Article Per Year (5 Year)
p-Index From 2021 - 2026
10.947
P-Index
This Author published in this journals
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
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

Published : 101 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 101 Documents
Search

 7   8   9   10   11 
Detection of Fraudulent Financial Statement based on Ratio Analysis in Indonesia Banking using Support Vector Machine Sibaroni, Yuliant; Ekaputra, Muhammad Novario; Prasetiyowati, Sri Suryani
JOIN (Jurnal Online Informatika) Vol. 5 No 2 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i2.646

Abstract

This study proposes the use of ratio analysis-based features combined with the SVM classifier to identify fraudulent financial statements. The detection method used in this study applies a data mining classification approach. This method is expected to replace the expert in forensic accounting in identifying fraudulent financial statements that are usually done manually. The experimental results show that the proposed classifier model and ratio analysis-based features provide more than 90% accuracy results where the optimal number of features based on ratio analysis is 5 features, namely Capital Adequacy Ratio (CAR), (ANPB) to total earning assets and non-earning assets (ANP), Impairment provision on earning assets (CKPN) to earning assets, Return on Asset (ROA), and Return on Equity (ROE). The contribution of the study is to complement the research of fraudulent financial statements detection where the classifier method used here is different compare to other research. The selection of banking cases in Indonesia is also unique in this research which distinguishes it from other research because the financial reporting standards in each country can be different. 
Performance Analysis of ACO and FA Algorithms on Parameter Variation Scenarios in Determining Alternative Routes for Cars as a Solution to Traffic Jams Sibaroni, Yuliant; Prasetiyowati, Sri Suryani; Fairuz, Mitha Putrianty; Damar, Muhammad; Salis, Rafika
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

Abstract

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 and  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.
LEARNING RATE AND EPOCH OPTIMIZATION IN THE FINE-TUNING PROCESS FOR INDOBERT’S PERFORMANCE ON SENTIMENT ANALYSIS OF MYTELKOMSEL APP REVIEWS Zaidan, Muhammad Naufal; Sibaroni, Yuliant; Prasetyowati, Sri Suryani
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2396

Abstract

With the advancement of the digital era, the growth of mobile applications in Indonesia is rapidly increasing, particularly with the MyTelkomsel app, one of the leading applications with over 100 million downloads. Given the large number of downloads, user reviews become crucial for improving the quality of services and products. This study proposes a sentiment analysis approach utilizing the Indonesian language model, IndoBERT. The main focus is on optimizing the learning rate and epochs during the fine-tuning process to enhance the performance of sentiment analysis on MyTelkomsel app reviews. The IndoBERT model, trained with the Indo4B dataset, is the ideal choice due to its proven capabilities in Indonesian text classification tasks. The BERT architecture provides contextual and extensive word vector representations, opening opportunities for more accurate sentiment analysis. This study emphasizes the implementation of fine-tuning with the goal of improving the model's accuracy and efficiency. The test results show that the model achieves a high accuracy of 96% with hyperparameters of batch size 16, learning rate 1e-6, and 3 epochs. The optimization of the learning rate and epoch values is key to refining the model. These results provide in-depth insights into user sentiment towards the MyTelkomsel app and practical guidance on using the IndoBERT model for sentiment analysis on Indonesian language reviews.
Geo-Sentiment Analysis of Public Opinion of X Users towards the Documentary Film Dirty Vote using the Bidirectional Long Short-Term Memory Method Salsabila, Syifa; Sibaroni, Yuliant; Prasetiyowati, Sri Suryani
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4195

Abstract

Presidential elections held every five years, often generates significant public discourse. The 2024 presidential election saw the release of the documentary Dirty Vote, which raised allegations of electoral fraud and sparked polarized opinions on social media, especially on X. This study aims to analyze public sentiment toward Dirty Vote using geo-sentiment analysis and the Bidirectional Long Short-Term Memory (Bi-LSTM) model. Data were collected from geotagged tweets, with sentiment classified as positive, negative, or neutral. The research explored various data processing techniques, including TF-IDF for feature extraction, FastText for feature expansion, and balancing methods like SMOTE and class weighting to address data imbalance. Results showed that the baseline Bi-LSTM model achieved an accuracy of 71.57% and an F1-Score of 74.05%. When enhanced with TF-IDF and FastText, accuracy increased to 77.07%, though the F1-Score dropped slightly to 72.95%. Applying SMOTE resulted in a decrease in accuracy to 76.45%, but significantly improved the F1-Score to 74.93%. Exploratory data analysis revealed that negative sentiment was most concentrated in Java Island, particularly Jakarta, and peaked during February 2024, coinciding with the documentary's release and the election period. This study significantly contributes to understanding how geographic locations influence public opinion on sensitive political issues. A lack of understanding of geographically-based sentiment patterns can hinder identifying regional needs, leading to poorly targeted policies. By integrating data analysis methods with geographical approaches, this research provides deep insights for designing more effective, data-driven public intervention strategies and supports policymaking that is more responsive to the dynamics of public opinion.
Performance of Time-Based Feature Expansion in Developing ANN Classification Prediction Models on Time Series Data Sri Suryani Prasetiyowati; Yahya, Arnasli; Rohmawati, Aniq Atiqi
International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023
Publisher : School of Computing, Telkom University

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

Abstract

The prediction problem in most research is the main goal, to estimate future events related to the field under study. Research on classification that involves the prediction process in it, with spatial-time data and influenced by many features, such as the problem of disease spread, climate change, regional planning, environment, economic growth, requires methods that can predict while solving the problem of features and time. To obtain a time-based classification prediction model using many features, this research uses machine learning methods, one of which is Artificial Neural Network (ANN). The scenario carried out is to develop a t+r classification prediction model by expanding features based on the time t-r of the previous period. The performance of feature expansion in the development of ANN classification prediction models is determined based on the optimal accuracy value of the combination of t-r classification prediction models for the previous time period. By implementing the model on the data, it is found that the performance of time-based feature expansion in ANN classification ranges from 3.5% to 11%. While the optimal accuracy value is obtained from the feature expansion scenario of 3 to 5 time periods earlier.
Revealing the Impact of the Combination of Parameters on SVM Performance in COVID-19 Classification Prasetiyowati, Sri Suryani; Harini, Sri; Nur Fadila, Juniardi; Fahlena, Hilda
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 1 (2024): Vol. 10 No.1 June 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i1.965

Abstract

Non-linear SVM functions to modify the kernel in the SVM. Each kernel function in linear and non-linear SVMs has several parameters that are used in the classification process. SVM is a method that has advantages in classification, but there are still obstacles in selecting optimal parameters. This research investigates the effect of parameter variations on SVM classification performance on the COVID-19 dataset, using linear, RBF, Sigmoid and polynomial kernels. The analysis shows that the polynomial kernel is superior with the highest performance compared to other kernels. The highest accuracy of 77.57% was achieved with a combination of C values ??of 0.75 and Gamma of 0.75, and an F1-Score value of 76.67% indicating an optimal balance between precision and recall. The performance stability produced by the polynomial kernel provides advantages in classifying the COVID-19 dataset, with more controlled fluctuations compared to other kernels. The interaction between the C and Gamma parameters shows that a Gamma value of 0.75 consistently provides good results, while adjusting the C parameter shows more controlled performance variations. This confirms that appropriate Gamma parameter settings are key in improving the accuracy and consistency of SVM model predictions in this case.
Classification Prediction of Dengue Fever Spread Using Decision Tree with Time-Based Feature Expansion Hawa, Iqlima Putri; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.1026

Abstract

In Indonesia, dengue hemorrhagic fever (DHF) has become a serious community health concern due to fluctuating incidence rates influenced by several factors. It requires comprehensive control strategies to prevent the rise of the incidence. This study seeks to classify the future spread of DHF in Bandung City, accompanied by optimal factors that influence the increase in its spread. This study proposes using Decision Tree to predict a classification of dengue hemorrhagic fever (DHF) spread with implementation of spatial time-based feature expansion. The developed scenario is to build a target class classification prediction model based on the previous time period. From the developed scenario, the selected model has optimal performance to form a classification prediction model in the future. The results obtained show that the performance of Decision Tree using time-based feature expansion is more than 90%. The contribution of this study is to inform the public and health institution regarding DHF spread for the future and influential factor so that the government can provide policies as early as possible to prevent DHF spread.
Prediction of Classification of Air Quality Distribution in Java Island using ANN with Time-Based Feature Expansion and Spatial Analysis Gutama, Soni Andika; Prasetiyowati, Sri Suryani; Sibaroni, Yuliant
International Journal on Information and Communication Technology (IJoICT) Vol. 10 No. 2 (2024): Vol.10 No. 2 Dec 2024
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v10i2.1035

Abstract

Air pollution is a major concern that significantly impacts human health and the environment, especially in densely populated and economically active areas like Java, Indonesia. Air pollution is primarily caused by motor vehicles and industrial activities, leading to higher concentrations of harmful pollutants such as carbon monoxide (CO), nitrogen oxides (NOx), and particulate matter (PM10). In this study, an Artificial Neural Network (ANN) model is employed to forecast air quality classifications across Java Island, utilizing time-based features and spatial analysis. The model achieves an impressive accuracy and an F1-score of 92.19%, demonstrating its capability in capturing the intricate dynamics of air quality. These results highlight the potential of the ANN model in supporting effective policy-making, crisis management, and the development of environmentally sustainable infrastructure.
Analisis Setting Over Current Relay Satu Fasa Ke Tanah Sistem 20 Kv Pada Recloser B 21 Penyulang Salju Gardu Induk Teluk Betung Berbasis Etap Son, Jeckson; Yenni Afrida; Sri Suryani
Electrician : Jurnal Rekayasa dan Teknologi Elektro Vol. 18 No. 2 (2024)
Publisher : Department of Electrical Engineering, Faculty of Engineering, Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/elc.v18n2.2683

Abstract

Gangguan jaringan sistem 20 kV selalu berpotensi terjadi dan penyebab yang paling sering menyebabkan gangguan adalah gangguan hubung singkat 3 fasa, 2 fasa, 2 fasa ke tanah dan 1 fasa ketanah. Dalam mengamankan jaringan dari gangguan-gangguan tersebut maka dibutuhkan peralatan proteksi pada jaringan sistem 20 kV yang sensitif, selektif, cepat, andal dan sederhana. Relai yang paling sering bekerja pada recloser B 21 adalah Over Current Relay (OCR) satu fasa ke tanah. Perhitungan dan Analisa waktu kerja OCR satu fasa ke tanah pada recloser B 21 penyulang Salju dilakukan pada outgoing dan incoming serta waktu kerja recloser. Setelah data-data tersebut didapatkan, dilanjutkan dengan menganalisa koordinasi setting OCR satu fasa ketanah antara recloser B 21 dengan outgoing penyulang Salju mengguankan software Etap. Teknik pengambilan data dilakukan dengan metode studi pustaka, literatur, wawancara, dan pemodelan sistem. Hasil perhitungan arus gangguan hubung singkat satu fasa tanah pada 1% jaringan atau 1.88 km sebesar 288.396 A, sedangkan arus gangguan hubung singkat satu fasa ke tanah yang mempunyai nilai minimum terletak pada 100% jaringan atau 18.8 km sebesar 202.65 A. Hasil perhitungan pada OCR satu fasa ke tanah incoming tms = 0.26, t = 0.7 detik, outgoing tms = 0.1, t = 0.3 detik dan recloser tms = 0.062, t = 0.2 detik dan setting eksisting dari PLN adalah incoming tms = 0.37, t = 1 detik, outgoing tms = 0.047, t = 0.15 detik dan recloser tms = 0.03, t = 0.09 detik. Dari hasil pengamatan data yang diperoleh, direkomendasikan untuk mempertimbangkan resetting pada recloser B 21, outgoing penyulang Salju dan incoming agar menaikkan setting relai sehingga ketika merasakan arus pick up relai tidak langsung trip.
Sentiment Analysis of Wondr by BNI App Reviews on Play Store using the CNN-LSTM Method Putra, Ihsanudin Pradana; Sibaroni, Yuliant; Prasetiyowati, Sri Suryani
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7477

Abstract

As the use of digital applications in banking services increases, user opinions about these applications become an important source of data to study Wondr by BNI, which receives many user reviews, is one of the applications studied in this research. This research aims to build an accurate sentiment classification model and compare the effectiveness of two word representation methods, Word2Vec and FastText, to automatically classify sentiment into two classes, positive and negative, from unstructured review text using informal language. The data was processed through pre-processing, labeling, and processing stages using a hybrid CNN-LSTM model with 20,000 reviews available on the Google Play Store. The training process is carried out using 5-fold cross-validation and hyperparameter optimization using the random search method. The results show that the model with FastText has an accuracy of 86.38%, precision of 86.82%, recall of 86.46%, and F1-score of 86.46%. In contrast, the model with Word2Vec has an accuracy of 85.90%, precision of 86.38%, recall of 85.80%, and F1-score of 85.87%. These results show that FastText is better in accuracy and performance consistency compared to Word2Vec. This research provides a better understanding of how word representation methods affect sentiment analysis in app reviews and is expected to be a reference for future development of similar models.
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