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<![CDATA[On the Neural Network Solution of One-Dimensional Wave Problem ]]>
<![CDATA[Aditya Firman Ihsan]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v5i6.3565
<![CDATA[Artificial neural network has become an emerging popular method to handle various problems, especially in case where it has deep multiple neural layers. In this study, we use a deep artificial neural network model to solve one-dimensional wave equation, without any external datasets. Different type of boundary conditions, i.e., Dirichlet, Neumann, and Robin, are used. We analyze the model learning capabilities in a set of settings, such as data setup and the model width and depth. We also present some discussions of advantages and disadvantages of the model in comparison with other matured existing techniques to solve wave equation.]]>
<![CDATA[Hate Speech Detection on YouTube Using Long Short-Term Memory and Latent Dirichlet Allocation Method ]]>
<![CDATA[Andi Fadil Adiyaksa]]>;
<![CDATA[Donny Richasdy]]>;
<![CDATA[Aditya Firman Ihsan]]>
<![CDATA[ Journal of Information System Research (JOSH) Vol 3 No 4 (2022): Juli 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/josh.v3i4.1875
<![CDATA[YouTube social media is one of the popular media for all people to become a platform as a means of information and expressing opinions. Opinions can be categorized as hate if they attack something targeted. Hate speech is a behavior, word or action that is prohibited, because it causes violence to any individual and group. Expressing opinions in the form of hate speech is a problem that is still very difficult for the authorities to overcome because it is very common. Therefore, in this study a system was created to detect hate speech in the youtube comment column, using the Long Short-Term Memory and Latent Dirichlet Allocation. In this study, several methods were carried out that aimed to get the best accuracy value and carried out the topic modeling process using Latent Dirichlet Allocation to produce a total of three topics containing words that often appear in youtube comments. Based on the tests that have been obtained, the best accuracy is 0.657 or 66%.]]>
<![CDATA[The Organization Entity Extraction Telkom University Affiliated using Recurrent Neural Network (RNN) ]]>
<![CDATA[Muhammad Daffa Regenta Sutrisno]]>;
<![CDATA[Donni Richasdy]]>;
<![CDATA[Aditya Firman Ihsan]]>
<![CDATA[ Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi ]]>
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DOI: 10.47065/bits.v4i2.1956
<![CDATA[In the news portal text, there is a lot of important information such as the name of the person, the name of the organization, or the name of the place. To obtain information in text documents manually, humans must read the contents of the entire news text. To overcome this issue, information extraction, commonly called Named Entity Recognition (NER) was used. The extraction of information expressly for the NER field is used to make it easier to process word or sentence data. It helps search engines and also helps to classify places, times, and organizations. There is a limited number of NER in Indonesian texts using only the Recurrent Neural Network (RNN) method. Similar previous studies only employed other versions of RNN such as LSTM (Long Short Term Memory), BiLSTM (Bidirectional Long Short Term Memory), and CNN (Convolutional Neural Network). NER is one of the answers to the problems that exist in a large number of news portal texts to obtain information effectively and efficiently. The results of this study indicate that the NER system using the RNN method in Indonesian news texts has an F1 -Score of 80%]]>
<![CDATA[Part-of-Speech Tagging Implementation on Telkom University News using Bidirectional LSTM Method ]]>
<![CDATA[Rheza Ramadhan Putra]]>;
<![CDATA[Donni Richasdy]]>;
<![CDATA[Aditya Firman Ihsan]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023 ]]>
Publisher : <![CDATA[Universitas Budi Darma ]]>
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DOI: 10.30865/mib.v7i1.5506
<![CDATA[News is a tool used to disseminate information through various media, one of which is the internet. Various kinds of news articles have words that are not recognized in the dictionary such as slang words and have foreign words that do not exist in the corpus. How can a POS tagging model built on the corpus be able to handle word class labeling in Indonesian news. The research was conducted to check the results of POS tagging on a collection of news about Telkom University which was selected manually. By using the bidirectional LSTM model, three test scenarios were attempted to improve the performance of the built model, the test scenarios were applying the best padding for the corpus, comparing the performance results of the modified corpus model with the original corpus model, and determining the dimensions of the Word2vec vector. Then the selected model from each corpus is implemented on the news that has been labeled manually. One of the best scenario tests is obtained by modifying the corpus by removing double words in the word class "X" and changing some of the word classes "X" which are more likely to be foreign words so that they are changed to the word class "FW". The best performance results in the implementation of news about Telkom University using the bidirectional LSTM model which was built based on the modified corpus get accuracy values of 92.74%, precision of 92.85%, recall of 92.74%, and F1-score 92.48%.]]>
<![CDATA[Fisher Kolmogorov Equation Theory Simulation Using Deep Learning ]]>
<![CDATA[Conny Tria Shafira]]>;
<![CDATA[Putu Harry Gunawan]]>;
<![CDATA[Aditya Firman Ihsan]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023 ]]>
Publisher : <![CDATA[Universitas Budi Darma ]]>
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DOI: 10.30865/mib.v7i1.5432
<![CDATA[Neural Networks (NNs), a powerful tool for identifying non-linear systems, derive their computational power through a parallel distributed structure. The Physics-Informed Neural Network (PINN) technique can solve the Partial Differential Equation (PDP) in the Fisher Kolmogorov equation. By testing several hyperparameter changes, the formula is correct, and the visualization results can be consistent. Shows that an accurate value can be obtained from the results of the Mean Squared Error (MSE) on the formula loss value (loss f) and data loss (loss u). In experiment 1 the MSE obtained was 0.00001657 (Loss f) and 0.00000038 (loss u), as well as the MSE values obtained in experiment 4, is 0.00005865 (Loss f) and 0.00000216 (Loss u). It can be said to be accurate if the MSE value is close to 0. A formula is proven correct if it displays consistent data in random input data, but with the condition that it uses the same parameters. The author conducted research to simulate the Fisher-Kolmogorov equation with deep learning using the PINN technique. So the purpose of the research conducted was to simulate the Fisher-Kolmogorov equation with the deep learning method using the PINN technique. From the research, it can be concluded that Fisher-Kolmogorov's equation proves to be true if the simulation is carried out in deep learning and produces a visualization that is consistent with the functions used for visualization.]]>
<![CDATA[Tagging Efficiency Analysis of Part of Speech Taggers on Indonesian News ]]>
<![CDATA[Djatnika Widia Nugraha]]>;
<![CDATA[Donni Richasdy]]>;
<![CDATA[Aditya Firman Ihsan]]>
<![CDATA[ JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023 ]]>
Publisher : <![CDATA[Universitas Budi Darma ]]>
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DOI: 10.30865/mib.v7i1.5384
<![CDATA[Part of speech tagging (POS tagging) is a part of Natural Process Language (NLP). POS tagging is the process of automatic labeling of a word in a sentence according to the word class. There are various tagger methods in POS tagging, each tagger method has its own characteristics in its application. The research method used is Conditional Random Fields and Hidden Markov Model. The training of the two method models uses the Indonesian language corpus and Indonesian news texts as test data to determine which method is the most efficient based on the results of the accuracy and training time of each model. The method that has the best value is the CRF method with an accuracy value of 97.68 on the evaluation of the corpus test data and 90.02% for the sample Indonesian news dataset with a training time of 146.90 seconds, then there is the HMM method which has the highest accuracy value with a value of 94.25 % and shorter training time relatively shorter at 32.45 seconds and for the sample sentences containing 116 tokens, CRF method produces 90.05% accuracy which is higher than the HMM method which produces 79.31% accuracy.]]>
<![CDATA[Javanese Script Letter Detection Using Faster R-CNN ]]>
<![CDATA[Muhammad Helmy Faishal]]>;
<![CDATA[Mahmud Dwi Sulistiyo]]>;
<![CDATA[Aditya Firman Ihsan]]>
<![CDATA[ Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023 ]]>
Publisher : <![CDATA[Universitas Islam Negeri Sultan Syarif Kasim Riau ]]>
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DOI: 10.24014/ijaidm.v6i2.24641
<![CDATA[The Javanese script is now rarely used, and some people no longer recognize it. The construction of a Javanese script recognition system based on digital image processing is one of its preservation efforts. This study proposes a model capable of detecting and recognizing Javanese characters using Faster R-CNN to help people who are not familiar with the Javanese script. Faster R-CNN was chosen because it does not require additional processing compared to the previous method and Faster R-CNN has better accuracy and the ability to detect small objects. Faster R-CNN shows good results in text detection, but the use of Faster R-CNN in detecting Javanese script has not been found which makes its performance unknown, so this study will show how Faster R-CNN performs in detecting Javanese script. In this study, Faster R-CNN was able to show good performance by obtaining mean average precision (mAP) values up to 0.8381, accuracy up to 96.31%, precision up to 96.53%, recall up to 96.38 %, and F1-Score up to 96.41%. These results indicate that Faster R-CNN has better results than the previous method and can detect Javanese characters well.]]>
<![CDATA[Pengenalan Tulisan Tangan Aksara Bali Menggunakan Faster R-CNN ]]>
<![CDATA[Alif Adwitiya Pratama]]>;
<![CDATA[Mahmud Dwi Sulistiyo]]>;
<![CDATA[Aditya Firman Ihsan]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v7i6.5176
<![CDATA[In Balinese culture, the ability to read Balinese script is one of the challenges young generations face. Advances in machine learning have proposed handwriting detection systems using both traditional and deep learning models. However, the traditional approach is usually impractical and is prone to inaccurate identification results. Convolutional neural network (CNN)-based models integrate feature extraction and classification into an end-to-end pipeline to increase performance. This research proposes that recognizing characters through an object detection approach makes an end-to-end process of localizing and classifying several characters simultaneously using the Faster R-CNN. Four CNN models, including ResNet-50, ResNet-101, ResNet-152, and Inception ResNet V2, were tested to detect 28 Balinese characters in a single form that covers 18 consonants and 10 digits using Intersection over Union (IoU) thresholds: 0.5 and 0.75. ResNet-50 and Inception ResNet V2 achieve 0.991 mAP at IoU of 0.5, while Inception ResNet V2 excels at IoU of 0.75. Further analysis showed that the class ‘nol’ had the lowest Recall due to many undetected ground truths. Meanwhile, class ‘ba’ had the lowest Precision due to its similarity to classes “ga” and “nga”. This research contributes to the experiment with Faster R-CNN in detecting handwritten Balinese scripts.]]>
<![CDATA[Clustering Content Types and User Motivation Using DBSCAN on Twitter ]]>
<![CDATA[Made Mita Wikantari]]>;
<![CDATA[Yuliant Sibaroni]]>;
<![CDATA[Aditya Firman Ihsan]]>
<![CDATA[ Journal of Computer System and Informatics (JoSYC) Vol 4 No 4 (2023): August 2023 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/josyc.v4i4.3750
<![CDATA[We are currently in an era full of information and communication technology. One of the communication media used is Twitter. Twitter is a microblogging service that is used by its users to express their thoughts on a topic called a tweet. Tweets that are posted can be either positive tweets or negative tweets. One of the topics that is currently being discussed by Twitter users is Anies Baswedan as a 2024 Indonesian Presidential Candidate. Many people have tweeted this but it is not known how many users support or reject Anies Baswedan to run as a 2024 Indonesian presidential candidate. To assist the analysis, use the method clustering namely algorithm (Density-Based Spatial Clustering of Application with Noise). DBSCAN has the advantage of being able to detect data that is not included in a cluster and will be considered noise. This can improve the accuracy of the grouping because the data in the cluster will be cleaner. The TF-IDF Vectorizer is used to make it easier for programs to manage data because it can turn sentences into vectors that can be processed by the algorithm. To determine the evaluation of the program, the silhouette score method will be used. The results of calculating the silhouette score show a value of 0.29 with the formation of 3 clusters. Then an analysis is carried out based on the top words from each cluster and it can be identified that cluster 0 has a positive category supporting Anies Baswedan to run for the 2024 Presidential Candidate and cluster 1 has a negative category that does not support Anies Baswedan not advancing for the 2024 Presidential Candidate.]]>
<![CDATA[Clustering Content Types and User Roles Based on Tweet Text Using K-Medoids Partitioning Based ]]>
<![CDATA[Raisa Benaya]]>;
<![CDATA[Yuliant Sibaroni]]>;
<![CDATA[Aditya Firman Ihsan]]>
<![CDATA[ Journal of Computer System and Informatics (JoSYC) Vol 4 No 4 (2023): August 2023 ]]>
Publisher : <![CDATA[Forum Kerjasama Pendidikan Tinggi (FKPT) ]]>
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DOI: 10.47065/josyc.v4i4.3751
<![CDATA[In this modern era, the spread of information occurs rapidly through social media. One of the channels for disseminating information is through the Twitter platform. Many Twitter users respond to existing content with positive, negative and neutral responses. One of the hot content to respond to is political content. This content is currently being discussed considering the approaching election of the 2024 Presidential Candidate of the Republic of Indonesia. One of the candidate pairs discussed was Anies Baswedan. With so many responses from Twitter users, it will be difficult to track whether users support Anies Baswedan to run as a presidential candidate due to the large number of responses. This study aims to determine the response of twitter users to the advancement of Anies Baswedan as a presidential candidate. The method used in this study is the K-Medoids Partitioning-Based algorithm based on twitter user text. This algorithm was chosen because it is easy to implement considering the basis of K-Medoids development is the K-Means algorithm but the K-Medoids algorithm can overcome the shortcomings of the K-Means algorithm which is sensitive to outliners. The evaluation will be done using Silhouette Score which produces a value of 0.35 with the number of clusters is 2. Then an analysis of each cluster is carried out by looking at the words in the cluster. As a result, from the two clusters formed, both clusters contain positive content and show that Twitter users support Anies Baswedan to run as a 2024 presidential candidate.]]>
