Detection Of Malaria Parasites In Human Blood Cells Using Convolutional Neural Network

Malaria is a blood disease caused by the Plasmodium parasite which is transmitted by the bite of the female Anopheles mosquito. The diagnosis of malaria is carried out by a microscopist through examination of human blood cells. Their level of accuracy depends on the quality of the tool, expertise in classifying and counting infected and uninfected parasite cells. The disadvantages of examining this way include the difficulty in making a diagnosis on a large scale and the poor quality of the results. The dataset used in model evaluation is a dataset developed by LHNVBC which contains 27,558 cell image data. The malaria dataset will be processed through data science processing using a Convolutional Neural Network with the ResNet architecture. The model will conduct training on the dataset and then the model will be able to recognize malaria parasites in human blood cells. The model will be trained by optimizing multinomial logistic regression using Stochastic Gradient Descent (SGD) and Nesterov momentum values. The results of training data validation accuracy from model training with 50 epochs were obtained at 96.23% and 97% after being tested on data testing

Komparasi Ekstraksi Fitur dalam Klasifikasi Teks Multilabel Menggunakan Algoritma Machine Learning

Ektraksi fitur dan algoritma klasifikasi teks merupakan bagian penting dari pekerjaan klasifikasi teks, yang memiliki dampak langsung pada efek klasifikasi teks. Algoritma machine learning tradisional seperti Na¨ıve Bayes, Support Vector Machines, Decision Tree, K-Nearest Neighbors, Random Forest, Logistic Regression telah berhasil dalam melakukan klasifikasi teks dengan ektraksi fitur i.e. Bag ofWord (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Documents to Vector (Doc2Vec), Word to Vector (word2Vec). Namun, bagaimana menggunakan vektor kata untuk merepresentasikan teks pada klasifikasi teks menggunakan algoritma machine learning dengan lebih baik selalumenjadi poin yang sulit dalam pekerjaan Natural Language Processing saat ini. Makalah ini bertujuan untuk membandingkan kinerja dari ekstraksi fitur seperti BoW, TF-IDF, Doc2Vec dan Word2Vec dalam melakukan klasifikasi teks dengan menggunakan algoritma machine learning. Dataset yang digunakan sebanyak 1000 sample yang berasal dari tribunnews.com dengan split data 50:50, 70:30, 80:20 dan 90:10. Hasil dari percobaan menunjukkan bahwa algoritma Na¨ıve Bayes memiliki akurasi tertinggi dengan menggunakan ekstraksi fitur TF-IDF sebesar 87% dan BoW sebesar 83%. Untuk ekstraksi fitur Doc2Vec, akurasi tertinggi pada algoritma SVM sebesar 81%. Sedangkan ekstraksi fitur Word2Vec dengan algoritma machine learning (i.e. i.e. Na¨ıve Bayes, Support Vector Machines, Decision Tree, K-Nearest Neighbors, Random Forest, Logistic Regression) memiliki akurasi model dibawah 50%. Hal ini menyatakan, bahwa Word2Vec kurang optimal digunakan bersama algoritma machine learning, khususnya pada dataset tribunnews.com

Sistem Informasi Promosi dan Penjualan Pupuk, Beras dan Benih Berbasis Web

  Sistem informasi telah banyak digunakan oleh beberapa perusahaan untuk beralih dari sistem manual ke sistem komputerisasi. Penjualan dan promosi online saat ini berkembang pesat. Dengan memanfaatkan teknologi berbasis web, proses penjualan dapat dilakukan secara online dan promosi produk akan memiliki jangkauan yang lebih luas. PT.Sang Hyang Seri Pekanbaru merupakan perusahaan yang bergerak di bidang agribisnis yang bekerjasama dengan dinas pertanian provinsi Riau untuk mendistribusikan hasil pertanian. Perusahaan memiliki tempat produksi sendiri untuk beras, pupuk dan benih. PT.Sang Hyang Seri Pekanbaru memiliki kendala dalam proses bisnis penjualan yang masih bersifat konvensional (offline) dimana perusahaan harus datang langsung ke rekanan sehingga prosesnya terbatas dan memakan waktu lama. Dengan begitu PT. Sang Hyang Seri Pekanbaru akan kalah saing dengan perusahaan yang sudah menggunakan e-commerce. Dengan cara ini peneliti mengusulkan suatu sistem informasi promosi dan penjualan pada PT.Sang Hyang Seri Pekanbaru dengan model pengembangan sistem waterfall yang dapat digunakan perusahaan sebagai wadah penjualan dan promosi produk pertanian.Information systems have been widely used by several companies to switch manual systems to computerized systems. Online sales and promotions are currently growing rapidly. By utilizing web-based technology, the sales process can be carried out online and product promotion will have a wider range. PT.Sang Hyang Seri Pekanbaru is a company engaged in the agribusiness sector that collaborates with the Riau provincial agricultural office to distribute agricultural products. The company has its own production sites for rice, fertilizers and seeds. PT.Sang Hyang Seri Pekanbaru has problems in the sales business process which is still conventional (offline) where the company has to come directly to partners, so the process is limited and takes a long time. That way PT. Sang Hyang Seri Pekanbaru will lose competitiveness with companies that already use e-commerce. In this way the researchers propose a promotion and sales information system at PT.Sang Hyang Seri Pekanbaru with a waterfall system development model that the company can use as a forum for sales and promotion of agricultural products

Hybrid Model for Sentiment Analysis of Bitcoin Prices using Deep Learning Algorithm

Bitcoin is a decentralized digital currency, which is not controlled by a single authority or government. Bitcoin uses blockchain technology to verify transactions and guarantee user security and privacy. The fluctuating value of bitcoin is influenced by opinions that develop because many people use these opinions as a basis for buying or selling bitcoins. Knowledge to find out the market conditions of bitcoin based on public opinion is very necessary. This study aims to develop a hybrid model for bitcoin sentiment analysis. The dataset used came from comments on the Indodax website chat room, as many as 2890 data were successfully collected, then do data preprocessing, translate to english, text labeling and used hybrid parallel CNN and LSTM using word embedding glove 100 dimensions. Results of the experiments conducted, at 90:10 data splitting and 100 epochs is the best model with 88% accuracy, 86% precision, 78% recall and 81% f1-score, while the classification of opinion text comments on indodax chat results in 64.22% neutral comments, 21.14% positive comments and 14.63% negative comments. Based on research results, use of a parallel hybrid model provides a high accuracy value in classifying text, from these results positive comments are more than negative so that investors are advised to buy bitcoins. &nbsp