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All Journal IKRA-ITH ABDIMAS Jurnal Kesehatan dan Pengelolaan Lingkungan Journal of Engineering Environtmental Energy and Science Journal Serambi Engineering (JSE)
Nur Faizaturrohmah
Institut Teknologi Sumatera
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Social Sciences

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Analisis Dinamika Penelitian Pencemaran Air Sungai berdasarkan Studi Bibliometrik 10 tahun terakhir Mia Erpinda; Nur Faizaturrohmah; Suci Wulandari
Jurnal Serambi Engineering Vol. 9 No. 4 (2024): Oktober 2024
Publisher : Faculty of Engineering, Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study analyses the research dynamics on river water pollution over the last decade (2014-2024) using bibliometric analysis. Data were collected using Publish or Perish software with Google Scholar as the main source, using keywords such as 'river pollution', 'river water quality', 'river contamination' and 'river water pollution'. Articles published between 2014 and 2024 were selected, resulting in a total of 1,000 relevant publications. The bibliometric analysis was conducted using VOSviewer version 1.6.20, which allowed mapping of research trends and visualisation of key themes through network, overlay and density visualisation. The results identified five major clusters in river pollution research, with three dominant themes: "water quality index", "metal pollution" and "microplastics". The overlay visualisation showed an increasing focus on emerging themes such as urbanisation and climate change since 2017, and microplastics as a major concern since 2019. This bibliometric analysis provides valuable insights to guide future research by identifying trends and gaps in river pollution studies. It also supports the development of evidence-based environmental policies and highlights the need for interdisciplinary collaboration to effectively address pressing water pollution challenges.
Modeling Quality and Pollution Load Capacity of the Musi River Using QUAL2Kw Rafles Ami Poza; Nur Faizaturrohmah; Erlina Kurnianingtyas; Isoralla; Dion Awfa; Rifka Noor Azizah; Muhammad Ulin Nuha; Arif Rohman; Aulia Try Atmojo; Daffa Adli Nauval Purnomo
Journal of Engineering Environtmental Energy and Science Vol. 5 No. 2 (2026): JUNI 2026
Publisher : LPPMP Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/1e9rfg06

Abstract

The aim is to analyze water quality and determine the pollutant load of the Musi River in Palembang City using the QUAL2K model. The method involved collecting water-quality data (TSS, NH₃, COD, and pH), calibrating and validating the model using hydraulic and water-quality parameters, and simulating the burden on the power capacity of the polluted river. The research results show that the QUAL2Kw model has high accuracy and performs well under representative field conditions. As for the spatial distribution, TSS and NH3 concentrations tend to increase from upstream to downstream due to anthropogenic activity, but they remain within safe limits. For COD, conditions tend to exceed the river’s capacity, with marked negative values. This indicates a high organic pollution pressure and the potential to degrade the quality of the aquatic ecosystems. This model can be used as a good basis for sustainable management of river water quality.
Pengurangan Limbah Tekstil melalui Edukasi Mode Berkelanjutan dan Program Berbagi Sandang Layak Pakai Mia Erpinda; Suci Wulandari; Nur Faizaturrohmah; Irhamni Irhamni; Yuni Lisafitri; Aulia Annas Mufti; Erlina Kurnianingtyas; Rifka Noor Azizah; Dion Awfa; Devi Kurnia Sari
IKRA-ITH ABDIMAS Vol. 10 No. 1 (2026): IKRAITH-ABDIMAS Vol 10 No 1 Maret 2026
Publisher : Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37817/ikra-ithabdimas.v10i1.7174

Abstract

Permasalahan limbah tekstil dan konsumsi pakaian berlebihan merupakan isu lingkungan yang semakin mendapat perhatian karena berkontribusi terhadap peningkatan timbulan sampah serta penggunaan sumber daya alam yang tidak berkelanjutan. Perkembangan tren fesyen yang cepat mendorong masyarakat untuk membeli pakaian secara berulang, sementara pakaian yang masih layak pakai sering kali tidak dimanfaatkan kembali. Rendahnya kesadaran mengenai konsumsi sandang yang bertanggung jawab turut meningkatkan potensi limbah tekstil yang berdampak pada lingkungan. Kegiatan Pengabdian kepada Masyarakat (PKM) ini bertujuan untuk meningkatkan pemahaman masyarakat mengenai konsep mode berkelanjutan (sustainable fashion) serta menumbuhkan kepedulian sosial melalui Program Berbagi Sandang Layak Pakai. Kegiatan ini dilakukan dengan pendekatan edukatif dan partisipatif melalui sosialisasi mengenai dampak lingkungan dari limbah tekstil, prinsip mode berkelanjutan, konsumsi pakaian yang bijak, serta praktik penggunaan kembali (reuse) pakaian layak pakai. Selain edukasi, kegiatan ini mencakup pengumpulan, penyortiran, pengemasan, dan penyaluran pakaian layak pakai kepada mahasiswa yang membutuhkan. Kegiatan ini memanfaatkan momentum bulan Ramadhan sebagai sarana untuk memperkuat nilai empati, solidaritas, dan kepedulian sosial. Hasil kegiatan menunjukkan adanya peningkatan pemahaman peserta mengenai pentingnya memperpanjang usia pakai produk pakaian melalui praktik penggunaan kembali dan donasi pakaian yang masih layak pakai. Edukasi ini dapat mendorong partisipasi masyarakat dalam kegiatan sosial sekaligus mengurangi potensi limbah tekstil. Integrasi edukasi mode berkelanjutan dan kegiatan berbagi sandang layak pakai dapat menjadi pendekatan yang efektif dalam menanamkan nilai keberlanjutan serta mendukung implementasi SDG 12 (Responsible Consumption and Production) dan SDG 13 (Climate Action).
Pemodelan Hybrid Radial Basis Function Neural Network Berbasis Ensemble Learning dalam Prediksi Parameter Nitrat (NO₃⁻) pada Sungai di Wilayah Tropis Rifka Noor Azizah; Azahra Sukesih; Isti Dina Febriyanti; Nur Faizaturrohmah; Fuji Lestari
Jurnal Kesehatan dan Pengelolaan Lingkungan Vol. 7 No. 2 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jkpl.v7i2.16292

Abstract

Nitrate (NO₃⁻) concentration is one of the key indicators for evaluating river water quality, particularly in tropical regions with relatively high temperatures, which accelerate the nitrification process in the nitrogen cycle. Increasing human activities, such as residential development, agricultural practices, livestock farming, and aquaculture near rivers, can increase the nitrogen nutrient load, posing a risk of degrading water quality. Traditional methods such as the Pollutant Index (PI) and Water Quality Index (WQI) only reflect current conditions and cannot accurately predict changes in water quality. Therefore, this study aims to analyze the effectiveness of the Radial Basis Function Neural Network (RBFNN) model in predicting nitrate concentrations based on water quality parameters in rivers in tropical regions. This study was conducted by analyzing 164 publications providing data on water physico-chemical parameters, such as temperature, pH, TDS, TSS, DO, BOD, COD, nitrite, ammonia, and electrical conductivity. The obtained data were then processed through data cleaning, logarithmic transformation, feature engineering, and model training using an RBFNN combined with Gradient Boosting and Random Forest methods via an ensemble approach. Model evaluation was performed using the coefficient of determination (R²). The results of the study indicate that this model exhibits excellent predictive capability, with an R² value of 0.9907 for residential riverbanks and 0.9953 for agro-aquatic riverbanks, outperforming conventional ANN models in similar studies in Indonesia, which generally yield R² values in the range of 0.90–0.97, and comparable to the best RBFNN models ever reported in the international literature. Sensitivity analysis indicates that the parameter most influential on nitrate concentration is nitrite (40.25%), followed by electrical conductivity (19.80%) and TDS (11.10%). These results indicate that the RBFNN is effective in modeling nonlinear relationships among water quality parameters and has the potential to be developed as a core component in an early warning system for nitrate pollution, a decision support system for water resource managers, and a more efficient and adaptive data-driven water quality monitoring instrument to support the sustainable management of tropical rivers. Keywords: radial basis function, nitrate, tropical rivers, water quality, neural network
Co-Authors Arif Rohman Aulia Annas Mufti Aulia Try Atmojo Azahra Sukesih Daffa Adli Nauval Purnomo Devi Kurnia Sari Dion Awfa Dion Awfa Erlina Kurnianingtyas Erlina Kurnianingtyas Fuji Lestari Irhamni Isoralla Isti Dina Febriyanti Mia Erpinda Muhammad Ulin Nuha Rafles Ami Poza Rifka Noor Azizah Rifka Noor Azizah Rifka Noor Azizah Suci Wulandari Yuni Lisafitri, Yuni