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All Journal Information Technology and Telematics Dinamik Jupiter Publikasi Eksternal Jurnal Buana Informatika Pixel : Jurnal Ilmiah Komputer Grafis JUITA : Jurnal Informatika Proceeding SENDI_U Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL ILMIAH INFORMATIKA JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknik Informatika UNIKA Santo Thomas INTECOMS: Journal of Information Technology and Computer Science Building of Informatics, Technology and Science Jurnal Teknologi Informasi dan Terapan (J-TIT) Jurnal Manajemen Informatika dan Sistem Informasi Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Teknik Elektro dan Komputasi (ELKOM) JATI (Jurnal Mahasiswa Teknik Informatika) Aiti: Jurnal Teknologi Informasi Dinamika Informatika: Jurnal Ilmiah Teknologi Informasi Jurnal Teknik Informatika (JUTIF) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) International Journal of Social Learning (IJSL) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Informatika Teknologi dan Sains (Jinteks) Maritime Park: Journal Of Maritime Technology and Socienty Jurnal Pengabdian Masyarakat Intimas (Jurnal INTIMAS): Inovasi Teknologi Informasi Dan Komputer Untuk Masyarakat Eduvest - Journal of Universal Studies Seminar Nasional Teknologi dan Multidisiplin Ilmu Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) INOVTEK Polbeng - Seri Informatika
Eri Zuliarso
Universitas Stikubank
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Neuroscience Public Health Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
Rainfall Forecasting Using SSA-Based Hybrid Models with LSSVR and LSTM for Disaster Mitigation Ruslana, Zauyik Nana; Zuliarso, Eri
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.4963

Abstract

Accurate rainfall forecasting is crucial for addressing the increasing risk of hydrometeorological disasters, particularly in tropical regions such as Semarang City, Indonesia. However, conventional forecasting models often struggle with inaccurate data and observations. This study proposes a novel hybrid combination of SSA-NMF with LSSVR and LSTM, offering high-resolution rainfall forecasting over multiple monitoring stations, to predict daily rainfall. As a preprocessing step, 15 years of daily rainfall data from six observation stations were denoised and decomposed using Singular Spectrum Analysis (SSA) combined with Non-Negative Matrix Factorization (NMF). This approach effectively handled data with many zero values, identified seasonal patterns or high-rainfall locations, and extracted key patterns. The prediction models were trained and validated using parameters optimized through RandomizedSearchCV for LSSVR and Keras Tuner for LSTM. Model performance was evaluated using MSE, RMSE, MAE, and Nash-Sutcliffe Efficiency (NSE). The results showed that the SSA-LSTM model consistently outperformed SSA-LSSVR model, with the highest average NSE value being 0.9 across six monitoring locations in Semarang City. Furthermore, the predicted rainfall values were spatially visualized using Inverse Distance Weighting (IDW) interpolation within a Geographic Information System (GIS) environment, producing informative rainfall distribution maps that support early warning systems and disaster mitigation efforts. In conclusion, the hybrid approach combining SSA-NMF preprocessing with LSTM-based deep learning significantly improves the accuracy and reliability of daily rainfall forecasting. This novel SSA‑NMF + LSSVR/LSTM framework delivers high‑resolution, reliable rainfall forecasts that directly empower disaster risk reduction systems and readily transfer to similar climatic regions.
COMBINATION K-MEANS AND LSTM FOR SOCIAL MEDIA BLACK CAMPAIGN DETECTION OF INDONESIA PRESIDENTIAL CANDIDATES 2024 Priambodo, Wisnu; Zuliarso, Eri
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1635

Abstract

Social media has become the main platform for the public and political figures to voice opinions and run political campaigns. Despite its positive impact, social media also has negative impacts, particularly in the spread of Black Campaigns. This phenomenon has become critical, especially about the 2024 elections in Indonesia that target presidential candidates. Black campaigns can trigger conflict and damage the image of presidential candidates in the eyes of the public. Therefore, it is important to detect black campaigns against presidential candidates. This research develops a Black Campaign detection model using the K-means clustering algorithm and the Long Short-Term Memory (LSTM) approach. K-means is implemented to cluster text data on Twitter social media, while LSTM is used to learn word order patterns and detect text. The result is that K-means can effectively prepare the data, and classification using LSTM shows an accuracy of 90.28%. The comparison with Ensemble Learning classification model achieved an accuracy of 94.31%. Evaluation involved accuracy, precision, recall, and F1-score, with the result that Ensemble Learning was slightly superior in the evaluation matrix. However, compared to Ensemble Learning, LSTM has an advantage in understanding word order, which can be achieved by utilizing the advantages of Deep Learning Recurrent Neural Network architecture. Testing on sample data shows the similarity between LSTM and Ensemble Learning models in detecting Black Campaigns on Twitter social media post text data.
Co-Authors . Sulastri . Suwarsito Aditya Bobby Rizki Ahmad Fathoni Allaam, Ekananda Naufal Amalina, Hana Anefia Mutiara Atha ariadi, hastomo Arief Jananto Ariyani, Dewi Ayu Arya Sena Setyanegara Astuti, Vivi Rizki Indri Bambang Wiranto, Joko budi hartono Cahyono Purbomartono Dewi Handayani Untari Ningsih Dian Kristiawan Nugroho Dwi Agus Diartono Dwi Budi Santoso Edy Winarno Fathoni, Aliffatul Majid Februarianti, Herny Fidiniari, Fathia Gunadi, Ilham Hastomo Ariadi Hermawan, Taufan Herny Februariyanti Herny Februariyanti Hersatoto Listiyono Hindayati Mustafidah Indah Widhi Prastika Indriani, Cheryllista Isworo Nugroho Khabib Mustofa Kogoya, Erminus Kusuma Satria, Hafiyan Nafan Mardi Siswo Utomo Munna, Aliyatul Nur Rohim Nurmakhlufi, Alfin Hilmy Priambodo, Wisnu Putra Alva, Ilyasa Garuda Putri, Indah Lissiana R. Soelistijadi Radyanto, Mohammad Riza Rara Sri Artati Redjeki Ratmoko, Hari Rezal Arminto, Edo Rina Candra NS Rizky Abdul Malik Rosyida, Elviana Rudi setyo P Ruslana, Zauyik Nana S Sunardi Safra, Icha Adellia Safra, Kyla Kaneshia Sariyun Naja Anwar Sri Eniyati Sri Hartati Sudiantoro, Adhi Viky Sugiyamto Sugiyamto, Sugiyamto Sulastri Sulastri Sulastri Sulastri Sulatri sulis, Sulistiyowati Sunardi Sunardi Toman, Sarah Husain Velamentosa, Desvio Wahyu Prasetyo Wibowo, Sayogo Wismarini T.D. Wismarini, Th Dwiati Yassin Achmad Nur Aziz Yohanes Suhari Yunus Anis Yunus Anis, Yunus Yuwan, Ridho Pangestu Zulfa Febriana Dewi Mellinia