Analisis Perubahan Penutupan Lahan di Daerah Aliran Sungai Kelara Menggunakan Citra Sentinel 2

Daerah Aliran Sungai (DAS) Kelara terletak di Kabupaten Gowa dan Kabupaten Jeneponto Provinsi Sulawesi Selatan yang diduga mengalami perubahan tutupan lahan dikarenakan penggunaan lahan yang tidak sesuai dengan rencana pola ruang sehingga dapat mengakibatkan kerusakan yang berdampak pada fungsi hidrologi yang berujung terjadinya bencana alam. Perubahan penggunaan lahan dilakukan dengan menganalisis citra sentinel 2 tahun 2017 dan 2021 melalui proses digitasi on screen yang kemudian dilanjutkan dengan proses tumpang susun (overlay). Tujuan penelitian ini adalah untuk mengetahui perubahan penutupan lahan pada DAS Kelara pada tahun 2017 – 2021. Hasil penelitian menunjukkan bahwa terdapat delapan kelas penggunaan lahan di DAS Kelara yaitu hutan, lahan terbuka, pemukiman, perkebunan, pertanian lahan kering campur, sawah, tambak dan tubuh air, dan. Penutupan lahan yang mengalami penambahan luasan yaitu, pemukiman (3,79%), pertanian lahan kering campur (3,25%), sawah (0,49%), tambak (0,01%), dan tubuh air (0,33%). Penutupan lahan yang mengalami penurunan luasan yaitu hutan (1,98%), lahan terbuka, (0,70%) dan perkebunan (5,18%). Nilai matrik konfusi menunjukkan overall accuracy tertinggi pada tahun 2017 sebesar 92,23% dan terendah pada tahun 2021 sebesar 91,71%

SISTEM ZONASI DALAM PENERIMAAN PESERTA DIDIK BARU DI SMA NEGERI 1 NGABANG

AbstractNew student admission with zoning is the process of accepting new students with predetermined qualifications using the residential zone from school to house. This study aims to determine the process of registration and acceptance of PPDB with the zoning system in SMA Negeri 1 Ngabang. This research uses a qualitative approach and type of case study research. The subjects in this study were the principal, the vice principal for student affairs, and the head of the PPDB committee. Data collection procedures, namely observation, interviews and documentation. The findings from the results of this study are: (1) Student recruitment with a zoning system is a PPDB system that is seen based on the distance between the prospective students' residence and the school, not based on the UN score. (2) Selection is a process carried out to recruit students according to the rules to be accepted in school. (3) Placement is a follow-up to the results of the selection process to be accepted into a school and does not have specific criteria for placement. (4) Evaluation is an assessment process so that the PPDB acceptance process in the future can run better. (5) PPDB constraints can cause the PPDB acceptance process to not be carried out properly. The conclusions from the research results are: Acceptance of the PPDB zoning system using zones makes it easier for parents to register their children because of the close distance between school and home. Can be done online. Keywords: Acceptance of New Students,  Zonation Syste

WEBINAR STUDENT PRESENCE SYSTEM BASED ON REGIONAL CONVOLUTIONAL NEURAL NETWORK USING FACE RECOGNITION

World health organization announce Covid-19 as a pandemic so On March 15th 2020, the social distancing has been established with working, learning, and praying from home. Webinar is one of the solutions so those activities still can be done face to face and conference-based. With webinar, users can interact each other in an online meeting from home. Student presence is part of a webinar. The purpose of this research is to design an accurate student presence with a face recognition system using R-CNN method. The object of this research is a human face with sufficient light, medium, and the face must be facing the camera. This research proposed for a webinar student presence system is using face recognition with Regional Convolutional Neural Network (R-CNN). With object detection and several scenarios used in this method, the webinar student presence system using R-CNN will be more accurate than the methods that have ever been used before. This research has done four scenarios to obtain the best parameters like 45 of total layers, test data of the whole dataset percentage as 10%, RMSProp as model op- timizer, and 0.0001 learning rate. With those parameters, it have resulted the best system performance including 99.6% accuration, 1 × 10-4 loss, 100% precision, 99% recall, and 99.5% F1 Score

Implementation of Waste Management Policy with 3R Principles (Reduse, Reuse, Recycle) in the Gorontalo City

This study aims to obtain information about the Effectiveness of Terminal Type A. Isimu in Gorontalo District, the research method uses qualitative type. Data collection techniques were carried out through interviews with a number of informants and observations and recording secondary data related to the research problem. The results of the study concluded that the achievement of the objectives showed that the existence of the Isimu Type A Terminal had not been effectively seen from the human resources owned and the infrastructure owned by the terminal was incomplete and not maintained. Whereas the other factor is the low awareness of the community in utilizing the terminal. So that the achievement of objectives has not been effective. While the Integration shows that communication between users and related parties is less than the maximum, it needs to be improved through socialization to increase understanding of service users. The Adaptation Indicator shows the benchmarks of the procurement and workforce filling process in supporting employee performance is not yet effective, seen from the lack of personnel and the lack of quality resources to support the effectiveness of the Isimu type A terminal

Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion models

Large annotated datasets are required for training deep learning models, but in medical imaging data sharing is often complicated due to ethics, anonymization and data protection legislation (e.g. the general data protection regulation (GDPR)). Generative AI models, such as generative adversarial networks (GANs) and diffusion models, can today produce very realistic synthetic images, and can potentially facilitate data sharing as GDPR should not apply for medical images which do not belong to a specific person. However, in order to share synthetic images it must first be demonstrated that they can be used for training different networks with acceptable performance. Here, we therefore comprehensively evaluate four GANs (progressive GAN, StyleGAN 1-3) and a diffusion model for the task of brain tumor segmentation. Our results show that segmentation networks trained on synthetic images reach Dice scores that are 80\% - 90\% of Dice scores when training with real images, but that memorization of the training images can be a problem for diffusion models if the original dataset is too small. Furthermore, we demonstrate that common metrics for evaluating synthetic images, Fr\'echet inception distance (FID) and inception score (IS), do not correlate well with the obtained performance when using the synthetic images for training segmentation networks.Comment: 20 Pages. arXiv admin note: text overlap with arXiv:2211.0408

Beware of diffusion models for synthesizing medical images -- A comparison with GANs in terms of memorizing brain MRI and chest x-ray images

Diffusion models were initially developed for text-to-image generation and are now being utilized to generate high-quality synthetic images. Preceded by GANs, diffusion models have shown impressive results using various evaluation metrics. However, commonly used metrics such as FID and IS are not suitable for determining whether diffusion models are simply reproducing the training images. Here we train StyleGAN and diffusion models, using BRATS20, BRATS21 and a chest x-ray pneumonia dataset, to synthesize brain MRI and chest x-ray images, and measure the correlation between the synthe4c images and all training images. Our results show that diffusion models are more likely to memorize the training images, compared to StyleGAN, especially for small datasets and when using 2D slices from 3D volumes. Researchers should be careful when using diffusion models for medical imaging, if the final goal is to share the synthe4c imagesComment: 12 Pages, 6 Figure

Does an ensemble of GANs lead to better performance when training segmentation networks with synthetic images?

Large annotated datasets are required to train segmentation networks. In medical imaging, it is often difficult, time consuming and expensive to create such datasets, and it may also be difficult to share these datasets with other researchers. Different AI models can today generate very realistic synthetic images, which can potentially be openly shared as they do not belong to specific persons. However, recent work has shown that using synthetic images for training deep networks often leads to worse performance compared to using real images. Here we demonstrate that using synthetic images and annotations from an ensemble of 10 GANs, instead of from a single GAN, increases the Dice score on real test images with 4.7 % to 14.0 % on specific classes.Comment: 5 pages, submitted to ISBI 202

Development of front and back rolling mobile learning media for class x students of SMAN 1 Pamekasan

Many students do not understand the gymnastic movements being taught and become bored with the movement, as well as the learning method. This study aims to produce a learning media device in physical education, sports and health in the form of mobile learning floor exercise with front roll and back roll material for student’s class X of SMAN 1 Pamekasan so that it can be a teaching material. In this study, the Research and Development (R&D) from Lee and Owens was. So that only 5 steps are in accordance with the conditions in the field. (1) needs analysis, (2) product design, (3) development related to the material to be developed (4) application/implementation, (5) product evaluation by experts and product trials. Results of product development data obtained the first result (91.3%) with 32 students and the second group (87.4%) with 115 students. From the results of these data, it can be concluded that the criteria for learning media products for floor exercise materials based on mobile learning have very valid criteria and are suitable for use in learning activities in schools