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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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Implementation of SVM, k-NN, and DT for Toxicity and Sentiment Classification of AWA Vlog Content in Wasur National Park Singgalen, Yerik Afrianto
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7434

Abstract

This study delves into the response of viewers to video content focusing on Wasur National Park in Papua, Indonesia, with a particular emphasis on its implications for livelihood and ecology. The increasing popularity of online platforms such as YouTube has provided a medium for content creators to showcase natural landscapes and cultural heritage, potentially influencing viewers' perceptions and behaviors toward conservation efforts. Employing the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework, this research systematically analyzes a specific video from the AWA channel, known for its documentaries on environmental and cultural topics. The methodology involves sentiment analysis to gauge viewers' emotional responses, toxicity assessment to identify harmful content, and thematic coding to categorize comments based on recurring themes. The analysis reveals that viewers engage with the content positively, expressing appreciation for the video's educational and visually compelling nature. Moreover, the study identifies various dimensions of toxicity within the dataset, including Toxicity (0.05364), Severe Toxicity (0.00629), Identity Attack (0.02250), Insult (0.03534), Profanity (0.03589), and Threat (0.01280). Furthermore, the performance of the Support Vector Machine (SVM) with Synthetic Minority Over-sampling Technique (SMOTE) is highlighted, demonstrating its effectiveness in classifying sentiment with an accuracy of 93.86%, precision of 100.00%, recall of 87.73%, f-measure of 93.44%, and an Area Under the Curve (AUC) value of 1.000. This research underscores the significance of balanced media portrayals in fostering positive attitudes toward environmental conservation and cultural preservation.
Analisis Perbandingan Metode Teorema Bayes dan Certainty Factor Pada Diagnosis Gangguan Kecemasan Gobel, Citra Yustitya; Lasena, Marlin; Puspa, Misrawati Aprilyana; Utiarahman, Siti Andini
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7683

Abstract

Anxiety disorders are a mental health problem that is increasing in prevalence, but the lack of public knowledge about anxiety disorders means that many people are not aware of anxiety disorders, so information technology is needed to understand symptoms and diagnosis using more relevant methods in intelligent systems. Intelligent systems are able to help in analyzing various symptoms and identifying initial diagnosis results with wider accessibility, but the problem of this research is focused on selecting the most effective method for intelligent systems as a basis for clinical data analysis, so in this research we will compare the level of accuracy of applying the method, namely Bayes' theorem and Certanty factor for the diagnosis of anxiety disorders. Bayes' Theorem is a classic statistical approach, offering a structured and measurable framework for calculating the probability of disease based on clinical evidence, while the Certainty Factor is a method for proving the certainty value of a fact in the form of a metric in an intelligent system. The aim of this research is to analyze the performance of the Bayes Theorem method and certainty factor by examining the percentage results obtained by applying the two methods. that the percentage result of the Bayes Theorem calculation method is higher, namely 84%, compared to the percentage result of the certainty factor, namely 70%, so it can be concluded that the application of Bayes' theorem is better than the certainty factor, especially in the diagnosis of people with anxiety disorders.
Optimasi Algoritma KNN dengan Parameter K dan PSO Untuk Klasifikasi Status Gizi Balita Rochman, Bagus Fathur; Rahim, Abdul; Siswa, Taghfirul Azhima Yoga
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7841

Abstract

The toddler years are a crucial phase that requires constant nutritional monitoring, because rapid growth and development require optimal nutritional intake. Nutritional problems in toddlers can hinder physical growth and can even be fatal. In assessing the nutritional status of toddlers, it is important to use efficient methods. One approach that can be used is machine learning, which can help determine the nutritional status of toddlers. K-Nearest Neighbors (KNN) is an algorithm commonly used in object classification based on nearest neighbors. Even though it is simple, determining the correct K value is very important because it can significantly influence KNN performance. This research emphasizes the importance of choosing the right parameters to increase the accuracy of the KNN model in classifying the nutritional status of toddlers. The test results show that the optimal combination for KNN is at K=4, using the 'distance' weight and distance metric p=1, producing the highest accuracy of 91.15% on the test data. Furthermore, the research applied Particle Swarm Optimization (PSO) to optimize KNN parameters, and it was found that the optimal combination was with K=6, 'distance' weight, and distance metric p=1, achieving a mean accuracy of 93.44% and a test accuracy of 93.98%. PSO is proven to be effective in finding the best parameters that increase model generalization to test data. Test results with a training data ratio of 80% and testing 20% show the best accuracy of 93.98%. .The use of PSO for parameter optimization succeeded in increasing model accuracy by 3.10% compared to the model without optimization
Analisis Clustering Global Living Cost Berdasarkan Socioeconomic Status Menggunakan Algoritma DBSCAN Perdana Putra, Siswadi; Aryo Anggoro, Dimas
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7567

Abstract

The development of the world economy is currently undergoing a very complex phase characterised by very different changes. These changes have a significant impact on the cost of living and quality of life of people in various countries around the world. Factors such as government policies, economy, income, transport, fuel play an important role in various changes in the world economy. Then this research aims to cluster the cost of living on the "Global Living Cost" dataset against socioeconomic status and also determine the performance of the DBSCAN algorithm on several types of "Global Living Cost" datasets that have been modified by perturbing using jitter position, combination of jitter position and scale, and noise perturbation then also using several dimensionality reduction techniques such as PCA, UMAP, t-SNE, and ICA. The data used is taken from "Kaggle. com", then obtained similarity results using the jaccard similarity method that the combination of clustering and dimensionality reduction algorithms for the "Global Living Cost" dataset from the best is DBSCAN and ICA with the highest similarity score of several types of datasets, namely 1.0, DBSCAN and PCA with the highest similarity score of 0.998769735493131, DBSCAN and t-SNE with the highest similarity score of 0.9995897435897436, and the last is DBSCAN and UMAP with the highest similarity score of 0.8065233506300964. The conclusion obtained is that the DBSCAN algorithm is able to work very well for different types of datasets.
Prediksi Kepribadian Big Five Pengguna Twitter Menggunakan Metode Decision Tree dengan Pendekatan Semantik BERT Widyanto, Jammie Reyhan; Setiawan, Erwin Budi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7311

Abstract

Individual personality can be seen easily in this day. There are several approaches in classifying personality, one of which is the big five personality. The big five personality consists of 5 dimensions, namely Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. One way of knowing an individual's personality can be seen from their social media, because today almost all individuals have social media. One of the social media that is still widely used is Twitter. Twitter is a social media that contains tweets from each individual with a maximum of 280 characters per tweet. There have been several studies related to the big five personalities of Twitter users. Based on previous big five personality research problems, this study carried out predictions of the big five personalities of Twitter users using the Decision Tree Classification And Regression Tree (CART), Term Frequency Inverse Document Frequency (TF-IDF), Synthetic Minority Oversampling Technique (SMOTE), Linguistic Inquiry Word Count (LIWC), and Bidirectional Encoder Representations from Transformers (BERT) methods. The study aims to determine the application of the methods used in this study to the prediction of big five personalities and to get better accuracy results than previous studies. Data obtained from 315 twitter users and 672,866 tweets obtained from surveys and have been labeled with big five personalities, resulting in an accuracy of 97.62% from the baseline with an increase of 23.1%, by applying the CART+TF-IDF+SMOTE+LIWC+BERT method.
Analyzing the Sentiment of the 2024 Election Sirekap Application Using Naïvee Bayes Algorithm Muhammad, Isa Ali; Rakhmawati, Desty; Wijaya, Anugerah Bagus
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7773

Abstract

One of the most recent types of elections is the 2024 election, which includes the election of the president as well as legislative members. Along with the development of technology, an application called Sirekap emerged which is used to recapitulate the results of the vote. Although the app only has a one-star rating on the Play Store, reading all the user reviews to know the quality takes quite a while. Therefore, sentiment analysis can be an alternative to get an overview of user reviews so that it can help in making better decisions then, the method that will be used in conducting sentiment analysis in this study is the naïve Bayes algorithm. This research aims to identify and categorize user sentiment, as well as evaluate the quality of the app based on reviews provided on the Playstore. This research contributes by providing an efficient method to analyze user reviews of the Sirekap app, which can assist app developers and other stakeholders in making better decisions regarding app development and improvement. In addition, the results of this study confirm that the app's one-star rating is accurate, with evaluation metrics such as precision, memory, and f1 score reaching 1.00 each.
Perbandingan Kernel Polynomial dan RBF Pada Algoritma SVM Untuk Analisis Sentimen Skincare di Indonesia Yunanda, Vinsensius Dendi; Hendrastuty, Nirwana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7425

Abstract

Skincare is considered a crucial need in maintaining and caring for skin health, given its role not only in cosmetics but also in contributing to the well-being and protection of the skin from external factors. Sentiment analysis of skincare reviews on social media helps understand consumer perspectives, guiding skincare manufacturers in product improvement. The comparison of polynomial and RBF kernels in SVM is relevant to enhance sentiment analysis of skincare in Indonesia, ensuring the model's accuracy in classifying product sentiments. The dataset used consists of 2168 data obtained through social media scraping. After obtaining the data, text preprocessing processes such as case folding, cleaning, tokenization, stemming, and data labeling were performed. The data was divided into an 80:20 ratio for comparison, with 1734 training data and 434 testing data. The accuracy results using the SVM method with RBF and Polynomial kernels were obtained, with the highest accuracy found in the RBF kernel at 86,17%, and the polynomial kernel achieving an accuracy result of 84,56%.
Analisis Sentimen Terhadap Penggunaan Chatgpt Berdasarka Twitter Menggunakan Algoritma Naïve Bayes Transiska, Dwi; Febriawan, Dimas; Hasan, Firman Noor
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7540

Abstract

Chatbots can assist by fostering collaboration between users and companies or organizations. In today's world, artificial intelligence through chatbots has become one of the mainstays in handling various problems. One of the well-known types of chatbots is ChatGPT, which utilizes NLP (Natural Language Processing) technology to understand and respond to user queries and requests. ChatGPT, as a device that is very easy to use for all circles, has a fairly simple interface but does not cause boredom, and the speed in responding to commands given, this is an added value of ChatGPT. Despite the myriad of conveniences offered, ChatGPT also raises concerns on the negative side. The negative side is that there are many concerns that arise, starting from the rampant spread of hoaxes and misunderstandings on social media. The advantages and disadvantages that have been explained above, researchers are encouraged to find out the truth from the public's response regarding ChatGPT more deeply so that this sentiment analysis research is made. Moreover, research related to sentiment analysis can be said to be quite an answer to the confusion of public responses outside related to ChatGPT. This research also starts from the process of Data retrieval on Twitter social media using Rapidminer, in this process the researcher uses the Twitter API token on the Rapidminer application so that it can be obtained. The data that has been obtained is then cleaned through the preprocessing process using the features available in Rapidminer, the result of this process is that the data becomes clean. After being cleaned through preprocessing, it is then labeled as positive or negative which will later be classified by the Naïve Bayes algorithm. This classification aims to divide between positive sentiment and negative sentiment. After performing classification, the data is then evaluated using a confusion matrix and the results are obtained with an accuracy value of 96.55%, a precision value of 89.19%, and a recall value of 95.18%.
Penerapan Artificial Neural Network Untuk Memprediksi Error dalam Perancangan Aplikasi Monitoring Tetes Cairan Infus Astutik, Liya Yuni; Syafii, Imam
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7724

Abstract

Decision-making in error prediction during the design process of infusion drip monitoring applications plays a crucial role. Designing this application is necessary because manual monitoring by medical staff is prone to errors and inaccuracies. Therefore, the need for accurate predictions in both planning and error management must be further investigated. This research discusses the benefits of the Artificial Neural Network (ANN) methodology in addressing error values in infusion drip monitoring applications during the design process. ANN is chosen for its ability to handle data complexity and non-linear patterns in infusion drip rates. Errors in infusion dosage can be fatal, ranging from patient instability to severe complications. Designing infusion drip monitoring applications automates the process and ensures accuracy, reducing the workload of medical staff and enhancing patient safety. This application also allows for more consistent and real-time monitoring, enabling quicker medical intervention when issues arise. The ANN methodology used includes both forwardpropagation and backpropagation, employing a binary sigmoid activation function with a learning rate of 0.03 and a maximum epoch setting of up to 1000. The research results indicate that the model-building procedure consists of several stages: (1) Determining input based on infusion drip rate readings. (2) Splitting the data into training and testing datasets. (3) Normalizing the data. (4) Building the forwardpropagation and backpropagation algorithm by determining the number of hidden layers, optimal input, and model weights. (5) Denormalizing the data. (6) Testing the model's accuracy. The ANN simulation revealed the best network structure using a 3-40-1 configuration (3 input variables, 40 hidden layers, and 1 output). The results achieved an average error prediction accuracy of 98.6%.
Meningkatkan Akurasi Deteksi Berita Palsu dengan Pendekatan Berbasis Lexicon dan LSTM melalui Text Preprocessing dan Model Training Prastyo, Edwin Hari Agus; Faisal, M
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7847

Abstract

Hoax news is an issue that is troubling the global community, including Indonesia. The spread of hoax news can cause various negative impacts, such as social division, public unrest, and can even endanger life safety. Hoaxes have become an epidemic in Indonesia, with 11,357 hoax issues identified by the Ministry of Communication and Information from August 2018 to March 2023. The combined approach of Lexicon-Based and LSTM results in improved accuracy in detecting hoax news. The combination of lexicon filters and pre-trained LSTM enables the model to identify hoax keywords and classify news with an accurate final score. Experimental results show that the use of Adam's optimizer produces high accuracy, achieving precision =1.0, recall=1.0, F1-score =1.0, and accuracy of 0.99. The model is able to perfectly distinguish between hoax and non-hoax news, demonstrating the effectiveness of using combined techniques and the right optimizer. However, there are some drawbacks that need to be considered, such as the reliance on a lexicon that may be incomplete and the potential for overfitting of the LSTM model. The results of this study provide insight into the importance of combined techniques in fake news detection, as well as the need for parameter adjustments and optimization strategies to minimize the drawbacks.

Page 105 of 119 | Total Record : 1182

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