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All Journal Inovasi Fisika Indonesia (IFI) Jurnal Pijar MIPA Public Health Risk Assesment Journal Calamity: A Journal of Disaster Technology and Engineering Journal of Civil Engineering, Planning, and Design (JCEPD)
Hermanto, Ady
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Aerospace Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Control & Systems Engineering Dentistry Earth & Planetary Sciences Electrical & Electronics Engineering Health Professions Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics Public Health Veterinary

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 1 
The impact of climate change on potential rob floods and its effect on regional spatial planning on the Surabaya coast Okvitasari, Astri Rino; Fatoni, Arrizal R.; Bahtiar, Ahmad; Faridatussafura, Nurzaka; Hermanto, Ady; Aulady, M. Ferdaus N.
Calamity: A Journal of Disaster Technology and Engineering Vol. 1 No. 2: January (2024)
Publisher : Institute for Advanced Science, Social, and Sustainable Future

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61511/calamity.v1i2.2024.320

Abstract

Climate change is a global issue that is of concern to the world. One of the impacts of climate change is sea level rise. Rising sea levels can cause tidal floods, especially in coastal areas. Based on data from the Meteorology, Climatology and Geophysics Agency (BMKG), one evidence of climate change is an increase in sea level. Satellite altimetry measurements show a trend of sea level rise in Indonesia from 1992-2022 averaging around 4 mm/year. Rising sea levels have an impact on increasing the frequency of coastal flooding, retreating coastlines, and the disappearance of national borders. The city of Surabaya is one of the coastal cities in Indonesia which has the potential to experience an increase in the height of tidal floods due to climate change. The potential for tidal floods due to climate change could harm the spatial planning of coastal areas in Surabaya. The results of this research show that the coast of Surabaya has a moderate level of tidal flood vulnerability with an area of 8230.77 ha, a high category with an area of 1739.21 ha, and a very high category with an area of 178.13 ha. The area is dominant from most of the coast of the Semampir subdistrict to Benowo and the border of the Bulak and Mulyorejo subdistricts. Tidal floods can cause the submergence of productive lands, such as settlements, fish farms, and warehouses. This can cause economic and social losses for coastal communities. Therefore, adaptation efforts are needed to reduce the impact of tidal floods due to climate change. These adaptation efforts can be carried out through improving spatial planning and environmental planning for coastal areas.
The application of PPE at the Juanda meteorological station (BMKG) Fatoni, Arrizal R.; Bahtiar, Ahmad; Faridatussafura, Nurzaka; Hermanto, Ady; Aulady, M. Ferdaus N.
Public Health Risk Assesment Journal Vol. 1 No. 2: (January) 2024
Publisher : Institute for Advanced Science, Social, and Sustainable Future

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61511/phraj.v1i2.2024.316

Abstract

The application of Personal Protective Equipment (PPE) at the Meteorology, Climatology, and Geophysics Agency (BMKG) is an important step in maintaining employees’ safety and health in their work environment. Even though BMKG is not involved in heavy industrial activities, there are still potential hazards and risks that need to be addressed in carrying out meteorological, climatological, and geophysical tasks. This study aims to describe the importance of implementing PPE in the BMKG environment, especially the Aviation Meteorological Station, where we took a case study at the Juanda Meteorological Station. First, we identify potential hazards that may be faced by BMKG employees, such as radiation risk, potential for hazardous gas explosions or fires, extreme weather conditions, and risks of physical injury. In the face of these risks, the use of proper PPE can help protect employees from hazardous exposures, and gas explosions and prevent injury. Next, we discuss the factors that need to be considered in implementing PPE at the BMKG. This includes a comprehensive risk assessment, identification of appropriate PPE for specific hazards, training of employees on the use and care of PPE, and regular monitoring and maintenance of the PPE used. Therefore, by implementing the Occupational Health and Safety Management System, in this case, the application of PPE, BMKG can ensure that employees have a safe and healthy work environment, reduce the risk of work accidents, and comply with applicable regulations and standards related to work safety.
The Application of PPE at the Juanda Meteorological Station (BMKG) Fatoni, Arrizal R.; Bahtiar, Ahmad; Faridatussafura, Nurzaka; Hermanto, Ady; Aulady, MFN
Journal of Civil Engineering, Planning and Design Vol 2, No 2 (2023): November
Publisher : Faculty of Civil Engeneering and Planning - ITATS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.jcepd.2023.v2i2.4659

Abstract

The application of Personal Protective Equipment (PPE) at the Meteorology, Climatology, and Geophysics Agency (BMKG) is an important step in maintaining employees’ safety and health in their work environment. Even though BMKG is not involved in heavy industrial activities, there are still potential hazards and risks that need to be addressed in carrying out meteorological, climatological, and geophysical tasks. This study aims to describe the importance of implementing PPE in the BMKG environment, especially the Aviation Meteorological Station, where we took a case study at the Juanda Meteorological Station. First, we identify potential hazards that may be faced by BMKG employees, such as radiation risk, potential for hazardous gas explosions or fires, extreme weather conditions, and risks of physical injury. In the face of these risks, the use of proper PPE can help protect employees from hazardous exposures, and gas explosions and prevent injury. Next, we discuss the factors that need to be considered in implementing PPE at the BMKG. This includes a comprehensive risk assessment, identification of appropriate PPE for specific hazards, training of employees on the use and care of PPE, and regular monitoring and maintenance of the PPE used. Therefore, by implementing the Occupational Health and Safety Management System, in this case, the application of PPE, BMKG can ensure that employees have a safe and healthy work environment, reduce the risk of work accidents, and comply with applicable regulations and standards related to work safety.
Tidal Flood Prediction in Surabaya Based on Hydrometeorological Data Using Gradient Boosting and Logistic Regression Setyaningrum, Kartika Dwi Indra; Masfufah, Kiki Syalasyatun; Rahmawati, Endah; Hermanto, Ady
Jurnal Pijar Mipa Vol. 20 No. 6 (2025)
Publisher : Department of Mathematics and Science Education, Faculty of Teacher Training and Education, University of Mataram. Jurnal Pijar MIPA colaborates with Perkumpulan Pendidik IPA Indonesia Wilayah Nusa Tenggara Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jpm.v20i6.10068

Abstract

This research aims to develop a predictive model for tidal inundation at Tanjung Perak Port in Surabaya, a region identified as critical and highly susceptible to such events. The foundational data incorporated comprises hydrometeorological indicators, such as lunar cycles, tidal patterns, and precipitation levels, which were sourced from the BMKG Tanjung Perak Maritime Meteorological Station. A dataset comprising 26,275 individual data points was compiled and subsequently partitioned into training sets (80% of the data) and validation sets (20%) via randomization. This apportionment is intended to support the robustness and applicability of the developed model. The initial data preparation phase involved techniques such as data normalization, imputation of missing values, and the determination of variable weights based on their respective degrees of impact. Subsequently, two distinct machine learning methodologies were employed to construct the predictive framework: Gradient Boosting (specifically, XGBoost) and Logistic Regression. The efficacy of the resultant models was rigorously assessed using various metrics, including accuracy, confusion matrix analysis, ROC-AUC scores, and feature significance analysis. Analysis of the outcomes indicated that the Gradient Boosting model achieved a superior accuracy of 99.96%, whereas Logistic Regression attained 99.85%. An examination of the features revealed that lunar cycles and tidal conditions were the principal determinants of tidal inundation, with precipitation exerting a comparatively minor effect. These observations substantiate the efficacy of integrating suitable data preparation techniques with machine learning methodologies to achieve precise predictive outcomes. The principal contribution of this investigation is the establishment of a computational framework to facilitate the development of an advanced warning system for tidal flooding, thereby aiding hazard reduction and limiting adverse societal, financial, and operational consequences in littoral regions.
Identifikasi Banjir Rob menggunakan Metode Klasifikasi dengan Model Random Forest dan Decision Tree di Pelabuhan Surabaya Tahun 2021-2023 -, kiki syalasyatun masfufah; Setyaningrum, Kartika Dwi Indra; Rahmawati, Endah; Hermanto, Ady
Inovasi Fisika Indonesia Vol. 15 No. 1 (2026): Inpress Vol 15 No 1
Publisher : Prodi Fisika FMIPA Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/ifi.v15n1.p21-29

Abstract

Abstrak Banjir rob merupakan salah satu jenis bencana hidrometeorologi yang kerap terjadi di wilayah pesisir, terutama akibat kombinasi antara pasang laut tinggi, fase bulan tertentu, dan curah hujan ekstrem. Penelitian ini bertujuan untuk mengidentifikasi potensi banjir rob di kawasan Pelabuhan Surabaya dengan menggunakan algoritma klasifikasi Random Forest dan Decision Tree. Data yang digunakan meliputi parameter hidrometeorologi seperti fase bulan, pasang surut air laut, dan curah hujan, yang diperoleh dari Stasiun Meteorologi Maritim Tanjung Perak Surabaya. Sebelum dimodelkan, data melalui tahapan pre-processing berupa normalisasi dan pembobotan untuk menyeragamkan skala antar variabel serta menilai tingkat kontribusi masing-masing parameter. Model dikembangkan dengan pembagian data 80% pelatihan dan 20% pengujian. Hasil evaluasi Random Forest menunjukkan akurasi sebesar 99,96% dan Decision Tree sebesar 99,94% dengan tingkat kesalahan yang sangat rendah. Analisis feature importance menunjukkan bahwa fase bulan dan pasang surut merupakan faktor dominan dalam prediksi banjir rob, sedangkan curah hujan memiliki pengaruh minimal. Temuan ini membuktikan bahwa Random Forest merupakan metode yang efektif dan andal untuk klasifikasi banjir rob serta memiliki potensi untuk diimplementasikan dalam sistem peringatan dini. Penelitian ini juga merekomendasikan integrasi data geografis, seperti informasi kerentanan tanah, morfologi wilayah, dan elevasi permukaan untuk meningkatkan akurasi dan generalisasi model di masa mendatang.   Abstract Tidal flooding is a type of hydrometeorological disaster that often occurs in coastal areas, mainly due to a combination of high tides, certain lunar phases, and extreme rainfall. This study aims to identify the potential for tidal flooding in the Surabaya Port area using Random Forest and Decision Tree classification algorithms. The data used include hydrometeorological parameters such as lunar phases, tides, and rainfall, obtained from the Tanjung Perak Maritime Meteorology Station in Surabaya. Before being modeled, the data went through pre-processing stages such as normalization and weighting to standardize the scale between variables and assess the level of contribution of each parameter. The model was developed by dividing the data into 80% training and 20% testing. The evaluation results of Random Forest showed an accuracy of 99.96% and Decision Tree at 99.94% with a very low error rate. Feature importance analysis showed that lunar phases and tides are the dominant factors in predicting tidal flooding, while rainfall has a minimal influence. These findings prove that Random Forest is an effective and reliable method for tidal flood classification and has the potential to be implemented in early warning systems. This study also recommends the integration of geographic data, such as soil vulnerability information, regional morphology, and surface elevation to improve the accuracy and generalization of future models.
Co-Authors -, kiki syalasyatun masfufah Ahmad Bahtiar, Ahmad Aulady, M. Ferdaus N. Aulady, MFN Endah Rahmawati Faridatussafura, Nurzaka Fatoni, Arrizal R. Masfufah, Kiki Syalasyatun Okvitasari, Astri Rino Setyaningrum, Kartika Dwi Indra