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All Journal Jurnal Informatika Jurnal Teknik Komputer AMIK BSI Paradigma Information System for Educators and Professionals : Journal of Information System Jurnal Informatika dan Rekayasa Perangkat Lunak Health Care : Jurnal Kesehatan Jurnal Digit : Digital of Information Technology Jurnal AbdiMas Nusa Mandiri Abditeknika - Jurnal Pengabdian Kepada Masyarakat Computer Science (CO-SCIENCE) IMTechno: Journal of Industrial Management and Technology Jurnal Abdimas Komunikasi dan Bahasa Jurnal Minfo Polgan (JMP) Paradigma INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Sinergi: Jurnal Pengabdian Kepada Masyarakat Jurnal Komputer Antartika Informatics and Software Engineering (ISE) Rayah Al-Islam : Jurnal Ilmu Islam
Siti Marlina
Universitas Bina Sarana Informatika
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : computer science co-science

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Perancangan Aplikasi Presensi Karyawan Berbasis Mobile Dengan Qrcode Dan Otentikasi Biometrik Nur Muhamad Rizalul Fahmi; Siti Marlina; Yoseph Tajul Arifin
Computer Science (CO-SCIENCE) Vol. 2 No. 1 (2022): Januari 2022
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v2i1.776

Abstract

The level of performance can be assessed through the level of attendance of employees of a company, attendance is one of the parameters in evaluating employee performance. Several companies or government agencies will always maximize facilities in supporting their work activities, including the employee attendance system. Unfortunately, not all agencies or companies can implement the system properly. Some of them still use the traditional way of recording attendance. This of course can cause some problems both in terms of users or data management, especially if the number of users who make attendance is very large. In addition to data management problems, other problems such as an authentication system that is less secure and prone to fraud can occur. The purpose of this study is to design and implement an android application to make it easier for users to carry out the presence process that is installed on registered devices with the support of scanning a QR code that is generated randomly for each session with biometric authentication support to ensure only verified users run the application. Based on the statement, the application of application-based presence can speed up users in carrying out the presence process with just a few steps, and increase security and user validation.
Optimization of Crop Recommendation Model Using Ensemble Learning Techniques for Multiclass Classification Siti Marlina; Titik Misriati; Riska Aryanti
Computer Science (CO-SCIENCE) Vol. 6 No. 1 (2026): January 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/co-science.v6i1.10044

Abstract

Crop recommendation systems play a crucial role in modern agriculture by helping farmers make data-driven decisions to maximize yield, optimize resource use, and ensure sustainable farming practices. By analyzing environmental and soil parameters, these systems can suggest the most suitable crops for specific conditions, reducing the risks of crop failure and improving overall productivity. This study evaluates the performance of five ensemble learning algorithms—Random Forest, Extra Trees, CatBoost, XGBoost, and LightGBM—for multiclass classification in a crop recommendation system. All models achieved high accuracy above 98%, with Random Forest demonstrating the best and most stable performance. The feature importance analysis revealed that climatic factors, particularly rainfall and humidity, contributed the most to prediction outcomes, followed by macronutrients such as potassium, phosphorus, and nitrogen. In contrast, temperature and soil pH showed relatively lower influence. These findings highlight the dominance of climatic factors over soil chemical properties and demonstrate the capability of ensemble learning methods to capture complex data patterns. Random Forest is recommended as the primary model to support more effective land management and crop cultivation strategies.
Co-Authors Agus Junaidi Ahmad Setiadi Ahmad Syauki Ahmad Yani Aisyah Aisyah Alwi, Fahrizal Azzahro, Fatimah Bela Klaudia Pohwain Delisa, Apifah Dewi Pratiwi Elvan Adinata Erwin Trio Wijaya Fatimah Azzahro Fatimah Azzahro febryan_wiraputra febryan Fuad Rahman Imron Maulana Istaqim Mulkhoir Jekson Rudianto Jenri Wandi Situngkir Khumair Ammanullah Madyan Maulana, Andry Meta Rosaulina Muchamad Ridwan Muhammad Faisal Muhammad Iqbal Aditya Muhammad Rifansyah Muhammmad Qomaruddin Norma Yunita Norma Yunita Numan Musyaffa Nunung Hidayatun Nur Muhamad Rizalul Fahmi Rahmat Hidayat Riska Aryanti Sari Hartini Sari Hartini Syarif Hidayatulloh Syarif Hidayatulloh Syarif Hidayatulloh Syarif Hidayatulloh Titik Misriati Ummu Radiyah Waeisul Bismi Wida Prima Mustika WITRIANA ENDAH PANGESTI Yoseph Tajul Arifin Yuyun Yuningsih