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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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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.
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.
Multi-Aspect Sentiment Analysis of Movie Reviews Using BiLSTM on Platform X Data Sinaga, Astria M P; Sibaroni, Yuliant; Prasetyowati, 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.v7i1.7509

Abstract

The film industry generates scores of movie reviews annually, reflecting viewer opinion towards various aspects of movies such as story, music, performances, and so on. They are a good source to publicly analyze opinion automatically. Aspect-based and sentiment analysis of movie reviews based on a multitask classification model rooted in the Bidirectional Long Short-Term Memory (BiLSTM) structure is the theme of this study. The objective of this research is to develop and evaluate a multitask BiLSTM-based model capable of simultaneously classifying sentiment polarity and movie review aspects to enhance fine-grained opinion mining. Data was collected from Platform X through web crawling and subjected to various text preprocessing steps before feeding them into the model. Unlike traditional approaches that treat sentiment and aspect classification as independent operations, the method proposed in this work is performing both simultaneously—sentiment prediction (positive, neutral, negative) and aspect categories (plot, music, actors, others). The model was compared between three different sizes of BiLSTM layers—32, 64, and 128 units—to investigate the influence of model capacity on performance. A 10-fold cross-validation scheme also implemented to confirm the reliability and robustness of results. Experiment findings reveal that the 128-unit BiLSTM model outperformed other models across the board, particularly at picking up subtle contextual relationships, to achieve the highest accuracy score in both tasks. Although this model significantly longer to train, its improved generalization—most notably for difficult sentiment- aspect pairs such as neutral or low-resource categories—validated the trade-off. The findings validate the effectiveness of BiLSTM-based multitask learning for comprehensive movie review analysis, demonstrating the importance of model capacity in tackling complex language patterns and fine-grained opinion identification.
Single Page Aplikasi Website Prediksi Kualitas Udara What The Air Jauzy, Muhammad Abdurrahman Al; Prasetyowati, Sri Suryani; Sibaroni, Yuliant
eProceedings of Engineering Vol. 10 No. 2 (2023): April 2023
Publisher : eProceedings of Engineering

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

Abstract

Abstrak-Polusi udara biasa diartikan sebagai pencemaran udara dimana jumlah bahan pencemar berada diluar batas. Kualitas udara saat ini adalah salah satu faktor penting dalam kehidupan sehari - hari. Terlalu banyak menghirup udara dengan kualitas yang rendah dapat berdampak buruk pada kesehatan. Dengan menggunakan alat pengukur kualitas udara kita bisa mengukur tingkat indeks kualitas udara, namun kenapa hanya berhenti disitu jika kita bisa menggunakan Machine Learning untuk melakukan Prediksi dalam beberapa tahun kedepan. Di studi ini digunakan metode Support Vector Machine yang akan melakukan klasifikasi terhadap data yang didapat dari sensor. SVM dipilih karena dinilai baik dalam mengklasifikasikan data yang berupa kelas - kelas. Data yang diolah adalah SO2, NO2, CO, PM10, PM25 dan O3. Kemudian data hasil klasifikasi akan diproses untuk prediksi dengan teknik perluasan model. Penelitian ini akan menghasilkan pemetaan prediksi polusi udara di provinsi jakarta untuk tahun 2022, diharapkan penelitian ini dapat membantu masyarakat untuk mengetahui tentang kondisi udara.Kata Kunci— kualitas udara, support vector machine, klasifikasi, machine learning
Combatting Misinformation: Leveraging Deep Learning for Hoax Detection in Indonesian Political Social Media Sibaroni, Yuliant; Mahadzir, Shuhaimi; Prasetiyowati, Sri Suryani; Ihsan, Aditya Firman
JURNAL INFOTEL Vol 16 No 2 (2024): May 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i2.1139

Abstract

The rampant spread of hoax news in social media, especially in the political domain, poses a significant challenge that requires immediate attention. To address this issue, automatic hoax news detection using machine learning-based artificial intelligence has emerged as a promising approach. With the approaching presidential election in Indonesia in 2024, the need for effective detection methods becomes even more pressing.This research focuses on proposing an efficient deep learning model for detecting political hoax news on Indonesian social media. Word2vec feature representation and three deep learning models – LSTM, CNN, and Hybrid CNN-LSTM – are evaluated to determine the most effective approach. Experimental results reveal that the CNN-LSTM hybrid model outperforms the others, achieving an accuracy of 96% in detecting hoax news on Indonesian social media in the political domain. By leveraging state-of-the-art deep learning techniques, particularly the CNN-LSTM hybrid model, this study contributes to the advancement of hoax news detection in Indonesia's political landscape. The findings underscore the importance of utilizing sophisticated machine learning methods to combat the spread of misinformation, particularly during crucial political events such as elections.
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