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Bidirectional Long Short-Term Memory and Word Embedding Feature for Improvement Classification of Cancer Clinical Trial Document Jasmir Jasmir; Willy Riyadi; Silvia Rianti Agustini; Yulia Arvita; Despita Meisak; Lies Aryani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 4 (2022): Agustus 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (340.414 KB) | DOI: 10.29207/resti.v6i4.4005

Abstract

In recent years, the application of deep learning methods has become increasingly popular, especially for big data, because big data has a very large data size and needs to be predicted accurately. One of the big data is the document text data of cancer clinical trials. Clinical trials are studies of human participation in helping people's safety and health. The aim of this paper is to classify cancer clinical texts from a public data set. The proposed algorithms are Bidirectional Long Short Term Memory (BiLSTM) and Word Embedding Features (WE). This study has contributed to a new classification model for documenting clinical trials and increasing the classification performance evaluation. In this study, two experiments work are conducted, namely experimental work BiLSTM without WE, and experimental work BiLSTM using WE. The experimental results for BiLSTM without WE were accuracy = 86.2; precision = 85.5; recall = 87.3; and F-1 score = 86.4. meanwhile the experiment results for BiLSTM using WE stated that the evaluation score showed outstanding performance in text classification, especially in clinical trial texts with accuracy = 92,3; precision = 92.2; recall = 92.9; and F-1 score = 92.5.
PENENTUAN HARGA JUAL PASIR SILIKA DENGAN METODE REGRESI LINIER SEDERHANA BERBASIS WEB Wiken Winata; Sharipuddin Sharipuddin; Jasmir Jasmir
Jurnal Informatika Dan Rekayasa Komputer(JAKAKOM) Vol 1 No 2 (2021): JAKAKOM Vol 1 No 2 September 2021
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (687.81 KB) | DOI: 10.33998/jakakom.2021.1.2.8

Abstract

ABSTRACT Batanghari Persada Makmur Jambi is a company located in the Jambi area and is engaged in the business of selling silica sand. The many types of sand sizes that have different selling prices cause problems, namely the difficulty in determining an accurate selling price for each type of sand size so that there will be uncertainty in determining the appropriate price, in addition if there are changes in production costs and profit determination, the manual way of pricing is very inefficient. Therefore, this study aims to provide a solution to the problems that occur by offering a decision support system to determine the selling price of sand using the PHP programming language and MySQL database. The author develops systems with the waterfall method and uses a system model approach to unified model language using usecase diagrams, activity diagrams, class diagrams and flowchart diagrams. The new system produces output that can display sand data, order data, admin profile data, customer data, sand price data, and sand price calculations using the Simple Linear Regression method. computerized system design will help company operations more efficiently. Keywords : Design, Decision, Price, Sand ABSTRAK Batanghari Persada Makmur Jambi merupakan salah satu perusahaan yang berlokasi di daerah Jambi dan bergerak dalam usaha penjualan pasir silika. Banyaknya jenis ukuran pasir yang memiliki harga jual yang berbeda-beda menyebabkan adanya permasalahan yaitu sulitnya menentukan harga jual yang akurat untuk setiap jenis ukuran pasir tersebut sehingga akan timbul ketidakpastian dalam menentukan harga yang sesuai, selain itu bila ada perubahan pada biaya produksi maupun penetapan laba maka cara penentuan harga secara manual sangat tidak efisien. Oleh karena itu, penelitian ini bertujuan memberkan sulusi untuk permasalahan yang terjadi dengan menawarkan sistem pendukung keputusan untuk menentukan harga jual pasir menggunakan bahasa pemrograman PHP dan database MySQL. Penulis melakukan pengembangan sistem dengan metode waterfall dan menggunakan pendekatan model sistem unified model language menggunakan usecase diagram, activity diagram, class diagram dan flowchart diagram. Sistem baru menghasilkan output yang dapat menampilkan data pasir, data pesanan, data profil admin, data pelanggan, data harga pasir, dan hasil perhitungan harga pasir dengan metode Regresi Linier Sederhana. perancangan sistem secara terkomputerisasi akan membantu operasional.perusahaan lebih efisien. Kata Kunci : Perancangan, Keputusan, Harga, Pasir
DESIGN OF SALES AND SERVICE INFORMATION SYSTEMS AT THE DELIMA BIKE SHOP Ferry Aldiansah; Jasmir Jasmir; Despita Meisak
International Conference on Business Management and Accounting Vol 1 No 1 (2022): Proceeding of International Conference on Business Management and Accounting (Nov
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/icobima.v1i1.2842

Abstract

Delima Bike Shop is a shop that sells various bicycles and bicycle accessories. In the current system, the Delima Bike Shop in recording sales and purchase data uses writing on paper, namely notes, while stock calculations still use the traditional system using a stock book. Therefore, this study aims to design a sales and service information system using the PHP programming language, MySQL database. This research has stages, namely problem identification, literature study, problem formulation, data collection, system design and report generation. So as to produce a system that can overcome the problems that occur at the Delima Bike Shop in managing its operational activities such as data on suppliers, users, stock of goods, buying and selling, and making reports, making it easier to control data so that it helps the Delima Bike Shop in managing data to be better and orderly.
PREDICTION PERFORMANCE OF AIRPORT TRAFFIC USING BILSTM AND CNN-BI-LSTM MODELS Willy Riyadi; Jasmir Jasmir
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 1 (2023): JITK Issue August 2023
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i1.4191

Abstract

The COVID-19 pandemic has had a significant and enduring impact on the aviation industry, necessitating the accurate prediction of airport traffic. This study compares the predictive accuracy of biLSTM (Bidirectional Long Short-Term Memory) and CNN-biLSTM (Convolutional Neural Network-Bidirectional Long Short-Term Memory) models using various optimization techniques such as RMSProp, Stochastic Gradient Descent (SGD), Adam, Nadam, and Adamax. The evaluation is based on Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) indices. In the United States, the biLSTM model utilizing the Nadam optimizer achieved an MAPE score of 9.76%. On the other hand, the CNN-biLSTM model utilizing the Nadam optimizer demonstrated a slightly improved MAPE score of 9.62%. For Australia, the biLSTM model using the Nadam optimizer obtained an MAPE score of 31.52%. However, the CNN-biLSTM model employing the RMSprop optimizer had a marginally higher MAPE score of 33.33%. In Chile, the biLSTM model using the Adam optimizer obtained an MAPE score of 44.04%. Conversely, the CNN-biLSTM model using the RMSprop optimizer had a slightly higher MAPE score of 44.09%. Lastly, in Canada, the biLSTM model using the Nadam optimizer achieved a comparatively low MAPE score of 14.99%. Similarly, the CNN-biLSTM model utilizing the Adam optimizer demonstrated a slightly better MAPE score of 14.75%. These results highlight that the choice of optimization technique, model architecture, and balanced dataset can significantly influence the prediction accuracy of airport traffic.