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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 40 Documents
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Search results for , issue "Vol 8, No 2 (2024): April 2024" : 40 Documents clear
Sentimen Analisis Masyarakat Terhadap Pembangunan IKN Menggunakan Algoritma Lexicon Based Approach dan Naïve Bayes Setiawan, Samuel Budi; Isnain, Auliya Rahman
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.v8i2.7506

Abstract

The relocation and construction of IKN (Capital City of the Archipelago) as a center for state administration activities has many benefits and shortcomings, starting from the selection of locations, the ratification of laws that are considered too hasty then raises pros and cons by the Indonesian people. President Joko Widodo decided to move the country's capital outside Java in a meeting on April 29, 2019. The location of the IKN development was determined in East Kalimantan. This research was conducted by retrieving data via Twitter with the keyword "IKN Development". The data that has been collected totals 3,680 tweets. Data analysis was carried out with two methods, namely Naïve Bayes Classifier and Lexicon Based, and the best accuracy value was found between the two methods in analyzing data on public responses to IKN Development. The initial step of the data analysis process is the preprocessing process which contains stages such as labelling, case folding, cleaning, tokenizing, stopword removal, stemming. It is known that the results obtained from the analysis of the Naïve Bayes Classifier method have an accuracy value of 79%, and Lexicon Based has an accuracy value of 76%. Sentiment analysis of the two methods has Positive, Negative, and Neutral sentiments. With the stages of the analysis process using the Naïve Bayes Classifier and lexicon based methods, it can be seen that the Naïve Bayes Classifier method shows a Positive sentiment of 47.18%, Negative of 6.33%, and Neutral of 46.49%, while for Lexicon Based, Positive sentiment reaches 54.15%, Negative 29.36%, and Neutral 16.49%. It should be noted that the highest positive polarity result is found in the Lexicon Based algorithm at 54.15%, while in the Naïve Bayes Classifier 47.18%. It can be concluded from the results of both methods that Naïve Bayes Classifier has a better analysis compared to Lexicon-Based analysis.
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 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.
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%.
Improving Infant Cry Recognition with CNNs and Imbalance Mitigation Indrawan, Michael; Luthfiarta, Ardytha; Fahreza, Muhammad Daffa Al; Rafid, Muhammad
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.v8i2.7370

Abstract

The classification of baby cries using machine learning is essential for developing automated systems that can assist caregivers in identifying and responding to the needs of infants promptly and accurately. This study aims to improve upon previous research relating to the Cry Baby Dataset, which has highly imbalanced data. We combine oversampling and undersampling techniques using SMOTE and ENN, along with data augmentation through pitch shifting and noise addition to address the data imbalance issue. The processed data was then modeled using Convolutional Neural Networks (CNN). The study yielded an overall accuracy of 88%, with balanced accuracy observed across all classes, effectively mitigating data imbalance. This represents a notable advancement compared to previous research, which often encountered challenges with unbalanced accuracies across classes. The classes identified include recordings of baby cries attributed to belly pain caused by colic, recordings related to burping, recordings associated with discomfort or other symptoms, recordings of hungry baby cries, and recordings indicating fatigue or the need for sleep. This shows a significant improvement from previous studies, which had very unbalanced accuracy for each class.
Klasterisasi Perguruan Tinggi LLDIKTI V Berdasarkan Indikator Kinerja Utama dan PDDIKTI Menggunakan K-Means Clustering Fatmawaty, Virdiana Sriviana; Riadi, Imam; Herman, Herman
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.v8i2.7497

Abstract

Pertumbuhan jumlah perguruan tinggi yang terus meningkat menjadi salah satu faktor krusial untuk memastikan mutu pendidikan tinggi agar berdaya saing. Komposisi perguruan tinggi di Provinsi Daerah Istimewa Yogyakarta terdiri atas 77% Perguruan Tinggi Swasta (PTS) dan sisanya adalah Perguruan Tinggi Negeri (PTN). Masing-masing perguruan tinggi memiliki Indikator Kinerja Utama (IKU) yang wajib dilaporkan dan dipenuhi, serta melakukan pendataan aktivitas pembelajarannya pada Pangkalan Data Pendidikan Tinggi (PDDIKTI). Data IKU dan data PDDIKTI ini menjadi  bahan evaluasi dan analisis untuk menentukan baseline dalam aktivitas pembinaan di LLDikti Wilayah V khususnya bagi PTS. Salah satu model analisis yang dapat dilakukan adalah dengan metode klasterisasi. Metode ini biasa digunakan pada data mining untuk mengelompokkan data berdasarkan kesamaan karakteristik data. Penelitian ini melakukan klasterisasi PTS di LLDIKTI Wilayah V menggunakan algoritma K-Means Custering. Hasil penelitian ini menunjukkan bahwa berdasarkan kesamaan karakteristik data IKU dan data PDDIKTI terbentuk empat klaster PTS, yaitu klaster 1 terdiri dari 4 PTS, klaster 2 terdiri dari 46 PTS, klaster 3 terdiri dari 21 PTS, dan klaster 4 terdiri dari 33 PTS.  Hasil analisis ini akan sangat bermanfaat bagi LLDIKTI Wilayah V dalam melakukan fungsi pembinaan kepada PTS.
Analisis Sentimen Opini Terhadap Tools Artificial Intelligence (AI) Berdasarkan Twitter Menggunakan Algoritma Naïve Bayes Oktavia, Ingrid; Isnain, Auliya Rahman
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.v8i2.7524

Abstract

This research aims to analyze public sentiment towards artificial intelligence (AI) tools via the Twitter platform using the Naive Bayes classifier algorithm. Twitter is a popular social media platform for sharing opinions and thoughts, making it suitable for sentiment analysis. Sentiment analysis is the process of analyzing and understanding opinions, attitudes, or feelings contained in text, such as tweets, product reviews, or other social media posts. The problems discussed in sentiment analysis can vary depending on the context. Tests carried out using the Naïve Bayes Classifier algorithm can conclude that the data collected was 2119. In this research, there are several steps that must be taken to analyzethe data, starting with crawling, labeling, preprocessing, splitting data, dividing test data, and training data, and finally applying the Naïve Bayes Classifier Algorithm. The results of the data analysis were divided into two categories: positive and negative, with 58.41% positive data and 12.43% negative data. In the analysis experiment, the Naïve Bayes accuracy value reached 79.41%, with a precision of 88% and a recall of 88%. The aim of the results of this research is to examine the public's response regarding artificial intelligence tools using the Naïve Bayes Classifier Algorithm to provide better sentiment results. So many see AI as a technology that carries great potential to improve human life. On the other hand, there are concerns about AI's negative impact on employment, privacy, and even its potential to take over human control. Ethical concerns also arise regarding the use of AI in decision-making that can affect human lives without adequate control. So artificial intelligence tools can be accepted by society because they have many benefits. Therefore, sentiment analysis and natural data processing use the Python programming language to categorize user comment data through a breakdown process.
Komparasi Metode LSTM dan GRU dalam Memprediksi Harga Saham Meri Aryati, Ni Wayan; Wiguna, I Komang Arya Ganda; Putri, Ni Wayan Suardiati; Widiartha, I Komang Kurniawan; Ginantra, Ni Luh Wiwik Sri Rahayu
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.v8i2.7342

Abstract

The rapid development of technology has an impact on the economy of society, one of which is investing in stocks. Stocks are evidence of ownership of an individual's assets in a company. However, stock prices have very high levels of fluctuation, requiring accurate methods to assist in predicting stock prices. LSTM and GRU were chosen for their intrinsic ability to handle long-term and short-term problems in time series data. LSTM has a complex memory structure that allows decision-making based on long and short-term information. Meanwhile, GRU has a simpler structure with a focus on gate mechanisms to control information flow, resulting in lighter and faster models. Therefore, this study will compare two RNN methods, Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU), in predicting stock prices using MAPE and RMSE evaluation metrics. The combination of parameters used to evaluate the MAPE and RMSE values in this study includes learning rate, timestamps, batch size, and epoch. The results of this study show that the GRU method is more accurate compared to the LSTM method. This is evidenced by the evaluation results of the LSTM method with the lowest MAPE value of 2.42% and the lowest RMSE value of 0.01807, while the evaluation results of the GRU method with the lowest MAPE value of 2.14% and the lowest RMSE value of 0.01775. The combination of parameters used in this study also has an influence on the final MAPE and RMSE results, especially in the use of learning rates of 0.001 and 0.0001. Therefore, it can be concluded in this study that the GRU method is more accurate and effective compared to the LSTM method in predicting stock prices.

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