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All Journal Jurnal Ilmiah KOMPUTASI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Mantik Jurnal Teknologi Informasi dan Multimedia Journal of Computer System and Informatics (JoSYC) Jurnal PkM (Pengabdian kepada Masyarakat)
Arie Gunawan
Universitas Nasional
Arts Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences

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Journal : journal of computer system and informatics josyc

 1 
Diagnosa Gejala Kecanduan Bermain Game Online dengan Metode Fuzzy Tsukamoto dan Fuzzy Sugeno Rivaldi Okta Pratama; Agung Triayudi; Arie Gunawan
Journal of Computer System and Informatics (JoSYC) Vol 4 No 2 (2023): Februari 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i2.3002

Abstract

In this increasingly developing era, online games are a type of game that can provide its own pleasure for its users. Because of that, many people play online games continuously until they forget the time, it can even become addictive. Not many people have the opportunity and willingness to consult an expert to overcome their addiction. Therefore, an expert system is needed to diagnose online game addiction symptoms. With this research, an expert system was produced to diagnose symptoms of addiction to playing online games, which makes it easy to use. This expert system produces output in the form of a diagnosis whether a person is addicted to playing online games or not. Tsukamoto's fuzzy logic method is one method for a good problem solving control system to implement. The Sugeno fuzzy logic used in the expert system in this study functions to seek and find solutions to symptoms of addiction to playing online games. The results of this study can compare the Sugeno fuzzy with the Tsukamoto fuzzy to find the intended value or success by comparing the two fuzzy to find the highest value in this study. The results of this study obtained an accuracy value of 100% from 100 sample data.
Komparasi Metode Weighted Product (WP) Dan Simple Additive Weighting (SAW) Pada Sistem Pendukung Keputusan Dalam Menentukan Pembangunan Infrastruktur Desa Yandi Makmur; Agung Triayudi; Arie Gunawan
Journal of Computer System and Informatics (JoSYC) Vol 4 No 2 (2023): Februari 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i2.3016

Abstract

Government initiatives to give villages more power include village development. In accordance with local village government policies, there are specific criteria for allocating village infrastructure development funds. The goal is for village infrastructure development to be more equitable and directed. Priorities for developing village infrastructure must be decided. Decisions on village infrastructure development are still made by voting, and often more significant developments have to be postponed due to a vote loss. To determine priorities for village infrastructure development, village officials can use a decision support system (Decision Support System). Village infrastructure development priorities are determined using the Simple Additive Weight (SAW) and Product Weight (WP) methodologies. Each proposal will be assessed according to the criteria selected by the village to set development priorities. It is hoped that by creating a decision support system it will be easier, more accurate and faster to determine development priorities in the village of Rawa Panjang when using the SAW and WP methodologies to prioritize village infrastructure development. Results Based on the research, it is recommended to use the WP method to determine the ranking, because the level of suitability of data in the field is higher than SAW which was found to be 99.87 WP and SAW was determined to be 99.27%
Diagnosa Kecanduan Gadget Pada Anak Usia Dini dengan Metode Fuzzy Sugeno dan Fuzzy Mamdani Rama Setiawan; Agung Triayudi; Arie Gunawan
Journal of Computer System and Informatics (JoSYC) Vol 4 No 2 (2023): Februari 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i2.3018

Abstract

The Tsukamoto fuzzy logic method is one method for problem solving control systems, which is good to implement with a system method that will use human knowledge that can be entered into the system on a computer to find and solve problems that usually require expertise or expertise. looking for a truth. The purpose of this study is to analyze and find methods that can be used in diagnosing gadget addiction in early childhood, and to compare two fuzzy methods that will be used as methods to diagnose gadget addiction in early childhood. The methods used in this study are expert system methods, namely fuzzy sugeno and fuzzy mamdani. The results showed that many children experience addiction to gadgets with a susceptibility to 1-4 hours of use time in one day. The level of addiction in boys is somewhat higher than in girls, the large number of children who are addicted to gadgets at a very early age to recognize and experience addiction to gadgets is the author's main goal to make this thesis if it can be used to monitor the level of gadget addiction in early childhood, research on the contribution of the method of applying the method the fuzzy sugeno and fuzzy mamdani algorithms in diagnosing gadget addiction in early childhood and the contribution to this method is the application of the fuzzy mamdani method and the fuzzy sugeno method to the fuzzy algorithm and the problem of this research is to determine the results of defuzzification of the data that has been obtained.
Sistem Pendukung Keputusan dalam Mendiagnosa Gejala TBC dengan Metode WASPAS dan CPI Akhmad Primulyana; Agung Triayudi; Arie Gunawan
Journal of Computer System and Informatics (JoSYC) Vol 4 No 2 (2023): Februari 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i2.3068

Abstract

Mycobacterium bacteria is the cause of tuberculosis, an infectious disease that attacks the respiratory tract in humans and can easily spread through the air. In 2012, WHO data showed that tuberculosis is an infectious disease that causes the second largest health problem in the world. Tuberculosis is a disease that can attack other parts of the body as well as the lungs. The sooner a person finds out he has tuberculosis and gets tested, the more likely he will recover faster. There are many detection methods, but many are time consuming. A decision support system is needed using the WASPAS method and the CPI method which are capable of diagnosing tuberculosis. To overcome this problem. By forming a decision tree represented by rules, this decision support system implements Rank Order Centroids, one of the classification techniques in machine learning used in the data mining process. This study produces a system that is expected to make it easier for the general public to obtain timely and accurate information for diagnosing tuberculosis. A decision support system for diagnosing tuberculosis was developed as a result of this study. After being tested with 100 patient data, 50 as training data and 50 as testing data, the Confusion Matrix produces an accuracy value of 90%.
Penerapan Artificial Intelligence Untuk Klasifikasi Penyakit Kulit Dengan Metode Convolutional Neural Network Berbasis Web Dimas Aryanto Wijaya; Agung Triayudi; Arie Gunawan
Journal of Computer System and Informatics (JoSYC) Vol 4 No 3 (2023): May 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v4i3.3519

Abstract

Skin is one of the human organs that functions to regulate body temperature in humans, as well as to protect all organs in the human body. There are many factors that affect skin health conditions that cause skin diseases. A system was developed to help people detect skin diseases. This system is Artificial Intelligence with Convolutional Neural Network method so that it will produce a very significant image. The network will be trained to find angles, edges, shapes, and also features. The results of system performance in this study using adam optimizer with a learning rate of 0.0001 get the highest value of data accuracy reaching a value of 97%. So that the identification of skin diseases is quite good.
Co-Authors Agung Triayudi Agus Iskandar Agus Rahayu Akhmad Primulyana Chaerunnisa Chaerunnisa Deny Hidayatullah Dhieka Avrilia Lantana Dimas Aryanto Wijaya Endah Tri Esti Handayani Lili Adi Wibowo Munawaroh Namira Ismi Tirana Novi Dian Nathasia Olipa Sarta Matilda Purba Olipa Sarta Matilda Purba Rama Setiawan Rivaldi Okta Pratama Rosyidah Rahmah Rosyidah Rahmah Sari Ningsih Yandi Makmur Yusuf LN, Syamsu