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All Journal dCartesian: Jurnal Matematika dan Aplikasi Jurnal Informatika dan Rekayasa Perangkat Lunak Journal of Engineering, Computer Science and Information Technology (JECSIT) Indonesian Journal of Intelligence Data Science (IJIDS) Jurnal Teknoinfo
Mahardika Inra Takaendengan
Universitas Sam Ratulangi
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Library & Information Science Mathematics

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Journal : Jurnal Teknoinfo

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Implementation of Integrated Registration System at BBI Using Zoho CRM and ISO 9001:2015 Marli Trivana Layan; Winsy Christo Deilan Weku; Stephano Caesar Wenston Ngangi; Mahardika Inra Takaendengan
Jurnal Teknoinfo Vol. 20 No. 1 (2026): Period January 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/teknoinfo.v20i1.270

Abstract

This study aims to improve the registration system at Brilliant Brain Indonesia (BBI) by implementing Zoho CRM, aligned with ISO 9001:2015 Clause 8 requirements. The existing system faced challenges including time-consuming paper-based forms, frequent data entry errors due to manual input, and fragmented data management across two unintegrated systems. Using the Business Process Improvement (BPI) methodology, which includes Organizing for Improvement, Understanding the Process, and Streamlining phases, this research analyzed and optimized the registration workflow. The Wilcoxon signed-rank test was applied to evaluate the statistical significance of improvements before and after system implementation. The customized Zoho CRM platform utilized features such as webforms, workflow automation, automated validation, and real-time analytics. Results showed a significant reduction in registration processing time from an average of 20 minutes 44 seconds to 17 minutes 55 seconds per registration (14% decrease), and a dramatic reduction in data input time from 4 minutes 3 seconds to 4 seconds per record (98% decrease). These improvements enhanced operational efficiency, data accuracy, and compliance with ISO 9001:2015 Clause 8. Although further module development is needed to fully complete documentation processes, this study demonstrates that integrating cloud-based CRM systems with quality management frameworks can substantially improve educational service operations.
Web-Based Library Information System Using ReactJS: Case Study at the Faculty of Mathematics and Natural Sciences, Sam Ratulangi University   Freya Emily Theresia Tombokan; Benny Pinontoan; Mahardika Inra Takaendengan; Gifriend Yedija Talumingan
Jurnal Teknoinfo Vol. 20 No. 1 (2026): Period January 2026
Publisher : Universitas Teknokrat Indonesia

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Abstract

Digital transformation has reshaped the library landscape, transitioning from manual to digital systems. This research aims to develop a web-based library information system for the Faculty of Mathematics and Natural Sciences (FMIPA) at Sam Ratulangi University (UNSRAT) using ReactJS, NestJS, and PostgreSQL. The system is designed to address the inefficiencies of manual library management and enhance book information accessibility. The Rapid Application Development (RAD) method was applied, encompassing requirements planning, user design, construction, and cutover. Data were collected through interviews, observations, and surveys. The results demonstrate that the system facilitates online book searching and borrowing, accelerates administrative processes, and improves service efficiency. User satisfaction evaluation through surveys showed high levels of approval. This system is expected to support teaching and learning processes and enhance the quality of library services at FMIPA UNSRAT.
Classification of Green Apple Varieties using Convolutional Neural Network based on RGB Color with MobileNetV2 Alnofri Rano Masiku; Nelson Nainggolan; Siska Ayu Widiana; Mahardika Inra Takaendengan
Jurnal Teknoinfo Vol. 20 No. 1 (2026): Period January 2026
Publisher : Universitas Teknokrat Indonesia

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Abstract

Manual classification of green apple varieties is often time-consuming, labor-intensive, and prone to human subjectivity. This research aims to develop an automated classification model for green apple types based on RGB color features using Convolutional Neural Network (CNN) with MobileNetV2 architecture. The dataset comprises 1,170 images of three green apple varieties: Golden Delicious, Granny Smith, and Manalagi. Image preprocessing steps include cropping, resizing, background removal, and RGB conversion to enhance feature extraction. The model training and evaluation utilize 5-fold Cross Validation to ensure robustness and generalization. Experimental results demonstrate that the proposed model achieves an average accuracy of 96%, precision of 96.33%, recall of 96.33%, and F1-Score of 96.33%. Furthermore, the model is implemented in a web-based application using the Flask framework to predict apple varieties from input images. Testing on new images shows classification confidence levels of 80.92% for Granny Smith, 87.38% for Manalagi, and 78.43% for Golden Delicious apples. This study confirms that CNN with MobileNetV2 and RGB color features effectively classifies green apple varieties, offering practical implications for agricultural automation and quality control.
Co-Authors Adrian Philip Marthinus, Adrian Philip Alnofri Rano Masiku Benny Pinontoan Chriestie E.J.C. Montolalu Christian Alderi Jeffta Soewoeh Dodisutarma Lapihu Eliasta Ketaren Freya Emily Theresia Tombokan Gifari I. S. Awam Gifriend Yedija Talumingan Jullia Titaley Khafidlin Triatama Lusyane Humune Marli Trivana Layan Muhammad Najib Dwi Satria Nelson Nainggolan Nur Fadli Utomo Parjito Parjito Ramadhan Paninggalih Sanriomi Sintaro Siska Ayu Widiana Stephano Caesar Wenston Ngangi Tenda, Edwin Winsy Christo Deilan Weku Wisard Widsli Kalengkongan Zakki Pribadi