter lingkungan. Sistem ini dilengkapi dengan pengambil keputusan menggunakan metode fuzzy logic. Parameter lingkungan yang diukur mencakup suhu ruangan, asap dan api yang kemudian dapat dimonitor secara real-time melalui web interface menggunakan Internet of Things platform. Pengujian menunjukkan bahwa detektor dapat mendeteksi api dengan jarak hingga 100 cm dengan akurasi mencapai 100%. Pengujian sensor suhu menunjukkan akurasi 98.79%, sementara itu detektor asap memperoleh akurasi 77.81%. Sistem ini mampu mengirimkan data dengan rata-rata delay transmisi 0.62 detik. Sistem usulan ini diharapkan dapat menyediakan pemantauan kondisi suatu ruangan secara real-time. Kata kunci: Kebakaran, Real-Time, Deteksi, Fuzzy, Internet Of Things ABSTRACT House fires are often caused by human error. Therefore, we need a system that can detect fires online real-time. In this study, a fire detection system with a number of sensors is designed and implemented to measure several environmental parameters. This system is equipped with a decision maker using the fuzzy logic method. The environmental parameters measured include room temperature, smoke and fire which can then be monitored in real time via a web interface using the Internet of Things platform. Tests show that the detector can detect fires with a distance of up to 100 cm with an accuracy of up to 100%. The temperature sensor test shows an accuracy of 98.79%, while the smoke detector generates an accuracy of 77.81%. This system is capable of sending data with an average transmission delay of 0.62 seconds. This proposed system is expected to provide realtime monitoring of the condition of a room. Keywords: Fire, Real-time, detection, Fuzzy, internet of things

One of the environmental parameters that need to be monitored is ocean waves. Waves that occur at sea have random, dynamic, and complex movements. Height measurements and wave periods are difficult to formulate accurate and simplification is needed through mathematical equations in the form of sinusoidal. Therefore, the marine information system is very important for data storage media, data processing, and visualization of measurement data so that it is monitored in real-time. This research was made to build a website-based coastal wave height monitoring information system for wave height data display data from the measurement of the wave buoy instrument. This research was conducted by utilizing data from a wave height measuring instrument that was transmitted to an online database. Wave height data is displayed in the form of real-time data, real-time graphs with data sampling of 30 minutes, average data for one day, and average data for one month. Keywords: Wave height, system information, real-time

ABSTRAK Pada studi ini diimplementasikan sebuah sistem untuk memantau ketinggian air di dalam drum secara online real-time menggunakan platform Internet of Things (IoT). Sistem ini terdiri dari sensor ultrasonik untuk estimasi ketinggian air, kemudian data tersebut dikirim ke firebase cloud database, untuk diakses oleh perangkat monitoring atau mengakses halaman website. Level air yang tersisa direpresentasikan dalam nilai persen (%). Rata-rata kesalahan pembacaan sensor adalah tidak lebih dari 2%. Delay pengiriman yang digenerate adalah 39,06 ms, sesuai dengan rekomendasi ITU-T untuk komunikasi real-time. Sistem informasi web dapat menampilkan data ketinggian air dalam bentuk numerik dan grafik. Sistem ini telah diterapkan di sekolah menengah pertama Al-Azhar kota Bandung dan diharapkan dapat diperluas penerapannya. Kata kunci: drum, ketinggian air, real-time, IoT ABSTRACT In this study, a real-time online monitoring of the water level in the drum was implemented using the internet of things (IoT) platform. This system consists of ultrasonic sensors to estimate the water level, then the data is sent to the Firebase cloud database, to be accessed by monitoring devices or accessing a website page. Water level is represented as a percent (%). The average sensor reading error is not more than 2%. The generated delivery delay is 39.06 ms, according to ITU-T recommendations for real-time communication. The web information system can display water level data in numerical and graphic form. This system has been implemented in Al-Azhar junior high school in Bandung and it is hoped that its application can be expanded. Keywords: drums, water level, real-time, IoT

ABSTRAK Sistem monitoring adalah hal yang sangat penting dan harus dimiliki oleh setiap pemilik rumah. Sehingga diperlukan alat untuk dapat mengawasi rumah secara real-time. Perancangan sistem menggunakan Raspberry-Pi sebagai pusat komputasi dan menggunakan smartphone berbasis android untuk melakukan monitoring. Sistem ini dilengkapi dengan beberapa fitur diantaranya pergerakan kamera secara horisontal dan vertikal menggunakan motor servo, sistem recording video yang dapat dikendalikan oleh user, serta sistem deteksi gerakan yang dapat menjadi alert untuk pengguna. Pengujian sistem dilakukan dengan cara melakukan pengujian network dilihat dari parameter bandwidth dan jumlah client dalam suatu jaringan untuk mendapatkan suatu sistem monitoring yang real-time, reliable dan smart. Dalam hasil pengujian dengan maksimum 2 klien dalam 1 network, sistem dapat menangkap rata-rata 10.7 frames per second, serta memiliki respon pergerakan kamera < 1 second. Pengujian fitur perekaman dan motion detection juga berfungsi dengan baik, sehingga sistem monitoring ini menjadi sistem yang smart dan reliable. Kata kunci: Real-time, Rasberry-Pi, Smart Monitoring. ABSTRACT The monitoring system is very important and must be owned by every homeowner. Therefore, it needed some tools to be self-sufficient in real-time. The design of the system using Raspberry-Pi as a computing center and use an Android smartphone to perform monitoring. Equipped with a servo motor, a user-controlled video recording system, and a motion detection system that can generate alert to the user. Testing is done by doing network testing seen from the bandwidth parameters and number of clients in a network for a real-time, reliable and smart monitoring system. In the test results with maximum 2 clients in a network, the system can capture an average of 10.7 frames per second and has a camera movement response of

Stasiun Pengisian Bahan Bakar Umum (SPBU) sementara ini masih belum bisa diakses oleh pengguna secara real-time. Sebuah sistem monitoring level BBM secara realtime diperlukan untuk memenuhi kebutuhan pelanggan atas kebutuhan BBM sehingga dapat menentukan alternatif SPBU lain jika ternyata SPBU terdekat kehabisan stok pada salah satu atau keseluruhan jenis BBM. Teknologi Augmented Reality (AR) memungkinkan pengguna dengan mudah mencari informasi dan lokasi SPBU terdekat. Pemanfaatkan teknologi Augmented Reality (AR) serta sensor GPS yang berinteraksi dengan google maps API, pada perangkat smartphone mampu memberikan informasi secara detail, akurat dan tampak nyata bagi pengguna. Perangkat sensor nirkabel dan kontrol oleh Arduino, mampu memberikan kemudahan dalam pengukuran kondisi stok BBM serta dapat menggantikan proses manual pengukuran tangki penyimpan BBM yang selama ini menggunakan tongkat besi pengukur. Komunikasi data jarak jauh dan uji coba tingkat akurasi data menunjukkan bahwa sistem monitoring mampu memberikan informasi akurat secara real-time. Kata Kunci: Sistem monitoring, SPBU, Augmented-reality, real-time, Arduino. AUGMENTED-REALITY MONITORING SYSTEM ON GAS STATION ABSTRACT Gas stations information are still not accessible to the user in real-time. A fuel level monitoring system in real time is noteworthy to meet customer needs. So, people can determine other alternative fuel stations if the nearest station is out of stock on one or all of the fuel types. Augmented Reality (AR) allows users to easily search for informations and the location of the nearest gas station. This research apply wireless sensor network technology and Augmented Reality (AR) by using GPS sensors that interact with google maps API, wireless sensor and controlled by Arduino. The system is also able to replace the manual process of measuring fuel storage tanks. The result showed that the monitoring system-App is able to provide accurate information in real-time. Keywords: Monitoring system, gas station, Augmented-reality, real-time, Arduino