Article Per Year (5 Year)
p-Index From 2020 - 2025
0.778
P-Index
This Author published in this journals
All Journal CCIT (Creative Communication and Innovative Technology) Journal Journal Sensi: Strategic of Education in Information System
Muhammad Suzaki Zahran
Universitas Raharja
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Gamification Based On Blockchain Technology To Enhance Student Centered Learning Qurotul Aini; Ninda Lutfiani; Muhammad Suzaki Zahran
CCIT (Creative Communication and Innovative Technology) Journal Vol 14 No 1 (2021): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (370.775 KB) | DOI: 10.33050/ccit.v14i1.1268

Abstract

Gamification, the application of mechanical game techniques in non-game contexts, has become the primary choice underlies current education. Educational gamification can provide solutions to problems that arise due to traditional learning methods considered less under current human behavior. Blockchain has become a hot topic of discussion that is overgrowing in recent years. Blockchain comes from a world community and company that acts as infrastructure technology that develops in various fields, both industry and education. The main advantage of blockchain is that it is free from third parties so that the security, transparency, and integrity of the blockchain is quite high. At present, research on the blockchain that implemented in the field of educational gamification is still minimal in number. Our paper will identify and discuss the main problems related to education by implementing educational gamification applied to the Blockchain system. This paper proposes an initial gamification model as a foundation for the development of future applications and research.
Deep Learning on Facial Expression Detection : Artificial Neural Network Model Implementation Hendra Kusumah; Muhammad Suzaki Zahran; Paksi Ryandana Cholied; Muhammad Surya Alkusna; Naufal Alwan Hafidhi
CCIT Journal Vol 16 No 1 (2023): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (939.203 KB) | DOI: 10.33050/ccit.v16i1.2518

Abstract

The moods, emotions, and even medical issues of a person can frequently be seen directly reflected in their facial expressions. The fields of social science and human-computer interaction have recently begun to pay more attention to facial emotion detection as a result of this. The primary focus of this study is on the automatic recognition of human facial expressions using an artificial neural network (ANN) model and a technique based on straightforward convolution. The dataset utilized is a self-mined dataset that was obtained by utilizing the web scraping approach on Google Image with the help of the Selenium package for Python. A dataset containing six categories of fundamental human expressions that are likely to be met on a daily basis, namely anger, confusion, contempt, crying, sadness, disgust, and happiness, with a total of 6,016 photos being used. The goal of this research is to determine how accurate the model of artificial neural networks can be in predicting.
Deep Learning Pada Detektor Jerawat: Model YOLOv5 Hendra Kusumah; Muhammad Suzaki Zahran; Kadek Naufal Rifqi; Devi Alawiyah Putri; Ety Meina Wakti Hapsari
Journal Sensi: Strategic of Education in Information System Vol 9 No 1 (2023): Journal Sensi
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1047.273 KB) | DOI: 10.33050/sensi.v9i1.2620

Abstract

Jerawat (Acne Vulgaris) merupakan masalah utama yang sulit untuk dihindari pada masyarakat daerah perkotaan, seperti Jakarta dan sekitarnya. Penyebab utama dari jerawat yaitu tingginya polusi udara yang disebabkan oleh hasil pembakaran transportasi dan sektor industri. Sisa pembakaran ini umumnya mengandung PM (Particulate Matter) dengan ukuran yang cukup kecil (PM2.5 dan PM10) yang mampu masuk ke dalam kulit melalui pori-pori dan bereaksi dengan beberapa senyawa diudara sehingga menyebabkan banyak permasalahan kulit lainnya. Penelitian ini berfokus pada pendeteksian jerawat dengan menggunakan model Deep Learning, yaitu YOLOv5. YOLOv5 dilatih dengan menggunakan tiga optimizer berbeda (SGD, Adam, dan AdamW) sebanyak 100 epochs. Setelah dilakukan pelatihan, didapatkan hasil F1-score dengan optimizer SGD sebesar 43%, Adam 39%, dan AdamW sebesar 40%. Pada penelitian ini, optimizer SGD memiliki nilai F1 tertinggi sehingga dijadikan sebagai optimizer teroptimum yang dapat digunakan pada permasalahan di penelitian ini.
Co-Authors Aris Aris Devi Alawiyah Putri Ety Meina Wakti Hapsari Hendra Kusumah iketut gunawan Ilamsyah Ilamsyah Kadek Naufal Rifqi Muhammad Surya Alkusna Mulyati Mulyati Naufal Alwan Hafidhi Ninda Lutfiani Paksi Ryandana Cholied Qurotul Aini