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Yohannes Yohannes
UNIVERSITAS MULTI DATA PALEMBANG

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Perbandingan Performa Algoritma Minimax dan Breadth First Search Pada Permainan Tic-Tac-Toe Setiawan, Jerry; Famerdi, Farhan Agung; Udjulawa, Daniel; Yohannes, Yohannes
Jurnal Teknik Informatika dan Sistem Informasi Vol 4 No 1 (2018): JuTISI
Publisher : Maranatha University Press

#### Abstract

Tic-Tac-Toe is one of the board games that can hone the motor skills of the brain. This game uses 2 pawns, there are X and O. The game started with X’s pawn as the player who first turns, the game got win condition if the player or the enemy put the 3 pawns in a diagonal, vertical or horizontal line. While the game got draw if there is no player or enemy who put 3 pawns in a diagonal, vertical or horizontal line. The game’s problems are the player should think about the next best step to win and defend with put pawn to block enemy’s steps to win. To solve the problems, the game needs some algorithms, there are Minimax algorithm and Breadth First Search algorithm. Minimax algorithm explores node from deepest level and evaluates the scores using minimum or maximum value. Breadth First Search algorithm is an algorithm which explores node widely and compares evaluation scores to the deepest level. In this research, each algorithm is tested to response time and number of nodes needed on a game board with 3×3, 5×5, 7×7, and 9×9 size as much as 16 scenarios. Based on the test results, Breadth First Search algorithm is superior to Minimax on 3×3 board size in terms of response time and the number of nodes required. While the Minimax algorithm is superior to Breadth-First Search on 5×5 and 9×9 board size in terms of response time and the number of nodes required. In the first turn, the algorithm will trace the number of nodes larger than the next step so that the placement of the algorithm for the first turn affects the final result of the node number parameter.
Klasifikasi Wajah Hewan Mamalia Tampak Depan Menggunakan k-Nearest Neighbor Dengan Ekstraksi Fitur HOG Yohannes, Yohannes; Sari, Yulya Puspita; Feristyani, Indah
Jurnal Teknik Informatika dan Sistem Informasi Vol 5 No 1 (2019): JuTISI
Publisher : Maranatha University Press

#### Abstract

Mammal is a type of animal that has many diverse characteristics, such as vertebrates and breastfeeding. In this study, the HOG feature and the k-NN method were proposed to classify 15 species of mammals. This study uses the LHI-Animal-Faces dataset which has fifteen species of mammals, where each type of mammal has 50 images measuring 100x100 pixels. The image will be conducted the process by the HOG feature extraction process and continued into the classification process using k-Nearest Neighbor. The performance of the HOG and k-NN features that get the best value is in deer and monkey, the best results for precision, recall, and accuracy are at k=3 where HOG feature extraction provides good vector features to be used in the classification process using the k-NN method.
Penerapan Speeded-Up Robust Feature pada Random Forest Untuk Klasifikasi Motif Songket Palembang Yohannes, Yohannes; Devella, Siska; Pandrean, Ade Hendri
Jurnal Teknik Informatika dan Sistem Informasi Vol 5 No 3 (2019): JuTISI
Publisher : Maranatha University Press

#### Abstract

Songket is a historical heritage in the city of Palembang. Where Songket has many different types and motifs. Besides having historical value, Palembang's original Songket has high quality and complexity in the manufacturing process. As known Palembang Songket has a lot of motives, one of the ways to recognize Palembang Songket is through its motives, so that research was conducted for the classification of Palembang Songket motifs. The method used to extract features is the Speeded-Up Robust Feature (SURF), while the classification method is Random Forest. The process of forming the SURF feature is divided into two stages, the first stage is Interest Point Detection, which consists of Integral Images, Hessian Matrix Based Interest Points, Scale Space Representation and Interest Point Localization, the second stage of Interest Point Description consists of Orientation Assignment and Descriptor Based on Sum Haar Wavelet Responses. The resulting feature is used for the Random Forest classification. This study used 345 images of Palembang Songket motifs, among others, Bunga Cina, Cantik Manis and Pulir. The images taken are based on 5 colors from each Palembang Songket motif. For the separation of data there are 300 images used as data train and 45 images for testing data. From the tests that have been done the results of the overall overall accuracy are 68.89%, per class accuracy 79.26%, precision 69.27, and recall 68.89%.
Rancang Bangun Edugame "History of Shodanco Supriyadi": Sejarah Perlawanan Pasukan PETA Blitar Terhadap Jepang Azarya, Philips Denny; Pandi, Pandi; Yohannes, Yohannes; Yoannita, Yoannita
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 1 (2020): JuTISI
Publisher : Maranatha University Press

#### Abstract

Games are not only for entertainment but games can be a means of learning. Historical subjects are often considered boring and uninteresting lessons because there are no innovations to attract students' curiosity. Therefore, a learning media was created and an introduction to the history of the resistance of the Blitar PETA forces through an adventure edugame. The methodology used is an iteration with three increments, each of which consists of the analysis, design, code, and test phases. The game design uses Unity 3D as a tool. Tests carried out include integration testing, system testing, and acceptance testing. From the results of these tests it was found that the edugame application that had been developed was able to assist students in introducing the history of the resistance figure PETA Blitar named Supriyadi.
Klasifikasi Lukisan Karya Van Gogh Menggunakan Convolutional Neural Network-Support Vector Machine Yohannes, Yohannes; Udjulawa, Daniel; Febbiola, Febbiola
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 1 (2021): JuTISI
Publisher : Maranatha University Press

#### Abstract

Painting is a work of art with various strokes, textures, and color gradations so that a painting that is synonymous with beauty is created. The various paintings created have characteristics, such as the paintings by Van Gogh, which have tightly arranged strokes, creating a repetitive and patterned impression. This study classifies paintings by Van Gogh or not by using the VGG-19 and ResNet-50 feature extraction methods. The SVM method is used as a classification method with two optimizations, namely random and grid optimization in the linear kernel. The data set used consisted of 124 Van Gogh paintings and 207 paintings by other painters. The use of VGG-19 feature extraction using grid optimization has the best value of 93,28% using the use of random optimization which has a value of 92,89%. The use of ResNet-50 using grid optimization with the best value of 90,28% using the use of random optimization which has a value of 90,15%. The extraction feature of VGG-19 is better than ResNet-50 in paintings by Van Gogh or not.
Pemanfaatan Scale Invariant Feature Transform Berbasis Saliency untuk Klasifikasi Sel Darah Putih Yohannes, Yohannes; Devella, Siska; Hadisaputra, William
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 2 (2021): JuTISI
Publisher : Maranatha University Press

#### Abstract

White blood cells are cells that makeup blood components that function to fight various diseases from the body (immune system). White blood cells are divided into five types, namely basophils, eosinophils, neutrophils, lymphocytes, and monocytes. Detection of white blood cell types is done in a laboratory which requires more effort and time. One solution that can be done is to use machine learning such as Support Vector Machine (SVM) with Scale Invariant Feature Transform (SIFT) feature extraction. This study uses a dataset of white blood cell images that previously carried out a pre-processing stage consisting of cropping, resizing, and saliency. The saliency method can take a significant part in image data and. The SIFT feature extraction method can provide the location of the keypoint points that SVM can use in studying and recognizing white blood cell objects. The use of region-contrast saliency with kernel radial basis function (RBF) yields the best accuracy, precision, and recall results. Based on the test results obtained in this study, saliency can improve the accuracy, precision, and recall of SVM on the white blood cell image dataset compared to without saliency.
Deteksi Penyakit Malaria Menggunakan Convolutional Neural Network Berbasis Saliency Yohannes Yohannes; Siska Devella; Kelvin Arianto
JUITA : Jurnal Informatika JUITA Vol. 8 Nomor 1, Mei 2020
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

#### Abstract

Malaria adalah penyakit mematikan yang menjadi masalah di berbagai negara. Metode yang paling umum untuk mendeteksi malaria adalah dengan memeriksanya secara manual, yang memakan waktu. Convolutional Neural Network (CNN) adalah salah satu solusi untuk deteksi malaria. CNN telah terbukti memberikan hasil yang sangat baik dalam klasifikasi gambar dan telah banyak digunakan dalam penelitian sebelumnya dan memiliki hasil yang baik. Sebelum proses klasifikasi, pra-pemrosesan gambar dapat digunakan untuk mendapatkan hasil klasifikasi yang lebih baik. Salah satu metode dalam pra-pemrosesan adalah arti-penting. Saliency adalah metode yang dapat mengambil bagian penting dari suatu gambar. Pada penelitian ini dilakukanlah pengujian terhadap metode saliency dan CNN untuk masalah pendeteksian penyakit malaria. Skenario pengujian dilakukan dengan membandingkan metode saliency, yaitu Region Contrast Saliency, Frequency-tuned saliency, Spectral Residual, dan Histogram Contrast. Metode saliency terbaik dalam mendeteksi penyakit malaria didapatkan oleh metode frequency-tuned saliency dengan akurasi sebesar 90,32% dibandingkan dengan metode saliency yang lain, yaitu 62,67% untuk region contrast saliency, 50% untuk spectral residual saliency, dan 79,06% untuk histogram contrast saliency.Kata-kata kunci: Klasifikasi; CNN; Malaria; Saliency
Brain Tumor Classification Using Gray Level Co-occurrence Matrix and Convolutional Neural Network Wijang Widhiarso; Yohannes Yohannes; Cendy Prakarsah
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) Vol 8, No 2 (2018): October

#### Abstract

Image are objects that have many information. Gray Level Co-occurrence Matrix is one of many ways to extract information from image objects. Wherein, the extracted informations can be processed again using different methods, Gray Level Co-occurrence Matrix is use for clarifying brain tumor using Convolutional Neural Network. The scope in this research is to process the extracted information from Gray Level Co-occurrence Matrix to Convolutional Neural Network where it will processed as Deep Learning to measure the accuracy using four data combination from TI1, in the form of brain tumor data Meningioma, Glioma and Pituitary Tumor. Based on the implementation of this research, the classification result of Convolutional Neural Network shows that the contrast feature from Gray Level Co-occurrence Matrix can increase the accuracy level up to twenty percent than the other features. This extraction feature is also accelerate the classification process using Convolutional Neural Network.
Analisis Perbandingan Algoritma Fuzzy C-Means dan K-Means Yohannes Yohannes
Annual Research Seminar (ARS) Vol 2, No 1 (2016)
Publisher : Annual Research Seminar (ARS)

#### Abstract

Klasterisasi merupakan teknik pengelompokkan data berdasarkan kemiripan data. Teknik klasterisasi ini banyak digunakan pada bidang ilmu komputer khususnya pengolahan citra, pengenalan pola, dan data mining. Banyak sekali algoritma yang digunakan untuk klasterisasi data. Algoritma yang sering digunakan untuk klasterisasi data  pada umumnya adalah Fuzzy C-Means dan K-Means. Algoritma Fuzzy C-Means merupakan algoritma klasterisasi dimana data dikelompokkan ke dalam suatu pusat cluster data dengan derajat keanggotaan masing-masing cluster. Sedangkan algoritma K-Means merupakan teknik mengelompokkan data dengan mempartisi data ke dalam beberapa cluster dengan menetapkan sejumlah objek data terdekatnya. Pada penelitian ini akan dilakukan perbandingan algoritma Fuzzy C-Means dan K-Means dalam hal klasterisasi data dengan jumlah klaster dan jumlah data yang berbeda.
Analisis Pengaruh Daya Mesin Dan Campuran Bahan Bakar Oli Bekas Dan Dexlite Terhadap Emisi Gas Buang CO2 Mesin Diesel Dongfeng Model R175 Karina Yolanda; Aryo Sasmita; Yohannes Yohannes
Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains Vol 8 (2021): Edisi 2 Juli s/d Desember 2021
Publisher : Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains

#### Abstract

Used oil is a potential alternative fuel and can reduce the waste of used oil. Used oil utilization has been carried out at The Production Technology Laboratory, Department of Mechanical Engineering, Riau University on modified diesel engines. The problem is that the resulting emissions still exceed the quality standard at a power load of 2000 W. In the present work, CO2 emission produced by Dongfeng R175 diesel engine with the mixed used oil and dexlite as alternative fuels was investigated. A total of three fuel samples, such as D10 (10% dexlite), D20 (20% dexlite), D30 (30% dexlite) respectively are used. CO2 emissions were analyzed with varied power loads, starting at idle, 1000 W, and 2000 W. The result was analyzed using a Microsoft Excel application with graphical output of the emission levels. The results showed that the best mixture was D10 where the highest loading power of 2000 W produced CO2 emission of 2,5%. While the highest CO2 emission produced by the D30 mixture at a power load of 2000 W of 6,6%.Keywords : Diesel Engine, Used Oil, Dexlite, Exhaust Emissions, CO2.