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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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Buzzer Detection on Indonesian Twitter using SVM and Account Property Feature Extension Yuliant sibaroni; Sri Suryani Prasetiyowati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 4 (2022): Agustus 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (460.97 KB) | DOI: 10.29207/resti.v6i4.4338

Abstract

The rapid use of Twitter social media in recent times has an impact on the faster dissemination of disinformation which is very dangerous to followers. Detection of disinformation is very important to do and can be done manually by conducting in-depth information analysis. But given the huge amount of information, this approach is less effective. Another, more effective approach is to use a machine learning-based approach. Several studies on hoax information detection based on machine learning have been carried out where some studies analyze the content of a tweet and some others analyze hashtags which are the context of a tweet. The feature usually used to analyze hashtag sentiment data is the property feature of the creator's account. The creator accounts of disinformation are called buzzer accounts. This research proposes account property feature expansion of buzzer accounts combined with the SVM classifier which in several previous similar studies has a very good performance to detect the buzzer hashtag. The experimental results show that expanding the proposed feature can increase SVM's performance in detecting hashtag buzzers by more than 24% compared to using the baseline feature, and the average F1 score obtained from the combination of methods is 84%.
Classification Classification of COVID-19 Monthly Cases Using Artificial Neural Network (ANN) Method Christina Natalia; Aniq A. Rohmawati; Sri Suryani Prasetyowati
Journal of Information System Research (JOSH) Vol 4 No 1 (2022): October 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (771.691 KB) | DOI: 10.47065/josh.v4i1.2236

Abstract

This study proposes the MLP type ANN method with the influence of Cross-Validation evaluation using three K-Fold tests, namely 3, 5, and 8. The data used are the data that relate to COVID-19 cases issued from the Bandung Public Health Office, climate report from Bandung Meteorological, Climatological, and Geophysical Agency (BMKG), population data from Bandung Population Office, citizen’s educational history from the Bandung Education Office and the West Java Open Data website. The data also was gathered from 151 sub-districts in Bandung City, with a total of 22 attributes collected from November 2020 to December 2021. The ANN method is included in the deep learning process. Therefore, the number of hidden layers utilized has a significant impact on the performance of the model being constructed. The implementation of Cross-Validation evaluation with K=8 results in an accuracy value of up to 98% and an error metric measurement of 0.3404 for MAE and 0.5994 for RMSE. This study's objective is to provide information on the optimal K-Fold Cross Validation parameters used in this ANN method to provide better performance during building a classification model for confirmed Covid-19 patients.
Performance Analysis of Air Pollution Classification Prediction Map with Decision Tree and ANN Rizky Fauzi Ramadhani; Sri Suryani Prasetiyowati; Yuliant Sibaroni
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2117

Abstract

Jakarta is a city in Indonesia that has a high population density that must pay attention to its health condition. Good air quality provides positive benefits to support public health so that they can be more productive at work and create fresh and healthy air. This study uses Machine Learning to classify air based on certain attributes. Then, the development of a prediction model based on time data is designed to produce a predictive map of air pollution in Jakarta area for the next 3 years. The methods applied are Decision Tree and Artificial Neural Networks. As a result, the Decision Tree and Artificial Neural Network models show very good accuracy for predictions from 2024 to 2026. The Decision Tree and Artificial Neural Network models get an accuracy of 98% and 94%. In 2025 the Decision Tree and Artificial Neural Network models get 99% and 93% accuracy. In 2026 the Decision Tree and Artificial Neural Network models get an accuracy of 94% and 93% which can be seen from the Decision Tree model which is superior to the Artificial Neural Network with a difference of 1 - 6%.
Identify User Behavior Based on The Type of Tweet on Twitter Platform Using Gaussian Mixture Model Clustering Ridha Novia; Sri Suryani Prasetyowati; Yuliant Sibaroni
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2208

Abstract

Social media has now become a place for social interaction to exchange information about business, politic, and many other. Twitter is one of the social media platforms that provides services for their users to share information and opinions on certain topics. The topic that will be discussed in this study is about politic by collecting tweet data about the student demonstration movement and SemuaBisaKena campaign. By using the word weighting method TF-IDF Vectorizer and Gaussian Mixture Model Clustering, it is possible to identify whether the user behavior is positive (support) or negative (blasphemy). To achieve the final result, there are several stages that must be passed. Such as data preprocessing, feature extraction using TF-IDF Vectorizer, Gaussian Mixture Model Clustering algorithm and data visualization. The results are there is 1 cluster identified as positive behavior and there are 2 clusters identified as negative behavior.
PREDICTION AND MAPPING RAINFALL CLASSIFICATION USING NAIVE BAYES AND SIMPLE KRIGING Indra Kusuma Yoga; 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.v7i4.3264

Abstract

This study discusses the development of a prediction model for the classification of rainfall based on time in Java. The method used in this research is naive Bayes and simple kriging. Naive Bayes is used for classification prediction, while simple kriging is an interpolation method used for mapping. There are two scenarios used, that is building a prediction model for daily and monthly rainfall classification, with data taken from 27 weather stations on the island of Java from 2010 to 2021. The results obtained in the classification process are an accuracy value of 67% for the daily model and 88% for the monthly model. The daily model data uses a spherical semivariogram with an average RMSE of 1,021. For the monthly model data using a Gaussian semivariogram with an average RMSE of 0,34. Then interpolation using simple kriging for mapping rainfall. The results of this study are predictions for the classification and mapping of daily rainfall models from April 1 to April 7 2022 and monthly models from April to September 2022. The contribution of this research is to provide predictive information and mapping of future rainfall so that public people can anticipate more.
Personality Classification Of Social Media Users Based On Type Of Work And Interest In Information Rizky Yudha Pratama; 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.v7i4.3196

Abstract

Social media is a platform that makes it easier for users to interact and get to know each other because in social media there are profiles, statuses, and user uploads. Therefore, many studies utilize social media because there is much information that can be explored on social media, one of which is research on the personality classification of social media users. However, many studies related to personality classification of social media users have failed due to too many model target classes, which result in low accuracy. In this research, the author uses the Myers-Briggs Type Indicator (MBTI) model, which is focused on only two personality classes, namely "Introvert/Extrovert" and "Sensor/Intuitive" with the features type of work and interest in information which are feature representations of the personality class used to reduce the target class. The best accuracy result is 95.87% after classifying using two personality classes.
Prediction of Rainfall Classification of Java Island with ANN-Feature Expansion and Ordinary Kriging Irfani Adri Maulana; Sri Suryani Prasetiyowati; Yuliant Sibaroni
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.v6i4.4556

Abstract

Precipitation is one of the most important climatic variables in many aspects of our daily lives. High rainfall intensity can cause floods, landslides, and other natural disasters. Therefore, rainfall prediction is important for predicting natural disasters, assisting farmers in production decisions, and crop harvesting. In this research, a system is built to create a rainfall prediction map using a machine learning approach and spatial interpolation algorithms in Java, Indonesia. In the field of weather prediction, the artificial neural network approach is a popular machine learning method. The artificial neural network (ANN) method is a method that has the advantage of studying connections in the previously unknown hidden layer between input data and output data through training procedures. By using the ANN method, historical weather and climate data can be applied to create a classification model and predict rainfall classes. The classification of data is determined based on the attributes of historical weather and climate data, namely temperature, humidity, air pressure, evaporation, sunlight, and the level of rainfall in the time range per day and month. From the results of the ANN modeling, it was found that the 5C month model with an accuracy value of 89% as the best monthly ANN model, and the 6C day model with an accuracy value of 81% as the best daily ANN model. After going through ANN modeling, there is a spatial interpolation algorithm that is often used to estimate rainfall, namely Ordinary Kriging. The Ordinary Kriging approach is used to reduce the estimated variance and estimate the rainfall value in the case study area. After going through Ordinary Kriging modeling, a rainfall prediction map for the next six months and seven days is made based on the coordinates as a result of the research. The results of this research are rainfall prediction maps for the next six months and the next seven days on Java Island.
Prediction of Bandung City Traffic Classification Using Machine Learning and Spatial Analysis Adhitya Aldira Hardy; Aniq Atiqi Rohmawati; Sri Suryani Prasetyowati
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.v6i4.4538

Abstract

This research proposes a visualization of Bandung City congestion map classification using machine learning and kriging interpolation methods. The machine learning methods used are Naive Bayes and Artificial Neural Network (ANN) for the congestion classification process. The kriging interpolation used is simple kriging to create a spatial location map visualization on the congestion classification prediction. They are based on the classification results of both methods. Naïve Bayes is ideal supervised learning for classification, while ANN is ideal unsupervised learning for prediction. The classification was performed on arterial and collector roads with 11 intersections that are congestion points. The data used is traffic counting data for Bandung City in April 2022. The congestion classification is divided into four categories based on the congestion level. This category division causes data imbalance, so the Random Oversampling technique is used to overcome data imbalance. The result is that the ANN method has better performance, with an accuracy rate of 93% and an RMSE value of 0.9746, while the Naïve Bayes method has an accuracy rate of 90% and an RMSE value of 0.9381. The resulting classification map shows that in April 2022, the southern area of Bandung City experienced the highest congestion compared to the northern, western and southern areas. This research provides the best algorithm between the two methods. It provides information on congestion in Bandung City by visualizing the congestion classification map to reduce traffic congestion in the city of Bandung.
Land Price Classification Map in Jakarta Using Random Forest and Ordinary Kriging Naufal Alvin Chandrasa; 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.v4i2.1896

Abstract

This research provides information about land prices in Jakarta by classifying using the Random Forest method. Where Random Forest is a data mining technique that is usually used to perform classification and regression. Random Forest is one of the best classification methods. It is found that classification accuracy will increase dramatically as a result of voting to select class types and ensemble tree growth. The method helps in providing information about the classification of land prices with the class of land prices per meter less than IDR 15 million, land prices per meter with a price range of IDR 15 to 25 million and land prices per meter more than IDR 25 million. With a fairly good accuracy of 82%, this method can classify where the permeter land price data that is tested will match the predicted classification accurately. Classification is performed on unbalanced data which is then oversampled using the ADASYN method. Assisted by doing spatial interpolation with the Ordinary Kriging method using Semivariogram, information about the classification of land prices can be seen on the distribution of the Jakarta area map. Ordinary Kriging can predict the estimated price per meter of land around the area of land that has a known price. The Root Mean Square Error (RMSE) results of the best Semivariogram model are obtained from the lowest RMSE value, namely the Spherical model with a value of 1.014896e7. The contribution of this research is to provide information about a reliable classification method, namely Random Forest and Ordinary Kriging performance as a spatial analysis method that can predict land prices per meter at unknown points so as to provide information about the distribution of land prices in Jakarta with each price class.
Performance Analysis of Bandung City Traffic Flow Classification with Machine Learning and Kriging Interpolation Nuraena Ramdani; 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.v4i2.1972

Abstract

This research focuses on making classification maps using the Classification And Regression Trees (CART), Random Forest and Ordinary Kriging methods. The dataset used is data from the Area Traffic Control System (ATCS) of the Bandung City Transportation Agency and the Google Maps application in April 2022. After the dataset is obtained, then the data pre-processing process will be carried out then the CART and Random Forest classification learning models will be made, after the CART and Random Forest classification learning is complete. From the CART and Random Forest classification models, traffic congestion classification map will then be made using the ArcMap application with the Ordinary Kriging interpolation method. The results of the comparison of classification maps made with Ordinary Kriging interpolation with the Gaussian Model semivariogram in both methods, namely CART and Random Forest. With the CART method has an accuracy of up to 88% while the classification map made with the Random Forest method has an accuracy of up to 90%. This proves that in this study the Random Forest method is far superior in building classification maps compared to the CART method
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