PENGEMBANGAN APLIKASI MOBILE UNTUK DETEKSI CACAT BIJI KOPI ROBUSTA BERDASARKAN STANDAR NASIONAL INDONESIA

Puja Hanifah; Hanif Ifran Antoni; Shumaya Resty Ramadhani; Yuliska Yuliska

doi:10.35143/jkt.v10i2.6472

Authors

Puja Hanifah Caltex Riau Polytechnic
Hanif Ifran Antoni Caltex Riau Polytechnic
Shumaya Resty Ramadhani Caltex Riau Polytechnic
Yuliska Caltex Riau Polytechnic

DOI:

https://doi.org/10.35143/jkt.v10i2.6472

Keywords:

Android, Coffee Bean, Convolutional Neural Network (CNN), Mobile, Object Detection

Abstract

Coffee is one of the most important raw materials for beverages worldwide. Indonesia is one of the world's leading coffee producers and exporters, and coffee consumption is currently increasing. To maintain the taste of coffee, it is necessary to preserve the quality of coffee beans. The government has established the Indonesian National Standard (SNI) 01-2907-2008 regarding coffee beans. The process of determining the quality of beans is carried out by processors who manually follow the steps outlined in the SNI. Therefore, to accelerate the quality determination process and provide knowledge to the general public, the author has developed a mobile-based coffee bean quality detection application to determine the grade of coffee beans using an Android platform. This application incorporates a machine learning model based on object detection. For the machine learning implementation, TensorFlow and Convolutional Neural Network (CNN) architecture are used. From the training data results, an average accuracy of 84% was achieved, indicating that the application generally detects correctly. Additionally, testing was conducted on several smartphones and at varying detection distances. The testing results showed that the optimal distance for detection is 15 cm to obtain accurate and detectable data. Furthermore, the smartphone with the best performance was the Smartphone A30, featuring a 16 MP resolution camera.

Downloads

Download data is not yet available.

Author Biographies

Shumaya Resty Ramadhani, Caltex Riau Polytechnic

Program Studi Teknik InformatikaPoliteknik Caltex Riau
Yuliska, Caltex Riau Polytechnic

Program Studi Teknik InformatikaPoliteknik Caltex Riau

References

“Industri Kopi di Indonesia - Analisis Produksi, Ekspor & Konsumsi | Indonesia Investments,” https://www.indonesia-investments.com/id/bisnis/komoditas/kopi/item186. Accessed: Nov. 21, 2024. [Online]. Available: https://www.indonesia-investments.com/id/bisnis/komoditas/kopi/ item186

Kemenperin, “Kemenperin: Industri Pengolahan Kopi Semakin Prospektif,” Kemenperin.Go.Id. Accessed: Nov. 21, 2024. [Online]. Available: https://kemenperin.go.id/artikel/21117/Industri-Pengolahan-Kopi-Semakin-Prospektif

B. M. Elliot, “Proses Pengolahan Biji Kopi: Perjalanan dari Biji hingga Kopi yang Nikmat - Sajian Kopi,” https://sajiankopi.com/proses-pengolahan-biji-kopi-perjalanan-dari-biji-hingga-kopi-yang-nikmat/. Accessed: Nov. 21, 2024. [Online]. Available: https://sajiankopi.com/proses-pengolahan-biji-kopi-perjalanan-dari-biji-hingga-kopi-yang-nikmat/

M. W. A. Kesiman and I. Sulaiman, Semi-automatic Ground Truth Image Construction for Coffee Bean Defects Classification Based on SNI 01-2907-2008, vol. 1. Atlantis Press International BV, 2023. doi: 10.2991/978-94-6463-122-7_43.

“Standar Nasional Indonesia (SNI) Biji Kopi: SNI 01-2907- 2008 - Coffee & Cacao Training Center.” Accessed: Nov. 21, 2024. [Online]. Available: https://www.cctcid.com/2018/08/14/standar-nasional-indonesia-sni-sni-01-2907-1999/

R. Sistem, P. Citra, B. Jagung, T. Elektro, P. Magister, and U. Gunadarma, “Penerapan Convolutional Neural Network Deep Learning dalam,” Jurnal Resti, vol. 1, no. 10, pp. 265–271, 2021.

M. I. I. Fata and D. Avianto, “Penerapan Metode Naive Bayes pada Sistem Klasifikasi Kualitas Biji Kopi Robusta,” Jurnal Indonesia?: Manajemen Informatika dan Komunikasi, vol. 5, no. 1, pp. 512–524, 2024, doi: 10.35870/jimik.v5i1.515.

U. UNGKAWA and G. AL HAKIM, “Klasifikasi Warna pada Kematangan Buah Kopi Kuning menggunakan Metode CNN Inception V3,” ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, vol. 11, no. 3, p. 731, 2023, doi: 10.26760/elkomika.v11i3.731.

I. Alfiantama, M. I. Kresnawan, and A. P. Handoko, “Klasifikasi Tingkat Roasting Biji Kopi Dengan Metode CNN,” Prosiding Seminar Nasional Teknologi Dan Sains Tahun 2024, vol. 3, pp. 285–290, 2024.

M. T. Hidayat, P. E. P. Utomo, and B. F. Hutabarat, “Implementasi Model Terlatih CNN pada Aplikasi Android Pendeteksi Jenis Kopi dengan Input Foto Biji, Buah dan Daun,” Digital Zone: Jurnal Teknologi Informasi dan Komunikasi, vol. 15(1), no. 1, pp. 42–52, 2024.

Kamil Fadli, “Pengolahan Citra Digital Menggunakan Metode Yolo Untuk Mendeteksi Kualitas Dari Biji Kopi Berbasis Android,” Jurnal Artificial Inteligent dan Sistem Penunjang Keputusan, vol. 1, no. 1, pp. 120–125, 2023.

G. A. Pratama, E. Y. Puspaningrum, and H. Maulana, “Convolutional Neural Network Dan Faster Region Convolutional Neural Network Untuk Klasifikasi Kualitas Biji Kopi Arabika,” Jurnal Informatika dan Teknik Elektro Terapan, vol. 12, no. 3, Aug. 2024, doi: 10.23960/jitet.v12i3.4887.

I. Nihayatul Husna, M. Ulum, A. Kurniawan Saputro, D. Tri Laksono, and D. Neipa Purnamasari, “Rancang Bangun Sistem Deteksi Dan Perhitungan Jumlah Orang Menggunakan Metode Convolutional Neural Network (CNN),” Seminar Nasional Fortei Regional, vol. 7, pp. 1–6, 2022.

Adi Nugroho, Agustinus Bimo Gumelar, Adri Gabriel Sooai, Dyana Sarvasti, and Paul L Tahalele, “Perbandingan Performansi Kinerja Algoritma Pengklasifikasian Terpandu Untuk Kasus Penyakit Kardiovaskular,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 4, no. 5, pp. 998–1006, 2020, doi: 10.29207/resti.v4i5.2316.