Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
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This Author published in this journals
All Journal Journal of Informatics, Information System, Software Engineering and Applications (INISTA) ICTEE (Engineering Journals of Information, control, telecommunication and electrical) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Afzal Ziqri
Institut Teknologi Telkom Purwokerto
Computer Science & IT Electrical & Electronics Engineering

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Evaluasi User Experience Aplikasi Google Meet Menggunakan Metode Usability Testing Afzal Ziqri; Aditya Abi Riestianto; Ariq Cahya Wardhana
Journal ICTEE Vol 3, No 2 (2022): Juli
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jictee.v3i2.2603

Abstract

The pandemic forced almost everyone in the world to work from home. Almost all work is done online from home. With the Google Meet application, it can help make it easier for everyone to do their work even online. Since 2020 the Google Meet application has become a very popular application among the public. Google Meet is one of the many meeting applications that have been widely used by the public. To find out the usability value of the application, it is necessary to carry out a testing process through a usability test. SUS (System Usability Scale) is an evaluation that is used as a result of software usability. 
Expert System for Identifying Pregnant Using Forward Chaining Gilang Aditia; Afzal Ziqri; Aldhan Tri Maulana; Faisal Dharma Adhinata
Journal of INISTA Vol 5 No 2 (2023): May 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v5i2.494

Abstract

Pregnancy is a biological process in which sperm and eggs meet each other to fertilize, and the fetus is formed in the uterus. But it's difficult; pregnant mothers sometimes have problems or discomfort during pregnancy. In addition, in areas far from the city, there are many obstacles to consulting an obstetrician. Therefore, it will be dangerous if mothers experience problems and find it difficult to get first aid. This research aims to create an expert system for pregnant women where it is not difficult for a mother to go to the doctor to ask about her complaints. The solution offered in this study is easy to access to the SP BUMIL website and automatically enters all mothers' complaints into the system. This system also provides a diagnosis and advice to pregnant women as to the best steps and an explanation of what the pregnant woman is suffering from. This expert system uses the forward chaining method, which has the advantage of producing a solution to a problem; in other words, being able to consider a problem and draw conclusions according to the facts. On this website, there is a disease information menu and also the results of the diagnosis
Klasifikasi Sampah Organik dan Non-Organik Menggunakan Convolutional Neural Network Abdurrahman Ibnul Rasidi; Yolanda Al Hidayah Pasaribu; Afzal Ziqri; Faisal Dharma Adhinata
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 1 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i1.4314

Abstract

Garbage is a unique problem in Indonesia. From ordinary waste to limited emergency plastic waste, Indonesia is the second-largest source of plastic waste in the world. Separate collection and disposal of waste is one way to reduce the waste generated by society and industry in Indonesia. Sorting out the types of waste is the first step before the recycling process. In the field of Computer Vision research, it is difficult to see the type and form of waste with a camera, therefore this study aims to overcome this problem by using Deep Learning technology which is expected to be implemented in the whole of Indonesia starting from some of the largest waste-producing cities. Deep Learning is a computer (AI) technique for learning like a human - with experiments being a Part of Machine Learning that can be used to classify images. The method used in this study uses the Convolutional Neural Network (CNN) method which can be used to detect and recognize objects in an image, which can be used to create an automatic waste classification system. Broadly speaking, CNN utilizes the convolution process by moving a convolution kernel (filter) of a certain size to an image, the computer gets new representative information from the results of multiplying that part of the image with the filter used. The test results show that the CNN method can classify inorganic waste with accuracy. 96% and organic waste with an accuracy of 62%.
Co-Authors Abdurrahman Ibnul Rasidi Aditya Abi Riestianto Aldhan Tri Maulana Ariq Cahya Wardhana Faisal Dharma Adhinata Gilang Aditia Yolanda Al Hidayah Pasaribu