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All Journal Scientific Journal of Informatics JURNAL MEDIA INFORMATIKA BUDIDARMA
Cahya, Nilam
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Implementasi Framework Codeigniter Pada Perancangan Chatbot Interaktif Menerapkan Metode Waterfall Cahya, Nilam; Triayudi, Agung; Benrahman, Benrahman
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 1 (2021): Januari 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i1.2623

Abstract

In delivering information related to lecture activities at the National University, the Information Systems Study Program has prepared a special website, namely http://si.ftki.unas.ac.id/. The website contains a lot of information about important activities or announcements, it's just that the need for other supporting information makes users have the need to ask further questions regarding the information contained on the website. Actually the Website http://si.ftki.unas.ac.id/ has provided the RoomChat feature for question and answer sessions between admin and users, but this feature is still ineffective, such as there is a time lag between questions and responses or unable to provide services for 24 jam. In this study, the authors wanted to design the chatbot feature using the waterfall method, which was carried out sequentially starting from the planning, analysis, design, implementation, testing to maintenance stages. While working on the application, this chatbot was created using the Codeigniter framework with the mysqli database. This chatbot feature will result in a more interactive question and answer session, so that two-way communication with users can run smoothly.
Application of Deep Learning Using Convolutional Neural Network (CNN) Method For Women’s Skin Classification anton, Anton; Nissa, Novia Farhan; Janiati, Angelia; Cahya, Nilam; Astuti, Puji
Scientific Journal of Informatics Vol 8, No 1 (2021): May 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i1.26888

Abstract

Facial skin is skin that protects the inside of the face such as the eyes, nose, mouth, and others. Facial skin consists of several types, including normal skin, oily skin, dry skin, and combination skin. This is a problem for women because it is difficult to recognize and distinguish their skin types this is what causes some women to find it difficult to determine the right make-up and care products for their skin types. In this study, the Convolutional Neural Network (CNN) method is the right method for classifying women's skin types from the age of 20-30 years by following several stages using Python 3.5 programming with a depth of three layers and the results of this research using the CNN method get the results of the accuracy value good at 67%
Application of Deep Learning Using Convolutional Neural Network (CNN) Method for Women’s Skin Classification Anton, Anton; Nissa, Novia Farhan; Janiati, Angelia; Cahya, Nilam; Astuti, Puji
Scientific Journal of Informatics Vol 8, No 1 (2021): May 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i1.26888

Abstract

Purpose:  Facial skin is the skin that protects the inner part of the face such as the eyes, nose, mouth, and other. The skin of the face consists of some type, among others, normal skin, oily skin, dry skin, and combination skin. This is a problem for women because it is hard to get to know and distinguish the type of peel, this is what causes some women’s, it is hard to determine which product cosmetology and proper care for her skin type. Methods: In this study, the method of the Convolutional Neural Network (CNN) is an appropriate method to classify the type of the skin of women of age 20 – 30 years by following a few stages using Python 3.5 with a depth of three layers. In this study, the method used CNN to distinguish the type of skin of the label object of the type of skin that a normal skin type, oily, dry and combination. A combination skin type is composed of normal and dry skin types. Result: The process of learning network CNN to get the results of the value by 67%. As for the classification of Normal skin 100%, the type of the skin of the face 100% Dry, kind of Oily facial skin 100% and combination skin type (Normal and Dry) to 100%. Novelty: It can be concluded that the use of the method of CNN in automatic object recognition in distinguishing the type of leather as a material consideration in determining the object of the image. And the classification method using CNN with the Python program to be able to classify well.
Co-Authors Agung Triayudi Anton Anton Benrahman Janiati, Angelia Nissa, Novia Farhan Puji Astuti