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All Journal ANDHARUPA JITEK (Jurnal Ilmiah Teknosains) Jurnal Teknologi Informasi MURA Martabe : Jurnal Pengabdian Kepada Masyarakat EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) Journal of Computer System and Informatics (JoSYC) Jurnal Nasional Ilmu Komputer Jurnal Pengembangan Sistem Informasi dan Informatika Jurnal Abdimas Bina Bangsa Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Ngabdimas : Pengabdian Pada Masyarakat Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Knowbase : International Journal of Knowledge in Database Ahsana: Jurnal Penelitian dan Pengabdian kepada Masyarakat Jurnal Teknologi Informasi Mura Ngabdimas
Gusmaliza, Debi
Institut Teknologi Pagar Alam, Pagar Alam
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Journal : EDUMATIC: Jurnal Pendidikan Informatika

 1 
Sistem Identifikasi Kualitas Biji Kopi Robusta berbasis Image Processing dengan Support Vector Machine Gusmaliza, Debi; Aminah, Siti
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.28008

Abstract

Pagar Alam is a region producing robusta coffee, a superior coffee variety in Indonesia with a strong taste and high caffeine quality. However, the selection process for robusta coffee beans in Pagar Alam is still traditional. It needs to be more consistent, impacting product quality, causing economic losses, and damaging the region's reputation as a producer of quality robusta coffee. Therefore, innovation is needed in the coffee bean selection process to improve the quality and competitiveness of robusta coffee from Pagar Alam. This study aims to build an image processing-based identification system for the quality of Pagar Alam robusta coffee beans. Identification is made by extracting visual features of coffee beans, including colour, shape, and size. Implementation of the Software Life Development Cycle (SDLC) through the stages of analysis, design, implementation, testing, and maintenance as a method of system development and identification process using a Support Vector Machine (SVM) with a kernel radial basis function (RBF) to extract visual features such as colour, shape, and size of coffee beans. In the system feasibility test, a percentage of 80% was obtained, a dataset of 170 data with a division ratio of 80:20, accuracy reached 91.17%, precision 100%, recall 91.17%, and F1-score 94.79%. These findings show great potential in improving the efficiency of coffee bean selection and the quality of Pagar Alam robusta coffee bean products by utilizing the support vector machine (SVM) algorithm.
Sistem Cerdas Deteksi Status Gizi Anak melalui Eksplorasi Algoritma C.45 dan Forward Feature Selection Arif, Alfis; Gusmaliza, Debi
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.28014

Abstract

The problem of high rates of child malnutrition remains in Pagar Alam City. The lack of understanding of nutritional interventions and the limited ability of Posyandu cadres to conduct accurate nutritional assessments are the main factors. This situation makes it difficult for the community to monitor the nutritional status of children and provide appropriate nutritional intake. This research aims to create an intelligent system for detecting children's nutritional status through the exploration of C.45 algorithm and Forward Feature Selection in Pagar Alam City. This system is designed to detect children's nutritional status and provide recommendations for appropriate nutritional intake based on detection results with variables of children's weight and height. The data used amounted to 7519 data obtained from the Pagar Alam City Health Office. The model we use to build this system is waterfall with stages of planning, analysis, design, development, testing and implementation. Then the method we apply to this system is CRISP-DM and the C.45 algorithm and Forward Feature Selection technique. Our results are in the form of an intelligent system for detecting children's nutritional status, with the results of system testing using test data and training data showing 100% accuracy. In addition, black box testing also proves that the system works well as expected.
Co-Authors Aisah, Elpita Alfis Arif Alfis Arif Arizki, Angga Azhari, Habi Barrorotul Azizah Dedi Setiadi Dedi Setiadi Desi Puspita Elpita Aisyah Fitri Ramadhani Inda Anggraini Jangcik, Idi Lucky Febrian Masdalipa, Risnaini Muntari, Siti Nanda S. Prawira Panyahuti Matondang Puspita, Desi Rahmadhani, Fitri Rony, Zahara Tussoleha Sasmita Sasmita Sherin Junisthia Siti Aminah Syahri, Riduan Yadi, Yadi Yogi Isro Mukti, Yogi Isro