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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 SVM dan Decision Tree Menggunakan SMOTE Untuk Mengklasifikasi Sentimen Masyarakat Mengenai Pinjaman Online Rismawati Nurul Ikhsani; Ferian Fauzi Abdulloh
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6809

Abstract

With the development of technology, many applications and social media make it easier for users to do various desires, one of which is borrowing money online on the Online Loan Application with easy terms. The convenience provided causes many violations committed by irresponsible people, such as the breach of important information and data of online loan application users. This causes many people to express their comments and opinions on social media, especially on twitter. Sentiment analysis is conducted to see the tendency of public opinion to fall into negative, neutral, or positive sentiment. Furthermore, public opinion will be classified using two algorithms, namely the Support Vector Machine and Decision Tree algorithms. The aim of this research is to compare the performance of SVM and Decision Tree classification algorithms on the tendency of public opinion on twitter regarding online loans. Furthermore, optimization is carried out using SMOTE to optimize the accuracy of the two algorithms. The results obtained neutral sentiment as much as 78.96%, positive sentiment as much as 14.98%, and negative sentiment as much as 6.06%, people are more inclined to neutral sentiment. Then classification using SVM gets an accuracy of 87% and on Decision Tree gets an accuracy of 89%.. Then to optimize the performance results of the two algorithms, optimization using SMOTE is carried out. After SMOTE optimization, the accuracy produced by SVM is 99% and Decision Tree is 97%. Optimization using SMOTE proves that the SVM algorithm is better than Decision Tree. 
Klasifikasi Masalah Pada Komunitas Marah-Marah di Twitter Menggunakan Long Short-Term Memory Dian Sukma Hani; Chanifah Indah Ratnasari
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6755

Abstract

The rapid development of social media goes hand in hand with the increase in social media users. Among the social media platforms widely used in Indonesia, Twitter is one of the most popular. On Twitter, users are free to share every moment they experience or what they think. Many users use Twitter as a medium to express their emotions, as is what happens in angry communities. There are no special requirements to join as a community member other than getting admin approval. This community provides a place for its members to vent all kinds of anger they feel. This research classifies angry community tweets to find out the types of problems in these tweets. The results of this research can help in understanding communication and behavior patterns in angry communities, which can provide deeper insight into the social dynamics within them.Text data is retrieved via web scraping techniques, and then processed through a series of preprocessing steps, including unnecessary character removal, normalization, and tokenization. The classification uses the Long Short-Term Memory (LSTM) algorithm with six problem category classes, namely Study, Romance, Family, Career/Work, Person/Personal, and Swearing. After modeling, the model accuracy was 91.94%. The model was built using an embedding layer, Long Short-Term Memory (LSTM) layer, dense layer, and dropout layer which was run for 10 epochs. Model evaluation is carried out using metrics such as accuracy, precision, recall, and F1-score to measure model performance. The value resulting from the evaluation results using the confusion matrix is more than 50, this indicates that the LSTM model is able to classify the problem well.
Penerapan Metode Weighted Aggregated Sum Product Assessment (WASPAS) dengan Rank Order Centroid (ROC) Dalam Rekomendasi Barbershop Terbaik Wahyuni Wahyuni; Siti Lailiyah; Reza Andrea
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6769

Abstract

Barbershop is an innovation or development of a service previously known as a barber or barbershop. Barbershop operations are led by a barber or hairstylist, also known as a hairstylist, who has special skills in shaving and creating a variety of men's hairstyles. The barbershop business is experiencing rapid growth in this era. While there may be different variations of the name or brand, this business promises very attractive prospects in the long term. With so many barbershop options available, finding the best one can be a daunting task. In choosing a barbershop, there are several criteria to consider, such as the skill of the hairstylist, price, services offered, facilities provided, and level of cleanliness. To organize data and provide recommendations regarding the best barbershop, it is necessary to use an effective information system. The term "Decision Support System" (DSS) is often used to describe these information systems. The main objective of the DSS system is to improve the decision-making process and make it more effective and efficient by providing information, analysis and data modeling. The data needed to provide the best barbershop recommendations in Samarinda City were collected using the WASPAS (Weighted Aggregated Sum Product Assessment) and ROC (Rank Order Centroid) methods in this study. The replacement for the BS4, Sir Salon Barbershop, has the highest rating of 0.9815, making it a top barbershop recommendation.
Analisis Komparatif Kinerja Algoritma Machine Learning untuk Deteksi Stunting Syahrani Lonang; Anton Yudhana; Muhammad Kunta Biddinika
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6553

Abstract

Stunting is a serious problem caused by chronic malnutrition in children under five, causing stunted growth and having a negative impact on long-term health and productivity. Therefore, early detection of stunting is very important to reduce its negative impacts. Previous studies utilizing machine learning have proven the success of this method in various health applications, such as disease detection and the prediction of medical conditions. The results of this research are a comparative evaluation of five classifications, namely Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM), in classifying stunted toddlers. The dataset used contains four important attributes: age, gender, weight, and height of toddlers, as well as a binary class label that differentiates between toddlers who are stunted and those who are not. The evaluation results show that KNN at K = 3 produces the highest accuracy of 94.85%, making it the best model for classifying stunting in toddlers. Apart from accuracy, other metrics such as precision, recall, and F1-score are used to analyze the algorithm's ability to solve this problem. KNN stands out as the best model, with the highest F1-score of 89.47%. KNN also manages to maintain a balance between precision and recall, making it an excellent choice for treating stunting in toddlers. Apart from that, the use of the AUC metric from the ROC curve also shows the superiority of KNN in differentiating between stunted and non-stunting toddlers. With a combination of consistent evaluation results, both in terms of accuracy and other evaluation metrics, this research proves that KNN is the best choice for overcoming the task of classifying stunting in toddlers.
Effectiveness of Using Autoencoder Method on Recommender System in E-Commerce Domains Jayana Citra Agung Pramu Putra; Z K A Baizal
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.v7i3.6346

Abstract

The growth of large data in the online market can cause problems for users, one of which is in finding products that are to their liking. Recommender systems can overcome this problem by providing specific product recommendations to be promoted and offered to buyers, for example with Collaborative Filtering. The Collaborative Filtering Paradigm consists of Memory-based and Model-based techniques. Model-based techniques are considered to be able to complement the shortcomings of memory-based because of their high scalability, accuracy, and reduced dimensions. The type of model-based that is best known for having good results is Singular Value Decomposition (SVD) and what has been frequently used recently is deep learning, especially Autoencoder. The both models are very popular for use in dimension reduction so they are suitable for making recommendations. The advantage of Deep Learning is this method can be done without preprocessing so it can minimize the process that must be done. The evaluation results show that the errors produced by SVD and Autoencoder are lower compared to other studies. RMSE is 0.7 and MAE is 0.5. Even though the RMSE and MAE on the Autoencoder are greater than the SVD, the results of the T-Test show that there is no significant difference in the two error results. Autoencoders have been shown to have good results without preprocessing and are more effective with shorter processes and there are no significant differences with SVDs. Thus, Autoencoder can be said to be worthy of use and more effective in giving recommendations.
Sentiment Analysis Netizens on Social Media Twitter Against Indonesian Presidential Candidates in 2024 Using Naive Bayes Classifier Algorithm Ismail Ismail; Mario Adi Nugroho; Rini Sulistiyowati
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.v7i3.6536

Abstract

The democratic system is highly respected in Indonesia. The Indonesian state government organizes its government in a democratic manner, namely, it is run by, for, and with the consent of the people. Democracy is held through general elections to occupy leadership and power seats, followed by political parties. The high enthusiasm of Twitter social media users with the electability of presidential candidates proposed by political parties in the 2024 general election and the existence of a survey of potential presidents from national institutions, as well as mass and social media coverage of presidential candidates suggested by the political parties, generates opinions, attitudes, and emotions among people from all walks of life through tweets. The availability of abundant data on Twitter and other social media can provide useful information. Tweet data is obtained by crawling data using the help of the Python library, namely snscrape. The sentiment analysis uses a mixed method, namely by using machine learning and Lexicon Based, through the process of fine-grained sentiment analysis using the technique, namely, knowing the level of opinion polarity by grouping netizen responses and opinions into three parts: positive, neutral, and negative, with the help of machine learning and natural language processing. The results of the study were carried out by experimenting with four scenarios by dividing test and training data by 60:40, 70:30, 80:20, and 90:10. Measurement of the accuracy value results in the classification of the Nave Bayes Classifier Algorithm as 68%, 67%, 70%, and 71%. From the tweet data, it is clear that positive sentiment is dominant on the research topic. 
Perbandingan Algoritma K-Means Dan K-Medoids Untuk Pemetaan Hasil Produksi Buah-Buahan Eka Prasetyaningrum; Puji Susanti
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6477

Abstract

In general, in 2019-2020 fruit production in Kotawaringin Timur district has decreased. Based on the data on fruit production, the amount of fruit production decreased, resulting in scarce fruit stocks and expensive fruit prices. Based on these problems, fruit production will be grouped according to the type of production in East Kotawaringin district using data mining techniques with clustering techniques using the K-Means algorithm and K-Medoids algorithm in order to optimize and increase fruit production. The results of grouping fruit production will be divided into 3 clusters, namely the highest cluster, the medium cluster, and the lowest cluster, making it easier for the Food and Agriculture Security Service in East Kotawaringin district to calculate and increase agricultural yields, especially in the horticulture sector. Based on the test results using data in 2019-2022, totaling 29 data in the Rapidminer application version 9.9 by comparing the DBI (Davies Bouldin Index) values of the two algorithms with so that the conclusion in determining the best value for the number of clusters (K) is that the fourth experiment shows 0.296 DBI (Davies Bouldin Index) values with six clusters. If the DBI value is smaller or closer to 0, then the cluster results obtained are more optimal. The results obtained in the K-Means algorithm get a smaller DBI (Davies Bouldin Index) value with a value of 0.296 while the K-Medoids algorithm results with a DBI (Davies Bouldin Index) value of 0.507. The best algorithm for clustering fruit production in Kotawaringin Timur district is the K-Means algorithm based on the DBI values obtained.
Analisis Perbandingan Teorema Bayes dan Certainty factor dalam Mendiagnosa Von Hippel-Lindau Disease Kusno Harianto; Azahari Azahari
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6778

Abstract

This research aims to compare the effectiveness of two diagnostic methods in identifying Von Hippel-Lindau (VHL) disease, namely the Bayes' Theorem and Certainty Factor. VHL is a rare disease that affects organs such as the brain, eyes, and kidneys. Health is a precious asset for humans, and often the assistance of specialized doctors is required to diagnose complex conditions like VHL. In the era of modern technology, the fusion of medical science with artificial intelligence has provided a fresh impetus in the development of expert systems to support faster and more accurate medical diagnoses. The Bayes' Theorem method is a statistical technique used to calculate the level of certainty in medical data. It helps measure the probability of whether someone has VHL or not. On the other hand, Certainty Factor is another method that gauges the level of confidence in diagnosing a disease using specific metrics, such as how certain symptoms are related to the disease. This research will conduct experiments on both methods using existing medical data and VHL cases. We will compare the accuracy, efficiency, and speed of both methods in diagnosing VHL. The results of this study are expected to provide valuable insights into which method is better suited to support VHL diagnosis. The implementation of expert systems in the field of medicine is crucial as it can assist doctors in making better decisions. The findings of this research can contribute to the development of more advanced and accurate medical expert systems, which, in turn, will enhance the care and prognosis of patients with VHL and other diseases. Thus, this research has the potential for significant impact in the fields of health and computer science. The results of the study indicate two methods in diagnosing Von Hippel-Lindau disease: "Certainty Factor" with a certainty level of 97.44%, and "Bayes' Theorem" with a certainty level of 41.22%. This provides insights into the relative effectiveness of both methods in diagnosing the disease, with "Certainty Factor" appearing to be more reliable.
Sistem Pendukung Keputusan Penerapan Metode EDAS Dalam Menyeleksi Konten Youtube Terbaik Untuk Anak Usia Dini Salmon Salmon; Pitrasacha Adytia; Muhammad Fahmi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 4 (2023): Oktober 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i4.6747

Abstract

Youtube is an online video platform that has become one of the most popular and widely used websites in the world. YouTube's main goal is to provide facilities for content creators to upload the videos they create, interact with their audiences and provide users with a better and innovative video viewing experience, with features such as video browsing tailored to the user's interests and improved video quality. YouTube content is a term that refers to videos or other material uploaded to the YouTube platform, which is currently one of the largest websites in the world for sharing videos. In selecting Youtube content for early childhood, there are several criteria, namely: entertaining, not pornographic elements, increasing insight, not being violent, being creative. The results showed that the EDAS (Evaluation Based on Distance From Average Solution) method can provide an objective and accurate assessment by considering various relevant criteria. So as to produce YouTube content Omar and Hana ranked first (A2) with a score obtained 0.084 is YouTube content that is appropriate for children at an early age.
Perbandingan Metode MADM dalam Memilih Pegawai Terbaik dengan Pembobotan Objektif Andre Hasudungan Lubis; Juanda Hakim Lubis; Dinda Rizky Aprillya
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.v7i3.6232

Abstract

Nowadays, MADM or Multi-Attribute Decision Making as the part of decision-making theory has been used in various studies to examine decision making problems. Several methods such as SAW, ARAS, and MABAC are the most popular method to be selected to solve these decision-making problems, especially for personnel selection in a company or institute. However, these methods will certainly present various results. Hence, it is necessary to perform a comparison of the most optimal ranking results between these methods. The study focused on comparing those three methods in handling the personnel selection problem through the objective weighting by using SWARA method. The RSI method also employed to ensure the proper method to be used to solve the MADM problem. Five attributes are selected as the references to select best personnel among 38 of them, including Attendance, Discipline, Performance, Punishment, and Achievement. The study reveals that all of the three methods have the identical of RSI score. The results showed that the three methods had almost the same RSI values. The SAW method has the highest RSI value compared to other methods, namely 0.999489; the MABAC method has an RSI value of 0.999416, and the ARAS method with the lowest RSI value, namely 0.999052. Theoretical and practical implications are presented and discussed, along with suggestions for future research.

Page 92 of 119 | Total Record : 1182

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