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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Ilmu Komputer dan Informasi Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jurnal Matematika Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA JMM (Jurnal Masyarakat Mandiri) Visualita : Jurnal Online Desain Komunikasi Visual Charity : Jurnal Pengabdian Masyarakat I-Com : Indonesian Community Journal eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT)
Putu Harry Gunawan
Telkom University
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Public Health Social Sciences Other

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Journal : JURNAL MEDIA INFORMATIKA BUDIDARMA

 1 
Fisher Kolmogorov Equation Theory Simulation Using Deep Learning Conny Tria Shafira; Putu Harry Gunawan; Aditya Firman Ihsan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5432

Abstract

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.
Sentiment Analysis of the Jakarta - Bandung Fast Train Project Using the SVM Method Muhammad Daffa Dhiyaulhaq; Putu Harry Gunawan
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.v7i4.6855

Abstract

Web growth contributes greatly to user-generated content such as user feedback, opinions and reviews. The construction of the Jakarta-Bandung High Speed Train is both an icon and a momentum for Indonesia to modernize mass transportation in an era of continuous progress. Sentiment analysis is one of the text-based research field solutions suitable for addressing satisfaction issues based on user reviews. In this research, the system will be made with review sentences from users and produce output in the form of positive and negative classes. The method used by the author is classification using the Support Vector Machine (SVM) method and Word2Vec extraction features. In addition, a comparison of the accuracy value between the Support Vector Machine method, Naïve Bayes method and TF-IDF extraction features is carried out. The data studied came from several news websites containing user reviews of the Jakarta-Bandung High Speed Train. This method is used because it represents words in a vector, besides that the training process is faster when compared to other extraction features. This research resulted in the performance of accurasy, precision, recall, and f1-score, namely accurasy of 82.74%, precision of 75.68%, recall of 97.67%, and f1-score of 85.28%. These results were obtained using the best tuning hyperparameters, namely ('C': 10, 'gamma': 0.1, 'kernel': 'rbf'). Then in the second scenario a comparison is made with the Naïve Bayes method. It was found that the accuracy of the Support Vector Machine method using the TF-IDF extraction feature obtained better and stable performance results than the other three performance results, which amounted to 86.90%. So the author concludes that the Support Vector Machine method using the TF-IDF extraction feature is better when compared to the Naïve Bayes method and the Word2vec extraction feature.
Stress Detection Due to Lack of Rest Using Artificial Neural Network (ANN) Lukman Nurwahidin; Putu Harry Gunawan; Rifki Wijaya
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.v7i4.6642

Abstract

Currently, many people feel symptoms of stress due to lack of adequate rest. Which at this time the person will carry out activities that are very heavy both from tasks that are too heavy, work pressure that accumulates, and much more. People who experience stress symptoms sometimes don't know what causes stress. Through this research a learning machine will be made, using the Artificial Neural Network algorithm, will analyze heart rate data or BPM from 7 patient data per day, using a Fitbit smart watch will display several data such as falling asleep, waking up, REM (Rapid Eyes Movement) and, well, from the results of the data collected from the patients. Total data in this research are 36224. This research process will show the best accuracy results from several types of Artificial Neural Network algorithms. At the processing stage of the patient's heartbeat dataset, a comparison will be made between the types of Artificial Neural Network algorithms. The research will obtain the highest accuracy value of 81% from the results the Artificial Neural Network algorithm.
YouTube Viewership Increation Analysis and Prediction using Facebook Prophet Model Rezqie Hardi Pratama; Putu Harry Gunawan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7240

Abstract

YouTube, a widely accessed video-sharing platform available through both mobile applications and web interfaces, serves as a medium for content creators, commonly referred to as YouTubers, to engage with their audience. The success of a YouTuber is intricately tied to their audience engagement, encompassing metrics such as total views, comments, and likes garnered by their videos. This study involves the analysis of 7,600 English-language videos uploaded on YouTube between August and September 2020. To assess the predictive success value of a video, the study employs the Facebook Prophet method. Focusing on the upload time as a primary parameter, this method forecasts the growth in the number of YouTube viewers using datasets obtained from the YouTube API. Leveraging Time Series modeling, Facebook Prophet processes data by considering audience interactions throughout a video broadcast. The results derived from the Facebook Prophet model indicate a predictive trend of increasing viewership on YouTube in the coming months. The evaluation of model linearity, measured using the R² score to gauge data reliability, reveals a score of 0.39 or 39% which indicates a positive linearity score. And using Pearson correlation it gives 75 accuracy score. This signifies the model's capability to reasonably predict the growth in the number of viewers, contributing valuable insights into the dynamics of YouTube audience engagement over time.
Classification of Company Level Based on Student Competencies in Tracer Study 2022 using SVM and XGBoost Method Tyo Revandi; Putu Harry Gunawan
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7237

Abstract

Assessing the quality level of companies where graduates are employed is crucial for understanding the impact of academic programs on career placements. The use of methodologies that do not match the research objectives may lead to inaccurate or irrelevant analysis. When company classification methods are not aligned with the nature of the data collected in a tracking study, the risk of misinterpretation and the formulation of invalid generalizations becomes apparent. This study utilizes the 2022 Tracer Study Data from Telkom University, encompassing responses from 4306 graduates working across Local, National, and Multinational companies. The research employs support vector machine (SVM) and XGBoost algorithms to analyze and classify the company levels of the surveyed graduates. The primary objective is to enhance the accuracy of company level classification, thereby facilitating a more precise analysis of the Tracer Study dataset. The SVM and XGBoost algorithms are rigorously tested, and the results indicate an accuracy improvement with the XGBoost method, yielding a 2% increase over the SVM method. The evaluation is conducted with a data separation of 20% test data and 80% training data. This research not only contributes to the refinement of company level classification in the context of Tracer Studies but also underscores the potential of machine learning algorithms, specifically SVM and XGBoost, in providing valuable insights into graduates' professional trajectories. The findings of this study pave the way for more informed decision-making processes in academic and career development initiatives.
Co-Authors Abi Rafdhi Hernandy Ade Romadhony Ade Romadhony Aditya Firman Ihsan Adrin, Athaya Fatharani Agung Ferdiana Agung Toto Wibowo Agung Toto Wibowo Aniq Atiqi Rohmawati Anis Zainia Farabiba Annisa Aditsania Ardhito Utomo Ardhito Utomo Arnanti Primiana Yuniati Bagus Gigih Adisalam Bambang Ari Wahyudi Bambang Pudjoatmodjo Bambang Pudjotatmodjo Bedy Purnama Conny Tria Shafira Dede Tarwidi Deni Saepudin Devi Munandar Didit Adytia Dinda Fitri Irandi Djoko Murdowo Dodi Wisaksono Sudiharto Dodi Wisaksono Sudiharto Ema Rachmawati Ema Rachmawati Ema Rachmawati Fadhil Lobma Fat'hah Noor Prawira Fat’hah Noor Prawira Fat’hah Noor Prawira Fazmah Arif Yulianto Fazmah Arif Yulianto Fenty Alia Fityanul Akhyar Friska Fristella Friska Fristella Gregorius Vito Hasbi Rabbani I Gde Made Bagus Nurseta Wijaya Iryanto Iryanto Jondri _ Lazuardy Azhari Bacharuddin Noor Ledya Novamizanti Lukman Nurwahidin M. Sofyan Bahrum Juniardi Mahmud Imrona Muhammad Arzaki Muhammad Arzaki Muhammad Daffa Dhiyaulhaq Muhammad Hablul Barri Naila Al Mahmuda Nur Nining Aulia Nurul Ikhsan palupi, irma Prabasworo, Bhanu Prawita, Fat’hah Noor Rachmad Ryan Feryal Rajib Sainan Zulkifli Ratri Wulandari Rezqie Hardi Pratama Rifki Wijaya Rimba Whidiana Ciptasari Rimba Whidiana Ciptasari Rita Purnamasari Satria Mandala Selly Meliana Siti Fitria Yonalia Sri Soedewi Tb Dzulfiqar Alhafidh Tora Fahrudin Tyo Revandi Vina Putri Damartya Yoreza Mandala Putra