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On the Experiment of Path Planning Using Multi-way Points with A* Algorithm for Autonomous Surface Vehicle
Get PDFCommonly, surveillance activities on lake waters is mostly carried out by using a surface vehicle as special-designed vehicle, especially to conduct water quality measurements, underwater surveys, and bathymetry mapping. However, conventional survey and monitoring still involves humans on the site. If a survey is conducted during strong wind conditions, it could jeopardize surveyor’s safety. Therefore, a vehicle must have several criteria, e.g., it must be pretty spacious and comfortable to carry surveyors, free from engine vibrations, stabilized and easy to maneuver, and the surveyor's safety can be guaranteed. This paper discusses preliminary research aiming to develop an Autonomous Raft Vehicle (ARV), a type of autonomous unmanned surface vehicle. The ARV is equipped with autonomous control based on multi-way-points with an A* algorithm. Thus, a user only requires giving a command once initially during path planning. A* algorithm over multi-way-point could improve ARV navigation when there are obstacles along the predetermined trajectory. Hence the predetermined trajectory will be maintained throughout the mission. It is a significant contribution to this paper
The Strategic Planning IS/IT to Improve Business Competitiveness in Public Hospital
Get PDFHospital competition is getting tighter, making good hospital strategic planning very important so that the Hospital can survive to develop better in a changing environment. Today's competitive advantage cannot be separated from Information Systems (IS) and Information Technology (IT). IS / IT in hospitals is currently used as a tool to create and develop innovative products, systems, and services for hospitals. The problem that occurs today is a large number of IS / IT that is made without looking at the financial side of the Hospital, thus making a number of uses of IS / IT not optimal in Hospital operational and business activities. Based on these problems, IS / IT strategic planning that also considers financial capability is needed. In this research, IS/IT strategic planning that takes into account the financial aspects is achieved using the approach of Ward and Peppard combined with Cost-Benefit Analysis. Value Chain and Critical Success Factor (CSF) analysis methods are used to analyze the current internal condition of the Hospital. Meanwhile, PEST and Porter's Five Forces analysis used external business analysis of the Hospital. The results of this study are in the form of IS business strategy recommendations, IS/IT management strategies, IT strategies, IS application portfolios, and IS/IT investment roadmaps compiled based on costs and benefits. There are 25 IS strategy recommendations, the IT strategy recommends 14 proposals to support the IS strategy based on the cost-benefit, and the IS / IT management strategy recommendations consist of 16 suggestions to support the IS strategy and IT strategy
Improving Automatic Essay Scoring for Indonesian Language using Simpler Model and Richer Feature
Get PDFAutomatic essay scoring is a machine learning task where we create a model that can automatically assess student essay answers. Automated essay scoring will be instrumental when the answer assessment process is on a large scale so that manual correction by humans can cause several problems. In 2019, the Ukara dataset was released for automatic essay scoring in the Indonesian language. The best model that has been published using the dataset produces an F1-score of 0.821 using pre-trained fastText sentence embedding and the stacking model between the neural network and XGBoost. In this study, we propose to use a simpler classifier model using a single hidden layer neural network but using a richer feature, namely BERT sentence embedding. Pre-trained model BERT sentence embedding extracts more information from sentences but has a smaller file size than fastText pre-trained model. The best model we propose manages to get a higher F1-score than the previous models on the Ukara dataset, which is 0.829
Convolutional Neural Network with Hyperparameter Tuning for Brain Tumor Classification
Get PDFBrain tumor has been acknowledged as the most dangerous disease through all its circles. Early identification of tumor disease is considered pivotal to identify the spread of brain tumors in administering the appropriate treatment. This study proposes a Convolutional Neural Network method to detect brain tumor on MRI images. The 3264 datasets were undertaken in this study with detailed images of Glioma tumor (926 images), Meningioma tumors (937 images), pituitary tumors (901 images), and other with no-tumors (500 images). The application of CNN method combined with Hyperparameter Tuning is proposed to achieve optimal results in classifying the brain tumor types. Hyperparameter Tuning acts as a navigator to achieve the best parameters in the proposed CNN model. In this study, the model testing was conducted with three different scenarios. The result of brain tumor classification depicts an accuracy of 96% in the third model testing scenario
Forensic Analysis of Braking Classification Based on Acceleration, Jerk, and Velocity Data
Get PDFNowadays, four-wheeled vehicles are equipped with an event data recorder (EDR) device to record sensors data. With advances in-memory technology, EDR provides evidence for forensic analysis after an accident happens, that uses information technology to facilitate forensic analysis to provide complete and valuable results using digital investigations. Several types of research have been conducted to reconstruct accidents from forensic data and Fuzzy Logic is an alternative method for classifying crash data taken from the accelerometer due to less complexity of implementation. Vehicle braking data is one of the most important evidence for digital investigation, since braking is a complex process determined by many factors, such as the condition of the vehicle, road construction, and the driver’s physiological condition. However, the existing digital investigation still process vehicle speed, deceleration, and varia- tion time of deceleration (known as a jerk) in separated manner to determine braking distance, driver response time, and braking category. The problem identified in this paper is how to use deceleration, velocity, and jerk to categorize the braking evidence forensic analysis. In this paper, forensic analysis is limited to produce forensic evident of braking events based on the collected data. The contribution of this paper is to propose a braking detection model by combining acceleration, speed, and jerk data into a Fuzzy Inference System. As a result, a forensic analysis of braking data can better understand the braking maneuvers, which can be further developed to identify the cause of the accident and provide recommendations on which actions to include in future analyses
Skin Lesion Image Classification using Convolutional Neural Network
Get PDFClassification of skin cancer is an important task to detect skin cancer and help with the treatment of skin cancer according to its type. There are many techniques in imaging used to classify skin cancer, one of the superior deep learning (DL) algorithms for classification is the Convolutional Neural Network (CNN). One type of skin cancer is dangerous is melanoma. In this study, CNN is proposed to help classify this type of skin cancer. The dataset consists of 15103 images of skin cancer pigments with 7 different types of skin cancer. These three tests proved malignant skin lesions can be classified with higher accuracy than non-melanocytic skin lesions which is 90% and performance evaluation shows melanocytic and non-melanocytic skin lesions detected with the highest accuracy. The tests conducted in this study grouped several types of skin diseases namely the first tests conducted using a group of melanocytic and non-melanocytic skin disease, second testing using groups of melanoma and melanocytic nevus diseases, and the final testing using malignant and benign. The proposed CNN model achieved significant performance with a best accuracy of 94% on the classification of melanoma and melanocytic nevus
Deep Learning for Aspect-Based Sentiment Analysis on Indonesian Hotels Reviews
Get PDFTourism is one of the fastest-growing industries. Many travelers book hotels and share their experiences using travel e-commerce sites. To improve the quality of products and services, we can take advantage by analyzing their reviews. We can see the good and the bad thing reviews in every aspect of the hotel. However, research to analyze sentiment in every aspect using Indonesian hotel reviews is still relatively new. In this work, we propose to create an Aspect-based Sentiment Analysis (ABSA) using Indonesian hotel reviews to solve the problem. This research consists of four steps: collecting data, preprocessing, aspect classification, and sentiment classification. Our classification process compares with eight deep learning methods (RNN, LSTM, GRU, BiLSTM, Attention BiLSTM, CNN, CNN-LSTM, and CNN-BiLSTM). In aspect classification, we have six classes of aspects which are harga (price), hotel, kamar (room), lokasi (location), pelayanan (service), and restoran (restaurant). In sentiment analysis, we compared two scenarios to classify sentiments as positive or negative. The first one is to classify sentiment in all aspects, and the second one is to classify sentiment in every aspect. The results showed that LSTM achieved the best model for aspect classification with an accuracy value of 0.926. For sentiment classification, our experiments showed that classify sentiment in every aspect achieved a better result than classify sentiment in all aspects. The result showed that the CNN model gets an average accuracy score of 0.904
Internet of Things: Water Quality Classifying Based on Estimation Dissolved Oxygen Solubility and Estimation Unionized Ammonia for Small-scales Freshwater Aquaculture
Get PDFIn aquaculture, poor water quality can affect fish growth and mortality. Water quality parameters such as ammonia, temperature, pH, and dissolved oxygen must be controlled and monitored. There are available measuring devices for dissolved oxygen and ammonia levels, but measurements cost is not suitable for small-scale aquaculture and are manually process. Our experimental study proposes the Emerson formula to find the estimated value of unionized ammonia and the Benson-Krause formula to find the estimated dissolved oxygen solubility value without using an ammonia sensor or dissolved oxygen sensor. Internet of things can be applied to aquaculture to monitor and collect water parameter data without human intervention. The values of both estimates are validated using the Seneye Sensor. RMSE and MAE are used to calculate the performance evaluation between the Seneye value and the estimated value. Fuzzy logic clasify water quality derived from estimates of ionized ammonia and estimates of dissolved oxygen as input
The Potential Overview of PV System Installation at the Quarry Open Pit Mine PT. Bukit Asam, Tbk Tanjung Enim
Get PDFSouth Sumatra has an established state-own enterprise exploring the Quarry Open Pit Mine. This mining process leaves some abondance open field that has a high potential to install a PV system. The Government has undertaken an initial analysis of 200 hectares of ex-mining land for this purpose. This paper analyzes the potential for PV panels to be installed at PTBA Tanjung Enim Quarry Open Pit Mine. The location of the PV panel in the open pit Quarry for this analysis is at an altitude of +140 m above sea level. Meanwhile, the PTBA Tanah Putih Township housing site where the PV comparison panel is installed is located on the altitude of +100 m above sea level, and within 2.97 km from the mine. PV panels installed in Open Pit mines generate better electricity and efficiency compared to PV panels installed outside the mine (in PTBA Township Housing). The difference of Isc between PV Panel installed in Quarry Open Pit Mine and Township is 0.0075 A. The Iload measured on the Quarry Open Pit Mine PV panel is 0.5478 A higher than the Township PV panel. Meanwhile, Voc measured on Quarry Open Pit Mine PV panel is smaller than Township PV panel, approximately 0.266 V and Vload of Quarry Open Pit Mine PV panel is 0.266 V larger than Township PV panel. The produced power of Quarry Open Pit Mine PV panel is 11.02 W larger than the Township PV panel. From the total value, the average efficiency produced by Quarry Open Pit Mine PV panels is 2.31 % higher than the efficiency produced by Township PV panels. This research shows the possibility and advantage of installing a PV system on a Quarry Open Pit Mine
Gold price prediction using Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM)
Get PDFGold has an important role in worldwide economics. Gold is not only used in jewelry but also can be a good deal for investment however several factors can affect the fluctuation in gold which can make the risk of investing in gold is bigger for many people. Therefore, is very important to predict the gold price for people who invest in gold in order to help reduce the investment risk. This study will implement a hybrid method from Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). CNN can extract useful knowledge and learn the internal representation of time-series data, and LSTM networks will identify short-term and long-term dependencies effectively. This research will use daily time frame data and weekly time frame data. This research also tried some experiments to find the best hyperparameters of batch size and epochs in ratio data 60:40 and 80:20. The best result obtained in the daily time of ratio data 60:40 with RMSE 13.67953 and MAE 9,40998, while in ratio data 80:20 has RMSE 15,53199 and MAE 12,78120. In weekly time has obtained the RMSE 38,01949 and MAE 28,32035 for ratio data 60:40 while in ratio data 80:20 the result was RMSE 32,61283 and MAE 22,74638. Those results shows that CNN-LSTM model can predict the trend of daily time frame gold price