Selection of medicines that is inappropriate will lead to an empty result at medicines, this has an impact on medical services and economic value in hospital. The importance of an appropriate medicine selection process requires an automated way to select need based on the development of the patient's illness. In this study, we analyzed patient prescriptions to identify the relationship between the disease and the medicine used by the physician in treating the patient's illness. The analytical framework includes: (1) patient prescription data collection, (2) applying k-means clustering to classify the top 10 diseases, (3) applying Apriori algorithm to find association rules based on support, confidence and lift value. The results of the tests of patient prescription datasets in 2015-2016, the application of the k-means algorithm for the clustering of 10 dominant diseases significantly affects the value of trust and support of all association rules on the Apriori algorithm making it more consistent with finding association rules of disease and related medicine. The value of support, confidence and the lift value of disease and related medicine can be used as recommendations for appropriate medicine selection. Based on the conditions of disease progressions of the hospital, there is so more optimal medicine procurement.

Security becomes one of the major necessities in our lives nowadays however criminal activities are still at large with criminals unable to be persecuted without eligible proofs of their misdeeds. Surveillance Camera is one of the better solutions to these problems in which they can be positioned at every corner of a building even streets and alleys. Their functions can be enhanced by adding algorithms that can identify objects. Frame Differences method is an algorithm to identify an object’s motion. Using this algorithm, we could differentiate an object moving in the environment. Background subtraction is one of the methods suitable to further improve frame differences thus increasing its effectiveness and precision. After implementing the method on a camera, the luminosity was founded to influence the threshold value significantly, the threshold value of 35 is the optimal value.

Skin cancer is one of the most common deadly diseases worldwide. Hence, skin cancer classification is becoming increasingly important because treatment in the early stages of skin cancer is much more effective and efficient. This study focuses on the classification of three common types of skin cancer, namely basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and melanoma using EfficientNet architecture. The dataset is preprocessed and each image in the dataset is resized to 256×256 pixels prior to incorporation in later stages. We then train all types of EfficientNet starting from EfficientNet-B0 to EfficientNet-B7 and compare their performances. Based on the test results, all trained EfficientNet models are capable of producing good accuracy, precision, recall, and F1-score in skin cancer classification. Particularly, our designed EfficientNet-B4 model achieves 79.69% accuracy, 81.67% precision, 76.56% recall, and 79.03% F1-score as the highest among others. These results confirm that EfficientNet architecture can be utilized to classify skin cancer properly.

Vehicle identification and detection is an important part of building intelligent transportation. Various methods have been proposed in this field, but recently the YOLOv8 model has been proven to be one of the most accurate methods applied in various fields. In this study, we propose a YOLOv8 model approach for the identification and detection of 9 vehicle classes in a reprocessed image data set. The steps are carried out by adding labels to the dataset which consists of 2,042 image data for training, 204 validation images and 612 test data. From the results of the training, it produces an accuracy value of 77% with the setting of epoch = 100, batch = 8 and image size of 640. For testing, the YOLOv8 model can detect the type of vehicle on video assets recorded by vehicle activity at intersections with. However, the occlusion problem overlapping vehicle objects has a significant impact on the accuracy value, so it needs to be improved. In addition, the addition of image datasets and data augmentation processes need to be considered in the future

The development of telecommunications technology is so rapid has given such great benefits. With the telecommunication technology, distance and time no longer be a significant obstacle. One of the results of telecommunications technology that is well known is the Short Message Service. In this study developed an application on the mobile phone to modify the SMS message into ciphertext so that the information content of the SMS is not known by others. SMS delivery system for encrypting messages into ciphertext using a key that is entered by the sender then sends to the destination number. SMS reception system to decrypt it to others via SMS without the fear of information from these messages will be known by others. The method used in the system encrypt and decrypt the message is the algorithm Tiny Encryption Algorithm and implemented using the Java programming language. JDK 1.7 as the Java programming language ciphertext into plaintext using the key entered by the receiver and displays the original message to the recipient. This application can be used by someone who wants to send a confidential information and the Java compiler. Eclipse, a Java SDK and the Android SDK as a Java source code editor.

The success of the work of Generative Adversarial Networks (GAN) has recently achieved great success in many fields, such as stock market prediction, portfolio optimization, financial information processing and trading execution strategies, because the GAN model generates seemingly realistic data with models generator and discriminator .Planning for drug needs that are not optimal will have an impact on hospital services and economics, so it requires a reliable and accurate prediction model with the aim of minimizing the occurrence of shortages and excess stock, In this paper, we propose the GAN architecture to estimate the amount of drug sales in the next one week by using the drug usage data for the last four years (2015-2018) for training, while testing using data running in 2019 year , the classification results will be evaluated by Actual data uses indicators of Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). From the results of the experiment, seen from the value ​​of MAE, RMSE and MAPE, the proposed model has promising performance, but it still needs to be developed to explore ways to extract factors that are more valuable and influential in the trend disease progression, thus helping in the selection of optimal drugs