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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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Prediction of Bandung City Traffic Classification Using Machine Learning and Spatial Analysis Adhitya Aldira Hardy; Aniq Atiqi Rohmawati; Sri Suryani Prasetyowati
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4538

Abstract

This research proposes a visualization of Bandung City congestion map classification using machine learning and kriging interpolation methods. The machine learning methods used are Naive Bayes and Artificial Neural Network (ANN) for the congestion classification process. The kriging interpolation used is simple kriging to create a spatial location map visualization on the congestion classification prediction. They are based on the classification results of both methods. Naïve Bayes is ideal supervised learning for classification, while ANN is ideal unsupervised learning for prediction. The classification was performed on arterial and collector roads with 11 intersections that are congestion points. The data used is traffic counting data for Bandung City in April 2022. The congestion classification is divided into four categories based on the congestion level. This category division causes data imbalance, so the Random Oversampling technique is used to overcome data imbalance. The result is that the ANN method has better performance, with an accuracy rate of 93% and an RMSE value of 0.9746, while the Naïve Bayes method has an accuracy rate of 90% and an RMSE value of 0.9381. The resulting classification map shows that in April 2022, the southern area of Bandung City experienced the highest congestion compared to the northern, western and southern areas. This research provides the best algorithm between the two methods. It provides information on congestion in Bandung City by visualizing the congestion classification map to reduce traffic congestion in the city of Bandung.
Improved User Interface Design on Mobile Apps “X” Using the Goal Directed Design Method Gready Michael Martua; Mira Kania Sabariah; Danang Junaedi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4611

Abstract

Mobile Apps “X” is one of the supporting applications for the Telkom University community that has been downloaded more than 10,000 times and received a rating of 3.9 on the Google Play Store. There are several user reviews that expect an improvement in the Mobile Apps “X” user interface. The problem found is the complexity of seeing the class schedule because they have to go through several menus first. The aim of the research is to improve the application to be even better so as to eliminate the complexity when used. This is a matter of ease of use. After evaluating the application, the SUS score is 61, the Completion Rate for the task of viewing class schedules is 50%, the task of viewing attendance information is 43%, and the Overall Relative Efficiency is 54%. The solution in this research is to improve the user interface using the Goal Directed Design method which will focus on meeting the goals of the user. The results of the improvements are an increase in the SUS score to 82, the Completion Rate for the task of viewing class schedules to 100%, the task of viewing attendance information to 100%, and the Overall Relative Efficiency to 100%. When interviewed at the end of the evaluation, users said that this design had met their goals. Thus, this improvement has become a solution to application problems.
Sistem Pakar Diagnosa Penyakit Disebabkan Rokok dengan Menggunakan Metode Forward Chaining Kelvin Dino Prasetio; Iqbal Kamil Sireegar; Suparmadi Suparmadi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4755

Abstract

The high cost and takes a long time to consult with a lung specialist at the hospital and the diseases caused by smoking are classified as complaints that are dangerous for the continuation of the disease. So we need an expert system to adopt human knowledge to computers, so that computers can provide solutions like doctors can. This research uses qualitative research methods and is included in descriptive research. The method used in this study is the forward chaining method and the search method used is forward data tracing to find conclusions. Data collection techniques through interviews and literature study. This research uses PHP programming language with MySQL database. Based on the results of the comparison between literature studies and interviews, obtained 7 diseases caused by smoking along with their causes and solutions. With this research process, an expert knowledge system application will be produced to be able to diagnose for the treatment of the disease.
News Recommender System Based on User Log History Using Rapid Automatic Keyword Extraction Inggrid Resmi Benita; Z K A Baizal
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4554

Abstract

There are many ways to find information; one of them is reading online news. However, searching for news online becomes more difficult because we should visit multiple platforms to find information. Sometimes, the recommended news doesn't match the user's interests. In many prior works, news recommendations are based on trending. Thus, the recommended news may not necessarily match the user's interests. To overcome this, we built a web-based news recommender system to make it easier for users to find news. We use the Rapid Automatic Keyword Extraction (RAKE) method in the recommendation process because this method can recommend news based on user preferences by utilizing user history logs. RAKE converts the title and content of the news into vector representation using Count vectorizer and applies the Cosine Similarity function to compare similarities between news. The test results show that the average performance of our proposed system is 90.8%, this accuracy outperforms earlier systems in terms of performance by the purpose of the recommender system, i.e., diversity, novelty, and relevance.
Analysis of Multi-Layer Perceptron and Long Short-Term Memory on Predicting Cocoa Futures Price Abbsumarmanali Firyabi Sakhtiyani; Siti Saadah; Gia Septiana Wulandari
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4498

Abstract

Predicting the price of Cocoa Futures is needed by farmers and also the government in determining policies. The uncertainty of price movements can affect farmers’ income and also foreign exchange savings because Indonesia is the largest cocoa-producing country in the world. In this study, we use the cocoa futures dataset to train using Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) to make a prediction of the cocoa futures price. In that way, this study resolves the uncertainties using the MLP method and also the LSTM, where these two methods produce a model using the input of data train and data test to predict the price of cocoa futures contracts and then be compared to see which one is the right one for the cocoa dataset. The dataset used is quoted from the Investing.com page taken from 2003 to 2021. The result of this study is the best model between MLP and LSTM model, where the LSTM can produce the best model using 50-50 Train to test data ratio, 128 batch size, and 64 Neurons on the hidden layer with evaluation metrics value in RMSE is 2.27, MAE is 32.11, and MAPE is 1.29 or 98.71% accuracy. This is because the LSTM model has logic gates in the layers that have an advantage on time series data using memory, where the LSTM model could memorize the output and use the output again as an input to achieve the best output.
Sentiment Analysis of Telkom University as the Best BPU in Indonesia Using the Random Forest Method Irfan Budi Prakoso; Donni Richasdy; Mahendra Dwifebri Purbolaksono
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4567

Abstract

In this day and age, social media has become a necessity for every human being. By using social media networks, users can easily exchange information, especially on linkedin social media. Linkedin is a social media network that can search for information openly, mainly used for professional networking. It will be easier and more practical to connect with professionals worldwide. Like identity, LinkedIn is often used as a medium to introduce yourself or your business to potential colleagues or companies for various purposes. Social media networks are often used to deliver information in various institutions at State Universities (PTN) and Private Universities (PTS). For example, it conveys information about state and private universities' achievements (PTS) achievements. Telkom University uses Linkedin to convey the achievements that have been achieved. This triggers the public to see posts that are positive, negative, or neutral. This study aims to conduct a sentiment analysis about Telkom University which has become the best private university in Indonesia, based on opinions submitted on LinkedIn social media. The process carried out in this study is to process all opinion data about Telkom University, which is the best private university in Indonesia, from Linkedin and then classification using the Random Forest method based on the categories of positive, neutral, and negative sentiments. Sentiment analysis results that have been obtained using the Random Forest classification method are 92.85% accuracy, 83.33% precision, 91.67% recall, and 84.13% F1-score%.
Analisa dan Penerapan Metode Algoritma K-Means Clustering Untuk Mengidentifikasi Rekomendasi Kategori Baru Pada List Movie IMDb Abraham Situmorang; Arifin Arifin; Ilpan Rusilpan; Christina Juliane
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4729

Abstract

IMDb (Internet Movie Database) is a comprehensive website that offers information about movies from all over the world, as well as various information about director, actor, actress, and writer biographies and award nominations. Visitors to the IMDb website can browse ratings and reviews based on the movies they plan to watch. Top 250 Movies and Most Popular Movies are two categories on IMDb. Because the results of the highest rating and the largest votes are only displayed based on the highest order of votes or ratings, the two existing categories are judged less useful and irrelevant to the suggestions for visitors to choose and decide on a film. This is due to the results of the highest rating and the most numerous votes, as determined by the highest ruling on either the votes or the rating. As a result of this, data mining with the K-means clustering algorithm is used to geolocate data in order to view data and accuracy using Davies-Bouldin Index (DBI) to combine ratings and votes with average approach to determine the centroid. Based on the results of this study, it is concluded that the DBI population with the highest accuracy is Cluster K=2 with population 509, with a score of 0.456, based on the voting and rating information, it can be deduced that a new category of movies called Best Recommended Movie is being recommended to potential moviegoers on the imdb.com website.
Penerapan Metode SMARTER Pada Penentuan Media Literasi Pembelajaran Anak Berkebutuhan Khusus Paramadina Mulya Majid; St Hajrah Mansyur; Harlinda L
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4885

Abstract

Children with Special Needs (ABK) are part of society that must be empowered, both from their physical and mental limitations.  In learning activities, especially for abk, they need learning media tools. There are 4 types of abk, namely Deaf, Visually Impaired, Deaf and Deaf. There are many learning media that can be used to increase the potential of abk students at SLB, but the school does not know the learning literacy media that suits the needs of students. This study aims to provide recommendations for learning media for website-based ABK students by considering three aspects of the criteria, namely academic assessment, non-academic assessment, and developmental assessment. The method used in decision making is the Simple Multi Attribute Rating Technique Exploiting Rank (SMARTER). Weighting in the SMARTER method uses the Rank Order Centroid (ROC) formula. The results of determining abk learning literacy media using the SMARTER method from 52 ABK students obtained media recommendations that can be given by students based on the severity level determined based on the final grade range. Meanwhile, based on the results of the questionnaire assessment to 14 respondents consisting of 1 principal and 13 teachers, it showed that the results of testing the system with the blackbox testing method obtained data for interface aspects as much as 88%, application performance 91%, database 90%, missing / damaged functions 46% and initialization 94%. Thus, the overall average application produced an index of 82% which is included in the good criteria.
Analisis dan Penerapan Algoritma C4.5 Untuk Memprediksi Kualitas Penelitian dan Publikasi Ilmiah Nur Fauza Muhidin; Dudih Gustian; Sihabudin Sihabudin
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4463

Abstract

Research is an obligation for every lecturer in Indonesia. In the midst of increasing publications in Indonesia, however, there is a problem, namely the quality of Indonesian publications which is still low when compared to those countries in Southeast Asia. In this case the C4.5 algorithm is a method that can help predict problems that have been carried out by research on the quality of research and scientific publications. This research was conducted in order to assist the study program to assess the quality of research and scientific publications. From this research, it can be said that by using the C4.5 algorithm, the quality of research and scientific publications can be predicted with a fairly high accuracy, the results of measuring the accuracy of the data obtained from data training with Confusion Matrix with dataTraining testing 81.65%.
Depression Detection on Social Media Twitter Using Long Short-Term Memory Hafshah Haudli Windjatika; Warih Maharani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4457

Abstract

Mental health problems in the world, especially in Indonesia are still significant. According to the Ministry of Health of the Republic of Indonesia stated that depression is experienced by adolescents from the age of 15 to 24 years. The depression experienced by a person is sometimes not realized by the sufferer, so social media becomes an intermediary to express feelings in text form. From the available data, this case pushes the research to detect depression disorder. Detecting depression performs to know the Twitter user who experiences depression. Data used from 159 Twitter users for every username is taken from 100 tweets. In this research, we use Word2Vec and LSTM (Long Short-Term Memory) features as the classification method. The Word2Vec works in converting data as vector and seeing the relation for every word. LSTM is chosen since the dataset is used to collect tweet from the past tense and this method be able to save the data from the past doing prediction. The classification is performed by processing the data trained such as tweeting which becomes a model for processing the data trained test. Based on the test result produce the accuracy data is 77.95% and the F1-Score is 57.14%.

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