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All Journal International Journal of Electrical and Computer Engineering Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jurnal Teknik ITS IPTEK The Journal for Technology and Science Proceedings of KNASTIK JUTI: Jurnal Ilmiah Teknologi Informasi JTSL (Jurnal Tanah dan Sumberdaya Lahan) PROCEEDING IC-ITECHS 2014 JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Conference SENATIK STT Adisutjipto Yogyakarta SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal ULTIMA Computing JURTEKSI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Sewagati NERO (Networking Engineering Research Operation)
Ahmad Saikhu
Departemen Teknik Informatika Institut Teknologi Sepuluh Nopember Surabaya
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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PEMODELAN DATA RADIOSONDE MENGGUNAKAN STACKING ENSEMBLE UNTUK KLASIFIKASI HUJAN Hermansyah, Muhammad; Saikhu, Ahmad; Amaliah, Bilqis
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 2 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i2.7806

Abstract

Perubahan iklim telah meningkatkan frekuensi dan intensitas kejadian cuaca ekstrem di wilayah tropis seperti Indonesia, sehingga men-imbulkan tantangan dalam pemanfaatan data observasi meteorologi untuk mitigasi bencana hidrometeorologis. Data observasi permukaan sering kali kurang mampu merepresentasikan dinamika vertikal at-mosfer dalam analisis kejadian cuaca ekstrem, seperti hujan sedang hingga lebat. Penelitian ini bertujuan mengembangkan model klasifikasi intensitas hujan berbasis data observasi udara atas dari radiosonde dengan pendekatan stacking ensemble, yang mengintegrasikan algorit-ma Random Forest, XGBoost, LightGBM, dan SVM, serta menggunakan HistGradientBoosting sebagai meta-learner. Untuk mengatasi ketidakseimbangan kelas antara kondisi berawan-hujan ringan dan hujan sedang-lebat, diterapkan teknik Synthetic Minority Over-sampling Technique (SMOTE). Evaluasi performa dilakukan menggunakan metrik precision, recall, F1-score, dan kurva precision-recall. Hasil menunjukkan bahwa model stacking ensemble memberikan performa terbaik dengan nilai precision sebesar 0,9084, F1-score 0,8718, dan average precision untuk kelas hujan sedang-lebat sebesar 0,949, melampaui seluruh model individual. Temuan ini menegaskan keunggulan integrasi data atmosfer vertikal dan pendekatan multi-algorithm machine learning dalam mendeteksi hujan intensitas sedang hingga lebat secara lebih akurat. Model ini memiliki potensi tinggi untuk diimplementasikan dalam sistem peringatan dini cuaca ekstrem, khususnya di wilayah tropis dengan keterbatasan data observasi permukaan.
Enhancing Electricity Consumption Prediction with Deep Learning through Advanced Data Splitting Techniques Pratiwi, Adinda Putri; Ginardi, Raden Venantius Hari; Saikhu, Ahmad
International Journal of Artificial Intelligence Research Vol 8, No 2 (2024): December 2024
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i2.1204

Abstract

Energy consumption is increasing due to population growth and industrial activity, making electricity essential in human life. With limited natural resources, effective management of electrical resources is crucial to reduce energy usage amidst rising demand. The current trends on using deep learning as prediction can enhance the performances. To have good performance it needs correct preprocessing data, so it will produce a model with less overfitting. This research proposes a model using time-series cross-validation as the splitting data and correlation to choose the best features set for the prediction of electricity consumption. Experiments will compare time-series cross-validation and holdout methods to see the performances of splitting data and enhancing the multi-horizon data.  The experiment used 8 sets of feature lists, which are paired in combination based on correlation to ensure the best features that are related. The result is splitting data using time-series cross-validation can maintain good perfomances on mode and holdout can maintain a good evaluation performance across the horizon. Feature sets that include temporal features have excellent results, especially when combined with features that have the strongest correlation relationship with electricity consumption, leading to an enhanced R2. Among all the models tested, CNN-GRU had the best model for multistep prediction across various every horizons and feature sets.
Hybrid Decomposition ICEEMDAN-EWT Deep Learning Framework for Wind Speed Forecasting Alif Hidayat, Dedi Arman; Aditya Pradana , Muhamad Hilmil Muchtar; Saikhu, Ahmad
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10241

Abstract

Accurate wind speed forecasting plays a crucial role in supporting early warning systems for extreme wind events. However, the inherent non-linearity and non-stationarity of wind speed data pose significant challenges. This study addresses these issues by evaluating the effectiveness of targeted Empirical Wavelet Transform (EWT) denoising applied to specific Intrinsic Mode Functions (IMFs) derived from Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN). Daily wind speed data from 2000 to 2023 were decomposed using ICEEMDAN, and denoising was selectively applied to IMF1, IMF2, and IMF3. Each IMF was then modeled using a Bidirectional Long Short-Term Memory (BiLSTM) network under a time-series cross-validation framework. Among all model configurations, the ICEEMDAN+EWT(IMF1 & IMF2)+BiLSTM model achieved the highest predictive accuracy, with an R² of 0.8885, RMSE of 0.501, and MAPE of 7.64%. This result outperformed both the baseline BiLSTM model (R² = 0.0501) and the ICEEMDAN+BiLSTM model without EWT denoising (R² = 0.6433). Moreover, denoising on IMF1 alone also yielded a strong performance (R² = 0.8879), emphasizing the importance of early component selection. Conversely, applying EWT to IMF2 or IMF3 individually resulted in lower R² values of 0.6639 and 0.6327, respectively, indicating limited individual contribution. These findings confirm that selective denoising, especially on the high-frequency IMFs, substantially enhances forecasting accuracy. The proposed approach holds significant potential to improve the timeliness and reliability of wind-related early warning systems, thus contributing to more effective disaster risk reduction strategies.
Co-Authors Adi Setyo Nugroho Aditya Pradana , Muhamad Hilmil Muchtar afrizal laksita akbar, afrizal laksita Alif Hidayat, Dedi Arman Ambarwati, Lina - Anak Agung Istri Sri Wiadnyani Anggraini, Ratih Nur Esti Ardiansyah, Ramadhani Muhammad Yusuf Arya Yudhi Wijaya Arya Yudi Wijaya Arya Yudi Wijaya Azizah, Salsabila Nur Baskoro, Fajar Bilqis Amaliah Bryan Alfadhori Budiawan, Irvan Buliali, Joko Lianto Chastine Fatichah D'layla, Adifa Widyadhani Chanda Daniel Oranova Siahaan Dedy D. Prastyo Deneng Eka Putra Deni Ekel Ramanda Sembiring Pelawi Djoko Kuswanto F.X. Arunanto Fajar Baskoro Fajar Baskoro Fauzan, Hermawan FX Arunanto Ginardi, R.V. Hari Gracius Cagar Gunawan Hari Ginardi Hidayat, Ahmad Nur Imam Hambali Ivanda Zevi Amalia Joko Lianto Joko Lianto Laili Rochmah Muhammad Hermansyah Muhammad Muharrom Al Haromainy Muhammaf Sjahid Akbar Nanik Suciati Neni Alya Firdausanti Noor Fitria Azzahra Noor Fitria Azzahra NUR LAILA Pradana, Hilmil Pradana, Muhamad Hilmil Muchtar Aditya Pratiwi, Adinda Putri Putra Wiratama, Rangga Kurnia Qolby, Lailly Syifa'ul Rakhmat Arianto Ramlah Riyanarto Sarno Rully Soelaiman Rully Soelaiman Santi Puteri Rahayu Sarwosri Sarwosri Septi Sri Rahmawati Setiawan Setiawan Setyowati, Ratih Suwadi Suwadi Victor Hariadi Wijaya, Arya Yudi Yudhi Purwananto Zakiya, Nurotuz Zulfa, Ahmad