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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik Indonesia Symposium on Computing 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 Jurnal Kebidanan JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science Jurnal Fisika dan Terapannya JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) International Journal of Clinical Inventions and Medical Sciences (IJCIMS) INFOKUM Jurnal Bumigora Information Technology (BITe) Jurnal Teknik Informatika (JUTIF) Charity : Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Indonesia Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Journal of Innovation Research and Knowledge Jurnal Pengabdian Masyarakat Bhinneka eProceedings of Engineering Electrician : Jurnal Rekayasa dan Teknologi Elektro Indonesian Counseling and Psychology Jurnal INFOTEL IJoICT (International Journal on Information and Communication Technology)

Sri Suryani Prasetyowati

Universitas Telkom

Author-ID : 560975

Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Physics Public Health Social Sciences

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Multi-Aspect Sentiment Analysis Hotel Review Using RF, SVM, and Naïve Bayes based Hybrid Classifier I Putu Ananda Miarta Utama; Sri Suryani Prasetyowati; Yuliant Sibaroni
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 2 (2021): April 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i2.2959

In the hotel tourism sector, of course, it cannot be separated from the role of social media because tourists tend to share experiences about services and products offered by a hotel, such as adding pictures, reviews, and ratings which will be helpful as references for other tourists, for example on the media online TripAdvisor. However, tourists' many experiences regarding a hotel make some people feel confused in determining the right hotel to visit. Therefore, in this study, an aspect-based analysis of reviews on hotels is carried out, which will make it easier for tourists to determine the right hotel based on the best category aspects. The dataset used is the TripAdvisor Hotel Reviews dataset which is already on the Kaggle website. And has five aspects, namely Room, Location, Cleanliness, Registration, and Service. A review analysis was carried out into positive and negative categories using the Random Forest, SVM, and Naive Bayes based Hybrid Classifier methods to solve this problem. In this study the Hybrid Classifier method gets better accuracy than the classification using one algorithm on multi-aspect data, namely the Hybrid Classifier got an average accuracy 84%, Naïve Bayes got an average accuracy 82.4%, Random Forest got an average accuracy 82.2%, and use SVM got an average accuracy 81%

PENGARUH KUALITAS PELAYANAN TERHADAP KEPUASAN KONSUMEN DIMASA PANDEMI COVID 19 PADA STEFANI CITY HOTEL PEKANBARU Sri Suryani; Lie Othman
Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik Vol. 9: Edisi I Januari - Juni 2022
Publisher : Fakultas Ilmu Sosial dan Ilmu Politik Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar

This research was conducted at Stefani City Hotel Pekanbaru located at Jalan Taskurun No. 100, Wonorejo, Kec. Marpoyan Damai, Pekanbaru City, Riau. The purpose of this study was to determine the effect of service quality on consumer satisfaction at Stefni City Hotel Pekanbaru. In this research, the method used is descriptive quantitative analysis with SPSS program. The population in this study were all visitors who were visiting at Stefani City Hotel Pekanbaru in 2021 as many as 7,542 people. Where the sample in this study were all visitors to Stefni City Hotel Pekanbaru as many as 99 respondents. Data collection techniques through questionnaires, types and sources of data using primary data and secondary data, measurement data collection techniques using a Likert scale.The problem in this study is the decline in visitors to Stefani City Hotel Pekanbaru in the last year. It can be seen from the visitor and sales data and the score table given by the consumers. From the results of the tests conducted, it shows that the effect of service quality has a positive and significant effect on customer satisfaction at Stefani City Hotel Pekanbaru.Keywords: Service Quality, Consumer Satisfaction

Performance Analysis of ACO and FA Algorithms on Parameter Variation Scenarios in Determining Alternative Routes for Cars as a Solution to Traffic Jams Yuliant Sibaroni; Sri Suryani Prasetiyowati; Mitha Putrianty Fairuz; Muhammad Damar; Rafika Salis
JOIN (Jurnal Online Informatika) Vol 7 No 1 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i1.797

This study proposes several alternative optimal routes on traffic-prone routes using Ant Colony Optimization (ACO) and Firefly Algorithm (FA). Two methods are classified as the metaheuristic method, which means that they can solve problems with complex optimization and will get the solution with the best results. Comparison of alternative routes generated by the two algorithms is measured based on several parameters, namely alpha and beta in determination of the best alternative route. The results obtained are that the alternative route produced by FA is superior to ACO, with an accuracy of 88%. This is also supported by the performance of the FA algorithm which is generally superior, where the resulting alternative route is shorter in distance, time, running time andÂ there is no influence on the alpha parameter value. But in each iteration, the number of alternative routes generated is less. The contribution of this research is to provide information about the best algorithm between ACO and FA in providing the most optimal alternative route based on the fastest travel time. The recommended alternative path is a path that is sufficient for cars to pass, because the selection takes into account the size of the road capacity.

Performance Analysis of Hybrid Machine Learning Methods on Imbalanced Data (Rainfall Classification) Aditya Gumilar; Sri Suryani Prasetiyowati; Yuliant Sibaroni
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 3 (2022): Juni 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

This study proposes several methods to analyze the performance of the hybrid machine learning method using Voting and Stacking on rainfall classification. The two hybrid methods will combine five classification methods, namely Logistic Regression, Support Vector Machine, Random Forest, Artificial Neural Network, and eXtreme Gradient Boosting. The data used is Bandung City rainfall data for the years 2005 until 2021. The hybrid method is classified as an ensemble, which means combining several individual classification models to improve the performance of the built model. Voting algorithm has weaknesses in imbalanced data, while stacking does not. The results show that by combining five machine learning methods on an imbalanced dataset, the Stacking algorithm obtains an accuracy value of 99.60%. Meanwhile, with the addition of the SMOTE technique, the accuracy increases to 99.71%. This is supported by the performance of the Stacking method which is superior because it takes the best classification value for each individual model and can overcome the imbalance. Model evaluation does not only focus on accuracy, but also precision, recall, and f1-score. The contribution of this research is to provide information about the best Hybrid method between Voting and Stacking in obtaining model performance results on rainfall classification.

Performance Analysis of the Hybrid Voting Method on the Classification of the Number of Cases of Dengue Fever arief rahman; Sri Suryani Prasetiyowati
International Journal on Information and Communication Technology (IJoICT) Vol. 8 No. 1 (2022): June 2022
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v8i1.614

Dengue hemorrhagic fever (DHF) is a health problem in Indonesia. The region in Indonesia that has the highest number of cases in West Java with the highest ranking with 10,772 cases. The city of Bandung is recorded to have the highest number of cases at this time, namely 4,424 cases. Dengue fever can be caused by high rainfall. Judging from the high number of cases and fluctuations that occur, it is necessary to predict the spread of the disease so that in the future it can be anticipated by the government. Prediction of the spread of dengue fever in the city of Bandung using various classification algorithms has been done. Therefore, the author wants to make a new breakthrough by using hybrid ensemble learning using a hard voting method from three classification methods, namely Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree (DT). Using the Bandung City DHF disease dataset from 2012 to 2018. The results obtained using the Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Decision Tree (DT) were 84%, 87%, 79%. to improve the classification accuracy of the three methods using a hybrid classification with the hard voting method to get 91% results.

DHF Incidence Rate Prediction Based on Spatial-Time with Random Forest Extended Features Elqi Ashok; Sri Suryani Prasetiyowati; Yuliant Sibaroni
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 4 (2022): Agustus 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

This study proposes a prediction of the classification of the spread of dengue hemorrhagic fever (DHF) with the expansion of the Random Forest (RF) feature based on spatial time. The RF classification model was developed by extending the features based on the previous 2 to 4 years. The three best RF models were obtained with an accuracy of 97%, 93%, and 93%, respectively. Meanwhile, the best kriging model was obtained with an RMSE value of 0.762 for 2022, 0.996 for 2023, and 0.953 for 2024. This model produced a prediction of the classification of dengue incidence rates (IR) with a distribution of 33% medium class and 67% high class for 2022. 2023, the medium class is predicted to decrease by 6% and cause an increase in the high class to 73%. Meanwhile, in 2024, it is predicted that there will be an increase of 10% for the medium class from 27% to 37% and the distribution of the high class is predicted to be around 63%. The contribution of this research is to provide predictive information on the classification of the spread of DHF in the Bandung area for three years with the expansion of features based on time.

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)

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)

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

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

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.

Co-Authors Abduh Salam Adhe Akram Azhari Adhitya Aldira Hardy Aditya Andar Rahim Aditya Faudzan Aditya Firman Ihsan Aditya Gumilar Afrida, Yenni Akmal Muhamad Faza Aniq A. Rohmawati Aniq Atiqi Rohmawati Aqilla, Livia Naura arief rahman Arnasli Yahya Asdar, Meisya Putri Indrayani Asramanggala, Muhammad Sulthon Aufa, Rizki Nabil Azmi Aulia Rahman Bakara, Stella Maris Chamadani Faisal Amri Christina Natalia Claudia Mei Serin Sitio Damar, Muhammad Dede Tarwidi Derwin Prabangkara Diyas Puspandari Ekaputra, Muhammad Novario Ellisa Ratna Dewi Ellisa Ratna Dewi Elqi Ashok Erna Sri Sugesti Erwin Budi Setiawan Fairuz, Mitha Putrianty Fatha, Rizkialdy Fathin, Muhammad Ammar Fatri Nurul Inayah Gede Astawa Pradika Gilang Brilians Firmanesha Gusti Aji, Raden Aria Gutama, Soni Andika Hari Yudha Fanani Hawa, Iqlima Putri Haziq, Muhammad Raffif Hilda Fahlena Hilman Rama Pratama I Putu Ananda Miarta Utama Ibnu Muzakky M. Noor Ichwanul Muslim Karo Karo Indra Kusuma Yoga Indri Octavellia Wulanissa Irfani Adri Maulana Ita Susanti Jauzy, Muhammad Abdurrahman Al Jeckson, Jeckson Juniardi Nur Fadila Karno Karno Kartika Putri Septiyani Kasim, Jamila Lesmana, Aditya Lie Othman Linda Salma Mahadzir, Shuhaimi Maharani, Anak Agung Istri Arinta Mardha Al Nazhfi Ali Marissa Aflah Syahran Marissa Aflah Syahran Mitha Putrianty Fairuz Muh. Ilham Muh. Kiki Adi Panggayuh Muhammad Alauddin Angka Kurniawan Muhammad Damar Muhammad Gentur WitjaksonoKurniawan Muhammad Gentur WitjaksonoKurniawan Muhammad Ghifari Adrian Muhammad Hadyan Baqi Muhammad Ikram Kaer Sinapoy Muhammad Novario Ekaputra Muldani, Muhamad Dika Nanda Ihwani Saputri Naufal Alvin Chandrasa Nazhrin Nazarudin Achmad Nenny Lisbeth Minarno Ni Made Dwipadini Puspitarini Nur Fadila, Juniardi Nuraena Ramdani Nurul Aliyah Nurul Fajar Riani Oktovina Suka Pernanda Arya Bhagaskara S M Pilar Gautama, Hadid Purwanto, Brian Dimas Putra, Ihsanudin Pradana Putri, Pramaishella Ardiani Regita Rachmadania Irmanita Rafika Salis Rahim, Husnul Fahma Rahmanda, Rayhan Fadhil Ratna Rian Febrian Umbara Ridha Novia Ridho Isral Essa Rifaldy, Fadil Rizky Fauzi Ramadhani Rizky Yudha Pratama Rizky, Muhammad Zacky Faqia Salis, Rafika Salsabila, Syifa Shofura Shofura Sinaga, Astria M P Siti Rahmah Madusari Siti Uswah Hasanah Sri Harini Sri Harini Sri Harini Suhendar, Annisya Hayati Tuti Budiawati Willie Japaries Winico Fazry Wira Abner Sigalingging Yulian Sibaroni Yuliant Sibaroni Zaidan, Muhammad Naufal

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