Sistem Informasi Geografis Risiko Kemunculan Rip Current Menggunakan Decision Tree C4.5

One of the dangers that occur at the beach is rip current. Rip current poses significant danger for beachgoers. This paper proposes a method to predict the rip current's occurence risk by using decision tree generated using C4.5 algorithm. The output from the decision tree is rip current's occurrence risk. The case study for this research is the beach located at Rote Island, Rote Ndao, Nusa Tenggara Timur. Evaluation result shows that the accuracy is 0.84, and the precision is 0.61. The average recall value is 0.68 and the average F-measure is 0.59 in the range 0 to 1

Face Images Classification using VGG-CNN

Image classification is a fundamental problem in computer vision. In facial recognition, image classification can speed up the training process and also significantly improve accuracy. The use of deep learning methods in facial recognition has been commonly used. One of them is the Convolutional Neural Network (CNN) method which has high accuracy. Furthermore, this study aims to combine CNN for facial recognition and VGG for the classification process. The process begins by input the face image. Then, the preprocessor feature extractor method is used for transfer learning. This study uses a VGG-face model as an optimization model of transfer learning with a pre-trained model architecture. Specifically, the features extracted from an image can be numeric vectors. The model will use this vector to describe specific features in an image.  The face image is divided into two, 17% of data test and 83% of data train. The result shows that the value of accuracy validation (val_accuracy), loss, and loss validation (val_loss) are excellent. However, the best training results are images produced from digital cameras with modified classifications. Val_accuracy's result of val_accuracy is very high (99.84%), not too far from the accuracy value (94.69%). Those slight differences indicate an excellent model, since if the difference is too much will causes underfit. Other than that, if the accuracy value is higher than the accuracy validation value, then it will cause an overfit. Likewise, in the loss and val_loss, the two values are val_loss (0.69%) and loss value (10.41%)

APLIKASI E-SEWA BARANG BERBASIS MOBILE

Aktivitas sewa-menyewa merupakan suatu aktivitas memakai suatu barang yang bukan barang milik sendiri dengan membayarkan sejumlah uang kepada pihak penyewa dengan persetujuan kedua belah pihak dalam pemenuhan proses bisnis si penyewa, sewa-menyewa tidak lepas dari kehidupan sehari-hari dikarenakan efektivitas dan efisiensinya dalam membantu menyelesaikan proses bisnis setiap pelaku usaha karena pelaku usaha tidak perlu membeli barang tersebut yang membuat tidak banyak biaya yang dikeluarkan dalam pemenuhan perawatan barang tersebut. Penerapan sistem informasi sangat diperlukan agar dapat mengoptimalkan fungsi dan pemanfaatan aktivitas sewa-menyewa, sehingga lebih mudah dan efisien. Penelitian ini menghasilkan sebuah sistem informasi sewa barang yang dapat digunakan oleh pihak pelaku usaha dalam mencari barang yang akan disewa dalam membantu menyelesaikan proses bisnis mereka. Terdapat fungsi utama yaitu Pencarian Barang yang membantu user dalam menemukan barang yang dicari serta terdapat detail barang yang memudahkan user dalam memilih spesifikasi barang yang diinginkan, juga terdapat fungsi Riwayat yang mencatat setiap aktivitas yang dilakukan user pada sistem serta memudahkan mencari barang yang sama ketika ingin melakukan penyewaan terhadap barang yang pernah disewa. Proses perancangan sistem menggunakan metode Waterfall dengan perancangan terstruktur, menggunakan React Native sebagai framework dan menggunakan Unified Modelling Language sebagai bahasa dalam memvisualisasikan rancangan model sistem. Metode pengujian menggunakan model Black Box. Hasil pengujian menunjukkan performa sistem yang secara fungsional sangat baik

Sistem Informasi Geografis Risiko Kemunculan Rip Current Menggunakan Decision Tree C4.5

One of the dangers that occur at the beach is rip current. Rip current poses significant danger for beachgoers. This paper proposes a method to predict the rip current's occurence risk by using decision tree generated using C4.5 algorithm. The output from the decision tree is rip current's occurrence risk. The case study for this research is the beach located at Rote Island, Rote Ndao, Nusa Tenggara Timur. Evaluation result shows that the accuracy is 0.84, and the precision is 0.61. The average recall value is 0.68 and the average F-measure is 0.59 in the range 0 to 1

Analysis of Understanding and Utilization of Fintech in Millennial Generation

This study aims to examine the millennial generation's comprehension and utilization of e-wallets. The questionnaire method was used to acquire data from 28 participants of the millennial generation for this study. Data was collected to determine the extent to which millennials comprehend the concept of e-wallets, their usage frequency, and their preferences for the most desirable features. The results of the analysis indicate that 82.1% of millennials have a deep comprehension of e-wallets and utilize them as an essential tool in their daily financial activities. 94% of respondents have employed e-wallets for online transactions and payment activities. The influence of usability and application features on e-wallet usage preferences. This research can provide valuable insights for e-wallet service providers, businesses, and the government regarding the millennial generation's use of e-wallets. The implications of this study's findings can be used to enhance e-wallet innovation and services, as well as to devise more effective marketing strategies to overcome obstacles and optimize the use of this financial technology