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All Journal Jurnas Nasional Teknologi dan Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Engineering Indonesian Journal for Health Sciences (IJHS) Jurnal Hilirisasi IPTEKS (JHI) CHIPSET (Journal on Computer Hardware, Signal Processing, Embedded System and Networking) The Indonesian Journal of Computer Science
Lathifah Arief, Lathifah
Universitas Andalas
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Electrical & Electronics Engineering Engineering Health Professions Nursing Public Health Veterinary Other

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Journal : The Indonesian Journal of Computer Science

 1 
Evaluation of Cloud-Based Ethereum Network Performance Sundara, Tri; Arief, Lathifah
The Indonesian Journal of Computer Science Vol. 11 No. 3 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/IJCS.V10I2.446

Abstract

Ethereum, that enable development of decentralized applications, will likely to leverage cloud computing. In this research, we evaluate the performance of a cloud-based Ethereum network. We researched 3 Ethereum networks, namely: Ethereum mainnet, Ethereum testnet Ropsten, and Ethereum testnet Rinkeby. We analyze the computational resource utilization required to run an Ethereum node for a month as well as the costs involved. Research shows that the utilization of computing resources on the Main Net is generally higher than on the Test Net network. Computing resources used in the cloud cost thousands of dollars and this will increase as the number of nodes running to support the Ethereum network.
Portable Cough Classification System Based on Sound Feature Extraction Using Tiny Machine Learning Arief, Lathifah; Risky, Mutiah; Derisma; Kasoep, Werman; Puteri, Nefy
The Indonesian Journal of Computer Science Vol. 11 No. 1 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v10i2.3001

Abstract

Cough is one of the most common markers that can provide information in diagnosing a disease. More specifically, cough is a common symptom of many respiratory infections. There are several types of cough, including: dry cough, wet cough (cough with phlegm), croup cough and whooping cough. This study aims to create a system that can classify the sounds of coughing up phlegm, dry cough, whooping cough and croup cough. The system development uses the concept of tiny machine learning. In the system built, Arduino Nano 33 BLE Sense is used as a control device and LED is used as an output device. In this study, the classification of dry cough, wet cough, croup cough and whooping cough was performed using the MFCC voice feature extraction. In the process of classifying coughing sounds using the Neural Network Classifier, the system has a percentage of dataset training accuracy from a total of 5 classes (croup, dry, noise, wet, whooping) of 97.1% by applying an epoch value of 500, window size 3000ms and a window increase of 500ms.
Penerapan YOLO Untuk Identifikasi Dan Penayangan Informasi Peralatan Laboratorium Dalam Mendukung Merdeka Belajar Arief, Lathifah; Muhammad, Fauzan
The Indonesian Journal of Computer Science Vol. 11 No. 1 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v10i2.3015

Abstract

Computer Laboratory serves as the place to conduct experimentation in regards to Computer System. During the practical or experimentation, it is not rare to see the confusion or misunderstanding of what to do or what is needed. This problem comes from lack of knowledge and how one or more dsvice is identical to another. With the development of technology, the new method using machine learning is made for soving these problems. The system with You Only Look Once method will detect the device on camera, and then showing the file that contains datasheet explaining what that device is and how to use it. This system can also be used at anytime so everyone can learn through it and improves the efficieny of the study.
Utilizing Machine Learning and Cloud Services to Improve Disaster Information Systems Arief, Lathifah; Sundara, Tri
The Indonesian Journal of Computer Science Vol. 11 No. 1 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i1.3090

Abstract

Cloud services have enabled various information system developments. In this paper, we explore the use of Amazon Sagemaker cloud services and AWS Data Exchange in disaster information systems. We proposed cloud architecture for a disaster information system and found some of the datasets provided on AWS Data Exchange could be leveraged for such system.
Co-Authors Anjasmara, Rima Derisma Derisma Derisma Derisma Derisma Dini Ramadhani Salsabila Dodon Yendri Dody Ichwana Putra Fajril Akbar Fauzan Muhammad Hadelina, Rizka Hadi Candra Hafizh Ramli Hersyah, Mohammad Hafiz Heru Saputra Iqbal Saputra Kasoep, Werman M. Hafiz Hersyah M. Ridho Heranof Muhammad Abdul Hadi Muhammad, Fauzan Multri Okta Ilham, Randa Mutiah Risky Nefy Puteri Nefy Puteri Novani Nelia Afriyeni Novani, Nefy Puteri Purwoko, Agus Puteri, Nefy Rafika Oktova Ratna Aisuwarya Rauza Sukma Rita Rima Anjasmara Risky, Mutiah Salsabila, Dini Ramadhani Tri A Sundara Tri Sundara Tri Sundara, Tri Werman Kasoep Werman Kasoep Yolanda, Desta Yuni Anggraini