Article Per Year (5 Year)
p-Index From 2020 - 2025
3.507
P-Index
This Author published in this journals
All Journal International Journal of Advances in Applied Sciences Jurnal Sains dan Teknologi Nuklir Indonesia (Indonesian Journal of Nuclear Science and Technology) Jurnal Fisika Unand Jurnal Sains & Teknologi Lingkungan JC-T (Journal Cis-Trans): Jurnal Kimia dan Terapannya Warta Ardhia : Jurnal Perhubungan Udara Jurnal Environmental Science JOURNAL OF CIVIL ENGINEERING, BUILDING AND TRANSPORTATION Jurnal Meteorologi Klimatologi dan Geofisika Jurnal Rekayasa Hijau To Maega: Jurnal Pengabdian Masyarakat Variance : Journal of Statistics and Its Applications Jurnal EnviScience (Environment Science) Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Jurnal Kolaboratif Sains DiJITAC : Digital Journal of Information Technology and Communication Jurnal Kesehatan dan Pengelolaan Lingkungan Geosfera: Jurnal Penelitian Geografi Jurnal Widya Climago Jurnal Laut Pulau
Agung Hari Saputra
Sekolah Tinggi Meteorologi Klimatologi Dan Geofisika
Religion Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

Published : 23 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 23 Documents
Search

 1   2   3 
PEMANFAATAN MODEL WRF-CHEM DALAM ANALISIS SEBARAN ABU VULKANIK GUNUNG MERAPI (ERUPSI TANGGAL 23 MARET 2020) Yudistira, Ricko; Indah Sary; Agung Hari Saputra
Jurnal Meteorologi Klimatologi dan Geofisika Vol 6 No 3 (2019): Jurnal Meteorologi Klimatologi dan Geofisika
Publisher : Unit Penelitian dan Pengabdian Masyarakat Sekolah Tinggi Meteorologi Klimatologi dan Geofisika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36754/jmkg.v6i3.137

Abstract

Volcanic ash is the result of fusion on mountain released into the atmosphere. Volcanic ash has dangerous impact for several sectors as health, agriculture, until flight safety. Therefore, it is important to forecast the direction of volcanic ash distribution to reduce disadvantage caused by volcanic ash. One of the way to forecast volcanic ash is to use the WRF-Chem model. The WRF-Chem model is a numerical weather forecast model with chemistry parameter elements so that it can predict the direction of volcanic ash distribution. On March 27, 2020, Mount Merapi had an eruption which caused volcanic ash to reach a height of about 5000 meters from the crater of the mountain. The results of the WRF-Chem model show the distribution direction of each size and concentration of volcanic ash from the 700mb to 300mb levels. The model results show that the volcanic ash distribution of each levels moves to the Southwest in accordance with the advisory data released by VAAC Darwin. However, the results of the model have a time delay in the distribution of volcanic ash
EVALUATING METEOROLOGICAL DATA FOR NUCLEAR POWER PLANT (NPP) PUSPIPTEK SERPONG Deni Septiadi; Arief Yuniarto; Agung Hari Saputra
Jurnal Meteorologi Klimatologi dan Geofisika Vol 7 No 3 (2020): Jurnal Meteorologi Klimatologi dan Geofisika
Publisher : Unit Penelitian dan Pengabdian Masyarakat Sekolah Tinggi Meteorologi Klimatologi dan Geofisika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36754/jmkg.v7i3.197

Abstract

Precise and consistent meteorological data is needed to support safety and security as well as in an effort to meet safety requirements and criteria from the initial stages of siting, design, construction, even activities in the previous stages to the operation stage, handling safety during and during decommissioning and waste management radioactive. Therefore, the aim of this study is to identify and analyze the distribution of data distribution to see the extent to which meteorological data for nuclear site area provide accurate and precise data so that it can be used scientifically. In the present paper, the concentrations calculated by this method are compared with data observed over Portable Weather Station (PWS) and existing Automatic Weather Stations (AWS). Good agreement was confirmed in similar data observed and existing of PWS or AWS data due to statistically calculating test using correlation, deviation and Root Mean Square Error (RMSE). The two AWS tested, both Experimental Power Reactor (RDE) and Nuclear Serpong Area (KNS), gave fairly good scores statistically. Analysis on October 13, 2020, the value of RMSE, and the correlation between AWS RDE and KNS, respectively, is 361.2; 67.6 and 0.56. Then the data analysis on October 14, 2019 which compared AWS RDE and PWS, the value of Standard Deviation, RMSE, and the correlation between AWS RDE and PWS were 137.3; 8.65 and 0.48. The availability of good data is 98.3% for RDE and 95.3% for KNS, respectively.
Pengaruh Teknik Asimilasi Penakar Hujan Brandes Spatial Adjustment terhadap Quantitative Precipitation Estimation (QPE) Radar BMKG Padang Agung Hari Saputra; Nur Riska Lukita; Sirly Oktarina
Jurnal Fisika Unand Vol 11 No 3 (2022)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (765.967 KB) | DOI: 10.25077/jfu.11.3.373-379.2022

Abstract

Estimasi curah hujan kuantitatif (QPE) dengan tingkat akurasi spasial yang baik dan temporal yang tinggi dapat diperoleh dari instrumen radar cuaca. Namun, terdapat limitasi dalam pengamatan radar cuaca karena efek blocking dan adanya ground clutter. Sehingga pada penelitian ini akan dilakukan perbaikan kualitas data radar dengan mereduksi ground clutter menggunakan clutter map. Setelah perbaikan kualitas data radar akan dilakukan asimilasi data radar dengan data penakar hujan guna mengurangi kesalahan dan meningkatkan akurasi estimasi curah hujan. Data penakar hujan memiliki akurasi yang tinggi namun, informasi yang dikumpulkan masih berupa titik yang tidak terdistribusi secara merata sehingga tingkat keterwakilan spasialnya masih terbatas. Asimilasi curah hujan dengan data penakar hujan dapat dilakukan dengan metode Brandes Spatial Adjusment (BRA). Metode BRA memerhatikan jarak antara grid point dengan penakar hujan. Berdasarkan hasil penelitian diperoleh bahwa metode tersebut dapat mengurangi kesalahan estimasi namun tidak secara signifikan mengurangi kesalahan estimasi curah hujan.
A numerical simulation of PM2.5 concentration using the WRF-Chem model during a high air pollution episode in 2019 in Jakarta, Indonesia Rista Hernandi Virgianto; Rayhan Rivaniputra; Nanda Putri Kinanti; Agung Hari Saputra; Aulia Nisaul Khoir
International Journal of Advances in Applied Sciences Vol 11, No 4: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (860.751 KB) | DOI: 10.11591/ijaas.v11.i4.pp335-344

Abstract

Jakarta, as a megapolitan city, is always crowded with thousands of vehicles every day which results in decreased air quality due to combustion emissions and may have a significant impact on human health. Particulate matter (PM2.5) is a pollutant that has an aerodynamic diameter of fewer than 2.5 micrometers and is very easy to enter the human respiratory system so it can affect health. In the dry season, rain as the main natural mechanism for reducing PM2.5 occurs very rarely, causing an accumulation of PM2.5 concentrations in the atmosphere. The weather research and forecasting model coupled with the chemistry (WRF-Chem) model is a dynamic model that works with atmospheric chemistry combined with meteorological variables simultaneously. This study aims to simulate the concentration of PM2.5 in Jakarta during the high air pollution episode from 20 to 29 June 2019 with the WRF-Chem model based on the T1-MOZCART chemical scheme. Spatial analysis was conducted to determine the distribution of PM2.5 concentrations during high air pollution episodes in Jakarta. Validation of the simulation model was based on three observation sites, one in South Jakarta and two in Central Jakarta. The results showed that the highest correlation is 0.3 and the lowest root mean square error (RMSE) is 26.4, while the simulations still tend to overestimate the PM2.5 concentration.
Optimalisasi Edukasi Informasi Geohidrometeorologi Untuk Masyarakat Perkotaan (Studi Kasus: Kelurahan Jurang Mangu Timur, Kecamatan Pondok Aren, Kota Tangerang Selatan, Banten) Giarno Giarno; Agung Hari Saputra; Agustina Rachmawardani
To Maega : Jurnal Pengabdian Masyarakat Vol 5, No 3 (2022): Oktober 2022
Publisher : Universitas Andi Djemma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/tomaega.v5i3.1294

Abstract

Indonesia merupakan daerah rawan terhadap bencana hidrometeorologi, gempa bumi dan tsunami. Sebagai bagian dari Wilayah Indonesia, Kelurahan Jurang Mangu Timur, Kecamatan Pondok Aren, Kota Tangerang Selatan, Propinsi Banten merupakan tempat yang rawan banjir, terutama dengan perkembangan pemukiman yang sangat pesat sehingga merubah penggunaan lahan. Badan Meteorologi Klimatologi dan Geofisika (BMKG) sebagai lembaga yang berupaya menyediakan informasi peringatan dini sebelum kejadian bencana geo-hidrometeorologi. Namun demikian masih terdapat gab antara informasi yang disediakan, dengan akses dan pemahaman yang ada di masyarakat. Berdasarkan missing link tersebut, maka perlu adanya suatu upaya untuk memberikan pemahaman lebih baik lagi. Tujuan penelitian ini adalah meningkatkan optimalisasi informasi peringatan dini ini terutama di daerah perkotaan. Untuk menggali pemahaman masyarakat dilakukan melalui edukasi secara offline maupun online mengikuti perkembangan masyarakat perkotaaan yang terbiasa menggunakan sarana tersebut. Berdasarkan hasil survey menunjukkan pengetahuan masyarakat akan lembaga yang menangani bencana masih sangat kurang, dimana BMKG dan Sekolah Tinggi Meteorologi, Klimatologi dan Geofisika (STMKG) berturut-turut 49% dan 39%. Ketidaktahuan akan kedua lembaga ini sebanding dengan disinformasi terhadap istilah-istilah geohidrometeorologi terlihat dari jawaban responden pada pertanyaan-pertanyaan kejadian tornado, tsunami, gelombang panas, perubahan iklim, fenomena cuaca dingin, cuaca panas,  dan hubungan virus covid dengan cuaca panas yang dipahami kurang tepat.
PEMETAAN ZONA RAWAN BANJIR DI JAKARTA MENGGUNAKAN ANALYTIC HIERARCHY PROCESS (AHP) Azwar Makarim Aldimasqie; Agung Hari Saputra; Sirly Oktarina
Jurnal Environmental Science Vol 5, No 1 (2022): Oktober
Publisher : UNIVERSITAS NEGERI MAKASSAR

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (977.505 KB) | DOI: 10.35580/jes.v5i1.35759

Abstract

Hujan yang turun sebagian besar terinfiltrasi ke dalam tanah, dan sebagian lainnya akan menjadi banjir khususnya di wilayah Jakarta. Banjir dipengaruhi oleh beberapa faktor seperti curah hujan, tata guna lahan, kemiringan lahan, jenis tanah, geologi, dan kerapatan drainase. Faktor-faktor tersebut akan dikelompokkan dalam penelitian ini untuk mengetahui faktor yang berpengaruh terhadap banjir di Jakarta. Pengelompokan faktor dilakukan menggunakan teknik Analitik Hierarki Proses (AHP) dan Sistem Informasi Geografis (SIG), untuk memproyeksikan wilayah rawan banjir di Jakarta. Hasil menunjukkan faktor yang paling berpengaruh adalah curah hujan terhadap rawan banjir sekitar 40,54%. Sementara faktor yang memiliki pengaruh terkecil pada rawan banjir yaitu tata guna lahan sekitar 5,27%. Wilayah Jakarta memilki kerawanan yang sedang terhadap banjir dengan luas sebaran sekitar 296,19 km2, dimana wilayah yang rentan terhadap banjir berada di Jakarta Selatan dan Jakarta Timur
THE ANALYSIS OF LAPSE RATE PROFILE IN THE SITE CANDIDATE OF NUCLEAR POWER PLANT (NPP) AT GOSONG BEACH, BENGKAYANG REGENCY– WEST KALIMANTAN Deni Septiadi; Agung Hari Saputra; Rista Hernandi Virgianto; Arif Yuniarto; Muhammad Elifant Yuggotomo
Jurnal Sains dan Teknologi Nuklir Indonesia (Indonesian Journal of Nuclear Science and Technology) Vol 23, No 1 (2022): February 2022
Publisher : HIMNI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17146/jstni.2022.23.1.6578

Abstract

The lapse rate profile in the site candidate for the Nuclear Power Plant (NPP) at Gosong Beach Bengkayang, has been investigated to obtain a description of the lability of the atmosphere and upper air as part of a meteorological aspect safety study in the plan to develop a NPP site. The study of the lapse rate was carried out using air data on the reanalysis of the Global Data Assimilation System (GDAS) by extracting air temperature data at each altitude level so as to obtain a lapse rate of up to 25 km. Daily data was processed during 2021 and transformed in the monthly average profile data to describe the lapse rate profile in January – December 2021. Tropopause was identified with average altitude about 16.6 km and stratosphere at 20.5 km with a lapse rate about -0.21 ℃/100 m. The surface layer to 200 m have lapse rate from 0.7 ℃/100 m - 0.9 ℃/100 m at 00.00 Universal Time Coordinated (UTC) and 0.5 ℃/100 m -0.6 ℃/100 m at 12.00 UTC
Prediksi Karbon Monoksida Menggunakan Model Machine Learning Berdasarkan Perbandingan Model Time Series Studi Kasus DKI Jakarta: Carbon Monoxide Prediction Using Machine Learning Model Based on Time Series Model Comparison DKI Jakarta Case Study Ni Made Orcidia Wulaning Sari Sari; Hani Elindra; Agung Hari Saputra
Jurnal Kolaboratif Sains Vol. 7 No. 3: MARET 2024
Publisher : Universitas Muhammadiyah Palu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56338/jks.v7i3.4819

Abstract

DKI Jakarta sebagai pusat kegiatan ekonomi, merupakan daerah dengan dinamika aktivitas dan tingkat kepadatan penduduk yang sangat tinggi, yang mana berpengaruh terhadap kualitas udara yang ada di wilayah tersebut. Rata - rata partikel PM 2.5 di Jakarta sebesar 160, level yang termasuk berbahaya bagi kesehatan manusia. Penelitian ini bertujuan untuk mengetahui dan menguji coba tingkat keakuratan model prediksi time series yang didapatkan dari proses perbandingan model pada library Pycaret yang dilakukan pada dalam memprediksi parameter pencemar udara berupa gas karbon monoksida di wilayah DKI Jakarta. Data yang digunakan pada penelitian ini adalah data ISPU DKI Jakarta selama 11 tahun dari 2010 hingga 2021. Hasil penelitian menunjukkan bahwa perbandingan dari model Machine Learning dimana didapatkan 3 model terbaik yaitu model Huber Regressor, model Linear Regressor dan Ridge Regressor untuk memprediksi karbon monoksida. Dari hasil keseluruhan, model Huber Regressor masih belum dapat menghasilkan prediksi yang akurat dan optimal. Terbukti dari nilai MAE sebesar 5.3187, nilai RMSE sebesar 8.9838, nilai MASE sebesar 0.5699, nilai RMSSE sebesar 0.6571, serta sebesar 0.2364 dan 0.2061 untuk MAPE dan SMAPE. Model masih memiliki banyak keterbatasan, terutama dalam menangani outlier atau peristiwa tak terduga sehingga model dan metode pada penelitian ini masih membutuhkan penyesuaian serta pengembangan lebih lanjut.
PERBANDINGAN MODEL PREDIKSI DATA MINING DALAM MEMPREDIKSI KONSENTRASI POLUTAN KARBON MONOKSIDA (CO) DI JAKARTA Rendy Syahril Amanu; Faiz Ahza Ramadhan; Agung Hari Saputra
Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Vol. 18 No. 1 (2024): Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Inform
Publisher : Universitas Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47111/jti.v18i1.12451

Abstract

DKI Jakarta, as the capital of Indonesia, faces serious challenges in terms of air quality. Carbon monoxide (CO) is one of the main air pollutants in Jakarta that is harmful to human health and the environment. Data mining is a method that can be used to predict situations based on a model. The study aims to compare data mining models with the best-performing methods to predict carbon monoxide pollutants in Jakarta. The predictive data mining model of the python library is tested and evaluated based on the evaluation metrics of MASE, RMSSE, MAE, RMSE, MAPE and SMAPE values. The model test results showed that K Neighbors with the Conditional Deseasonalize & Detrending model had the best metric evaluation value to predict CO concentration with the value evaluation metrics of MASE 0.2942, RMSSE 0.2483, MAE 2.7362, RMSE 3.3863, MAPE 0.1975 and SMAPE 0.01993. Overall, K Neighbors with the Conditional Deseasonalize & Detrending model shows good performance to predict CO concentrations in Jakarta, but further adjustments are needed to improve accuracy.
The Utilization of HuberRegressor Machine Learning Model to Predict Carbon Monoxide Concentration in Surabaya City Cahya Sugiarto; Febby Debora Abigael; Yusron Faiz Athallah; Agung Hari Saputra
JOURNAL OF CIVIL ENGINEERING BUILDING AND TRANSPORTATION Vol. 8 No. 1 (2024): JCEBT MARET
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jcebt.v8i1.11262

Abstract

Carbon monoxide (CO) is one of the pollutant gases whose concentration currently continues to increase due to an increase in population and population activities, especially those that occur in the city of Surabaya, East Java. The purpose of this study is to make a prediction of CO gas concentration in Surabaya City in 2022. CO concentration air quality data was obtained from MERRA-2 Reanalysis through NASA's Giovanni platform. CO concentration data processing is carried out by Machine Learning methods using the Google Colaboratory platform with the HuberRegressor model. The results of the data processing carried out were obtained with details of MASE worth 0.6218, RMSSE worth 0.3657, MAE worth 0.0280, RMSE worth 0.0314, MAPE worth 0.0836, and SMAPE worth 0.0876. From the results of the evaluation of the model, it can be concluded that the HuberRegressor model can make a prediction of CO gas concentration in the city of Surabaya quite well.
Co-Authors Adzan, Muhizzadin Abdul Agustina Rachmawardani Arief Yuniarto Arif Yuniarto Aritonang, Binsar Hakim Arno, Giarno Aulia Nisaul Khoir Azwar Makarim Aldimasqie Cahya Sugiarto Deni Septiadi Disera, Tiara Emanuella Faiz Ahza Ramadhan Fauzi, Naufal Kamil Febby Debora Abigael Giananti, Attiya Shakila Hanggadireksa, Bimo Hani Elindra Hariadi Hariadi Indah Sary Jannata, Juan Idhar M. Arifudin, M. Arifudin Manullang, Safri Merdeka, Juang Muhammad Elifant Yuggotomo Muhammad Zaidan Hakim Musyaffa, Faqih Nanda Putri Kinanti Nesty Youwe, Angelina Serena Gracella Ni Made Orcidia Wulaning Sari Sari Novianti, Rima Novvria Sagita Nugraheni, Imma Redha Nur Riska Lukita Pangestu, Titis Angger Pramuji, Veimas Mahardika Putri, Ashifa Rayhan Rivaniputra Rendy Syahril Amanu Rista Hernandi Virgianto Rohman, Muhammad Faisal Rosyady, Muhammad Panji Sirly Oktarina Sirly Oktarina Syahputra, Rizki Syahrin, Khairummin Alfi Takdir, Muhammad Zaqi Fikanza Taruna, I Wayan Jyesta Jaya Taufiqi, Rafli Iqbal Thenu, Christy Anastasya Yosik Norman Yudistira, Ricko Yusron Faiz Athallah Zikri, Muhammad Aulia