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All Journal JURNAL KIMIA SAINS DAN APLIKASI IPTEK The Journal of Engineering JURNAL INTEGRASI PROSES IJEMS (Indonesian Journal of Environmental Management and Sustainability) Reka Buana : Jurnal Ilmiah Teknik Sipil dan Teknik Kimia Jurnal Rekayasa Hijau Jurnal Ilmiah Teknik Kimia Stannum : Jurnal Sains dan Terapan Kimia Darmabakti : Junal Pengabdian dan Pemberdayaan Masyarakat Equilibrium Journal of Chemical Engineering REACTOR : Journal of Research On Chemistry And Engineering Warta AKAB Jurnal Akademika Kimia Journal of Vocational Studies on Applied Research Research In Chemical Engineering Jurnal Teknik Kimia dan Lingkungan JPM-AKA Jurnal Teknologi Kimia Mineral Indonesian Journal of Chemical Science Jurnal Akademika Kimia Jurnal Rekayasa Proses
Fikrah Dian Indrawati Sawali
Program Studi Teknik Kimia Mineral, Politeknik Industri Logam Morowali
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Public Health Social Sciences Other

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Phytoremediation of Fe(III) Using Pistia stratiotes L. - Efficiency and Kinetic Insights Raisya Fadilah Putri; Fikrah Dian Indrawati Sawali; Moh Azhar Afandy
Indonesian Journal of Environmental Management and Sustainability Vol. 10 No. 2 (2026): June
Publisher : Magister Program of Material Science, Graduate School of Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26554/ijems.2026.10.2.79-89

Abstract

The increasing presence of heavy metals (dense metallic elements, such as iron, which can be toxic at high levels) in industrial wastewater poses a substantial threat to environmental and human health, requiring the development of effective, sustainable treatment solutions. This study analyzes the potential of Pistia stratiotes L. (also known as Kiambang, a floating aquatic plant) as a phytoremediation agent (an organism that removes pollutants from the environment) for extracting Iron (III) (the trivalent, oxidized form of iron) from synthetic wastewater (water with artificially added contaminants). The experiment was conducted in batch reactors (containers where reactions occur in set amounts) under controlled conditions, with modifications to the number of plants and contact length (the duration plants are exposed to contaminated water) to evaluate the effectiveness of iron removal. Iron concentrations were monitored spectrophotometrically (by measuring the amount of light absorbed by sample solutions) over time, and the phytoremediation kinetics (the rate and mechanism of pollutant removal) were investigated using zero, first, and second-order kinetic models (mathematical approaches to describe how quickly reactions occur). Results indicated that Pistia stratiotes L. was highly effective at reducing Fe (III) levels, achieving removal efficiencies exceeding 99% under optimal conditions-specifically with 10 plants and 5 days of contact time. Kinetic analysis indicated that the second-order model provided the best fit, suggesting a chemisorption-dominated process (removal primarily involves chemical bonding between the plant and iron ions). These findings emphasize the potential of Pistia stratiotes L. as a green and efficient solution for Fe (III) removal from wastewater and offer significant insight into optimizing phytoremediation system design for industrial applications.
Kinetic Modeling of Ni and Co Adsorption: A Comparative Study on Activated Zeolite Fikrah Dian Indrawati Sawali; Moh. Azhar Afandy
Rekayasa Hijau : Jurnal Teknologi Ramah Lingkungan Vol 10, No 1 (2026)
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/jrh.v10i1.1-11

Abstract

AbstractThe existence of heavy metals such as nickel (Ni) and cobalt (Co) can lead to environmental problems that have an immediate impact on human life, therefore it is crucial to carry out proper preventative strategies in wastewater treatment, one of the these utilizes the process of adsorption.  In the current investigation, thermally activated zeolite (ZT) by a carbonization process at a temperature of 550°C was utilized as a medium in the adsorption phase of Ni and Co from simulated wastewater with variation in a mass ratio of ZT.  Several kinetic models have been used for evaluating the kinetic parameters and mechanisms that control the adsorption process.  The outcomes received shown the adsorption process of Ni and Co by ZT followed the PSO kinetic model (R² > 0.99) with qe = 9.4877 mg.g⁻¹ and k₂ = 329.6075 g.mg⁻¹.min⁻¹ for Ni and qe = 7.3206 mg.g⁻¹ .min⁻¹ and k₂ = 127.9652 g.mg⁻¹.min⁻¹ for Co.  Based on the PSO kinetic model, it can be assumed that the adsorption process of Ni and Co by ZT is controlled by chemical interactions through ion exchange and the creation of coordination covalent bonds with active sites on the surface of the adsorbent.Keywords: Adsorption, Nickel, Cobalt, Zeolite
Comparative Study of Machine Learning Algorithms for Cr(VI) Adsorption Optimization: A Case Study Using KOH-Activated Wood Charcoal Moh. Azhar Afandy; Fikrah Dian Indrawati Sawali
Equilibrium Journal of Chemical Engineering Vol 10, No 1 (2026): Volume 10, No 1 July 2026 (First Online)
Publisher : Program studi Teknik Kimia UNS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/equilibrium.v10i1.108769

Abstract

The removal of toxic Cr(VI) ions from industrial wastewater remains a pressing environmental concern due to their high mobility and carcinogenic nature. This study presents a data-driven approach for modeling and optimizing Cr(VI) adsorption onto KOH-activated wood charcoal using various machine learning (ML) algorithms. A dataset derived from batch adsorption experiments was used, involving three operational parameters: initial Cr(VI) concentration (10–50 mg/L), contact time (40–120 min), and adsorbent dose (0.5–1.5 g). Six supervised regression models such as Linear Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), Gradient Boosting, and k-Nearest Neighbors (kNN) were evaluated. Model performance was assessed using the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) dan mean square error (MSE). Gradient Boosting and Decision Tree showed superior predictive accuracy, with R² values of 0.89 and 0.87, respectively. Feature importance analysis revealed initial concentration as the most influential factor, followed by contact time and adsorbent dosage. These findings highlight the potential of ML as an effective tool for predicting and optimizing adsorption processes in environmental remediation. The integration of ML methods supports efficient decision-making, particularly under constraints of limited experimental data, and aligns with digital transformation strategies in wastewater treatment.
Isolation of Flavonoids from Walang Sangit Leaves (Eryngium Foetidum) Using Methanol and Acetone Maceration with UV–Visible Spectrophotometric Analysis Suhirman Suhirman; Adna Ivan Ardian; Fikrah Dian Indrawati Sawali; Moh Azhar Afandy
Reka Buana : Jurnal Ilmiah Teknik Sipil dan Teknik Kimia Vol 11, No 1 (2026): EDISI MARET 2026
Publisher : Universitas Tribhuwana Tunggadewi Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33366/rekabuana.v11i1.8072

Abstract

Flavonoids are natural phytonutrient compounds in plants that exhibit antioxidant properties and play a role in scavenging free radicals. Walang sangit leaves have potential as a source of flavonoids, with samples consisting of young and mature leaves obtained from Pagebangan, Ciwandan District, Cilegon City. The sample preparation stage was carried out through extraction using the maceration method. The experimental procedure involved 10 g of dried walang sangit leaves ground to 60 mesh, which were macerated using 99.95 percent methanol and 80 percent acetone solutions. The volume of methanol and acetone used was 250 mL, with maceration times of 60 and 120 minutes in an Erlenmeyer flask. At 120 minutes, the methanolic extract yielded 2.81 mg QE/g (young leaves) and 4.12 mg QE/g (mature leaves), whereas the acetone extract only reached 1.03 mg QE/g and 2.88 mg QE/g, respectively. A similar pattern was also observed at 60 minutes, with methanol producing values of 1.39 – 2.32 mg QE/g, which were higher than those obtained with acetone (0.35 –0.78 mg QE/g). The highest extraction rate constant was obtained for acetone–mature leaves (0.0124 / min) and for methanol young leaves (0.0058 / min).
The Role of Artificial Intelligence in Enhancing Heavy Metal Removal Efficiency: A Bibliometric Perspective Moh Azhar Afandy; Fikrah Dian Indrawati Sawali
Research in Chemical Engineering Vol. 4 No. 2 (2025): Research in Chemical Engineering
Publisher : Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/rice.v4i2.272

Abstract

The problem of heavy metal pollution in wastewater has prompted the demand for more effective and sustainable treatment systems. In the recent decade, the integration of artificial intelligence (AI) in heavy metal adsorption processes has shown tremendous potential in enhancing efficiency and optimizing operational parameters. This study intends to identify global research trends on the application of AI in optimizing heavy metal adsorption processes by a bibliometric method for the period 2010 to 2024. Data were acquired from Google Scholar and filtered to include indexed papers, then analyzed using VOSviewer and Microsoft Excel software to evaluate annual publishing trends, as well as visualization of keyword co-existence. The findings of the investigation showed an impressive move in publications after 2019. The leading terms detected included “machine learning,” “neural networks,” and “optimization.” Despite demonstrating encouraging trends, research in this subject still confronts hurdles such as inadequate large-scale experimental data, minimal integration of AI with Internet of Things (IoT) systems, and lack of industrial-scale applications. This study shows the need of building hybrid AI-IoT systems, using big data analytics, and adaptive predictive models to increase the effectiveness of heavy metal adsorption systems in the future. These findings are likely to be a key reference for researchers and practitioners in creating smart and sustainable waste processing systems.
Co-Authors Adelina, Dina Adna Ivan Ardian Ardiansah, Ardiansah Arif Rahman Az Zahra , Clarisa Hany Clarisa Hany Az Zahra Devi Yuni Susanti Eryan Dwi Krisna Fery Setyawan Firman Aldani Hadiyatni Rita Priyantini Hary Sulistyo Herliawan, Deden Jajang Harly Khatimah, Khusnul Khusnul Khatimah Lestary, Rara Ayu Mega Mustikaningrum Mega Mustikaningrum Mega Mustikaningrum Moh. Azhar Afandy Muh. Azis Albar. J Muslikhin Hidayat Mustika, Pra Cipta Buana Wahyu Mustikaningrum , Mega Mustikaningrum, Mega Mustikaningrum, Mega Nirwansyah Nita Widyastuti Pangeran, Andi Baso Pra Cipta Buana Wahyu Mustika Pra Cipta Buana Wahyu Mustika Putra, Aditya Perdana Raisya Fadilah Putri Rara Ayu Lestary Rara Ayu Lestary Samuel Putra Mandiri Simanullang Saputra, Aditya Tri Sediawan, Wahyudi B. Septiawan, Muhammad Ridwan Simanullang, Samuel Putra Mandiri Suhirman Suhirman Susanti, Devi Y. Wahyudi B. Sediawan Wahyudi Budi Sediawan Yudi Siswanto