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All Journal Natural Science: Journal of Science and Technology Biocelebes JURNAL PHARMASCIENCE JURNAL PENGABDIAN KEPADA MASYARAKAT MPI (Media Pharmaceutica Indonesiana) PENA SAINS Daya Saing : Jurnal Ilmu Manajemen Jurnal Farmasi Sains dan Komunitas Jurnal Farmasi Galenika (Galenika Journal of Pharmacy) Jurnal Pengabdian Pada Masyarakat GHIDZA: Jurnal Gizi dan Kesehatan Pharmacology and Clinical Pharmacy Research JSFK (Jurnal Sains Farmasi & Klinis) Fullerene Journal of Chemistry Jurnal Ilmiah Medicamento Jurnal sains dan teknologi industri peternakan Jurnal Locus Penelitian dan Pengabdian Jurnal Economica: Media Komunikasi ISEI Riau Indonesia Law Review (ILREV) Journal of Health, Education, Economics, Science, and Technology (J-HEST) Jurnal Bisnis Kompetitif Tahqiqa: Jurnal Pemikiran Hukum Islam Molekul: Jurnal Ilmiah Kimia Satera: Jurnal Sains dan Teknik Terapan JIPI (Jurnal IPA dan Pembelajaran IPA) Jurnal Pengabdian Kompetitif Journal Of Chemical Process Engineering Jurnal Pengabdian Farmasi dan Sains Integrated and Sustainable Agriculture REGISTER JOURNAL Journal of Tropical Pharmacy and Chemistry Jurnal Sains dan Kesehatan Journal of Muhammadiyah’s Application Technology Journal Social Civilecial Eko dan Bisnis: Riau Economic and Business Review Journal of Pharmascience
Yusriadi Yusriadi
Universitas Tadulako
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : pena sains

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MACHINE LEARNING MODELS FOR PREDICTING STRESS VALUE IN THE TENSILE STRENGTH OF BIOFILMS FROM STARCH AND HAIR WASTE Utami, Hermin Hardyanti; Fitrah, Muhammad Aqdar; Yusriadi, Yusriadi; Ardiansah, Ardiansah; Arminas, Arminas; Lestari, Mega Fia; Towolioe, Sherly
JURNAL PENA SAINS Vol 11, No 2 (2024): Jurnal Pena Sains
Publisher : Program Studi Pendidikan IPA, Fakultas Ilmu Pendidikan, Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/jps.v11i2.26227

Abstract

Biofilms, structured communities of microorganisms, have emerged as a subject of significant interest across various industries due to their unique biodegradable and sustainable characteristics. Hair waste is an incredibly rich source of keratin, and this abundance makes it a promising candidate as a fundamental building block for the development of biodegradable plastics. This study focuses on sustainable biofilms derived from biodegradable materials, specifically a unique combination of starch and hair waste. Machine Learning models, implemented in RapidMiner, were utilized to predict the tensile strength of these biofilms, with the goal of enhancing quality control in their production. Neural Networks and Deep Learning methods were employed to compare their predictive capabilities, assessing both their strengths and limitations. Through rigorous data collection, feature identification, and detailed data analysis, critical factors influencing the quality of the biofilms were identified. The results revealed the remarkable predictive accuracy of the Neural Net model, particularly for Ratio 40, while the performance of the Deep Learning model varied across different ratios. The lower RMSE of the Neural Net model indicated a more precise alignment between the predicted and actual values, distinguishing it as the superior model. This research contributes to the advancement of sustainable biofilm development, offering eco-friendly solutions through the use of unconventional materials. Both models offer valuable predictive capabilities, and the choice between them may depend on the specific requirements and contexts of the application. In conclusion, the performance of the Neural Net and Deep Learning models in predicting stress in tensile strength varies across different ratios.
Co-Authors A. Khairinisa, Miski A.A. Ketut Agung Cahyawan W Abdul Rahman Adam, Aisyah Azhar Ade Fazliana Mantika Agustinus Widodo Akhmad Khumaidi Aldilla, Dilla Alowisya Futwembun Alwiyah Mukaddas Alwiyah Mukaddas Amal, Citra Amalia amelia rumi, amelia Ana Silviana Anak Agung Istri Sri Wiadnyani Andi Nuddin Ansar Aqdar Fitrah, Muhammad Ardiansah, Ardiansah Ari Prabowo Arminas, Arminas Armini Syamsidi Asriana Sultan Asty Ivon Samaliwu Azzahra, Najwa Salsabilla Bakhrul Amal Birdayanthi, Birdayanthi Cristadeolia Oktaviani Cristadeolia Oktaviani, Cristadeolia David Pakaya Desa, Susilo Sudarman Deviana, Alda Dinda Rahmiyati Dwi Lestari Handayani Dytha Okvhyanitha Evi Sulastri Febry Astuti Abu Fina Ulviani Friskilla Friskilla Gugun Gunawan Haryanto Atmowardoyo, Haryanto Hasniar Hasniar Hidayat, Novit Idrus, Irnawaty Ika Olivia Borman Ikmal, Ikmal Imam Mishbach Ince Agus Nurcholis Ingrid Faustine Iskandar Sulaiman, Iskandar Ismail, Nur Afia Jamaluddin Jamaluddin Jessica Jessica Joni Tandi, Joni Khaerunnisa, Munira Khildah Khaerati Lestari, Sri Ayu Liyatin Gea lubis, kartika sari M Sulaiman Zubair Mega Fia Lestari Miftahul Jannah Misnawati Misnawati Muhamad Rinaldhi Tandah Muhammad Faisal Muhammad Rinaldhi Tandah Muhammad Sulaiman Zubair Musili, Silvana Muthi’ah H Z Mutiani, Santi Nandiska Maladjili Natasya, Putri Nela Sharon Nur Amri, Azhari Nur Fuady, Muhammad Ikram Nurhajawarsi Nurhalifah Ibrahim Nurlaila Ramadhani Nuur Aanisah Paddiyatu, Nurhikmah Padhlun A Jerni Papriani, Nada Pertiwi Pebrianti Pebrianti Pramita Putri Pramita Putri Purnomo, Nurul Purwanti, Rani Ramadan, Rafsanjany Ramadhani, Nurlaila Ririen Hardani Ritha Pratiwi Rochaeti , Nur Sahabuddin Latif Saipul Maulana Salawali, Rismul Trianto Seprililianti Seprililianti Serli, Serli Siti Nurhafsah Sri Sulistiana Suhaeni Zainuddin Suherman Suherman, Suherman Syahsudarmi, Siti Syarifuddin, Nuryani Syariful Anam Tamba, Nesli Towolioe, Sherly Tri Novrianti Djanggola Tricia Andini Utami, Hermin Hardyanti Wardatul Nurjannah Yonelian Yuyun Yuli Arnida Pohan Yuliet Yuliet Yusri, Andi Zainuddin, Suhaeni