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All Journal Journal Software, Hardware and Information Technology Jurnal Ilmiah Edutic : Pendidikan dan Informatika
Elnursa, Dian Budi
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Computer Science & IT Education

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Sistem Rekomendasi Pemilihan Program MSIB Bagi Mahasiswa Pendidikan Informatika Elnursa, Dian Budi; Nofriana, Vesy; Syamsuri, Agus; Cahyani, Laili
Journal Software, Hardware and Information Technology Vol 3 No 2 (2023)
Publisher : Jurusan Sistem Informasi Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/shift.v3i2.92

Abstract

This study discusses a recommendation system using a content-based filtering method with cosine similarity to help informatics education students choose the right Certified Independent Study and Internship Program (MSIB). The data used is data based on student interests, program and course data available on the MSIB web portal. The content-based filtering method is used to consider the suitability between student preferences and the MSIB program curriculum, while the cosine similarity algorithm is used to calculate the similarity score between different contents. The development of this recommendation system can assist informatics education students in choosing the MSIB program that is in accordance with the preferences of the student's interest profile. The results of the system evaluation obtained an average precision level of 89.4%, indicating that the list of recommendations provided by the system is very good and very relevant according to user preferences.
Sistem Klasifikasi Citra Simplisia Fructus Dalam Obat Tradisional Madura Menggunakan Transfer Learning Pada Algoritma CNN Elnursa, Dian Budi; Tahir, Muhlis; Jakfar, Abdul Azis; Resnanda, Rio Meisya
EDUTIC Vol 10, No 1: November 2023
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v10i1.22957

Abstract

Herbal plants have long been a part of traditional medical practices in various cultures, including in Madurese traditional medicine. One crucial component in the preparation of traditional medicine is Simplisia Fructus. Accurate knowledge of Simplisia Fructus is often a challenge, especially for those unfamiliar with it, as recognizing the various forms of Simplisia Fructus can be difficult due to its numerous types. The utilization of artificial intelligence technology, such as Convolutional Neural Network (CNN), can be a solution to assist in identifying and introducing types of Simplisia Fructus. This research employs transfer learning tested on a small-scale dataset. The dataset comprises six classes: Piperis Nigri Fructus (Black Pepper), Piperis Albi Fructus (White Pepper), Cumini Fructus (Cumin), Amomi Fructus (Cardamom), Tamarindus Indica Fructus Piper Retrofractum Fructus (Javanese Chili), Capsici Frutescentis Fructus (Bird's Eye Chili). The total dataset for all classes is 979. Dataset preprocessing involves dividing it into three parts: 80% for training, 10% for validation, and 10% for testing. Model evaluation using a confusion matrix yielded an accuracy rate of 97%. Additionally, web system testing using blackbox testing resulted in a 99.17% rating in the "Highly Acceptable" category. The system implementation follows the software development life cycle (SDLC), specifically the waterfall model for software development and web coding using the Flask framework. The outcome of this research is a web-based application capable of recognizing types of Simplisia Fructus within the category of Madurese traditional medicine
Co-Authors Jakfar, Abdul Azis Laili Cahyani Nofriana, Vesy Resnanda, Rio Meisya Syamsuri, Agus Tahir, Muhlis