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All Journal Teknotan: Jurnal Industri Teknologi Pertanian TELKOMNIKA (Telecommunication Computing Electronics and Control) Rekayasa Mesin Indonesian Green Technology Journal Jurnal Keteknikan Pertanian Tropis dan Biosistem Jurnal Teknologi Pertanian Jurnal Ilmiah Rekayasa Pertanian dan Biosistem Rona Teknik Pertanian Advance Sustainable Science, Engineering and Technology (ASSET)
Gunomo Djoyowasito
Jurusan Keteknikan Pertanian, Fakultas Teknologi Pertanian, Universitas Brawijaya
Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering Other

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Characterization Of Bioplastic from Cellulose Acetate Empty Bunches Oil Palm and Canna Bulb Flour (Canna edulis Ker) With Addition of Variations of Glycerol Lutfi, Musthofa; Djoyowasito, Gunomo; Argo, Bambang Dwi; Perdana, Lita Puspita Rizka
Jurnal Ilmiah Rekayasa Pertanian dan Biosistem Vol 13 No 2 (2025): Jurnal Ilmiah Rekayasa Pertanian dan Biosistem
Publisher : Fakultas Teknologi Pangan & Agroindustri (Fatepa) Universitas Mataram dan Perhimpunan Teknik Pertanian (PERTETA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jrpb.v13i2.1167

Abstract

Bioplastics are environmentally friendly materials that serve as alternatives to synthetic plastics, capable of being decomposed by microorganisms. Starch, commonly sourced from tubers such as canna tubers, is one of the primary natural components used in bioplastic production. This study utilized a completely randomized factorial design with glycerol as a plasticizer and cellulose acetate from Oil Palm Empty Fruit Bunches (OPEFB) as a reinforcement agent. Glycerol was varied at concentrations of 1, 2, 3, 4, and 5 ml, while cellulose acetate OPEFB was varied at 1, 1.5, 2, 2.5, and 3 g. The resulting bioplastics underwent mechanical testing, including tensile strength, elongation, elasticity, water absorption, solubility, and biodegradability. The highest tensile strength (20.56 MPa) was observed with 2 ml glycerol and 1.5 g cellulose acetate OPEFB. The best elongation (33.33%) occurred with 4 ml glycerol and 1 g cellulose acetate OPEFB. Maximum elasticity (2.86 MPa) was achieved with 2 ml glycerol and 2.5 g cellulose acetate OPEFB. Optimal water absorption (12.54%) was recorded with 1 ml glycerol and 1 g cellulose acetate OPEFB, while the highest solubility (43.97%) was observed with 5 ml glycerol and 3 g cellulose acetate OPEFB. The greatest biodegradability (88.75%) was achieved with 5 ml glycerol and 1.5 g cellulose acetate OPEFB. These findings highlight the potential of starch-based bioplastics reinforced with cellulose acetate OPEFB to achieve desirable mechanical and environmental performance characteristics.
Prediction of Soil Nutrients from Different Soil Textures using Portable Spectrometer and Machine Learning Himawan, Harki; Nainggolan, Rut Juniar; Rakhmadi, Handono; Djoyowasito, Gunomo; Ubaidillah; Nopriani, Lenny Sri; Al Riza, Dimas Firmanda
Advance Sustainable Science Engineering and Technology Vol. 8 No. 1 (2026): November - January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i1.2166

Abstract

Soil nutrients, such as nitrogen, phosphorus, and potassium, are critical for plant growth and agricultural productivity. Conventional laboratory methods for measuring these nutrients are accurate but often time-consuming, costly, and environmentally taxing. This study explores the potential of portable visible-near infrared (Vis-NIR) spectrometer combined with machine learning algorithms as a rapid, cost-effective, and eco-friendly alternative for soil nutrient analysis. Soil samples of clay, clay loam, and sandy clay were collected and analyzed using artificial neural network (ANN) approach to predict soil nutrients. A total of 81 reflectance spectra data from each soil type were acquired using an AS7265x sensor and processed to develop a predictive model for nutrient content. ANN models demonstrated high accuracy, with R² values exceeding 0.8 in each type of soil texture. This study emphasizes the potential of portable Vis-NIR spectrometer and machine learning integration to revolutionize soil nutrient analysis, offering significant improvements in agricultural efficiency and sustainability.
Co-Authors Adamsyah Harika Putra Affan Zahirul Fawazi Alin Rosyidatul Afidah SR Ampri Lylyanto Khusna Andik Setiawan Arif Suryo Nugroho Ary Musthofa Ahmad Ary Musthofa Ahmad Ary Mustofa Ahmad Bambang Dwi Argo Bambang Susilo Cahyanto, Darmawan Dwi Darmanto Darmanto Dimas Firmanda Al Riza Dwi Purnomo Elka Musyarofatunnisa Frilantika Kusuma Wardani Herlambang Widarto, Marsa Hilmi, Miftahul Himawan, Harki Ida Ayu Widhiantari Indah Purnamasari, Desi Rahmawati Joko Prasetyo Juan ZHANG Khasanah, Asmaul Lazuardy Tembang, Lazuardy Lita Puspita Rizka Perdana Luchman Hakim Meilani Eka Marantika Miftahul Hilmi Mochamad Bagus Hermanto Muhamad ikhsanudin Musthofa Lutfi Nainggolan, Rut Juniar Natalia Simanjuntak Nugrayani, Thalia Reninang Perdana, Lita Puspita Rizka Rakhmadi, Handono Retno Damayanti Rifaldi Aprilianto Rini Yulianingsih Roni Hadi Wijaya Sandra Malin Sutan Sandra Sandra Sandra Sandra Shinta Rosalia Dewi Sukoyo, Agung Sumardi Hadi Sumarlan Tubagus Irfan Khiari Ubaidillah Vibi Rafianto Wahyunanto Agung Nugroho wenny andriyani Widhiantari, Ida Ayu Wiwik Handayani Yoga Aditya Pratama Yusron Sugiarto Yusuf Hendrawan Yusuf Wibisono Zaqlul Iqbal