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All Journal Infotech Journal JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Informatika Universitas Pamulang MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer BERNAS: Jurnal Pengabdian Kepada Masyarakat Tematik : Jurnal Teknologi Informasi Komunikasi DECODE: Jurnal Pendidikan Teknologi Informasi Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Jurnal INFOTEL Jurnal Informatika: Jurnal Pengembangan IT
Ardi Mardiana
Universitas Majalengka
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Journal : Jurnal Informatika: Jurnal Pengembangan IT

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Comparison of Machine Learning Algorithm for Enzyme Production Optimization from Industrial Waste Bastian, Ade; Fitriyani, Rofi; Susandi, Dony; Pangestu, Arki Aji; Mardiana, Ardi; Sujadi, Harun
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 2 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i2.8212

Abstract

The manufacture of industrial enzymes from trash provides a sustainable remedy for environmental issues. This work investigates machine learning methods to enhance enzyme production from industrial waste by examining critical factors such as waste type and chemical makeup. Three algorithms—Linear Regression, Decision Tree, and Neural Network—were used to estimate and forecast enzyme production. Evaluation criteria, such as Mean Squared Error (MSE) and Coefficient of Determination (R²), were used to evaluate model performance. The results indicated that the Decision Tree method was the most effective, exhibiting lowest error and enhanced accuracy in selecting ideal production factors such as fermentation temperature and time. This method improves efficiency, lowers operating expenses, and encourages sustainable waste management practices. The results highlight the potential of machine learning to convert trash into useful industrial goods, providing a route to more sustainable biotechnology. Future study may enhance hybrid algorithms, include new waste factors, and facilitate real-time implementation for wider industrial applicability.  
Co-Authors Aas Nuraisah Abrar, Abrar Wahid Aceng Jarkasih Ade Bastian Aji Permana Ayu Sunengsih Citra Pebrianti Fitria Dadan Zaliluddin Dede Abdurahman Dede Didin Dede Maulana Deffy Susanti Dinda Sri Wulansari Dony Susandi Enang Rusnandi Endah Prihartini Fahmi Aziz, Muhamamad Fitriyani, Rofi Gary Gibran Amnesty Geraldy Fatullah Hafsari, Zacky Harun Sujadi Intan Kusumadewi Iqbal Assegaf, Muhammad Jamhari Jamhari Mega Berliani Mochammad Bagasnanda Firmansyah Muhammad Dziya Dhiyauddin Muhammad Iqbal Assegaf Nunu Nurdiana Nunu Nurdiana, Nunu Pangestu, Arki Aji Priyadi, Deni Puput Putriani Rifki, Muhamamad Risa Risnawati Safari Yonasi Sandi Fajar Rodiyansyah Sandi Fajar Rodiyansyah Tarsono, Ano Tresna Irawan, Eka Tri Ferga Prasetyo Wahid, Abrar Wahono, Syiffa Safiera