DETECTION AND CLASSIFICATION OF RED BLOOD CELLS ABNORMALITY USING FASTER R-CNN AND GRAPH CONVOLUTIONAL NETWORKS

Research in medical imagery field such as analysis of Red Blood Cells (RBCs) abnormalities can be used to assist laboratory’s in determining further medical actions. Convolutional Neural Networks (CNN) is a commonly used method for the classification of RBCs abnormalities in blood cells images. However, CNN requires large number of labeled training data. A classification of RBCs abnormalities in limited data is a challenge. In this research we explore a semi-supervised learning using Graph Convolutional Networks (GCN) to classify RBCs abnormalities with limited number of labeled sample images. The proposed method consists of 3 stages, i.e., extraction of Region of Interest (ROI) of RBCs from blood images using Faster R-CNN, abnormality labeling and abnormality classification using GCN. The experiment was conducted on a publicly accessible blood sample image dataset to compare classification performance of pretrained CNN models (Resnet-101 and VGG-16) and GCN models (Resnet-101 + GCN and VGG-16 + GCN). The experiment showed that the GCN model build on VGG-16 features (VGG-16  + GCN) produced the best accuracy of 95%

LITERATURE REVIEW IOT SOFTWARE ARCHITECTURE ON AGRICULTURE

Context – Internet of Things (IoT) interrelates computing devices, machines, animals, or people and things that use the power of internet usage to utilize data to be much more usable. Food is one of the mandatory human needs to survive, and most of it is produced by agriculture. Using IoT in agriculture needs appropriate software architecture that plays a prominent role in optimizing the gain. Objective and Method – Implementing a solution in a specific field requires a particular condition that belongs to it. The objectives of this research study are to classify the state of the art IoT solution in the software architecture domain perspective. We have used the Evidence- Based Software Engineering (EBSE) and have 24 selected existing studies related to software architecture and IoT solutions to map to the software architecture needed on IoT solutions in agriculture. Result and Implications – The results of this study are the classification of various IoT software architecture solutions in agriculture. The highlighted field, especially in the areas of cloud, big data, integration, and artificial intelligence/machine learning. We mapped the agriculture taxonomy classification with IoT software architecture. For future work, we recommend enhancing the classification and mapping field to the utilization of drones in agriculture since drones can reach a vast area that is very fit for fertilizing, spraying, or even capturing crop images with live cameras to identify leaf disease

Aplikasi Location Based Service untuk menentukan rute terpendek lokasi ATM di Kota Malang menggunakan Algoritma Djikstra

ABSTRAK Perkembangan dunia teknologi informasi saat ini telah berkembang dalam bidang ekonomi, salah satunya adalah dengan penggunaan Kartu ATM. Penggunaan Kartu ATM ini diiringi pula dengan disediakannya lokasi mesin ATM oleh Bank yang terletak hampir di seluruh Kota Malang. Dengan semakin banyaknya lokasi ATM yang didirikan oleh Bank, maka pencarian mesin ATM oleh nasabah akan semakin banyak digunakan. Namun, banyaknya lokasi ATM Bank lain menyebabkan nasabah dapat terkena tambahan biaya (charge) saat melakukan transaksi di ATM Bank lain tersebut. Penelitian ini membangun sebuah aplikasi pada smartphone Android untuk mencari rute terpendek menuju lokasi ATM. Dalam aplikasi pencarian lokasi ditambahkan kategori pencarian berdasarkan nama Bank, dengan tambahan kategori pencarian ini maka charge dapat dihindari. Pada aplikasi pencarian lokasi mesin ATM ini, diterapkan Algoritma Dijkstra yang digunakan untuk mencari rute terpendek. Dengan aplikasi ini, nasabah dapat terbantu untuk mengurangi charge dan mencari rute terpendek lokasi mesin ATM Bank nasabah tersebut. ABSTRACT The development of Information Technology has been developed in economics, one of it is the use of ATM. With the huge use of ATM, it also accompanied with the ATM location by Bank in almost every place in Malang. With the increasing ATM location, the search for ATM by the costumes will be more widely used. But, many of ATM location can lead costumer additional cost (charge) when transacting at other Bank ATM. This research is to make android application to finding shortest route to the ATM location. In this ATM location search application also added with searching category by Bank’s name, with this additional category use then the additional cost can be avoided. In this ATM search application, the Dijkstra’s Algorithms is applied to finding the shortest route. With this application, the customer can be helped to avoid charge also finding shortest route to the ATM from the coustomer’s Bank

Deteksi Dan Klasifikasi Abnormalitas Sel Darah Merah Menggunakan Faster R-Cnn Dan Graph Convolutional Networks

Penelitian dalam bidang citra medis seperti analisis abnormalitas Sel Darah Merah dapat digunakan untuk membantu Analis dalam penentuan tindakan medis lanjutan. Metode Convolutional Neural Networks (CNN) merupakan metode yang umum digunakan untuk klasifikasi abnormalitas Sel Darah Merah (Red Blood Cells/RBCs) pada citra sel darah. Namun penerapan dari metode CNN ini memerlukan data yang cukup besar sebagai data pelatihan. Penelitian klasifikasi abnormalitas dari sel darah merah dengan hasil yang baik pada data yang terbatas merupakan suatu tantangan. Penelitian ini menggunakan semi-supervised learning yang didapatkan dari metode Graph Convolutional Networks (GCN) dimana dengan data sampel darah yang terbatas dapat mengklasifikasikan data yang belum memiliki label abnormalitas, yang direpresentasikan sebagai graph. Penelitian ini memiliki 3 tahap, yakni pengambilan Region of Interest (ROI) RBCs dari citra sampel darah menggunakan Faster R-CNN, labelisasi abnormalitas dan klasifikasi abnormalitas menggunakan GCN. Uji coba dilakukan pada dataset citra sampel darah yang dapat diakses secara publik. Dataset ini terdiri dari citra sampel darah dan label dari citra tersebut yang kemudian diproses oleh Faster R-CNN agar didapatkan ROI RBCs. ROI RBCs tersebut dilakukan labelisasi kelas abnormalitas dan digunakan sebagai ground truth dari GCN. Pengujian dilakukan dengan membandingkan beberapa model CNN (Resnet-101 dan VGG-16) dengan model GCN (GCN+Resnet-101 dan GCN+VGG-16) menggunakan metode evaluasi accuracy, precision dan recall. Metode GCN menghasilkan kinerja dengan akurasi 0.65 pada GCN+Resnet-101 dan 0.95 pada GCN+VGG-16. ===================================================================================================== Research in medical imagery field such as analysis of Red Blood Cells (RBCs) abnormalities can be used to assist analys in determining further medical actions. Convolutional Neural Networks (CNN) method is commonly used method for the classification of RBCs abnormalities in blood cells images. However, CNN method requires large data as training data. Then, research on RBCs abnormalities classification with good results in limited data is a challenge. This research uses semi-supervised¬ learning that obtained from Graph Convolutional Networks (GCN) method where with limited blood sample data it can classify data that don’t have an abnormality label, which implicitly represented as graph. this study has 3 stages, extraction of Region of Interest (ROI) RBCs from blood sample images using Faster R-CNN, abnormality labeling and abnormality classification using GCN. Trial was conducted on a publicly accessible blood sample image dataset. This dataset consists of blood sample images and labels, this images then processed by Faster R-CNN to obtain ROI RBCs. ROI RBCs were labeled with the abnormality class and used as GCN ground truth. The test was carried out by comparing several CNN models (Resnet-101 and VGG-16) with GCN model (GCN + Resnet-101 and GCN + VGG-16) using accuracy, precision and recall evaluation methods. GCN method produces performance accuracy of 0.65 on GCN + Resnet-101 and 0.95 on GCN + VGG-16