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INDONESIA
JURNAL MEDIA INFORMATIKA BUDIDARMA
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : 26145278     EISSN : 25488368     DOI : http://dx.doi.org/10.30865/mib.v3i1.1060
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering
Decission Support System, Expert System, Informatics tecnique, Information System, Cryptography, Networking, Security, Computer Science, Image Processing, Artificial Inteligence, Steganography etc (related to informatics and computer science)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,182 Documents
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Optimasi Convolutional Neural Network NASNetLarge Menggunakan Augmentasi Data untuk Klasifikasi Citra Penyakit Daun Padi Afiana Nabilla Zulfa; Jasril Jasril; Muhammad Irsyad; Febi Yanto; Suwanto Sanjaya
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.6056

Abstract

Diseases that attack rice are one of the elements that can reduce rice production. Rice diseases include Blast, Brown Spot, Leaf Smut, and so on. Distinguishing rice disease from sight has a weakness because rice disease has similar symptoms and characteristics. Farmers lack knowledge in identifying rice disease types so that technology is needed that can help distinguish rice diseases. The method used for rice image classification in this study is the Convolutional Neural Network NASNetLarge architecture. There are two classification processes, namely the classification process using data augmentation and without data augmentation. The data consists of 4 classes, namely Healthy, Leaf Smut, Blast, and Brown Spot with a total of 440 original images and 1320 augmented images. This study uses data augmentation, namely Horizontal Flips, Vertical Flips, and Contrast. The results for the classification process without data augmentation obtained the highest accuracy, namely 94.31%, 100% precision, 100% recall, and 100% f1-score at a ratio of 80:20, learning rate 0.1, dense 256, batch size 32, and optimizer Adam. While the accuracy obtained in the classification process using data augmentation is 98.73%, 96.11% precision, 100% recall, and 98.01% f1-score at a ratio of 70:30, learning rate 0.1, dense 16, batch size 128, and the Adagrad optimizer. The accuracy results show that the data augmentation and hyperparameters used can increase the accuracy in classifying rice leaf disease images.
Klasifikasi Pengenalan Wajah Siswa Pada Sistem Kehadiran dengan Menggunakan Metode Convolutional Neural Network Henri Kurniawan; Kusrini Kusrini; Kusnawi Kusnawi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.5958

Abstract

The student attendance system is useful for monitoring student attendance. The current technology is technology capable of detecting an object, such as fingerprints, voice, eye retinas, and faces. The author will create a model that can be used to detect student faces. In this study the authors used a modified Convolutional Neural Network (CNN) algorithm. The complexity of the CNN designed is in accordance with the specifications of the hardware and software used. Face data is taken directly from students in class (private dataset). Recording of students' faces using a standard quality webcam camera. The images produced by each student are 126 images with a total of 20 classes (labels). Taking pictures with various angles of the face, namely from above, below, front, left side and right side. The augmentation techniques used are flip, random rotation and affine techniques to enrich the data. Regularization techniques, such as dropout are also used. This is in order to increase accuracy, speed of model training and avoid overfitting of the built model. The evaluation results with the confusion matrix on the modified Convolutional Neural Network (CNN) algorithm produce a faster model training process with 5.31 hours and accuracy reaching 97.78%, the loss value is stable at 0.1177, loss validation with the number 0.0192, with as many iterations (epochs) as 60. The resulting model will be developed on a prototype of the student attendance system.
Klasifikasi Citra Stroke Menggunakan Augmentasi dan Convolutional Neural Network EfficientNet-B0 Nadila Handayani Putri; Jasril Jasril; Muhammad Irsyad; Surya Agustian; Febi Yanto
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.5981

Abstract

A stroke is a sudden onset of brain dysfunction, lasting for 24 hours or longer, resulting from clinically focal and global brain dysfunction. As many as 15 million people die from stroke each year. The stroke patients need an immediate treatment to minimize the risk of brain damage. One of the proponents for the stroke diagnosis is through a computed tomography (CT) image. In recent years, the image processing techniques capable to detect stroke patterns in a brain image, it can be useful for doctors and radiologists in doing diagnosis and treatment. This study aims to compare the level of accuracy using augmentation and without augmentation and hyperparameters using the Convolutional Neural Network in the EfficientNet-B0 architecture to classify ischemic, hemorrhagic, and normal brain stroke images. The data augmentation is produced by rotating, horizontal flipping, and contrast tuning of the original data. Testing data is provided as much as 20% of the portion of the original and augmented data, and the other 80% is used for the training process to find the optimal model. The model search is based on the composition of the training and validation data with a ratio of 70:30, 80:20 and 90:10. The experimental results show that the best performance is obtained for the combined original and augmented images, with accuracies of 97%, 93%, and 94%, respectively, for the three types of data-test: original, augmented, and combined. The merging of original and augmentated images for training data has shown that the model is robust enough in producing high accuracy results.
Sistem Pendukung Keputusan Penentuan Aplikasi Jasa Pemesanan Makanan Online Terbaik Dalam Menerapkan Metode OCRA Mohammad Aldinugroho Abdullah; Gandung Triyono; Rima Tamara Aldisa
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.5932

Abstract

In using online food ordering service applications, the existence of a Decision Support System (DSS) with the OCRA method is very helpful in choosing the best alternative. The application of a Decision Support System with the OCRA method gives important value to the predetermined criteria, so that the best alternative can be selected objectively and measurably. In choosing the best online food ordering service application. the use of DSS can increase the effectiveness and efficiency in decision making, thus bringing a positive impact on the development of the business. So from the results obtained from research regarding determining the best online food ordering service application is alternative A5 (GoFood) with a value of 0.2000.
Music Recommender System using Autorec Method for Implicit Feedback Muhamad Faishal Irawan; Z K A Baizal
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.5653

Abstract

As the number of music and users in music streaming services increases, the role of music recommender systems is getting important to make it easier for users to find music that matches their tastes. The collaborative filtering paradigm is the most commonly used paradigm in developing recommender systems. Many studies have proven that deep learning is able to improve the performance of matrix factorization. One such method in deep learning that has been adapted for use in Recommender Systems is Autorec, which is a variation of the Autoencoder technique. Autorec shows that it performs better than the baseline matrix factorization using Movielens and Netflix datasets. Therefore, in this study we propose the use of Autorec to develop a recommender system for music. The experimental results show that Autorec performs better than Singular Value Decomposition (SVD), with an RMSE difference of 0.7.
Perbandingan Metode K-NN Dan Metode Random Forest Untuk Analisis Sentimen pada Tweet Isu Minyak Goreng di Indonesia Christina Purnama Yanti; Ni Wayan Eva Agustini; Ni Luh Wiwik Sri Rahayu Ginantra; Dewa Ayu Putri Wulandari
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.5900

Abstract

Along with the development of technological advances, a lot of social media is used by humans, one of which is Twitter social media. On Twitter social media, we can find a lot of text data, opinions and public opinion, as the issue of cooking oil is currently hot in Indonesia. In this study, the K-NN and Random Forest methods were used, and the purpose of this study was to compare the two methods in sentiment analysis on the issue of cooking oil. The results of the accuracy of these two methods are not too far apart. Each of the two methods used will be divided into three research scenarios, the first is scenario 1, a collection of 500 data, scenario 2, a collection of 800 data, and scenario 3, a collection of 1,000 data, where the ratio of training data and test data is 80:20. The test results for the K-NN method in scenario 2 are superior with an accuracy presentation of 74.58%, 56.75% precision and 44.57% recall and the lowest result is the K-NN method scenario 1 with an accuracy presentation of 71. 50%, 47.83% precision and 37.45% recall. The average test results for the K-NN method are 72.86% accuracy, 52.26% precision and 41.04% recall. While the average results of the random forest method are 73.37% accuracy, 52.26% precision and 34.28% recall
Penggunaan Algoritma K-Means Pada Aplikasi Pemetaaan Klaster Daerah Pariwisata Lion Ferdinand Marini; Christian Dwi Suhendra
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.5558

Abstract

The Teluk Wondama Regency has various potentials in the field of religious and natural tourism. There are 13 districts in Teluk Wondama, where some districts are included in the Teluk Cendrawasih National Park area. This makes Teluk Wondama regency visited by many tourists every year. However, this potential is not well maximized by the local government. This is due to the long distance between each district, with a travel time of about 1-2 hours. This research will group districts that have potential to be maximized by the local government using the K-Means clustering algorithm. This algorithm will use the Elbow and Silhouette methods in the process of determining the most ideal cluster. The cluster results obtained will be presented in the form of web-based tourism area maps. The results obtained from the two cluster determination methods are 2 clusters. Of the 13 districts, after the normalization process is carried out by removing districts that do not have tourist data, only 7 districts remain. Based on the cluster analysis, there are 3 districts in cluster 1 and 5 districts in cluster 2. The cluster of tourism areas is presented in the form of a map created using the Shiny Web with R programming language.
Single-Label and Multi-Label Text Classification using ANN and Comparison with Naïve Bayes and SVM M. Mahfi Nurandi Karsana; Kemas Muslim L.; Widi Astuti
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.6024

Abstract

Machine learning has become useful in daily life thanks to improvements in machine learning techniques. Text classification as an important part in machine learning. There are already many methods used for text classification such as Artificial Neural Network (ANN), Naïve Bayes, SVM, Decision Tree etc.  ANN is a branch in machine learning which approximate the function of natural neural network. ANN have been used extensively for classification. In this research a simple architecture of ANN is used. But it needs to be pointed out that the architecture used in this research is relatively simple compared to the cutting edge in ANN development and research to show the potential that ANN have compared to other classification method. ANN, Naïve Bayes and SVM performance are measured using f1-macro. Performance of classification model is measured of multiple single-label and multi-label dataset. This research found that in single-label classification ANN have a comparable f1-macro with 0.79 compared to 0.82 for SVM. In multi-label classification ANN have the best f1-macro with 0.48 compared to 0.44 in SVM.
Komparasi Hasil Optimasi Pada Prediksi Harga Saham PT. Telkom Indonesia Menggunakan Algoritma Long Short Term Memory I Ketut Agung Enriko; Fikri Nizar Gustiyana; Rahmat Hardian Putra
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.5822

Abstract

To invest or buy and sell on the stock exchange requires understanding in the field of data analysis. The movement of the curve in the stock market is very dynamic, so it requires data modeling to predict stock prices in order to get prices with a high degree of accuracy. One of the steps to achieve this can be using a prediction system based on machine learning. There are several algorithms that can be used to predict stock values, one of which is the Long-Short Term Memory (LSTM) algorithm. This study aims to compare several optimization models, namely the Adam, SGD and RMSprop optimization models to analyze the accuracy of the LSTM algorithm in predicting stock price data and analyzing the number of epochs in forming an optimal model. The results of our research show that the LSTM algorithm has a good level of accurate prediction as shown in the Mean Absolute Percentage Error (MAPE) value and the data model obtained on variations in epochs values. Adam's optimization model shows that the higher the epoch value, the lower the loss value. The lower the loss value, the higher the prediction accuracy of the resulting stock data. Adam's Optimization Model is also the model with the highest accuracy value of 98.45%.
Analisis Dalam Pendukung Keputusan Seleksi Reporter dengan Menerapkan Metode EDAS dan Pembobotan ROC Pitrasacha Adytia; Muhammad Fahmi; Reza Andrea
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.6064

Abstract

The community's need for curiosity about the latest information makes news something that is always sought after and never goes extinct. One of the important elements in the world of news is the reporter. Reporters are people who are involved or play an important role in finding the latest information and covering everything that happens and conveying news or facts that happened at the scene directly to the public. In addition, reporters are also tasked with compiling various important information in a systematic and easy-to-understand manner so that the public can understand well about whatever is conveyed by the reporter. However, the amount of data makes it difficult for companies to select potential reporters. Because if done manually it is less effective. To solve these problems, a decision support system was created to assist the company in determining which applicants should be accepted. A decision support system (DSS) is a system that was created with the aim of helping parties who have difficulty making a decision or making an election with large amounts of data. In this study the method used is the EDAS method. the EDAS method is a method that functions to produce a ranking value from several choices so that a calculated value is obtained from each criterion attribute value. Based on the results of the research that has been carried out and has been carried out regarding the selection of prospective reporters who are eligible to be accepted by companies by optimizing the Decision Support System function by implementing the EDAS (Evaluation Based On Distance From Average Solution) method and the Rank Oder Centroid (ROC) weighting method, it is obtained the result is equal to 1.3093 with code B4 on behalf of syahputra as a reporter candidate that the company deserves to accept.

Page 83 of 119 | Total Record : 1182

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