One of the deadly diseases that attacks many women is breast cancer. It was recorded that breast cancer cases in 2020 were 2.3 million, with deaths accounting for 29% of these cases. The BSE technique is an easy way of early identification of breast cancer that can be done independently. However, this technique often goes wrong when practiced, making it ineffective. An early breast cancer detection system is proposed to make it easier for women to carry out early identification independently. Detection is carried out based on the measured temperature of the breast surface. The temperature difference at each point is a reference for the potential for breast cancer. This system was built in a bra and tested with a mannequin as a simulator subject. The MLX90614 temperature sensor, as the primary sensor, succeeded in measuring the surface temperature of the dummy with 99% accuracy. Final testing of the proposed system can also differentiate the temperature differences in each zone.

The main aim of the study is to give the reader the basic meaning of an “Internet of Things” itself, evaluate its main concepts, types, trends, and areas of application, as well as challenges. The study is a basic and fresh literature review from general sources and researches on the topic that has been done recently by the scientific community. Qualitative and quantitative methods of data collecting have been used. As a result, this paper can offer new interpretations, theoretical approaches, or other ideas. Mendeley referencing application was used to cite and give credits to the authors of a raw material used in this study. This term paper will give an excellent understanding to other researchers who are trying to build basic concepts within the topic, or to those who wish to begin their researches on “IoT” furthermore and will provide effective and accumulation knowledge. Also, can be useful as a raw material to the introductory courses regarding “IoT”.

Security is very important everywhere, including in the campus environment. To provide security and comfort for those who park their vehicles, a parking application is needed that can provide vehicle security while undergoing academic activities on campus. QR code (Quick Response Code) is a technology for converting written data into a two-dimensional code, which is printed on a more compact medium capable of storing various types of data. The most common individual part used to identify a person is the face because it has the unique characteristics of everyone. Histogram of Oriented Gradient (HOG) is a feature extraction used for face identification based on histogram of gradient orientation and gradient magnitude. This application is implemented using the Dlib library for facial recognition. The implementation of this method is expected to improve parking security and provide a record of parked vehicles. The results of testing the implementation of facial recognition methods into android applications show very satisfactory results. With the results of testing the QR code scanning accuracy of 100% and an accuracy of 90% for a 7% damage rate and an accuracy of 85% for a 15% damage rate, and the results of facial recognition testing of 90% on face photos wearing helmets and an accuracy of 92% on photo of face without helmet.

In the implementation of Digital Forensics, one of the derivatives of practice is the handling of Digital Evidence. Handling Digital Evidence requires important steps and procedures. Digital evidence is a source of artifacts in handling a digital-based crime case, one of which comes from digital storage. In this research, the author will design a framework for Digital Forensic investigations by simulating digital evidence in the form of a non-volatile architecture. The reference commonly used by researchers in previous articles is the National Institute of Justice (NIST). The framework is a reference and steps in the practice of acquiring digital evidence. The purpose of designing this framework is as a legal procedure that is specifically implemented in the practice of acquiring non-volatile digital evidence. In the design, the author conducted a literature study on the NIST SP 800-86 and ISO 27037:2012 standards and then combined them in a hybrid terminology. The output of this research is to combine the two standards to become framework as reference for handling and investigating Digital Forensic science.


 
 
 Network security on the webserver is the most important part to ensure integrity and service for users. Web servers are often the target of attacks that result in data corruption. One of them is the SYN Flood attack, which is a type of Denial of Service (DOS) attack that provides massive SYN requests to the webserver. This research is to analyze attack indications and maintain system security from the threat of data flooding. One way to maintain a computer network security system is to use Snort as an IDS (Intrusion Detection System). Snort is software that functions to detect intrusions. Data packets passing through network traffic will be analyzed first. Data packets detected as intrusions will trigger an alert which is then stored in a log file. That way, network administrators can find out intrusions that occur on computer networks. The method of testing flood attack data is using the penetration testing method. The three test samples are data flooding attacks against ICMP, UDP, and TCP protocols. The results obtained when testing flooding attack data where detection sensors can detect all attacks and all attack samples, while the warnings generated by Snort are shown in a web form which can be seen in the detail of each attack that occurred.
 
 

E-Commerce is a concept that quite developed in the field of information technology. E-Commerce gives more convenience and advantages when compared with conventional, including all the information needed by cunsomers can be accessed more detail, without being limited with place and time, and transactions process can be done much easier. So that the implementation of this system will make it easier and more profitable for many parties, both consumers and sellers. This development method in building E-Commerce application is based on the theory of the Waterfall Model. Waterfall is a software development methodology, which proposes an approach to systematic and sequential software which starts at the system progress level throughout the analysis, design, code, testing and maintenance. The purpose of this research is to produce Pekanbaru city snack E-Commerce application based on android. By implementing this system will make it easier and more profitable for many parties, both consumers and sellers.

The petroleum industry is developing technology to increase oil recovery in reservoirs. One of the technologies used is Enhanced Oil Recovery (EOR). Selecting an EOR method for a specific reservoir condition is one of the most challenging tasks for a reservoir engineer. This study tries to build a fuzzy logic-based screening system to determine the EOR method. It created the system intending to be able to assist in selecting and determining the appropriate EOR method used in the field. There are nine input criteria used to screen the EOR criteria, namely: API Gravity, Oil Saturation, Formation Type, Net Thickness, Viscosity, Permeability, Temperature, Porosity, Depth criteria. The output criteria generated from the calculation of the EOR screening criteria are 14 outputs, namely: CO2 MF Miscible Flooding, CO2 IMMF Immiscible Flooding, HC MF Miscible Flooding, HC IMMF Immiscible Flooding, N2 MF Miscible Flooding, N2 IMMF Immiscible Flooding, WAG MF Miscible Flooding , HC+WAG IMMF Immiscible Flooding, Polymer, ASP, Combustion, Steam, Hot Water, Microbial. In this system, 512 rules are generated to produce 14 different outputs of the EOR method, with Mamdani's Fuzzy Inference reasoning. This fuzzy-based screening system has an accuracy rate of 80.95%, so this system is suitable to be used to assist reservoir engineers in determining the appropriate EOR method to be used according to the conditions in the reservoir. The sensitivity level of the system only reaches 53.1%, while the specificity level reaches 94%.

In this new normal era, many activities began to operate again, such as offices, malls, etc. This creates a potential mass crowd. The public must follow health protocols as recommended by the government, including wearing masks and checking the temperature to anticipate the spread of the coronavirus. This study tested a tool that included image processing and artificial intelligence to help implement health protocols as recommended by the government. This tool connects Raspberry PI, Thermal Camera (amg8833), Pi Camera, an ultrasonic sensor with Multiple Linear Regression and Deep Learning algorithms. The purpose of this tool is to detect body temperature and detect the use of masks. The system will check on the pi camera frame whether the person is wearing a mask or not. The system is trained using the Deep Learning method to detect the use of masks. The system will check the temperature of the human body and the distance between humans and the tool. Temperature and distance data are entered in multiple linear regression formulas to get more accurate results. The processed results of the system will be displayed on the monitor screen if detected using a mask and the normal temperature will be green and if it is not detected it will be red and give a warning sound. The data is sent to the server and displayed via the web. We found that this tool succeeded in detecting body temperature within a distance of 1 to 3 meters with an accuracy of 99.49%, detecting people using masks with an accuracy of 94.71%, and detecting people not wearing masks with an accuracy of 97.7%.

In the Industrial 4.0 era, almost all activities and transactions are carried out via the internet, which basically uses web technology. For this reason, it is absolutely necessary to have a high-performance web server infrastructure capable of serving all the activities and transactions required by users without any constraints. This research aims to design a high-performance (high availability) web server infrastructure with low cost (low cost) and energy efficiency. low power) using Cluster Computing technology on the Raspberry Pi Single Board Computing and Docker Container technology. The cluster system is built using five raspberry Pi type 4B modules as cluster nodes, and the Web server system is built using docker container virtualization technology. Meanwhile, cluster management uses Docker Swarm technology. Performance testing (Quality of Service) of the cluster system is done by simulating a number of loads (requests) and measuring the response of the system based on the parameters of Throughput and Delay (latency). The test results show that the Raspberry Pi Cluster system using Docker Swarm can be used to build a High Availability Server system that is able to handle very high requests that reach Throughput = 161,812,298 requests / sec with an Error rate = 0%.

Dalam kehidupan sehari-hari semua orang tidak terlepas dari berbagai macam informasi, terutama informasi yang dihasilkan dari sebuah website. Selain dari pemrograman yang handal, resource yang lain seperti webserver juga sangat perlu diperhatikan agar website dapat berjalan dengan baik. Seiring meningkatnya kebutuhan konten dan pengunjung website, maka website sering mengalami crash atau request yang overload. Hal ini dikarenakan masih menerapkan single server untuk menangani website tersebut. Untuk mengatasi permasalahan tersebut, perlu diterapkan sebuah load balance cluster, dimana beban kerja webserver tersebut dapat didistribusikan ke beberapa node cluster. Algoritma penjadwalan weighted round robin merupakan salah algoritma penjadwalan dimana beban kerja server dapat berjalan seimbang dengan cara memberikan jumlah bobot ke masing-masing node cluster.