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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Penelitian Pendidikan IPA (JPPIPA) Indonesian Journal of Artificial Intelligence and Data Mining Building of Informatics, Technology and Science Charity : Jurnal Pengabdian Masyarakat eProceedings of Engineering
Donni Richasdy
Telkom University
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Physics Social Sciences

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Path Smoothing With Support Vector Regression Donni Richasdy; Saiful Akbar
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 4, No 1 (2020): ---> EDISI JULI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (264.22 KB) | DOI: 10.31289/jite.v4i1.3856

Abstract

One of moving object problems is the incomplete data that acquired by Geo-tracking technology. This phenomenon can be found in aircraft ground-based tracking with data loss come near to 5 minutes. It needs path smoothing process to complete the data. One solution of path smoothing is using physics of motion, while this research performs path smoothing process using machine learning algorithm that is Support Vector Regression (SVR). This study will optimize the SVR configuration parameters such as kernel, common, gamma, epsilon and degree. Support Vector Regression will predict value of the data lost from aircraft tracking data. We use combination of mean absolute error (MAE) and mean absolute percentage error (MAPE) to get more accuracy. MAE will explain the average value of error that occurs, while MAPE will explain the error percentage to the data. In the experiment, the best error value MAE 0.52 and MAPE 2.07, which means error data ± 0.52, this is equal to 2.07% of the overall data value.Keywords: Moving Object, Path Smoothing, Support Vector Regression, MAE
Application of The Equivalent Partitioning Method in Testing for Automatic Test Case Generation on The Digi-OTA System Silfi Nur Amalia; Sri Widowati; Donni Richasdy
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 1 (2022): Januari 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i1.3326

Abstract

Software testing is one of the important phases in determining software quality. In the software development cycle, the testing phase takes more than 50% of the development time. The process of creating test cases in software testing is the most difficult process and determines the success of the testing phase. Test cases for software testing can be created based on the existing analytical modeling in the software specifications. This kind of testing technique is known as model-based testing, which is one of the black box testing approaches. In this study, the analytical model used is the UML Activity diagram. The reason for choosing UML Activity diagrams is because this diagram can model activities in software based on behaviors and conditions that are by the sequence. The output of this research is a prototype of a test case generator using an activity diagram. The analysis of the suitability of the test cases generated for the Digi-OTA application using the equivalence partitioning method is 100% when tested with valid data test specifications, while when tested with invalid test data specifications it produces 100% for approving actors, 95.98% for employee actors and 95.45% for detail officer actor.
Sentiment and Discussion Topic Analysis on Social Media Group using Support Vector Machine Salsabila Putri Adityani; Donni Richasdy; Widi Astuti
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 3 (2022): Juli 2022
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i3.4233

Abstract

The growth of social media in this modern era is increasingly rapid, where people are very active digitally interacting with each other. People who have a common interest or simply like to be in a community often gather in an online group, especially on Facebook. Alumni of Telkom University are no exception, who are also actively discussing and sharing information in Telkom University Alumni Forum Facebook group (FAST). By using their status from that group, sentiment and topic discussion analysis can be performed to determine whether the polarity is positive, neutral, or negative. In Addition, topic modeling extracts what topics are often discussed in the group. In this research, sentiment analysis was performed using the Support Vector Machine (SVM) method. Also, the classification process involved TF-IDF for word weighting and confusion matrix as performance measurement. Several testing scenarios were carried out to get the best accuracy value. Based on the tests performed on the preprocessing technique and feature extraction n-gram addition, the highest accuracy value obtained is 80.56%. The result indicates that the best performance is obtained by combining preprocessing techniques without the stopword removal process and feature extraction unigram. Moreover, the topics discussed based on topic modeling results were related to telecommunication and Telkom, Indonesia, alumni, and FAST.
Question Answering Chatbot using Ontology for History of the Sumedang Larang Kingdom using Cosine Similarity as Similarity Measure Rinaldi Jasmi; Z K A Baizal; Donni Richasdy
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4530

Abstract

Information can also be a means of learning for humans. Including information about history because history can be a means of learning for the younger generation to appreciate the nation's culture and build national identity. In the past, the Sumedang Larang kingdom was one of the many kingdoms in West Java, Indonesia, that could be used as much information as a lesson. Technological developments make more and more information available for study. We need the proper means to find the information we need. This study aims to build a Question Answering (QA) system to create a means for the younger generation to be more familiar with the history of the kingdom in the past. The QA system offers an information retrieval system that is easy to access and can immediately provide the answers we need. This QA system was built using ontology as a knowledge base and cosine similarity to determine the similarity between user questions and the dataset. The QA system that has been built is tested by providing a set of questions so that the system's performance can be measured, and the results of system testing get a precision value of 70% and a recall value of 90%.
Telkom University Opinion Topic Modeling on Twitter Using Latent Dirichlet Allocation During Covid-19 Pandemic Tandya Rizky Pratama; Donni Richasdy; Mahendra Dwifebri Purbolaksono
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4426

Abstract

In the current digital era, the development of information technology is growing rapidly. The development of information technology is followed by the development of social media, one of the social media that is on the rise is Twitter. Because there are many Twitter users around the world, Twitter stores a lot of data that can be used for something, one of which is to determine the category of public opinion about a company or university, in this study the focus is more on the category of public opinion about Telkom University. The public opinion can be grouped or categorized to make it easier to determine the topic being discussed. Determining opinions manually will take a long time due to the large number of tweets. Therefore, there must be another method to determine the categories of public opinion on Twitter. One of them is the Latent Dirichlet Allocation (LDA) method with a dataset of tweets of Indonesian-language Twitter users. With this method, grouping tweets on a large scale is more efficient. From the modeling made, the most optimum results obtained with a coherence score using the c_umass method of -15.33029 with a combination of 9 topics, 0.31 alpha value, and 0.01 beta value.
Partner Sentiment Analysis for Telkom University on Twitter Social Media Using Decision Tree (CART) Algorithm Sean Akbar Ryanto; Donni Richasdy; Widi Astuti
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4533

Abstract

Sentiment analysis is an analysis in terms of opinion and meaning in the form of writing. Sentiment analysis is very useful for expressing opinions from any individual or group to improve branding.  Branding is a process to promote and improve the name of a brand or brands to attract the attention of consumers to be interested in trying the services of a company that runs in academic terms such as Telkom University. However, this requires cooperation between other associations as partners so that the branding carried out can be effective. One form of cooperation is by providing opinions about Telkom University so that consumers are more familiar with Telkom University on Twitter social media which is the largest social media used by many people because it can provide any opinion freely. Therefore, this study aims to analyze the sentiment submitted by partners for Telkom University on Twitter which is the main factor for promoting themselves to consumers. The process carried out is to take all tweets about Telkom University submitted by partners and then carry out the TF-IDF weighting process and classified using the Decision Tree CART algorithm based on positive, negative, and neutral sentiment categories. The best results obtained by the Decision Tree model of the CART algorithm are the Accuracy value of 86.73%, Precision of 87.06%, Recall of 87.55%, and F1-Score of 86.52%.
BERT Implementation on News Sentiment Analysis and Analysis Benefits on Branding Muhammad Faris Abdussalam; Donni Richasdy; Moch Arif Bijaksana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4579

Abstract

The rapid development of information makes data processing easy and fast, especially in the business world, so many business brands have used the internet as a marketing medium for their operations. Now the business does not only depend on its operations; now, the opinion of the public media, especially on the news, has become an essential spotlight in today's business, especially against negative opinions that indirectly impact the image and product branding of the business, we need the proper means to help identifying and analyzing this kind of news. This study aims to identify and analyze sentiment with negative and positive indications on news titles from one of the sources of an Indonesian online news portal using the Bidirectional Representations from Transformers (BERT) sentiment analysis method, with the measurement of the confusion matrix metrics to measure and identify which headlines contains negative and positive indications. The sentiment analysis system offers identification and categorization with ease and immediately provide good results on identifying news. The results of this study, the sentiment model achieves an accuracy rate of 93% in identifying negative and positive news and F1-Score on negative identification rate of 92% and positive identification rate of 93%. The sentiment analysis system was built as effort to help analyzing against positive news indications or awful news as analysis benefits carried out to identifying alarming news indications towards branding.
Question Answering using Ontology for Sumedang Larang History with Support Vector Machine Based on Telegram Bot Erbina Selvia Br Perangin-Angin; Z. K. A Baizal; Donni Richasdy
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4574

Abstract

Technological developments affect many aspects, one of which is historical education. History lessons can shape students' personalities and encourage an interest in historical knowledge. There are many stories from Indonesian history, one of which is the Sumedang Larang Kingdom. The Sumedang Larang Kingdom is one of the Islamic kingdoms in Pasundan. However, not many people know about this kingdom. The millennial generation is technologically advanced, so they can take advantage of technological advances to quickly introduce the history of Sumedang Larang. One of them utilizes the telegram bot using the Application Programming Interface (API), which can connect the system to the telegram platform. In addition, this technology can be used as a history learning attraction using the question answering system (QA). Our research aims to build a QA system that can introduce the history of Sumedang Larang to the millennial generation. Because this system uses ontology knowledge with concepts related to the Sumedang Larang domain, it can focus on the history of Sumedang Larang. Applying the support vector machine (SVM) algorithm to process classification text can make it easier to search for text categories. The test results show the performance of the SVM method with a test size parameter of 0.5, such as 74% and 78%. The performance test results are accuracy scores in the subject category and object classification.
Analysis of Telkom University News Subjects on Popular Indonesian News Portals Using a Combination of Hidden Markov Model (HMM) and Rule Based Methods Rendhy Al-Farrel; Donni Richasdy; Mahendra Dwifebri Purbolaksono
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4566

Abstract

News media are often found in everyday life as a means of information for the public about something that is happening. In news articles, it is common to see several sentences that support the object to increase its popularity by being promoted by the subject. Part of Speech Tagging can determine the class of words in the sentence according to Tagsets provided by the corpus. That way, the search for the subject in the news article can be found from the word class obtained from a corpus. This research was focused on finding the subject "who" repeatedly spreading the news about Telkom University by using Part of Speech Tagging with the Hidden Markov Model and Rule Based on a news dataset from popular news portals about Telkom University. The process is taking all news about Telkom University on popular news portals and classifying it using the Hidden Markov Model and Rule-Based. We conducted to enhance the research results by changing the probability estimator on Hidden Markov Model. After running some scenarios, the best results obtained by the Hidden Markov Model and Rule-Based are the Accuracy of 94.96%, the Precision of 94.99%, the Recall of 94.96%, and the F1-Score of 94.95%.
Food and Beverage Recommendation in EatAja Application Using the Alternating Least Square Method Recommender System Elsa Rachel Dementieva; Z K A Baizal; Donni Richasdy
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4549

Abstract

EatAja is a startup in Indonesia that provides a mobile application-based food and beverage ordering solution for restaurants. The EatAja application uses transaction data to recommend food and beverage menus to customers. Previous studies have developed recommender systems using the Apriori and Collaborative Filtering methods. However, there are shortcomings in the recommendation system using both methods, i.e., the lack of personalization factors and low scalability. The learning method with matrix factorization can overcome the problem. In this study, we improve the food and beverage product recommender system in the EatAja application using the Alternating Least Square (ALS) matrix factorization method on Apache Spark. We will compare the results of the recommender system using the ALS method with the Collaborative Filtering method. The comparison uses the Mean Absolute Error (MAE) evaluation method. The results showed that the MAE value decreased by 0.07 with the ALS Matrix factorization method.
Co-Authors Ade Romadhony Aditya Firman Ihsan Amala, Ihsan Ahsanu Arfive Gandhi Arie Ardiyanti Suryani Aroman, Andika Djatnika Widia Nugraha Elsa Rachel Dementieva Erbina Selvia Br Perangin-Angin Firmansyah, Nanda H Hasmawati Hasmawati Hasmawati Hutari Gani, Prati Irfan Budi Prakoso Jofardho Adlinnas Kemas Muslim Lhaksmana Mahendra Dwifebri Purbolaksono Meillano, Zico Fachreza Moch Arif Bijaksana Muhammad Alwi Nugraha Muhammad Daffa Regenta Sutrisno Muhammad Faris Abdussalam Nazar Azmi Pratama, Reyhan Ramanti Dharayani Rendhy Al-Farrel Rendy Andrian Saputra Rheza Ramadhan Putra Ridho Akbar Rimbawan, Awan Rinaldi Jasmi Saiful Akbar Salsabila Putri Adityani Sa’adah, Siti Sean Akbar Ryanto Silfi Nur Amalia Silvia Atika Anggrayni Sri Widowati Tandya Rizky Pratama Widi Astuti Wijaya, Kurniadi Ahmad Winkie Setyono ZK Abdurahman Baizal