ComEngApp : Computer Engineering and Applications Journal
ComEngApp-Journal (Collaboration between University of Sriwijaya, Kirklareli University and IAES) is an international forum for scientists and engineers involved in all aspects of computer engineering and technology to publish high quality and refereed papers. This Journal is an open access journal that provides online publication (three times a year) of articles in all areas of the subject in computer engineering and application. ComEngApp-Journal wishes to provide good chances for academic and industry professionals to discuss recent progress in various areas of computer science and computer engineering.
Articles
215 Documents
<![CDATA[Point of Interest (POI) Recommendation System using Implicit Feedback Based on K-Means+ Clustering and User-Based Collaborative Filtering ]]>
<![CDATA[Sulis Setiowati]]>;
<![CDATA[Teguh Bharata Adji]]>;
<![CDATA[Igi Ardiyanto]]>
<![CDATA[ Computer Engineering and Applications Journal Vol 11 No 2 (2022) ]]>
Publisher : <![CDATA[Universitas Sriwijaya ]]>
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DOI: 10.18495/comengapp.v11i2.399
<![CDATA[Recommendation system always involves huge volumes of data, therefore it causes the scalability issues that do not only increase the processing time but also reduce the accuracy. In addition, the type of data used also greatly affects the result of the recommendations. In the recommendation system, there are two common types of data namely implicit (binary) rating and explicit (scalar) rating. Binary rating produces lower accuracy when it is not handled with the properly. Thus, optimized K-Means+ clustering and user-based collaborative filtering are proposed in this research. The K-Means clustering is optimized by selecting the K value using the Davies-Bouldin Index (DBI) method. The experimental result shows that the optimization of the K values produces better clustering than Elbow Method. The K-Means+ and User-Based Collaborative Filtering (UBCF) produce precision of 8.6% and f-measure of 7.2%, respectively. The proposed method was compared to DBSCAN algorithm with UBCF, and had better accuracy of 1% increase in precision value. This result proves that K-Means+ with UBCF can handle implicit feedback datasets and improve precision.]]>
<![CDATA[Automated Continuous IoT-based Monitoring System for Vaname Shrimp Cultivation Management ]]>
<![CDATA[Dahnial Syauqy]]>;
<![CDATA[Buce Trias Hanggara]]>;
<![CDATA[Welly Purnomo]]>;
<![CDATA[Widhy Hayuhardhika Nugraha Putra]]>;
<![CDATA[Nyoman Wira Prasetya]]>
<![CDATA[ Computer Engineering and Applications Journal Vol 11 No 2 (2022) ]]>
Publisher : <![CDATA[Universitas Sriwijaya ]]>
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DOI: 10.18495/comengapp.v11i2.402
<![CDATA[Shrimp cultivation in Indonesia has been increasing since the introduction of white leg shrimp or often known as vaname (Penaeus vannamei) from the South Pacific waters. The use of a cultivation model with a circular pond with a diameter of 10 meters has begun to attract shrimp farmers in the northern coastal areas of Java, including Tuban Regency. There are several water quality parameters that affects survival rate such as Dissolved Oxygen (DO), Temperature, and Total Dissolved Solids (TDS). Shrimp pond farmers in Tuban Regency have used digital measuring tools to monitor the environmental conditions. However, these measurements cannot be carried out continuously for 24 hours. This often causes delays in identifying problems that occur in ponds and eventually impacts on reducing biomass weight, resulting in not achieving harvest targets. In this study, a continuous monitoring system for water quality management was designed and implemented. The system consists of an IoT-based water quality monitoring device combined with a Shrimp Aquaculture Management Information System. Based on the system that has been built, it is found that the system has been able to acquire all sensor parameters and send them to the server. The results of calibration and prediction using linear regression show that the average data reading error is achieving 14% for DO sensors, and 1% each for temperature and TDS sensors. The aggregated data is presented in tabular and graphic formats so that daily monitoring and predictions can be carried out in ponds.]]>
<![CDATA[Segmentation of the Lungs on X-Ray Thorax Images with the U-Net CNN Architecture ]]>
<![CDATA[Teddi Pranata]]>;
<![CDATA[Anita Desiani]]>;
<![CDATA[Bambang Suprihatin]]>;
<![CDATA[Herlina Hanum]]>;
<![CDATA[Filda Efriliyanti]]>
<![CDATA[ Computer Engineering and Applications Journal Vol 11 No 2 (2022) ]]>
Publisher : <![CDATA[Universitas Sriwijaya ]]>
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DOI: 10.18495/comengapp.v11i2.404
<![CDATA[Lungs are one of the most important parts of the human body. They are very susceptible to various disorders and diseases. For this reason, it is necessary to detect or diagnose the lungs. In this study, we present a method for lung segmentation using the CNN method U-Net architecture. The initial stage was pre-processed did a 1-1 correspondence to equalize the amount of training data and testing data and resized the image so all images have the same size. The process continued with the CLAHE (Contrast Limited Adaptive Histogram Equalization), and after that, the segmentation process was carried out according to the method. This study used a dataset from the Kaggle website. The results used the CNN method of the U-Net architecture in data get an average accuracy of 91.68%, sensitivity 92.80%, and specificity 89.15%, precision 95.07, and F1-Score 93. 92%. Based on the performance evaluation results, it was concluded that the method proposed in the study is great and valid in the lungs segmentation on X-Ray Thorax images.]]>
<![CDATA[Performance Comparison of Feature Face Detection Algorithm on The Embedded Platform ]]>
<![CDATA[Ahmad Zarkasi]]>;
<![CDATA[Siti Nurmaini]]>;
<![CDATA[Deris Stiawan]]>;
<![CDATA[Bhakti Yudho Suprapto]]>;
<![CDATA[Huda Ubaya]]>;
<![CDATA[Rizki Kurniati]]>
<![CDATA[ Computer Engineering and Applications Journal Vol 11 No 2 (2022) ]]>
Publisher : <![CDATA[Universitas Sriwijaya ]]>
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DOI: 10.18495/comengapp.v11i2.405
<![CDATA[The intensity of light will greatly affect every process carried out in image processing, especially facial images. It is important to analyze how the performance of each face detection method when tested at several lighting levels. In face detection, various methods can be used and have been tested. The FLP method automates the identification of the location of facial points. The Fisherface method reduces the dimensions obtained from PCA calculations. The LBPH method converts the texture of a face image into a binary value, while the WNNs method uses RAM to process image data, using the WiSARD architecture. This study proposes a technique for testing the effect of light on the performance of face detection methods, on an embedded platform. The highest accuracy was achieved by the LBPH and WNNs methods with an accuracy value of 98% at a lighting level of 400 lx. Meanwhile, at the lowest lighting level of 175 lx, all methods have a fairly good level of accuracy, which is between 75% to 83%.]]>
<![CDATA[Brahmi Script Classification using VGG16 Architecture Convolutional Neural Network ]]>
<![CDATA[Vincen Vincen]]>;
<![CDATA[Samsuryadi Samsuryadi]]>
<![CDATA[ Computer Engineering and Applications Journal Vol 11 No 2 (2022) ]]>
Publisher : <![CDATA[Universitas Sriwijaya ]]>
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DOI: 10.18495/comengapp.v11i2.407
<![CDATA[Many Indonesians have difficulty reading and learning the Brahmi script. Solving these problems can be done by developing software. Previous research has classified the Brahmi script but has not had an output that matches the letter. Therefore, letter classification is carried out as part of the process of recognizing Brahmi script. This study uses the Convolutional Neural Network (CNN) method with the VGG16 architecture for classifying Brahmi script writing. Training results from various amounts of image data. Smooth model. The requested image data is a 224x224 binary image. This study has the highest quality, accuracy is 96%, highest recall is 98% and highest precision is 98%.]]>
<![CDATA[Parking System Optimization Based on IoT using Face and Vehicle Plat Recognition via Amazon Web Service and ESP-32 CAM ]]>
<![CDATA[Ilham Firman Ashari]]>
<![CDATA[ Computer Engineering and Applications Journal Vol 11 No 2 (2022) ]]>
Publisher : <![CDATA[Universitas Sriwijaya ]]>
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DOI: 10.18495/comengapp.v11i2.409
<![CDATA[Today's technology has developed rapidly. One application of technology is in the parking lot. Most parking lots in Indonesia can already recognize the vehicle plate image, but it is hoped that it can be even better by applying Internet of Things (IoT) technology that is integrated with facial recognition images. One of the parking problems is in the parking lot at the Sumatran Institute of Technology, where checking is still done manually by security officers. This of course will take time and the level of security is also not good, because when you enter there is no checking. Checks are only carried out at the time of exit and the officer who checks is not necessarily the same and memorized as the owner of the vehicle. The addition of this facial image recognition feature is expected to increase the security of the parking system. Facial image recognition can be assisted by Cloud services from Amazon Image Recognition. With this service, no training data is required. The system developed is only a prototype. The developed parking system can recognize facial images and vehicle license plates with 2 cameras using the ESP32-Cam when entering and exiting the parking lot. The use of the ESP32-cam can recognize facial images both during the day and at night. The results obtained by the system can work effectively with an increase of 21 %.]]>
<![CDATA[Simulation Design of Artificial Intelligence Controlled Goods Transport Robot ]]>
<![CDATA[Yurni Oktarina]]>;
<![CDATA[Destri Zumar Sastiani]]>;
<![CDATA[Tresna Dewi]]>
<![CDATA[ Computer Engineering and Applications Journal Vol 11 No 2 (2022) ]]>
Publisher : <![CDATA[Universitas Sriwijaya ]]>
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DOI: 10.18495/comengapp.v11i2.411
<![CDATA[Technological advances enable scientists and researchers to develop more automated systems for life's convenience. Transportation is among those conveniences needed in daily activities, including warehouses. The easy-to-build and straightforward transport robot are desired to ease human workers' working conditions. The application of artificial intelligence (AI), Fuzzy Logic Controller, and Neural Network ensures the robot is able to finish assigned tasks better and faster. This paper shows the concept design of an AI-controlled good transport robot applied in the warehouse. The design is made as fast and straightforward forward possible, and the feasibility of the proposed method is proven by simulation in Scilab FLT and Neuroph.]]>
<![CDATA[Performance Evaluation on Applied Low-Cost Multi-Sensor Technology in Air Pollution Monitoring ]]>
<![CDATA[Ade Silvia Handayani]]>;
<![CDATA[Nyayu Latifah Husni]]>
<![CDATA[ Computer Engineering and Applications Journal Vol 11 No 3 (2022) ]]>
Publisher : <![CDATA[Universitas Sriwijaya ]]>
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DOI: 10.18495/comengapp.v11i3.401
<![CDATA[This research aims to discuss the application of multi-sensor network technology for the monitoring of indoor air pollution. Indoor air pollution has become a severe problem that affects public health, especially indoor parking. The indoor air pollution monitoring system will provide information about vehicle exhaust emission levels. We have improved the system to identify six parameters of the vehicles' gas emissions within a different location at once. This research aimed to measure the parameter of Carbon Monoxide (CO), Carbon Dioxide (CO2), Hydro Carbon (HC), temperature and humidity, and levels of particulates in the air (PM10). The performance of this system shows good ability to compare the results of measurements of air quality measuring professionals. In this study, we investigated the performance of a custom-built prototype developed under the android-based application to detect air pollution levels in the parking area. Our objective was to evaluate the suitability of a low-cost multi-sensor network for monitoring air pollution in parking and the other area. The benefit of our approach is that its time and space complexity make it valuable and efficient for real-time monitoring of air pollution.]]>
<![CDATA[Detection of Diabetic Retinopathy Using Convolutional Neural Network (CNN) ]]>
<![CDATA[Rizq Khairi Yazid]]>;
<![CDATA[Samsuryadi Samsuryadi]]>
<![CDATA[ Computer Engineering and Applications Journal Vol 11 No 3 (2022) ]]>
Publisher : <![CDATA[Universitas Sriwijaya ]]>
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DOI: 10.18495/comengapp.v11i3.406
<![CDATA[One of the complications of Diabetes Mellitus, namely Diabetic Retinopathy (DR) damages the retina of the eye and has five levels of severity: Normal, Mild, Medium, Severe and Proliferate. If not detected and treated, this complication can lead to blindness. Detection and classification of this disease is still done manually by an ophthalmologist using an image of the patient's eye fundus. Manual detection has the disadvantage that it requires an expert in the field and the process is difficult. This research was conducted by detecting and classifying DR disease using Convolutional Neural Network (CNN). The CNN model was built based on the VGG-16 architecture to study the characteristics of the eye fundus images of DR patients. The model was trained using 4750 images which were rescaled to 256 X 256 size and converted to grayscale using the BT-709 (HDTV) method. The CNN-based software with VGG-16 architecture developed resulted in an accuracy of 62% for the detection and classification of 100 test images based on five DR severity classes. This software produces the highest Sensitivity value in the Normal class at 90% and the largest Specificity value in the Mild class at 97.5%.]]>
<![CDATA[Leukaemia Identification based on Texture Analysis of Microscopic Peripheral Blood Images using Feed-Forward Neural Network ]]>
<![CDATA[Wahyu Tri Puspitasari]]>;
<![CDATA[Dina Zatusiva Haq]]>;
<![CDATA[Dian C Rini Novitasari]]>
<![CDATA[ Computer Engineering and Applications Journal Vol 11 No 3 (2022) ]]>
Publisher : <![CDATA[Universitas Sriwijaya ]]>
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DOI: 10.18495/comengapp.v11i3.412
<![CDATA[ABSTRACT Leukaemia is very dangerous because it includes liquid tumour that it cannot be seen physically and is difficult to detect. Alternative detection of Leukaemia using microscopy can be processed using a computing system. Leukemia disease can be detected by microscopic examination. Microscopic test results can be processed using machine learning for classification systems. The classification system can be obtained using Feed-Forward Neural Network. Extreme Learning Machine (ELM) is a neural network that has a feedforward structure with a single hidden layer. ELM chooses the input weight and hidden neuron bias at random to minimize training time based on the Moore Penrose Pseudoinverse theory. The classification of Leukaemia is based on microscopic peripheral blood images using ELM. The classification stages consist of pre-processing, feature extraction using GLRLM, and classification using ELM. This system is used to classify Leukaemia into three classes, that is acute lymphoblastic Leukaemia, chronic lymphoblastic Leukaemia, and not Leukaemia. The best results were obtained in ten hidden nodes with an accuracy of 100%, a precision of 100%, a withdrawal of 100%.]]>
