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All Journal IKRA-ITH Informatika : Jurnal Komputer dan Informatika Jurnal Elektronika dan Teknik Informatika Terapan
Kenneth Liem Hardadi
Universitas Matana
Computer Science & IT

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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 Aplikasi Pengelola Smart Engine Untuk Deteksi Jenis Biji Kopi Dengan Menerapkan Web Service Kenneth Liem Hardadi; Simon Prananta Barus
Jurnal Elektronika dan Teknik Informatika Terapan Vol. 1 No. 2 (2023): Juni : Jurnal Elektronika dan Teknik Informatika Terapan ( JENTIK )
Publisher : Politeknik Kampar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59061/jentik.v1i2.297

Abstract

Coffee is one of the agricultural commodities that plays a vital role as a refreshing beverage and is categorized as a perennial crop. Currently, coffee is becoming a trend and an integral part of people's lifestyles. Coffee is divided into two main varieties: robusta and arabica, each with its distinct characteristics. Differentiating between robusta and arabica coffee beans can be challenging due to their similar physical appearance. Deep learning-based smart engines can offer a solution to this problem, although their complex user interfaces often hinder accessibility. To address this issue, a Spiral development method was employed to build an application using PHP programming language and Codeigniter 4 framework. This application facilitates easy detection of coffee bean types through both a website and a RESTful API, allowing users to access it online from any device. The application underwent comprehensive black box testing, demonstrating successful functionality aligned with the initial design objectives. It is expected that this application will solve the identified problem and provide significant assistance to coffee enthusiasts and the general public in easily distinguishing between various coffee bean types. Users can effortlessly recognize and differentiate between different coffee beans while obtaining useful information about their preferred coffee beans.
Co-Authors Barus, Simon Prananta Geraldo Julius Halim Paramitha Gunawan Stanley Tejadinata