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All Journal Jurnal Teknik Jurnal Teknik Kimia USU Telematika : Jurnal Informatika dan Teknologi Informasi OPSI Chemistry in Education Communications in Science and Technology Chemistry Indonesian Journal of Chemical Research CHEESA: Chemical Engineering Research Articles Walisongo Journal of Chemistry Warta Perkaretan Jurnal Teknik Kimia USU JPM-AKA
Uma Fadzilia Arifin, Uma Fadzilia
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Social Sciences

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Quality Evaluation of Bioplastic from Glutinous Rice Starch Reinforced with Bamboo Leaf Powder Arifin, Uma Fadzilia; Adetya, Nais Pinta; Pambudi, Wisnu; Ratnaningsih, Wahyu
CHEESA: Chemical Engineering Research Articles Vol. 5 No. 2 (2022)
Publisher : Universitas PGRI Madiun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25273/cheesa.v5i2.14235.82-91

Abstract

Plastics are widely used in various aspects of life due to their variety of superior properties. However, they contribute a negative impact on the environment, which leads to the search for an alternative solution such as the production of bioplastics as biodegradable plastics. Therefore, this study aims to evaluate the psycho-mechanic quality of bioplastic from glutinous rice starch reinforced with bamboo leaf powder. The bioplastic synthesis process was carried out using 0, 1, 3, 5, and 7% (w/w) variations of bamboo leaf powder on glutinous rice starch, respectively. The results showed that the best bioplastic composition was the addition of 3% (w/w) bamboo leaf powder to glutinous rice starch. This indicated that the addition of bamboo leaf powder in bioplastics can enhance the thickness, hardness, and tensile strength significantly. Meanwhile, the value of density, water vapor transmission rate, and elongation showed a slight increase, and the bioplastic also degraded more than 70% for 7 days.
Kinetic of Hexavalent Chromium (Cr(VI)) Removal by Corn Cob-Based Activated Carbon Modified with Nitric Acid Ratnaningsih, Wahyu; Aningtyas, Vebri; Shabrina, Husna Muizzati; Arifin, Uma Fadzilia; Prayogo, Wisnu; Amin, Muhammad
Indonesian Journal of Chemical Research Vol 12 No 3 (2025): Edition for January 2025
Publisher : Jurusan Kimia, Fakultas Sains dan Teknologi, Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/ijcr.2025.12-rat

Abstract

The presence of heavy metal Cr(VI) in water indicates environmental pollution. Heavy metal Cr(VI) that exceeds the standard can be harmful to health because it is toxic and carcinogenic. Activated carbon can be used as a heavy metal adsorbent. Modification of activated carbon using nitric acid can increase metal adsorption capacity. Therefore, this study aims to determine the kinetic of hexavalent chromium (Cr(VI)) removal by corn cob-based activated carbon modified with nitric acid. The modified activated carbon was characterized by Boehm titration and FTIR spectrophotometer. The adsorption capacity was identified in various parameters, involving the initial concentration of Cr(VI), pH value, contact time, and concentration of the adsorbent to obtain the optimal Cr(VI) removal efficiency value. The most optimal Cr(VI) adsorption was obtained at an activated carbon dosage of 3 g/L, pH value of 1, contact time of 140 minutes, and 100 mg/l Cr(VI) concentration. Based on adsorption kinetics data, the pseudo-second-order equation was obtained (R2 =0.994). The adsorption phenomenon followed the Langmuir isotherm model (R2 = 0.998) with an optimum adsorption capacity of 28.32 mg/g. Corn cob-activated carbon modified with nitric acid has many acidic groups that act as effective active sites for reducing Cr(VI) from water.
The Application of Artificial Intelligence in Waste Classification as an Effort In Plastic Waste Management Listyalina, Latifah; Utami, Ratri Retno; Arifin, Uma Fadzilia; Putri, Naimah
Telematika Vol 21 No 1 (2024): Edisi Pertama 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i1.11977

Abstract

Purpose: Sorting waste before it is deposited in the Final Disposal Site (TPA) is crucial to reduce the increasing amount of waste accumulation each year. This issue can be addressed by implementing machines capable of automatically sorting waste.Design/methodology/approach: This research is quantitative and utilizes secondary data, namely image data of various types of waste. The images will be classified into organic and inorganic waste using a deep learning model. The measurement conducted involves assessing the accuracy of the designed deep learning model in classifying waste images into appropriate categories.Fondings/results: Based on the available dataset, waste identification will be performed, including food waste, paper, wood, leaves, electronic waste, metal, plastic, and bottles. The overall accuracy of the model is 94.42%, indicating that the model correctly classifies 94.42% of waste samples.Originality/value/state of the art: This research can classify 8 types of waste classes successfully using deep learning.
Utilization Of Chlorella Pyrenoidosa As A Phytoremediator For Tannery Waste Adetya, Nais Pinta; Arifin, Uma Fadzilia; Anggriyani, Emiliana; Rachmawati, Laili
Walisongo Journal of Chemistry Vol. 7 No. 1 (2024): Walisongo Journal of Chemistry
Publisher : Department of Chemistry Faculty of Science and Technology UIN Walisongo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/wjc.v7i1.20749

Abstract

This study aims to analyze the effect of phytoremediation on the bioremoval of COD, ammonia, and Cr (VI) from tannery wastewater and examine its effect on the growth of Chlorella populations. The research method consisted of two stages: first, preparation of liquid waste media. The second is culturing pure cultures followed by microalgae cultivation using leather tanning liquid waste media with a concentration variation of 0%, 10%, 20%, and 30% v/v. Filtrate samples after harvest were analyzed for COD, ammonia, and Cr (VI). The results obtained in this study show that Chlorella can grow in tanning waste media. The highest exponential phase occurs at a concentration of 20% with a growth rate of 0.557. Tannery liquid waste contains inorganic minerals utilized by Chlorella pyrenoidosa cells for growth. Cultivation of Chlorella pyrenoidosa can reduce leather tanning liquid waste parameters, namely COD, ammonia, and Cr (VI).
Penurunan Waktu Siklus Proses Cetak Injeksi Produk Tutup Plastik Melalui Simulasi Moldflow Arifin, Uma Fadzilia; Suci Rahayu
Jurnal Teknik Vol 21 No 2 (2023): Jurnal Teknik
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37031/jt.v21i2.387

Abstract

The cycle time is the integral factors of the production process. The higher the cycle time, the longer the time for the production process so as to reduce the efficiency and effectiveness of the production process. Plastic cap production uses an injection molding machine. The time required to produce a plastic cap is relatively high is about 33 seconds. This study aims to reduce the cycle time of plastic caps production during the injection molding process through simulation using Autodesk Moldflow software. Simulation was conducted to determine the optimal temperature parameter where other parameters are kept constant. The design of this simulation experiment used the Taguchi method with an orthogonal array as the simulation matrix. The simulation results was analyzed using the Minitab 19 software to determine the response value of the S/N ratio in the smaller is better category. Based on the value of the S/N ratio, the optimal combination of process temperature parameters is 180°C for melt temperature and 32°C for mold temperature to be able to reduce cycle time by 9.34 seconds. The simulation results show that the cycle time required for plastic cap production is 23.66 seconds.
Development of a real-time plastic waste detection system based on deep learning to support the automation of industrial waste sorting processes Listyalina, Latifah; Sarisky, Mario; Arifin, Uma Fadzilia; Utarianingrum, Ratna; Chandra, Hekin Irfan
OPSI Vol 18 No 2 (2025): OPSI - December 2025
Publisher : Jurusan Teknik Industri, Fakultas Teknologi Industri UPN "Veteran" Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/opsi.v18i2.15682

Abstract

The accumulation of plastic waste has become one of the major environmental issues in Indonesia, where conventional waste management systems are still limited in handling and classifying various types of waste. This research aims to develop an automatic waste detection system using Artificial Intelligence (AI) and implement it in a mobile application capable of identifying plastic waste in real time. The model was trained using the WasteIn dataset, which contains annotated images of different waste categories, including plastic, paper, glass, metal, organic, and electronic waste. The YOLO11-Nano architecture was applied due to its lightweight structure and efficiency for mobile-based deployment. The trained model was then converted into TensorFlow Lite (TFLite) format and integrated into an Android Studio environment to enable real-time inference through smartphone cameras. Based on the evaluation of 36 test images, the system achieved an accuracy of 91.67%, with consistent performance in detecting plastic, paper, and organic waste. The inference time of less than 100 milliseconds per frame demonstrates the system’s feasibility for real-time mobile applications. The results indicate that the integration of deep learning and computer vision technologies can effectively support waste classification processes and contribute to sustainable waste management practices.
Co-Authors Adetya, Nais Pinta Agustian, Mertza Fitra Aningtyas, Vebri Arief, Diana Ross Chandra, Hekin Irfan Emiliana Anggriyani Endang Susilaningsih Isananto Winursito, Isananto Laili Rachmawati Listyalina, Latifah M. Djaeni Mario Sarisky Dwi Ellianto Muhammad Amin Pambudi, Wisnu Prayogo, Wisnu Putri, Naimah Rahmawati, Atiqa Ratnaningsih, Wahyu Ratri Retno Utami RR. Ella Evrita Hestiandari Sarisky, Mario Satwikanitya, Pani Shabrina, Husna Muizzati Subiyanto Hadisaputro, Subiyanto Suci Rahayu Supomo Supomo Suwarno, Yuli Utarianingrum, Ratna Wibowo, Midarto Dwi