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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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Penerapan Algorimta Backpropagation Untuk Prakiraan Cuaca Harian Dibandingkan Dengan Support Vector Machine dan Logistic Regression Ayu Zulfiani; Chairani Fauzi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6173

Abstract

To anticipate the impacts caused by extreme weather, the Meteorology, Climatology, and Geophysics Agency (BMKG) issues weather forecasts so that the community can be prepared when such extreme weather occurs. The application of Artificial Neural Network (ANN) techniques in weather forecasting significantly enhances the ability to explore vast amounts of big data in obtaining the necessary information, serving as a reliable assistant for forecasting and policymaking. The data used in this study consists of weather elements such as pressure, air temperature, humidity, wind direction and speed, as well as rainfall, obtained from the Radin Inten II Lampung Meteorological Station. The observational data has a data density per hour, spanning a period of 5 years from January 1, 2018, to December 31, 2022. The method employed in this research is Backpropagation Neural Network (BPNN). The research results indicate that BPNN can effectively predict classified rainfall compared to other methods, within recall value when slight rain 0.68, moderate rain 0.17, and heavy rain 0.03, meanwhile Support Vector Machine (SVM) and Logistic Regression (LR) method can predict only slight rain with recall value when slight rain is 0.51 and 0.47.
Perbandingan Algoritma Naïve Bayes Classifier Dan K-Nearest Neighbor Pada Sentimen Review Aplikasi Mobile JKN Citra Annisa; M. Afdal; Tengku Khairil Ahsyar
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6242

Abstract

BPJS Health must provide health services for the people of Indonesia. With the availability of the Mobile JKN application, it is useful to facilitate services for participants of the National Health Insurance-Indonesian Health Card (JKN-KIS). Mobile JKN is an innovation in electronic government health insurance services, making it easier for the public to access services and information quickly in the palm of their hand. With this innovation, many pros and cons flowed from the community, various comments appeared in the Play Store review column, sentiment analysis could be used to assess and rate applications. Therefore, these sentiments can be analyzed into information that can be used as material for evaluation and consideration by BPJS Kesehatan regarding Mobile JKN. This study aims to look at the results of the accuracy comparison between the Naïve Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) algorithms on the sentiment review of the Mobile JKN application on the Play Store. This study used the Naïve Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) methods with data scrapping techniques to collect Play Store data for the past year, namely 2,847 data and divided into 3 classes, namely positive, neutral and negative. Distribution of data using 10 K-Fold Cross Validation so that a comparison of the accuracy level of the Naïve Bayes Classifier (NBC) is 61.15%, while the accuracy level of K-Nearest Neighbor (KNN) is 87.59%.
Effectiveness of Word Embedding GloVe and Word2Vec within News Detection of Indonesian uUsing LSTM Muhammad Ghifari Adrian; Sri Suryani Prasetyowati; Yuliant Sibaroni
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6411

Abstract

In recent years the use of social media platforms in Indonesia has continued to increase. The increasing use of social media has several advantages and disadvantages. The advantage is that the news is easily accessible by anyone, while the disadvantage is that much information that is spread is hoax news. Hoax news must be detected because hoax news spreads false and misleading information. This undermines the integrity of the information and needs to be clarified for the public. By detecting hoax news, we can ensure the information being disseminated is accurate and trustworthy. In this study, the author will detect hoax news on Indonesian news media on Twitter using LSTM with word embedding GloVe and Word2Vec and compare the two-word embeddings to find the best performance in the LSTM model. The reason for choosing the GloVe and Word2Vec extraction features to be compared is that both are useful for representing vectors of words. Their performance may vary. Word2Vec might better capture semantic relationships between words, whereas GloVe might better capture distributional relationships and word co-occurrence. This study shows that LSTM with Word2Vec performs better than LSTM and GloVe in detecting Indonesian language news. LSTM and Word2Vec produced an average accuracy value of 95%, while LSTM with GloVe produced an average accuracy value of 90%.
Pengembangan Analisis Teknikal Untuk Trading Bursa Saham dengan Long Short Term Memory Faris Abdi El Hakim; Arna Fariza; Setiawardhana Setiawardhana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6410

Abstract

Stock price movements are difficult to predict and can change over time. Technical analysis is necessary to determine the right timing and company for stock investments. However, novice traders may face difficulties in analyzing stocks. Therefore, the author conducted research on the development of technical analysis for stock trading using Long Short-Term Memory (LSTM). The study utilized data from PT Bank Central Asia Tbk (BBCA.JK) stock prices, covering the period from July 1, 2004, to April 28, 2023. The LSTM method combined with technical analysis resulted in RMSE values of 136.759 for Open price, 126.52 for Close price, 317.968 for High price, 178.001 for Low price, and 189.669 for Adj. Close price, which outperformed the LSTM method without technical analysis. Furthermore, the LSTM method achieved better accuracy compared to Support Vector Regression (SVR) and K-Nearest Neighbors (KNN) using three different datasets. The RMSE values were 65.21 for LSTM, 313.56 for SVR, and 72.44 for KNN. The R2 values were 0.9919 for LSTM, 0.81 for SVR, and 0.990 for KNN. The results of the model were implemented in a web-based system using the Laravel framework and MySQL database.
Analisis Sentimen Pengguna Transportasi Online Maxim Pada Instagram Menggunakan Naïve Bayes Classifier dan K-Nearest Neighbor Dzul Asfi Warraihan; Inggih Permana; Mustakim Mustakim; Rice Novita
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6336

Abstract

Online transportation is a form of internet-based transportation that covers all aspects of the transaction process, including booking, route tracking, payment, and service assessment of the online transportation. Maxim is one of the popular online transportation providers in Indonesia so it will continue to improve its services to serve the needs of the entire community. In making developments, Maxim needs user opinions regarding its application or services. This research conducts sentiment analysis of Maxim users' opinions on Instagram using Naive Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) algorithms. Opinions are divided into 3 classes: negative, neutral, and positive. This research also uses the Random Over Sampling method and data sharing with 10-Fold Cross Validation. The accuracy results on sentiment data related to applications using the NBC algorithm are 81.03% and in the KNN algorithm with a value of k = 3 which is 80.72%. Meanwhile, sentiment data related to services produces an accuracy value in the NBC algorithm, namely 94% and the KNN algorithm with k = 3, namely 84%. It can be concluded that the NBC model is better than the KNN model in testing application-related sentiment data and service-related sentiment data after the Random Over Sampling method.
Penerapan Algoritma K-Medoids Pada Clustering Penerima Bantuan Pangan Non Tunai (BPNT) Tiara Ramayanti; Elin Haerani; Jasril Jasril; Lola Oktavia
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6475

Abstract

Bantuan Pangan Non Tunai (BPNT) is assistance distributed by the government to underprivileged communities to ease the financial burden that is increasingly burdening their lives. In a number of cases, it was found that the number of people who received BPNT was not properly targeted, so it was necessary to analyze the pattern of the characteristics of BPNT recipients so that the assistance was right on target. There are many criteria that must be considered to determine the people who are entitled to receive BPNT, so an appropriate algorithm is needed to determine the right cluster when analyzing characteristic patterns. This study applies the K-Medoids algorithm to classify BPNT data obtained from Firza Syahputra's research in 2020–2021, with a total of 732 attributes, so that the government can consider the factors that characterize beneficiaries. Perform tests using the Silhouette coefficient, which is useful for maximizing clustering results. The clustering result is three clusters, and the silhouette coefficient is 0.4439221599010089. The results of the analysis show that clustering performed using the K-Medoids algorithm can assume that clusters are grouped according to grouping: cluster 0 is eligible to receive BPNT, cluster 1 is considered, and cluster 2 is not eligible to receive BPNT.
Bank Central Asia (BBCA) Stock Price Sentiment Analysis On Twitter Data Using Neural Convolutional Network (CNN) And Bidirectional Long Short-Term Memory (BI-LSTM) Mansel Lorenzo Nugraha; Erwin Budi Setiawan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6120

Abstract

Stock investing has become popular among the public. Although this stock investment has significant risks, every year, investors keep increasing because the return from stocks is also quite promising. Social media also supports this stock investing, which can give information extensively and very quickly, so it can affect the stock price. The Efficient Market Hypothesis (EMH) theory defines that market information reflects stock prices. In this research, sentiment analysis uses a dataset crawled from Twitter to process the sentiment into helpful information. All the tweets related to stock prices are collected for sentiment analysis according to the appropriate sentiment type, whether it's a positive or negative sentiment. Many believe that sentiment influences stock price movements. This sentiment analysis process uses a hybrid method named Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) with feature expansion Word2Vec. Afterwards, the hybrid method analysis will establish the final accuracy obtained. This research uses 27.930 data and shows the hybrid CNN Bi-LSTM method result is 95.74%.
Hoax Detection of Indonesian News Media on Twitter Using IndoBERT with Word Embedding Word2Vec Pernanda Arya Bhagaskara S M; Sri Suryani Prasetiyowati; Yuliant Sibaroni
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6367

Abstract

Hoax is data that is added or deducted from the news that occurred. In the digital age, hoaxes are increasingly being spread, and people are very quickly affected by their spread, especially hoaxes circulating in Indonesian news media on social media. Disseminating information that has not been confirmed as accurate can cause public concern and anxiety. Virtual diversion has transformed into a correspondence key to begin thinking, talking, and moving around cordial issues. In this manner, exploration will be led by consolidating the IndoBERT model with the Word2Vec development highlight in arranging deception news in Indonesian news media. This model was constructed using K-Fold cross-validation to enhance model performance across extensive data sets. The information utilized comes from tweets shared on Twitter by the Indonesian public. The trials that have been carried out demonstrate that combining Word2Vec with IndoBERT is effective at detecting hoaxes, with an overall accuracy score of 88% for the entire dataset. This conclusion can be drawn from the classification results of Word2Vec with IndoBERT. Also, the best precision and incentive for every cycle is almost 99%. In addition, the study's objective is to identify hoax news in Indonesian news media disseminated via social media. This will encourage individuals to be more cautious when reading and disseminating news, as untrue information will significantly impact certain individuals.
Study of Feature Extraction Method to Detect Myocardial Infraction Using a Phonocardiogram Ashydiki Malik; Satria Mandala; Miftah Pramudyo
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6442

Abstract

Myocardial Infraction is one of the most dangerous and often fatal cardiovascular diseases. To detect this disease early, non-invasive methods based on Phonocardiogram (PCG) signals have become a significant focus of research. However, to present, research on feature extraction from PCG signals is still limited. In this research, we propose a study of feature extraction algorithms using Discrete Wavelet Transform (DWT), Mel Frequency Cepstral Coefficients (MFCC), and Entropy methods to detect heart attacks. In the pre-processing stage, we applied noisereduce to remove noise in the PCG signal. Further, we perform feature extraction using DWT, MFCC, and Entropy methods on the processed PCG signal. Following that, we used a detuned KNN with hyperparameters as the classification algorithm to classify the features into two categories: heart attack and non-heart attack. The test results show that DWT, MFCC, and Entropy-based feature extraction methods can make a significant contribution in detecting Myocardial Infraction. In comparison with other feature extraction algorithms, the test results show that the Entropy-based feature extraction method provides the best accuracy of 99%, with 99% sensitivity and 99% specificity. This research makes an important contribution to the development of heart attack detection methods using PCG signals. With promising results, the Entropy-based feature extraction method can be an effective and efficient approach in detecting coronary heart disease early, which in turn can improve patient prognosis and treatment.
Collaborative Filtering Based Food Recommendation System Using Matrix Factorization Muhammad Bayu Samudra Siddik; Agung Toto Wibowo
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6049

Abstract

A recommendation system is a method that provides suggestions of items that might users like. There are many domains that can be recommended, one of the most demanded domains by users today is food. In the era of big data, food choices from the large amount of data make it difficult for users to choose the right food for them. The collaborative filtering (CF) approach is considered capable of providing accurate and high quality item suggestions. One of the algorithms that can provide good performance results from the CF approach is Matrix Factorization (MF). This study aims to test a dataset that contains product ratings of food items using three MF algorithms, which are Singular Value Decomposition (SVD), SVD with Implicit Ratings (SVD++), and Non-Negative Matrix Factorization (NMF). Different latent factors are also used for the purpose of improving the performance of the proposed recommendation system algorithm. The dataset used is Amazon Fine Food Reviews. The study shows NMF and SVD++ as the best algorithm for generating user rating predictions for items. NMF has the smallest average prediction error as measured by MAE which is 0.7311. While SVD++ obtains the smallest prediction error value of 1.0607 as measured using RMSE. In addition to these results, the top-n evaluation also shows that the proposed algorithm performs quite well. The hit ratio value for each different n-item always increases proportionally to the number of recommended n-items. The highest hit ratio value is generated from the SVD++ algorithm of 0.0025 on n-item recommendations of 25 items. Overall it can be said that the proposed algorithm has good performance in providing item recommendations.

Page 86 of 119 | Total Record : 1182

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