Article Per Year (5 Year)
p-Index From 2021 - 2026
1.829
P-Index
This Author published in this journals
All Journal Agromet Indonesian Journal of Geography Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Sumberdaya Lahan Journal of Mathematical and Fundamental Sciences Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Jurnal Penelitian Pendidikan IPA (JPPIPA) LIMNOTEK - Perairan Darat Tropis di Indonesia International Journal of Remote Sensing and Earth Sciences (IJReSES) JURNAL METEOROLOGI DAN GEOFISIKA Indonesian Journal of Electrical Engineering and Computer Science Jurnal Sumberdaya Lahan Jurnal Tanah dan Iklim LIMNOTEK Jurnal Meteorologi dan Geofisika The International Journal of Remote Sensing and Earth Sciences (IJReSES)
Akhmad Faqih
Agrometeorology Division, Department Of Geophysics And Meteorology, Faculty Of Mathematics And Natural Sciences, IPB University, Campus IPB Dramaga, Indonesia
Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Earth & Planetary Sciences Education Energy Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

Published : 34 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 34 Documents
Search

 1   2   3   4 
Designing A Rainfall Potential Prediction Model Based on Atmospheric Stability in Airport Areas Yus Prihatinina; Perdinan; Akhmad Faqih; Supari
Agromet Vol. 40 No. 1 (2026): JUNE 2026
Publisher : PERHIMPI (Indonesian Association of Agricultural Meteorology)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/j.agromet.40.1.12-25

Abstract

Rainfall forecasting in airport areas plays a crucial role in ensuring flight safety and operational reliability, yet local characteristics effect on rainfall intensity are overlooked. This study aims to develop a rainfall occurrence and intensity prediction model using Artificial Neural Network (ANN) based on atmospheric stability indices including Showalter Index (SI), Lifted Index (LI), K Index (KI), Total Totals (TT), Convective Available Potential Energy (CAPE), Convective Inhibition (CIN), and Precipitable Water (PW). Data were obtained from radiosonde observations, Automated Weather Observing System (AWOS), and Meteorological Aerodrome Report (METAR) in Soekarno-Hatta International Airport January 2020-December 2024 at 00 and 12 UTC. We used two stage analysis (1) binary rainfall occurrence prediction and (2) three-class rainfall intensity prediction based on selected feature. Our results reveal that ANN is capable to simulate rainfall occurrence with high accuracy (>0.78), outperforming the minimal-feature model (0.738) and all other configurations. Physically, the atmospheric indices can be grouped into three categories: stability-related indices (SI, LI, KI, TT), energy-related indices (CAPE, CIN), and moisture-related indices (PW), representing key factors influencing convective rainfall in tropical regions. However, the model’s applicability may be limited in study site due to local climatic characteristics and temporal constraints of the dataset. These findings highlight the importance of selecting physically relevant atmospheric parameters and implementing robust data preprocessing to enhance rainfall prediction accuracy in operational aviation contexts.
Enhancing wind energy prediction accuracy with a hybrid Weibull distribution and ANN model: a case study across ten locations in Java Island, Indonesia Silvy Rahmah Fithri; Nurry Widya Hesty; Rudi P. Wijayanto; Bono Pranoto; Prima Trie Wijaya; Akhmad Faqih; Wisnu Ananta Kusuma; Agus Nurrohim; Agus Sugiyono; Yudiartono Yudiartono
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp180-190

Abstract

Accurate wind speed forecasting is essential for optimizing renewable energy (RE) systems, especially in coastal and island regions with high variability. This study proposes a hybrid predictive model that combines Weibull distribution parameters with artificial neural networks (ANN) to enhance forecasting accuracy. Using ten years of hourly NASA POWER data from 10 locations across Java Island, 24 scenarios were tested with varying combinations of Weibull and meteorological variables. Results demonstrate that incorporating both Weibull shape (k) and scale (c) parameters significantly improves performance, with the best configuration (Scenario 1) achieving a MAPE of 0.44% in Garut. Excluding one or both parameters sharply reduced accuracy, with errors rising up to 35.12%. Beyond technical accuracy, the findings emphasize the practical relevance of Weibull-informed ANN models for energy planning. Reliable forecasts support better wind resource assessment, grid integration, and investment decisions, reducing uncertainties that often hinder wind power deployment. By providing accurate and stable predictions across diverse locations, this approach offers policymakers and planners a robust tool to accelerate RE development and meet national energy targets.
EVALUASI MODEL IKLIM REGIONAL RegCM3 UNTUK REKONSTRUKSI DATA IKLIM HISTORIS Unggul Handoko; Akhmad Faqih; Rizaldi Boer; Wahyoe Soepri
LIMNOTEK Perairan Darat Tropis di Indonesia Vol. 21 No. 2 (2014)
Publisher : BRIN Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/limnotek.2014.13285

Abstract

Penelitian mengenai rekonstruksi data iklim historis dengan menggunakan model iklim regional RegCM3 telah dilakukan untuk wilayah Sub DAS Seluna, Provinsi Jawa Tengah. Rekonstruksi dilakukan pada data curah hujan dan suhu masing-masing untuk periode 1998-2010 dan 1990-1997. Sebagai bagian dari proses rekonstruksi, dilakukan penentuan faktor koreksi untuk analisis koreksi bias data iklim luaran model. Data luaran yang telah dikoreksi kemudian dievaluasi dengan data observasi dengan menggunakan beberapa metode pengujian statistik, diantaranya MSE, R2 dan NSE. Hasil evaluasi keluaran model yang telah dikoreksi tersebut menunjukkan hasil perbandingan yang cukup baik dengan data observasi sehingga dapat digunakan untuk rekonstruksi data iklim historis. Data yang telah direkonstruksi dapat diaplikasikan untuk analisis hidrologi dan analisis lainnya yang memerlukan input parameter cuaca/iklim.
Perbandingan Metodologi Koreksi Bias Data Curah Hujan CHIRPS Misnawati Misnawati; Rizaldi Boer; Tania June; Akhmad Faqih
LIMNOTEK Perairan Darat Tropis di Indonesia Vol. 25 No. 1 (2018)
Publisher : BRIN Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/limnotek.2018.13346

Abstract

Penggunaan data global makin meningkat dalam mengatasi permasalahan ketersedian data curah hujan observasi. Salah satu data global yang sering digunakan yaitu data Climate Hazards Group InfraRed Precipitation with Station (CHIRPS). Namun demikian, data CHIRPS tidak bebas dari permasalahan bias, sehingga perlu divalidasi dan dikoreksi dengan menggunakan data observasi hasil pengamatan di lapangan. Penelitian ini bertujuan untuk mengidentifikasi metode koreksi bias yang memberikan performa paling baik dalam memperbaiki inkonsistensi data curah hujan CHIRPS terhadap curah hujan observasi. Metode-metode yang digunakan dalam penelitian ini adalah metode interpolasi error, metode Piani, metode Lenderink dan metode regresi power. Evaluasi performa masing-masing metode tersebut dilakukan berdasarkan nilai R2 dan MSE. Hasil penelitian menunjukkan bahwa metode koreksi bias intepolasi error memberikan hasil yang terbaik dengan nilai R2 dan MSE paling kecil. Pola curah hujan harian dan bulanan CHIRPS terkoreksi metode interpolasi error juga menunjukkan konsistensi yang paling baik terhadap curah hujan observasi
Co-Authors Adyaksa Budi Raharja Agita Vivi Wijayanti Agus Buono Agus Nurrohim Agus Sugiyono Alexander Knohl Alif Akbar Syafrianno Alif Kurniawan Alimatul Rahim Amsari Mudzakir Setiawan Amsari Mudzakir Setiawan Amsari Mudzakir Setiawan Amsari Mudzakir Setiawan Ardhasena Sopaheluwakan Ashehad Ashween Ali Bono Pranoto Bony Septian Pandjaitan Christian Stiegler Daniel Murdiyarso Dodo Gunawan Dodo Gunawan Eddy Hermawan Ela Hasri Windari Elza Surmaini Erna Nur Aini Fildza Novadiwanti Fithriya Yulisiasih Rohmawati Furqon Alfahmi Furqon Alfahmi Gita Adhani Jadmiko, Syamsu Dwi Kartika, Qurrata A'yun Laode Nurdiansyah M. Mukhlis Mamenun Mamenun Misnawati Misnawati Misnawati, Misnawati Mulyono R. Prabowo Mulyono R. Prabowo Naziah Madani Nova Susanti Nurry Widya Hesty P. Perdinan Prima Trie Wijaya Rahma Dani Rahmat Hidayat Rini Hidayati Rizaldi Boer Rudi P. Wijayanto Santikayasa, I Putu Sanusi Sanusi Silvy Rahmah Fithri Sitanggang, Imas S. Supari Supari Supari Suria Tarigan Syamsu Dwi Jadmiko Tania June Tugiyo Aminoto Ummu Ma'rufah Unggul Handoko Wahyoe Soepri Wisnu Ananta Kusuma Yonny Koesmaryono Yudiartono Yudiartono Yus Prihatinina