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All Journal Techno.Com: Jurnal Teknologi Informasi Bulletin of Electrical Engineering and Informatics Prosiding SNATIF JAIS (Journal of Applied Intelligent System) Journal of Information System Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Teknik Informatika (JUTIF) Journal of Soft Computing Exploration Jurnal Informatika Polinema (JIP) Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Heru Lestiawan
Universitas Dian Nuswantoro
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Other

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Journal : JAIS (Journal of Applied Intelligent System)

 1 
Pattern Recognition on Vehicle Number Plates Using a Fast Match Algorithm Cahaya Jatmoko; Daurat Sinaga; Edi Sugiarto; Nur Rokhman; Heru Lestiawan
Journal of Applied Intelligent System Vol 6, No 2 (2021): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v6i2.4625

Abstract

Computer Vision was the fast developing apps in the world, it is make people make a lot of new algorithm. Before we can use in out app, we need to test the algorithm to make sure how effective and optimal the algorithm to solve every case we given. A lot of traffic system has implemented computer vision, they need fast and can work in every condition, because every vehicle who pass needs to be recognized. In this research Fast Match algorithm was chosen because they can solve some test and make a lot of image have a similarity with the template. It makes accuracy of the data can be achieved with this algorithm. For example on of the sample was have a SAD point for 0.5 and Overlap Error for 0.5 and can run in standard computer just for a couple second. It makes the template and the original image has a little similarity.
Learning Vector Quantization for Robusta and Arabica Coffee Classification Jatmoko, Cahaya; Sinaga, Daurat; Lestiawan, Heru; Hadi, Heru Pramono
Journal of Applied Intelligent System Vol. 8 No. 2 (2023): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i2.7343

Abstract

ANN or artificial neural network is a way to solve various kinds of problems to make decisions based on training. One of the methods of JSt which contains competitive and supervised learning. Where this layer will automatically learn the classification of the closest input distances and will be distributed to the same class. there are 2 types of coffee beans that are famous in the world, namely arabica and robusta, for some people or the layman it will be very difficult to distinguish these 2 types of coffee beans apart from the fact that the shape is almost the same the color looks almost the same but there are a number of differences in the two coffee beans which we can see from the shape of the seed. Robusta has a shape that tends to be round and smaller in size, and has a rougher texture. Arabica, on the other hand, is slightly flatter and longer in shape. The size is slightly bigger than Robusta but the texture of Arabica is smoother than Robusta. This is the basis of this study where the images of the two coffee beans will be extracted using the first-order texture feature extraction method based on MU parameters, standard deviation, skewness, energy, entropy, and smoothness. The method for collecting data was in the form of a quantitative method using images from each coffee bean, both Arabica and Robusta, with a total of 130 images. The comparison between training_data and test_data is 80:20. Through research conducted in the form of performance parameters with the best accuracy, including: Learning rate 0.01, max epoch or maximum iteration of 10 and 30%, the amount of training data used is 39 training images and 26 test images resulting in an accuracy presentation of 71% for the training process and error with a percentage of 96% for the test process.
Prediction of Sleep Disorders Based on Occupation and Lifestyle: Performance Comparison of Decision Tree, Random Forest, and Naïve Bayes Classifier Lestiawan, Heru; Jatmoko, Cahaya; Agustina, Feri; Sinaga, Daurat; Erawan, Lalang
(JAIS) Journal of Applied Intelligent System Vol. 8 No. 3 (2023): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v8i3.8987

Abstract

Health is a very important thing in life. Therefore, to maintain health, we need adequate rest. Without adequate rest, the body will not be healthy and fit. In this study, a person's sleep disorder prediction will be made based on their lifestyle and work. The predictions made will classify sleep disorders that are absent, sleep apnea and insomnia from certain lifestyles and work. The methods used to make predictions are decision tree classifier, random forest classifier and naïve Bayes classifier. The test was carried out using a total of 375 data which was broken down into 70% training data and 30% testing data. The results obtained after testing with test data are by using the decision tree classifier algorithm to get an accuracy of 89.431%, using the random forest classifier algorithm to get an accuracy of 90.244% and by using the naïve Bayes classifier algorithm to get an accuracy of 86.992%.
Co-Authors Abdussalam Abdussalam, Abdussalam Agustina, Feri Ardyan Pratama Ardytha Luthfiarta Aris Febriyanto Award Widya Laksana, Deddy Cahaya Jatmoko Candra Irawan Chaerul Umam Christy Atika Sari Doheir, Mohamed Edi Sugiarto Eko Hari Rachmawanto Erna Zuni Astuti Fauzi Adi Rafrastara Guruh Fajar Shidik Hadi, Heru Pramono Hussein, Jassim Nadheer Islam, Hussain Md Mehedul Lalang Erawan MARIA BINTANG Nur Rokhman Pulung Nurtantio Andono Purwanto Purwanto Putri, Ni Kadek Devi Adnyaswari Ramadhan Rakhmat Sani Reza Dwi Oktaf Purnama Saputro, Fakhri Rasyid Sinaga, Daurat Sinaga, Daurat Solichul Huda, Solichul suprayogi Suprayogi Suprayogi Syamsul Arifin Wellia Shinta Sari Wibowo Wicaksono Wildanil Ghozi Yaacob, Noorayisahbe Mohd