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All Journal Dinamik Jurnal Masyarakat Informatika Jurnal Sains dan Teknologi Semantik Techno.Com: Jurnal Teknologi Informasi JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Jurnal Ilmiah Dinamika Rekayasa (DINAREK) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Abdimasku : Jurnal Pengabdian Masyarakat Journal of Soft Computing Exploration Prosiding Seminar Nasional Hasil-hasil Penelitian dan Pengabdian Pada Masyarakat Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Community: Jurnal Pengabdian Pada Masyarakat Seminar Nasional Teknologi dan Multidisiplin Ilmu Jurnal Informatika Polinema (JIP) Transformasi Masyarakat : Jurnal Inovasi Sosial dan Pengabdian Scientific Journal of Informatics Kesejahteraan Bersama : Jurnal Pengabdian dan Keberlanjutan Masyarakat Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial Pelayanan Unggulan: Jurnal Pengabdian Masyarakat Terapan Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Cahaya Jatmoko
Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Physics Public Health Social Sciences 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.
Classification of Bird Based on Face Types Using Gray Level Co-Occurrence Matrix (GLCM) Feature Extraction Based on the k-Nearest Neighbor (K-NN) Algorithm Daurat Sinaga; Feri Agustina; Noor Ageng Setiyanto; Suprayogi Suprayogi; Cahaya Jatmoko
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.v6i2.4627

Abstract

Indonesia is one of the countries with a large number of fauna wealth. Various types of fauna that exist are scattered throughout Indonesia. One type of fauna that is owned is a type of bird animal. Birds are often bred as pets because of their characteristic facial voice and body features. In this study, using the Gray Level Co-Occurrence Matrix (GLCM) based on the k-Nearest Neighbor (K-NN) algorithm. The data used in this study were 66 images which were divided into two, namely 55 training data and 11 testing data. The calculation of the feature value used in this study is based on the value of the GLCM feature extraction such as: contrast, correlation, energy, homogeneity and entropy which will later be calculated using the k-Nearest Neighbor (K-NN) algorithm and Eucliden Distance. From the results of the classification process using k-Nearest Neighbor (K-NN), it is found that the highest accuracy results lie at the value of K = 1 and at an degree of 0 ° of 54.54%.
A Classification of Batik Lasem using Texture Feature Ecxtraction Based on K-Nearest Neighbor Cahaya Jatmoko; Daurat Sinaga
Journal of Applied Intelligent System Vol 3, No 2 (2018): 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.v3i2.2151

Abstract

In this study, batik has been modeled using the GLCM method which will produce features of energy, contrast, correlation, homogenity and entropy. Then these features are used as input for the classification process of training data and data testing using the K-NN method by using ecludean distance search. The next classification uses 5 features that provide information on energy values, contrast, correlation, homogeneity, and entropy. Of the two classifications, which comparison will produce the best accuracy. Training data and data testing were tested using the Recognition Rate calculation for system evaluation. The results of the study produced 66% recognition rate in 50 pieces of test data and 100 pieces of training data.
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%.
Completing Sudoku Games Using the Depth First Search Algorithm Alfany, Fauzan Maulana; Sari, Christy Atika; Jatmoko, Cahaya; Laksana, Deddy Award Widya; Irawan, Candra; Huda, Solichul
(JAIS) Journal of Applied Intelligent System Vol. 9 No. 1 (2024): Journal of Applied Intelligent System
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jais.v9i1.10017

Abstract

Sudoku is a digital game that is included in the type of logic-based puzzle game where the goal is to fill in the puzzle with random numbers. Therefore, in this research it is proposed to use Artificial Intelligence which contains the Depth First Search Algorithm to track the number of possible solutions that lead to only one so that it becomes efficient. This game has different levels of difficulty such as easy, medium and difficult. The time and complexity of execution will vary depending on the difficulty so it is proposed to use Android Studio software. The experimental results prove that there is an increase in playing the Sudoku game quickly and accurately by applying the Depth First Search Algorithm method. This is proven by the ability to complete this game using the Depth First Search Algorithm using the Android Studio programming language. The average time at the easy level is 11:04 minutes, at the normal level is 10:52 minutes, at the hard level is 25:46 minutes, and at the extreme level is 38 minutes.
Co-Authors Abdussalam Abdussalam, Abdussalam Agus Setiawan Agustina, Feri Akrom, Ahmad Alfany, Fauzan Maulana Alvin Faiz Kurniawan Award Widya Laksana, Deddy Candra Irawan Candra Irawan Christy Atika Sari Danu Hartanto Daurat Sinaga Daurat Sinaga De Rosal Ignatius Moses Setiadi Deddy Award Widya Laksana Doheir, Mohamed Edi Sugiarto Edi Sugiarto Edi Sugiarto Eko Hari Rachmawanto Elkaf Rahmawan Pramudya Erna Zuni Astuti Feri Agustina Feri Agustina Florentina Esti Nilawati Guruh Fajar Shidik Hadi, Heru Pramono Hedriyanto, Novi Heribertus Himawan Heru Lestiawan Heru Lestiawan Ifan Rizqa L. Budi Handoko Laksana, Deddy Award Widya Lalang Erawan Lalang Erawan Marjuni, Aris Noor Ageng Setiyanto, Noor Ageng Nova Rijati Nur Rokhman Nur Rokhman Pulung Nurtantio Andono Saifullah, Zidan Setia Astuti Sinaga, Daurat Sinaga, Daurat Sindhu Rakasiwi Solichul Huda, Solichul suprayogi Suprayogi Suprayogi Suprayogi Suprayogi Suprayogi Suprayogi Wellia Shinta Sari Widya Laksana, Deddi Award Yaacob, Noorayisahbe Mohd