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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 Jurnal Pengabdian Masyarakat Bhinneka eProceedings of Engineering Jurnal INFOTEL IJoICT (International Journal on Information and Communication Technology)
Sri Suryani Prasetiyowati
Universitas Telkom
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science 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.
Penerapan Data Sains untuk Analisis Preferensi Wisatawan dalam Pengembangan Paket Wisata Sibaroni, Yuliant; Prasetiyowati, Sri Suryani; Puspandari, Diyas
Jurnal Pengabdian Masyarakat Bhinneka Vol. 4 No. 3 (2026): Bulan Februari
Publisher : Bhinneka Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58266/jpmb.v4i3.1035

Abstract

Perkembangan teknologi informasi telah memberikan dampak signifikan pada industri pariwisata, terutama melalui media sosial yang memengaruhi preferensi wisatawan dalam memilih destinasi. Data digital dari aktivitas wisatawan di platform seperti Instagram, TikTok, dan YouTube dapat dimanfaatkan untuk memahami tren, minat, serta perilaku wisatawan. Kegiatan pengabdian masyarakat ini bertujuan untuk meningkatkan keterampilan siswa SMKN 3 Bandung dalam menganalisis data preferensi wisatawan menggunakan pendekatan data sains. Metode yang digunakan meliputi penentuan fitur preferensi, pemodelan berbasis konten, segmentasi wisatawan dengan K-Means, serta pembangunan paket wisata menggunakan pendekatan heuristik. Data yang dianalisis berasal dari konten media sosial wisata Bandung dan sekitarnya, kemudian dibersihkan dan dikategorikan berdasarkan jenis destinasi. Hasil pelatihan menunjukkan peningkatan pengetahuan peserta melalui pre-test dan post-test, serta keberhasilan dalam merancang paket wisata inovatif yang sesuai dengan minat wisatawan, seperti “Bandung Serenity Escape” dan “Garut Hidden Paradise.” Evaluasi kegiatan memperlihatkan lebih dari 90% peserta merasa pelatihan bermanfaat dan relevan dengan kebutuhan mereka. Dengan demikian, penerapan data sains terbukti efektif dalam mendukung pengembangan paket wisata berbasis preferensi wisatawan dan memberikan kontribusi praktis bagi industri pariwisata lokal.
A Comparative Study on Handling Imbalanced Data in Indonesian Hate Speech Detection Using FastText and BiLSTM Akmal Muhamad Faza; Yuliant Sibaroni; Sri Suryani Prasetiyowati
IJoICT (International Journal on Information and Communication Technology) Vol. 11 No. 2 (2025): Vol. 11 No. 2 Dec 2025
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v11i2.9513

Abstract

Online hate speech has become a serious threat to social harmony in Indonesia, with cases increasing significantly in recent years. This study develops and evaluates a system for detecting Indonesian hate speech using a Bidirectional Long Short-Term Memory (BiLSTM) deep learning model, complemented by FastText word embeddings. To address the common issue of data imbalance in hate speech datasets, this study implements and compares three oversampling techniques: Random Oversampler, Synthetic Minority Oversampling Technique (SMOTE), and Adaptive Synthetic Sampling (ADASYN). The research utilizes the Indonesian Hate Speech Superset, a dataset comprising 14,306 comments. The model's performance is evaluated using Stratified K-fold Cross-Validation, with metrics including Accuracy, Precision, Recall, and F1-score. Results, visualized using a Confusion Matrix to demonstrate that applying oversampling techniques enhances model performance, particularly by improving the Recall and F1-score metrics. These findings contribute to the development of hate speech classification systems that are fairer, more adaptive, and better suited to the unique characteristics of the Indonesian social media landscape.
Prediction and Classification of Vehicle Traffic Congestion in Bandung City Using the Random Forest and K-Nearest Neighbour Algorithm Muhammad Alauddin Angka Kurniawan; Sri Suryani Prasetiyowati; Yuliant Sibaroni
IJoICT (International Journal on Information and Communication Technology) Vol. 11 No. 2 (2025): Vol. 11 No. 2 Dec 2025
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v11i2.9602

Abstract

Traffic congestion remains one of the problems that continue to arise, especially in urban areas, oneof which is Bandung City, when the causes of the problem are not managed properly. Continuousmanagement of the causes of congestion problems will result in a controlled traffic system for theforeseeable future. This condition can be achieved if there is a congestion classification predictionsystem available. A reliable prediction and classification system can support the government informulating data-based traffic management strategies. The Random Forest and K-NearestNeighbour machine learning classification methods are strengthened with time-based featureexpansion to capture traffic behavior in various time frames, so that the objectives can be achieved.The dataset obtained from Area Traffic Control System Bandung includes traffic flow recorded at15-minute intervals at several intersections. Additional features such as red light duration, roadwidth, and spatial proximity to residential and commercial areas are included to improve modelperformance. The results show that the Random Forest classifier with time-based feature expansionoutperforms K-Nearest Neighbors, achieving the highest performance of 96%. These results showthe potential contribution in short-term traffic prediction and its effectiveness in supporting urbantraffic planning and congestion mitigation efforts in Bandung.
Co-Authors Abduh Salam Adhe Akram Azhari Adhitya Aldira Hardy Aditya Andar Rahim Aditya Firman Ihsan Aditya Gumilar Akmal Muhamad Faza 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 Diyas Puspandari 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 Alauddin Angka Kurniawan 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