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INDONESIA
Jurnal Teknologi Informasi dan Multimedia
Published by Puslitbang Sekawan Institute Nusa Tenggara
ISSN : 27152529     EISSN : 26849151     DOI : https://doi.org/10.35746/jtim.v2i1
Core Subject : Science,
Computer Science & IT
Cakupan dan ruang lingkup JTIM terdiri dari Databases System, Data Mining/Web Mining, Datawarehouse, Artificial Integelence, Business Integelence, Cloud & Grid Computing, Decision Support System, Human Computer & Interaction, Mobile Computing & Application, E-System, Machine Learning, Deep Learning, Information Retrievel (IR), Computer Network & Security, Multimedia System, Sistem Informasi, Sistem Informasi Geografis (GIS), Sistem Informasi Akuntansi, Database Security, Network Security, Fuzzy Logic, Expert System, Image Processing, Computer Graphic, Computer Vision, Semantic Web, Animation dan lainnya yang serumpun dengan Teknologi Informasi dan Multimedia.
Arjuna Subject : -
Articles 3 Documents
 1 
Search results for , issue "Vol. 2 No. 4 (2021): February" : 3 Documents clear
Penerapan Algoritma Decision Tree C4.5 Untuk Memprediksi Kelayakan Calon Pendonor Melakukan Donor Darah Dengan Klasifikasi Data Mining Yuda Irawan
Jurnal Teknologi Informasi dan Multimedia Vol. 2 No. 4 (2021): February
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v2i4.75

Abstract

Based on data from UDD PMI Kampar Regency, many donors must have provisions to become blood donors. So far, blood donor selection has been made manually to determine whether potential donors can donate blood or not. Meanwhile, today's information system has not yet explored further information from the large amount of data stored as knowledge. There is a need for organizational consolidation and continuous evaluation of the performance that has been carried out by PMI in dealing with social and humanitarian problems. By making a data mining application with a classification method using the Decision Tree C4.5 Algorithm in predicting someone worthy or not to donate blood, it can be calculated from the results of variables that are continuous or critical, such as variables of age, body weight, hemoglobin (HB) levels, blood pressure. (systolic and diastolic), The data that enters the information system is calculated using the Decision Tree C4.5 Algorithm formula, which results in detailed results and can produce valid and more accurate values. With the data mining application using the Decision Tree Algorithm C4.5 method, potential blood donors' eligibility can be classified based on age, body weight, hemoglobin, and blood pressure. Hemoglobin with the highest gain value (0.861212618) is the variable that most determines blood donation success.
Sistem Pendeteksi Gerak Menggunakan Sensor PIR dan Raspberry Pi Akbar Juliansyah; Ramlah Ramlah; Dewi Nadiani
Jurnal Teknologi Informasi dan Multimedia Vol. 2 No. 4 (2021): February
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v2i4.113

Abstract

Digital security and monitoring systems are entering a new era. Every industry is busy developing security systems according to their individual needs. What can be done is by providing a security perimeter around the assets to prevent unwanted things. There are currently many CCTV (Closed Circuit Television) based security systems; CCTV security systems also have less effective because they require more devices and large enough storage memory. Also, there are other solutions, namely systems that are built using PIR sensors and Raspberry Pi. The PIR sensor is used to detect infrared emissions from humans, so the target object is a human. The PIR sensor also receives heat radiation from humans, so when humans move, this sensor will receive changes in radiation emitted by humans. The purpose of this study is to simulate a solution to the problem of infrastructure design for the development of a physical asset security system using a Wireless Sensor Network and to find out how the security system works using a PIR sensor and Raspberry Pi Model B. The research method used is the Network Development Life Cycle (NDLC) approach. This study illustrates that the Raspberry Pi with hardware capabilities and Rasbian OS and the Python programming language support building a security system. The HC-SR501 PIR sensor can also detect moving objects from the right, left, and front. Email and SMS can be well integrated to produce reports according to the sensor's movement.
Klasifikasi Jenis Kendaraan Menggunakan Metode Extreme Learning Machine Rispani Himilda; Ragil Andika Johan
Jurnal Teknologi Informasi dan Multimedia Vol. 2 No. 4 (2021): February
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v2i4.118

Abstract

The number of vehicles in Indonesia has increased each year, both two-wheeled and four-wheeled vehicles; this is inversely proportional to the development of road infrastructure in Indonesia, which has not experienced much change or improvement. Supposedly, with the increase in the number of vehicles, road infrastructure must also keep pace so that things such as the accumulation of cars on the road do not occur, traffic accidents and congestion become obstacles to carrying out activities. Therefore, it is necessary to make a system to detect and classify vehicles' types in this study using two types of vehicles, namely cars and motorbikes. According to the Indonesian Central Statistics Agency (BPS), it is the highest number. The classification system uses digital image processing techniques, a science to study how an image is formed, processed, and analyzed by a computer to produce information that humans can understand. The method used in this research is the Extreme Learning Machine (ELM), a part of artificial intelligence in feedforward neural networks, where this method can solve regression and classification problems. The data used in this study are 25 images of cars and motorbikes as training data and 15 photos of cars and motorbikes as test data, respectively. The results obtained from this study are a system for classifying two types of vehicles, namely cars and motorbikes, with an accuracy rate of 86.6%.

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