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All Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika Jurnal Sistem Informasi dan Ilmu Komputer
Geraldo Julius Halim
Universitas Matana
Computer Science & IT Decision Sciences, Operations Research & Management

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Aplikasi Sistem Pakar Berbasis Web UntukMendeteksi Ras Kecoa Dengan Metode Forward Chaining Paramitha Gunawan; Geraldo Julius Halim; Kenneth Liem Hardadi; Stanley Tejadinata; Simon Prananta Barus
ikraith-informatika Vol 6 No 2 (2022): IKRAITH-INFORMATIKA Vol 6 No 2 Juli 2022
Publisher : Fakultas Teknik Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (980.176 KB)

Abstract

Kecoa merupakan salah satu jenis serangga yang mudah ditemui di sekitar tempat tinggalkita. Hingga saat ini, 4.500 dari spesies Kecoa telah teridentifikasi di seluruh dunia. Saat ini, banyakmasyarakat yang belum mengetahui berbagai jenis ras Kecoa. Banyak masyarakat sudah memilikismartphone yang dapat mengakses ke berbagai aplikasi web. Penelitian ini bertujuan untukmenyediakan aplikasi sistem pakar berbasis web yang berfungsi untuk mendeteksi ras Kecoa yangingin diketahui. Hasil deteksi dari aplikasi berdasarkan dari karakteristik yang disampaikan olehpengguna. Mesin inferensi menerapkan teknik forward chaining, yang diawali dengan penentuanfakta (data) kemudian berdasarkan basis pengetahuan (knowledge base) nya dihasilkankesimpulan. Pengembangan aplikasi ini menggunakan model prototyping, pengkodeanberbasiskan PHP dengan memanfaatkan framework CodeIgniter 4, gaya pengkodean denganprosedural / struktural. Aplikasi sistem pakar ini berhasil dibangun. Pengembangan lebih lanjut,pengujian oleh pakar, pengembangan aplikasi smartphone.
Rancang Bangun Smart Engine Untuk Mendeteksi Jenis Biji Kopi Dengan Menerapkan Model Convolutional Neural Network Geraldo Julius Halim
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 1 No. 3 (2023): Agustus: Jurnal Sistem Informasi dan Ilmu Komputer
Publisher : Universitas Katolik Widya Karya Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59581/jusiik-widyakarya.v1i3.910

Abstract

Coffee is an agricultural product that not only functions as a fresh drink, but also comes from an annual plant. Indonesia is known as one of the largest countries in coffee production in the world after Brazil, Vietnam and Colombia in 2017. There are two of the most commonly known varieties of coffee, namely arabica coffee (Coffea arabica) and robusta coffee (Coffea canephora). Due to the similarities between the two types of coffee beans, many people, especially those who are not experienced in the world of coffee, have difficulty telling the difference. Therefore, we need a tool that can help overcome this problem, such as smartengine which can classify arabica and robusta coffee beans. The development of this smartengine follows the Software Developer Life Cycle method using a spiral approach which involves several cycle stages. The first stage involves creating a deep learning model using the Deep Learning Life Cycle method which consists of several steps. In the second stage, deep learning models are provided as a service that can be used by other applications through application programming interfaces (APIs). For smartengine implementation, Google Colab with Keras API and TensorFlow backend is used. This smartengine has the ability to detect coffee beans and also allows the retrain process if needed. Testing is carried out using the blackbox method, where the feature functionality of the smartengine is tested. This research succeeded in developing a smartengine that can detect Arabica and Robusta coffee beans.
Co-Authors Barus, Simon Prananta Kenneth Liem Hardadi Paramitha Gunawan Stanley Tejadinata