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Journal : Internet of Things and Artificial Intelligence Journal

A Prototype of Water Turbidity Measurement With Fuzzy Method using Microcontroller Siregar, Victor Marudut Mulia; Sinaga, Kalvin; Hanafiah, M. Ali
Internet of Things and Artificial Intelligence Journal Vol. 2 No. 2 (2022): Volume 2, Issue 2, 2022 [May]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1063.081 KB) | DOI: 10.31763/iota.v2i2.539

Abstract

Water is a source of much-needed living things such as daily needs and transportation routes, and also as a source of energy. Water is also essential as a water quality factor. Good water for treating cold-water ornamental fish with temperatures below 20o Celcius has a maximum water turbidity value of 10 NTU (Nephelometric Turbidity Unit); if the turbidity level is above 10 NTU, the water will be declared cloudy and affect fish health. The object of this research is ornamental aquarium fish with the type of goldfish. The research method used is qualitative. The research flow begins with observing the problem, then designing and simulating the Arduino Uno as a place to process the measuring data. The prototype of this tool aims to show changes in the level of turbidity of water from the value of water turbidity. This prototype uses the fuzzy method to assist the testing process. This study's results for five days showed that 1 out of 5 tests indicated that the aquarium water was cloudy, namely on the fifth day. The results of this study are expected to be implemented in a prototype for measuring water turbidity using the fuzzy method using a microcontroller. The design of this water turbidity measuring instrument is expected to estimate the turbidity of water or liquid correctly, precisely, accurately, with a small error rate, and notify warnings for replacing ornamental fish aquarium water.
A Prototype of Garbage Picker Ship Robot Using Arduino Nano Microcontroller Siregar, Ivana Maretha; Yunus, Muhammad; Siregar, Victor Marudut Mulia
Internet of Things and Artificial Intelligence Journal Vol. 2 No. 3 (2022): Vol. 2 No. 3 (2022): Volume 2 Issue 3, 2022 [August]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1397.082 KB) | DOI: 10.31763/iota.v2i3.540

Abstract

This study aims to create a robot prototype for a garbage collection ship made using an Arduino nano microcontroller. The prototype of this garbage collection robot vessel is controlled by an Android smartphone application that is connected via Bluetooth. The prototype of the robot of the boat was made to deal with piles of garbage that can cause flooding. The method used in making the prototype ship robot along with the controller application begins with identifying the problem, namely the problem of piles of garbage, then needs analysis. Moreover, in this research, what will be developed is in terms of design, ship robot prototype, and ship robot controller design. Next is implementation and testing. The prototype of the garbage collection ship robot uses an Arduino nano microcontroller and in making a controller application using Android Basic 4. Then the trial stage was carried out on an Android smartphone. The results of this study are a prototype ship robot that can move according to commands through a controller application.
The Implementation of Fingerprint Sensors for Fingerprint Reader Prototypes Using a Microcontroller Siregar, Victor Marudut Mulia; Siagian, Nancy Florida
Internet of Things and Artificial Intelligence Journal Vol. 2 No. 1 (2022): Volume 2 Issue 1, 2022 [February]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (903.667 KB) | DOI: 10.31763/iota.v2i1.559

Abstract

 study aims to create a Fingerprint Sensor Implementation System for a Fingerprint Reader Prototype Using a Microcontroller. Implementing the Fingerprint Sensor for the Fingerprint Reader Prototype is run by an application connected by a USB cable. The Fingerprint Sensor Implementation System for the Fingerprint Reader Prototype is made for fast and more accurate attendance and cannot be manipulated. The Fingerprint Sensor Implementation System for the Fingerprint Reader Prototype is the problem. Namely, the attendance problem, which is still manual using books; after that, the design and manufacture of the Fingerprint Reader prototype using a Microcontroller or applications through NetBeans. Then implementation and testing. Then at the trial stage, the Arduino application was carried out. The result of this study is the Fingerprint Sensor Implementation System for the Prototype Fingerprint Reader Using a Microcontroller and development in the Internet of Things.
A Design of an Electric Light Control Device Using Arduino Uno Microcontroller-Based Short Message Service Siregar, Ivana Maretha; Siagian, Nancy Florida; Siregar, Victor Marudut Mulia
Internet of Things and Artificial Intelligence Journal Vol. 2 No. 2 (2022): Volume 2, Issue 2, 2022 [May]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1765.187 KB) | DOI: 10.31763/iota.v2i2.560

Abstract

This study aims to design a tool that can control electric lights remotely. The wastage of using lights often occurs due to the difficulty of the occupants of the house in controlling the lighting, especially when they have to leave the house due to various activities. Lighting needs to be controlled to turn on or not when you want to travel for a long time. In addition, lighting needs to be controlled to significantly reduce the service life of the lamp because it is on continuously. This remote light control system can make it easier for users to cut electricity usage. An electric light controller designed using SMS (Short Message Service) based on the Arduino Uno Microcontroller. This system only applies if the condition of the AC voltage originating from the local PLN is ON. System control is managed using the Atmega328 Arduino Uno Microcontroller. The language used for programming is C/Arduino language, and this tool works on GSM communication systems, especially SMS services. The result of designing this tool is that the user can control (ON/OFF) the lights without being limited by time and place as long as the cellular network is reachable.
A Decision Support System For Selecting The Best Practical Work Students Using MOORA Method Siregar, Victor Marudut Mulia; Hanafiah, M. Ali; Siagian, Nancy Florida; Sinaga, Kalvin; Yunus, Muhammad
Internet of Things and Artificial Intelligence Journal Vol. 2 No. 4 (2022): Vol. 2 No. 4 (2022): Volume 2 Issue 4, 2022 [November]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (474.472 KB) | DOI: 10.31763/iota.v2i4.562

Abstract

This research aims to solve the problem of selecting the best practical work students at the Politeknik Bisnis Indonesia. The current selection of the best practical work students at PBI does not yet use a decision support system approach. This problem is solved by building a Decision Support System using Multi-Objective Optimization based on Ratio Analysis (MOORA) method. The criteria used in this DSS consist of discipline, teamwork, skills, quality of work, and attendance. As for the results of data processing from this study, the three best alternative data were obtained, namely alternative Vivi (A6) as the 1st best Practical Work Students with a score of Yi = 36.5954, Hafiz (A1) as the 2nd best Practical Work Students with a score of Yi = 34.5339, Cahaya (A3) as the 3rd best PKL student with a score of Yi = 33.4767. Through this decision support system that has been built, the selection of the best practical work students can be made quickly and effectively.
Best Employee Selection Using The Additive Ratio Assesment Method Siregar, Victor Marudut Mulia; Sirait, Erwin; Sihombing, Lasminar Lusia; Siregar, Ivana Maretha
Internet of Things and Artificial Intelligence Journal Vol. 3 No. 1 (2023): Vol. 3 No.1 (2023): Volume 3 Issue 1, 2023 [February]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v3i1.589

Abstract

This study aims to solve the problem of selecting the best employees at Café Alvina. In order for employee performance to be further improved and more motivated in doing their work, the leadership gives awards to employees who have a good reputation in it so that all employees are motivated to be able to improve the quality of their respective work. The problem of selecting the best employees is done by building a decision support system. The DSS was built using the ARAS (Additive Ratio Assessment method) method. The criteria used consisted of discipline, responsible, diligent, and cooperation with the weight of each criterion being 0.28, 0.11, 0.19, 0.31, 0.11. The results obtained from this study are the best employee recommendations consisting of employee_004 with a score of 0.9246 ranked 1st, employee_006 with a score of 0.8244, ranked 2nd, and employee_002 with a score of 0.5446 ranked 3rd. Through this decision support system, Alvina's café manager was greatly assisted because it becomes easier to decide on the selection of the best employees at the Café.
Classification of Customer Satisfaction Through Machine Learning: An Artificial Neural Network Approach Siregar, Victor Marudut Mulia; Sinaga, Kalvin; Sirait, Erwin; Manalu, Andi Setiadi; Yunus, Muhammad
Internet of Things and Artificial Intelligence Journal Vol. 3 No. 3 (2023): Vol. 3 No. 3 (2023): Volume 3 Issue 3, 2023 [August]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v3i3.643

Abstract

This study aims to classify customer satisfaction data from Café Alvina using Machine Learning, specifically by implementing the Backpropagation Artificial Neural Network. The data used in this study consists of 70 training data and 30 testing data, with the input layer of the Artificial Neural Network having 5 neurons and the output layer having 2 neurons. The tested Artificial Neural Network models include the 5-5-2 model, 5-10-8-8-2 model, 5-5-10-2 model, and 5-8-10-2 model. Among the four models used in the testing process of the Backpropagation Artificial Neural Network system using Matlab, the 5-10-8-8-2 architecture model performed the best, achieving an MSE (Mean Squared Error) of 0.000999932 during training with 2920 epochs and a testing MSE of 0.000997829. After conducting the testing, the performance of the Artificial Neural Network models was as follows: the 5-5-2 model achieved 81%, the 5-10-8-8-2 model achieved 100%, the 5-5-10-2 model achieved 98%, and the 5-8-10-2 model achieved 96%. Through the implementation of Backpropagation Artificial Neural Network, the classification of customer satisfaction can be effectively performed. The trained and tested data demonstrate that the Artificial Neural Network can accurately recognize the input data in the system.