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All Journal Jurnal Sains dan Teknologi Jurnal Tekinkom (Teknik Informasi dan Komputer) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Masyarakat Indonesia Internet of Things and Artificial Intelligence Journal

Sinaga, Kalvin

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Author-ID : 1409584

Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Veterinary Other

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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)

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 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)

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.

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

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.

Decision Support System for Selecting Social Assistance Recipients using The Preference Selection Index Method Parapat, Eka Pratiwi Septania; Sinaga, Kalvin; Sirait, Erwin; Manalu, Andi Setiadi
Internet of Things and Artificial Intelligence Journal Vol. 3 No. 4 (2023): Vol. 3 No. 4 (2023): Volume 3 Issue 4, 2023 [November]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v3i4.662

This research aims to solve the problem of selecting social assistance recipients in the Nagori Moho area, Java Marajah Bah Subdistrict, Jambi, Simalungun District; in order to obtain the right targeted recipients of social assistance, the Nagori office carries out the selection of its residents, this selection is carried out by implementing a computer-based decision support system (DSS). The decision support system uses the PSI method. The criteria used in this method consist of economic condition, income, jobs, age, and dependents of the school children. The results obtained from this research are recommendations for the population receiving aid with results consisting of rank 1 with the alternative value S_Purba with a value of 0.9286, then rank two with the alternative F_Azhar with a value of 0.7599, and rank 3 is Jumiati with a value of 0.7163. This decision support system can make it easier for the Nagori office to select residents worthy of assistance.

Co-Authors Erwin Sirait, Erwin Fahmi Sulaiman Henny Pratiwi Henny Pratiwi, Henny M. Ali Hanafiah, M. Ali Manalu, Andi Setiadi Muhammad Ilham Riyadh, Muhammad Ilham Muhammad Yunus Parapat, Eka Pratiwi Septania Saragih, Roy Sahputra Siagian, Nancy Florida Siregar, Victor Marudut Mulia Sonang, Sahat

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