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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) IJoICT (International Journal on Information and Communication Technology) JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Bumigora Information Technology (BITe) Jurnal Teknik Informatika (JUTIF) Charity : Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Indonesia eProceedings of Engineering Jurnal INFOTEL
Sri Suryani Prasetiyowati
Universitas Telkom
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Public Health Social Sciences

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Prediction Map of Rainfall Classification Using Random Forest and Inverse Distance Weighted (IDW) Ibnu Muzakky M. Noor; Sri Suryani Prasetyowati; Yuliant Sibaroni
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.1978

Abstract

The amount of rainfall that occurs can affect natural disasters and even food production to economic activities. the factor of the area where the rain occurs is one of the main parameters for how the change occurs. So, it is necessary to have a rainfall prediction approach that aims to find out when and what type of rain will occur. Spatial classification and interpolation are two methods used to make predictions. Random Forest is a classification method that can be used to predict rainfall. and Inverse Distance Weighted is one of the stochastic interpolation techniques to calculate the estimated rainfall from the data points of rainfall that occur so that the distribution can be visualized. In the implementation of random forest, the model that is built on a daily basis gets the best level of accuracy in the 5D model sub model C with an accuracy of 0.8238 while the monthly model gets the best level of accuracy in the sub-model B 4M 0.9362. and the results of predictions and mapping using IDW show that daily predictions from June 1-4 2022 show that Most of Java Island will experience light rain, June 5-7 2022 most of Java Island will experience sunny cloudy days. And for monthly predictions, August and June 2022 show the distribution of monthly rainfall with predictions that most of Java is cloudy, while May, July, October, September have light rainfall in most of Java
Deteksi Berita Hoax Mengenai Vaksin Covid-19 dengan Menggunakan Levenshtein Distance Gilang Brilians Firmanesha; Sri Suryani Prasetyowati; Yuliant Sibaroni
Jurnal Bumigora Information Technology (BITe) Vol 4 No 2 (2022)
Publisher : Prodi Ilmu Komputer Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v4i2.2023

Abstract

The internet is a communication tool that we often use. The internet itself has brought many benefits. However, some people misuse it, for example, individuals or a group of people who spread hoaxes or fake news to incite and lead the publics’ opinions to their desired side. When COVID-19 spread in Indonesia and the government implemented mandatory vaccine obligations, the total of hoaxes on vaccination increased rapidly. Due to a large number of hoaxes on the Internet on COVID-19 vaccinations, As for several studies on the creation of a hoax detection system with various methods to try to overcome this problem, one of the studies with a system that detects hoax news and uses several methods, one of these methods is Levenshtein, getting a fairly low-performance result of 40% compared to other methods used. Therefore. Researchers are motivated to develop a hoax detection system with a similar method by adding Feature Extraction which aims to improve system performance from the previous research. In this study, 2 main experiments were conducted using Levenshtein distance as the main classification method, the results showed the best results in experiment-2 with an f1-score of 70.2% which was an increase compared to previous studies due to adding feature extraction using tf-idf.
SOCIAL MEDIA USER PERSONALITY CLASSIFICATION BASED ON HOW USER LIVE AND MAKE DECISION Chamadani Faisal Amri; Sri Suryani Prasetyowati; Yuliant Sibaroni
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 7, No 4 (2022)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v7i4.3204

Abstract

Personality classification is one of the ways in the field of Natural Language Processing (NLP) with a collection of data that can describe the user's personality through input sets of text documents such as status uploads. Social media is one way to interact online that can provide convenience for users, such as interacting, expressing themselves, and expanding friendships. Status posts on social media can be extracted into useful information in the personality classification process. This research performs classification based on how social media users live their lives and make decisions, which is a representation of the "Thinkers/Feelers" and "Judgers/Perceivers" class attributes of the Myers-Briggs Type Indicator (MBTI) model. Researchers are encouraged to develop a personality classification system with feature extraction that can improve system performance. In this research, there are three main experiments conducted, experiments using data with oversample techniques in the Thinker/Feelers (TF) and Judgers/Perceivers (JP) classes provide the best results compared to other experiments with f1-score and accuracy of 92% using the Random Forest classification method and Glove as the extraction feature.
BANDUNG CITY TRAFFIC CLASSIFICATION MAP WITH MACHINE LEARNING AND ORDINARY KRIGING Winico Fazry; Sri Suryani Prasetyowati; Yuliant Sibaroni
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 7, No 4 (2022)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v7i4.3219

Abstract

Congestion is a problem that occurs when the number of vehicles exceeds the capacity of the road and the vehicle speed slows down. This issue is one of the main issues in big cities, including Bandung. In this study, this study aims to reduce traffic congestion in the city of Bandung. The classification process in this study uses the Support Vector Machine (SVM), Naive Bayes, and Ordinary Kriging methods. The data used is traffic counting data from ATCS in Bandung and direct observation. The traffic count data obtained contains 3804 rows. Three experimental scenarios were carried out to validate the effectiveness of the model used, the performance of the first model without oversampling, the performance of the second model with oversampling, and the performance of the third model with hyperparameter adjustment. The experimental results show that the Support Vector Machine method has higher accuracy than the Naive Bayes method, which is 93%, while the Naive Bayes method has an accuracy of 90%. The application of hyperparameter tuning and over-sampling is proven to overcome the problem of data imbalance and get better classification results. In addition, the best classification results are used in making classification maps, namely the Support Vector Machine method, and assisted with ordinary kriging to predict the surrounding area. The results of the congestion classification map show that the southern area of the city of Bandung is more unstable than other areas of the city of Bandung.
Big Five Personality Detection Based on Social Media Using Pre-Trained IndoBERT Model and Gaussian Naive Bayes Ni Made Dwipadini Puspitarini; Yuliant Sibaroni; Sri Suryani Prasetiyowati
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.v7i1.5439

Abstract

A person's personality offers a thorough understanding of them and has a significant role in how well they perform at work in the future. No wonder it attracted the interest of the researcher to develop a personality detection system. Although much research about personality detection through social media was conducted, this task has been challenging to implement, especially using conventional machine learning. The issue is conventional machine learning still insufficient to make the personality detection system perform better. The purpose of this research is to detect Big Five personalities based on Indonesian tweets and increase its performance by combining machine learning with deep learning, which is Gaussian Naive Bayes and IndoBERT model. The proposed combined model in this research is summing the log probability vector on each model. Gathered 3.342 tweets from 111 Twitter accounts that were used as a dataset. This research also implemented min-max normalization to rescale the data. The result showed that for the entire dataset, the combined model has more accuracy score than Gaussian Naive Bayes by 5.42% and IndoBERT by almost 2%, which indicates the combined model is better than the Gaussian Naive Bayes and IndoBERT models.
Covid-19 Fake News Detection on Twitter Based on Author Credibility Using Information Gain and KNN Methods Nanda Ihwani Saputri; Yuliant Sibaroni; Sri Suryani Prasetiyowati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4871

Abstract

Twitter is one of the social media that is used as a tool to share various kinds of information about various kinds of things that are of concern to social media users. One of the information shared is information about COVID-19, which is known that the COVID-19 pandemic is currently spreading throughout the world at a very alarming rate. COVID-19 is an infectious disease caused by SARS-COV-2. The World Health Organization (WHO) claims that the spread of COVID-19 is supported by the spread of false/fake news. So to find out the truth of the news, a COVID-19 fake news detector is needed so that users don't fall for the hoaxes circulating. This study aims to classify COVID-19 news on Twitter based on author credibility. Credibility in question is a person's perception of the validity of information and is a multidimensional concept that is used as a means of receiving information to assess the source of communication. The method used in this research is Information Gain and KNN. KNN (K-Nearest Neighbor) is a supervised learning algorithm that works by classifying a set of data based on classified training data. Information Gain is used to ranking the most influential attributes, and KNN is used to classify data based on learning data taken from the nearest neighbors. The research consists of 6 main stages, namely data collection (crawling data), data preprocessing, feature extraction, feature selection, data split into training data and testing data, KNN stage, and data evaluation stage. The research carried out succeeded in obtaining an accuracy value of 91%, a correlation value between credibility and hoax of 0.115, and a p-value <0.005.
Comparative Analysis of Naive Bayes Model Performance in Hate Speech Detection in Media Social Twitter Muhammad Hadyan Baqi; Yuliant Sibaroni; Sri Suryani Prasetiyowati
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5493

Abstract

Twitter is a popular social media in Indonesia, and for some people, it is a place to find and disseminate information. Hate speech is aggressive behavior against individuals or groups such on race, gender, religion, nationality, ethnicity, sexual orientation, gender identity, or disability. In this study, hate speech is modeled using Naive Bayesian models, which consist of Multinomial, Bernoulli, and Gaussian Naïve Bayes Models. These methods were chosen because Naïve Bayes is a simple method but has good performance in the case of sentiment analysis. This research aims to get the method with the highest accuracy value in analyzing hate speech. Thus, the Naïve Bayes model can provide the best solution for hate speech problems. The process carried out in this study is to process all data which obtained from Twitter social media and then classify it using the Multinomial Naïve Bayes, Gaussian Naïve Bayes, and Bernoulli Naive Bayes models based on the classification of HS and non-HS sentiment categories.  In this study, to get the best accuracy, two different scenarios were used. The result of the analysis of the accuracy is 82.13% of the Multinomial Naïve Bayes model which is the best accuracy rate value compared with other models.
Comparison of Word2Vec with GloVe in Multi-Aspect Sentiment Analysis Classification of Nvidia RTX Products with Naïve Bayes Classifier Wira Abner Sigalingging; Sri Suryani Prasetyowati; Yuliant Sibaroni
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5528

Abstract

The increasing number of gamers has increased the demand for Graphics Processing Unit (GPU) products, one example of which is the Nvidia RTX product. Many users submit their reviews on social media Twitter in the form of tweets. These Tweets can be analyzed to determine the quality of a product. But most of the tweets talking about the product as a whole ignoring the category aspects of the product, making it difficult for both users and companies to pinpoint which aspects need attention. In this research, a multi-aspect based sentiment analysis will be carried out on tweets on Nvidia RTX products based on aspects of the product. The classification method used is Naive Bayes Classifier which will then compare feature extraction using Word2Vec and GloVe. Performance parameters are measured using a confusion matrix to produce values for accuracy, precision, recall, and f1-score. The highest accuracy results obtained were 60.71% on the price aspect, GloVe feature extraction, and classification with Gaussian Naive Bayes.Keywords: naive bayes classifier; Word2Vec; GloVe; confusion matrix; multi-aspect sentiment analysis
Performance of ANN and RNN in Predicting the Classification of Covid-19 Diseases based on Time Series Data Ridho Isral Essa; Sri Suryani Prasetyowati; Yuliant Sibaroni
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5557

Abstract

Indonesia is one of the countries with the highest confirmed cases of COVID-19. The city of Bandung is an area in Indonesia where the number of confirmed cases have continued to increase from 2021 to 2023. Currently there are around 103,574 cases with a total of deaths of around 1485 people. This is bad news for the city of Bandung because of the increasing number of confirmed cases. Various precautions against factors that might affect the rapid spread of COVID-19 in the city of Bandung have been carried out. But the confirmation cases still can't be stopped. Therefore, in this study we made a classification of the spread of COVID-19 in the city of Bandung with 25 features which will later be expanded using feature expansion techniques. This aims to analyze what factors have a major influence on the spread of COVID-19 in the city of Bandung. The method used are ANN and RNN methods. Where in this study the two methods were compared to determine which model had the best performance. Modeling is done by building models 2, 3, 4, and 5 months then the best model accuracy results from the ANN method are 79% and 81% for the RNN method. The author's contribution in this research is to build 2, 3, 4, and 5 month models, compare the performance results of ANN and RNN models, analyze the results of the confusion matrix, and make conclusions about what features are often used in each modeling.
PERFORMANCE ANALYSIS OF THE IMBALANCED DATA METHOD ON INCREASING THE CLASSIFICATION ACCURACY OF THE MACHINE LEARNING HYBRID METHOD Azmi Aulia Rahman; Sri Suryani Prasetiyowati; Yuliant Sibaroni
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 8, No 1 (2023)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v8i1.3286

Abstract

This study analyzes the performance of hybrid methods in improving accuracy on imbalanced data using Dengue Hemorrhagic Fever Case Data from 2017 to 2021 in Bandung City. The attributes used in this study consist of Total Population, Total Male, Elementary School Graduation, Junior High School Graduation, High School Graduation, College Graduation, Rainfall, Average Temperature, Humidity, Male Cases, Number of Cases, and Class. This research combines five Machine Learning methods, such as Decision Tree, Support Vector Machine, Artificial Neural Network, K-Nearest Neighbor, and Nave Bayes. Hybrid Methods used in this research are Voting and Stacking methods. The oversampling methods used to handle imbalanced data in this study are Random Oversampling and Adasyn. The results show that Voting and Stacking without Random Oversampling and Adasyn get the same accuracy of 88,88%. While using Random Oversampling, voting gets an accuracy of 95,37% and stacking gets an accuracy of 96,29%. While using Adasyn, voting gets an accuracy of 94,44% and stacking gets an accuracy of 97,22%. Based on the results obtained, it can be concluded that the Random Oversampling and Adasyn Method can improve the performance of the Machine Learning hybrid method on imbalanced data. The contribution of this research is to provide information on the study and analysis of the implementation of the Random Oversampling and Adasyn methods in improving the performance of the Voting and Stacking methods in hybrid classification.
Co-Authors Abduh Salam Adhe Akram Azhari Adhitya Aldira Hardy Aditya Andar Rahim Aditya Firman Ihsan Aditya Gumilar Aniq A. Rohmawati Aniq Atiqi Rohmawati Aqilla, Livia Naura arief rahman Arnasli Yahya Asramanggala, Muhammad Sulthon Aufa, Rizki Nabil Azmi Aulia Rahman Chamadani Faisal Amri Christina Natalia Claudia Mei Serin Sitio Damar, Muhammad Dede Tarwidi Derwin Prabangkara Ekaputra, Muhammad Novario Elqi Ashok Erna Sri Sugesti Fairuz, Mitha Putrianty Fatha, Rizkialdy Fathin, Muhammad Ammar Fatri Nurul Inayah Gede Astawa Pradika Gilang Brilians Firmanesha Gusti Aji, Raden Aria Gutama, Soni Andika Hawa, Iqlima Putri Haziq, Muhammad Raffif Hilda Fahlena I Putu Ananda Miarta Utama Ibnu Muzakky M. Noor Indra Kusuma Yoga Indri Octavellia Wulanissa Irfani Adri Maulana Jauzy, Muhammad Abdurrahman Al Juniardi Nur Fadila Lesmana, Aditya Mahadzir, Shuhaimi Maharani, Anak Agung Istri Arinta Mardha Al Nazhfi Ali Mitha Putrianty Fairuz Muh. Kiki Adi Panggayuh Muhammad Damar Muhammad Ghifari Adrian Muhammad Hadyan Baqi Muhammad Ikram Kaer Sinapoy Muhammad Novario Ekaputra Muldani, Muhamad Dika Nanda Ihwani Saputri Naufal Alvin Chandrasa Nenny Lisbeth Minarno Ni Made Dwipadini Puspitarini Nur Fadila, Juniardi Nuraena Ramdani Nurul Fajar Riani Pernanda Arya Bhagaskara S M Pilar Gautama, Hadid Purwanto, Brian Dimas Putra, Ihsanudin Pradana Putri, Pramaishella Ardiani Regita Rachmadania Irmanita Rafika Salis Rahmanda, Rayhan Fadhil Ridha Novia Ridho Isral Essa Rifaldy, Fadil Rizky Fauzi Ramadhani Rizky Yudha Pratama Rizky, Muhammad Zacky Faqia Salis, Rafika Salsabila, Syifa Sinaga, Astria M P Siti Uswah Hasanah Sri Harini Sri Harini Suhendar, Annisya Hayati Winico Fazry Wira Abner Sigalingging Yahya, Arnasli Yuliant Sibaroni Zaidan, Muhammad Naufal