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Yohannes Yohannes
Department of Civil Engineering, Parahyangan Catholic University

Published : 9 Documents
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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.
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.
Deteksi Teks Secara Otomatis Pada Natural Image Berbasis Superpixel Menggunakan Maximally Stable Extremal Regions dan Stroke Width Transform Yohannes, Yohannes
Jurnal Teknik Informatika dan Sistem Informasi Vol 3 No 2 (2017): JuTISI
Publisher : Maranatha University Press

#### Abstract

Text detection in natural image is something to do before performing character recognition. The process of text detection plays an important role in the acquisition of information in an image. This research aims to detect text automatically in natural image based on superpixels with Maximally Stable Extremal Regions (MSER) and Stroke Width Transform (SWT). The superpixel method used is Simple Linear Iterative Clustering (SLIC). The SLIC method is used for segmenting text images into superpixel spaces. Image segmentation to superpixel aims to group pixels into homogeneous regions that capture redundant images. SLIC is a technique that effectively divides images into homogeneous regions (superpixels). Furthermore MSER is used as a feature to locate the text candidate region in a segmented image with superpixel. Then edge detection is done to validate the text area that has been found. Next, the SWT method is used to distinguish both text and non-text image regions. The dataset used is ICDAR 2003. Based on test result, MSER with superpixel is able to detect region of text in natural image. SWT is also able to recover the region which is the candidate of the text in natural image.
BRAIN TUMOR CLASSIFICATION USING GRAY LEVEL CO-OCCURRENCE MATRIX AND CONVOLUTIONAL NEURAL NETWORK Widhiarso, Wijang; Yohannes, Yohannes; Prakarsah, Cendy
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.
The Tension Strength Experiment of Thread Connection Based on The Depth of Thread Penetration Sudjono, Agus Santosa; Tjong, Lydia F.; Yohannes, Yohannes
Civil Engineering Dimension Vol 10, No 1 (2008): MARCH 2008
Publisher : Institute of Research and Community Outreach - Petra Christian University

#### Abstract

Klasifikasi Mamalia Berdasarkan Bentuk Wajah Dengan k-NN Menggunakan Fitur CAS dan HOG Al Rivan, Muhammad Ezar; Yohannes, Yohannes
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 5 No 2 (2019): JATISI
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

#### Abstract

Klasifikasi pada jenis objek sudah banyak dilakukan pada beberapa jenis data citra. Klasifikasi jenis hewan telah dilakukan menggunakan pendekatan segmentasi dan tanpa segmentasi sebagai tahapan awal. Context Aware Saliency (CAS) merupakan metode yang mampu membuat wilayah objek menjadi lebih dominan dibandingkan dengan background dalam mode saliency sehingga dapat menjadi alternatif pengganti proses segmentasi objek. Fitur bentuk diambil berdasarkan citra hasil saliency menggunakan metode Histogram of Oriented Gradient (HOG). Metode K-Nearest Neighbors (K-NN) digunakan untuk klasifikasi jenis hewan mamalia berdasarkan fitur HOG dari citra saliency. Dataset yang digunakan pada penelitian ini adalah LHI-Animal-Faces. Hasil yang didapatkan menunjukkan bahwa jenis hewan yang dapat dikenali dengan baik, yaitu Kucing dan Harimau, sedangkan Domba, Anjing, dan Babi belum mampu dikenali dengan baik.
Pengembangan Sistem Kontrol Penggerak Stang Las Pada Sliding Adaptive Two Axis Mesin Pengelasan Smaw Berbasis Mikrokontroller Arduino Uno Prasetya, Surya Dita; Yohannes, Yohannes
Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains Vol 6 (2019): Edisi 1 Januari s/d Juni 2019
Publisher : Jurnal Online Mahasiswa (JOM) Bidang Teknik dan Sains

#### Abstract

SMAW (Shield Metal Arc Welding) is a welding technique using electric current that forms a current arc and webbed electrodes. In the SMAW welding process there are parameters that determine the quality of the welding results. This welding handlebar drive control system is made to simplify work, especially in the field of welding and get constant parameters. The research method is used the working principle of CNC 2 axis machines. The CNC 2 axis machine is able to move in the direction of the X and Y axis in the work plane and have high precision and precision. This welding handlebar drive control system is controlled by Universal G-Code Sender software. Making microcontroller-based welding handlebar drive control systems makes changes to the metal welding process easier, because operators only input data as desired. From the test results of the X and Y axis movements get the same results between the program and the actual, X and Y axis joint movements, limit switch testing and sliding movement speed that get results in the form of graphs and Tabels.Keywords : SMAW, Control System, Microcontroller ATMega 328P, Limit Switch, Arduino UNO
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.
PENGGUNAAN GLOBAL CONTRAST SALIENCY DAN HISTOGRAM OF ORIENTED GRADIENT SEBAGAI FITUR UNTUK KLASIFIKASI JENIS HEWAN MAMALIA Yohannes, Yohannes; Al Rivan, Muhammad Ezar
PETIR: Jurnal Pengkajian dan Penerapan Teknik Informatika Vol 13 No 1 (2020): PETIR (Jurnal Pengkajian Dan Penerapan Teknik Informatika)
Publisher : Sekolah Tinggi Teknik - PLN

#### Abstract

Mammal type can be classified based on the face. Every mammal?s face has a different shape. Histogram of Oriented Gradient (HOG) used to get shape feature from mammal?s face. Before this step, Global Contrast Saliency used to make images focused on an object. This process conducts to get better shape features. Then, classification using k-Nearest Neighbor (k-NN). Euclidean and cityblock distance with k=3,5,7 and 9 used in this study. The result shows cityblock distance with k=9 better than Euclidean distance for each k. Tiger is superior to others for all distances. Sheep is bad classified.