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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Mantik Data Sciences Indonesia (DSI)
Juliansyah Putra Tanjung
Universitas Prima Indonesia, Medan, Sumatera Utara
Computer Science & IT Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Physics

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Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

 1 
CLASSIFICATION OF FACIAL EXPRESSIONS USING SVM AND HOG Tanjung, Juliansyah Putra; Muhathir, Muhathir
JITE (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING) Vol 3, No 2 (2020): EDISI JANUARI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (581.755 KB) | DOI: 10.31289/jite.v3i2.3182

Abstract

The face is one of the human biometric which is often utilized as an important information of a person. One of the unique information of the face is facial expressions, expressions are information that is given indirectly about an expression of one's feelings. Because facial expressions have a unique pattern for each expression so that the pattern of facial expression will be tested with the computer by utilizing the Histogram of oriented gradient (HOG) descriptor as the extraction of existing features in each expression Face and information acquisition from HOG will be classified by utilizing the Support vector Mechine (SVM) method. The results of facial expression classification by utilizing the Extracaski HOG features reached 76.57% at a value of K = 500 with an average accuracy of 72.57%.
Shafiyyatul Amaliyyah School Student Face Absence Using Principal Component Analysis and K – Nearest Neighbor Aripin Rambe; Juliansyah Putra Tanjung; Muhathir Muhathir
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 2 (2022): Issues January 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i2.6214

Abstract

Pattern recognition is one of the sciences used to classify things based on quantitative measurements of the main features or properties of an object. Pattern recognition has been widely used in various fields of research. One of the pattern recognition that is often discussed is facial recognition. The face is one of the human biometrics that is often used as the main information of a person. Face recognition is a field of research with many applications in applications such as attendance, population data collection, security systems, and others. The research utilizes feature extraction of PCA (Principal Component Analysis), and K-NN (K – Nearest Neighbor) with variations of the distance formula by applying facial recognition attendance at the Safiatul Amaliyah School. This research is expected to get accurate results in detecting, recognizing, and comparing a person's face with a small error rate. The distance formula with accuracy level is presented with the equation Cityblock < Euclidian < Minkowski < Chebychev. The effect of applying the variation of the distance formula on the performance of the facial attendance recognition model is not too big, but it is better.
Classification of facial expressions using SVM and HOG Juliansyah Putra Tanjung; Muhathir Muhathir
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 3, No 2 (2020): EDISI JANUARI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v3i2.3182

Abstract

The face is one of the human biometric which is often utilized as an important information of a person. One of the unique information of the face is facial expressions, expressions are information that is given indirectly about an expression of one's feelings. Because facial expressions have a unique pattern for each expression so that the pattern of facial expression will be tested with the computer by utilizing the Histogram of oriented gradient (HOG) descriptor as the extraction of existing features in each expression Face and information acquisition from HOG will be classified by utilizing the Support vector Mechine (SVM) method. The results of facial expression classification by utilizing the Extracaski HOG features reached 76.57% at a value of K = 500 with an average accuracy of 72.57%.
Classification of Wheat Seeds Using Neural Network Backpropagation Algorithm Juliansyah Putra Tanjung
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 4, No 2 (2021): EDISI JANUARY 2021
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v4i2.4449

Abstract

There are various types of wheat scattered in the world. Usually it takes a long time to recognize the type of wheat seed by manual method because wheat germ has a physical appearance that looks the same as others. One method that can be used is an Artificial Neural Network. In this study, the data used were secondary data which consisted of data from the variable physical characteristics of wheat germ. The types of wheat seeds that are classified are 3. The Artificial Neural Network architecture used in this study is 5. By comparing the 5 Artificial Neural Network architectures, it is concluded that the architecture consisting of 3 layers and 4 layers is more precise in the classification of wheat germ types. The accuracy obtained by the 2 Artificial Neural Network architectures is 90% and 90%, respectively.
Numerical Analysis of Variations Distance Formulas on K Nearest Neighbors In Classifying Malaria Parasite Blood Cells Taufik Ismail Simanjuntak; Juliansyah Putra Tanjung; Mahardika abdi prawira tanjung; Cut Try Utari; Muhathir Muhathir
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 6, No 1 (2022): Issues July 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i1.5464

Abstract

Malaria is one of the numerous acute and chronic diseases. Even malaria can pose a threat to a person's safety. The original cause of malaria was an infection with a protozoan of the genus Plasmodium, which was transmitted by the bite of a mosquito. This Anopheles mosquito parasite infects red blood cells throughout the body, resulting in an enlarged spleen. This research aims to make it easier for physicians to classify blood images as malaria-infected or not. If the input is a blood image, then SURF Feature Extraction will be used to extract the blood image. We therefore obtained weight results based on the extraction results. The weighted results generated by the SURF extraction process will be classified using the KNN Algorithm to determine whether or not an individual is infected with malaria. This study's tests compared various distance formulas utilized by the KNN classification method. Comparing the results of malaria blood image classification using the KNN classification method with variations in the distance formula, it is evident in table 7 that correlation is the optimal distance formula for malaria parasite blood cells recognition, followed by cosine. According to the results of KNN's tests, it is not optimal at classifying blood images containing malaria, but these results are categorized as good
Implementation of Transfer Learning on CNN using DenseNet121 and ResNet50 for Brain Tumor Classification Putri, Farah Azhari Pranata Restia; Tanjung, Juliansyah Putra; Dharshini, N P
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 2 (2025): Issues January 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i2.13952

Abstract

Brain tumors are conditions characterized by abnormal cell growth in the brain, which can disrupt brain function. Early detection and accurate classification are crucial to ensuring effective treatment. This study aims to improve the accuracy of brain tumor classification by implementing Convolutional Neural Networks (CNN) using Transfer Learning approaches on DenseNet121 and ResNet50 models. Transfer Learning leverages knowledge from pre-trained models on larger datasets, thereby accelerating the training process and enhancing performance on the brain tumor dataset. The dataset used consists of medical images, including images of brain tumors and images without tumors. The data was divided into two parts, with 80% for training and 20% for validation. This split ensures that the model learns optimally from the training data and is tested on unseen data to objectively evaluate its performance. Experimental results show that the ResNet50 model achieved an accuracy of 98.44% on the validation data, while the DenseNet121 model achieved an accuracy of 96.31%. In conclusion, the ResNet50 model outperformed DenseNet121 in brain tumor classification. The implications of this study demonstrate that the Transfer Learning approach with ResNet50 can serve as an effective tool for automated brain tumor diagnosis, potentially improving patient outcomes through more accurate detection and classification
Co-Authors Ambarita, Jojor Putri Aripin Rambe Bayu Angga Wijaya Cut Try Utari Dharshini, N P Dosma Hutauruk Endang Sapriana Hutagalung Faldi, Mhd. Rio Fanema Putra Hartaret Harefa Mahardika Abdi Prawira Tanjung Muhammad Ridho Muhammad Sholahuddin Muhathir, Muhathir Muhathir, Muhathir N P Dharshinni Nandrawan, M. Anjas Asmara Panggabean, Ari Wahyuda Putri, Farah Azhari Pranata Restia Rika Saljuni Saputra Silitonga Sitompul, Haggai Tampubolon, Fenny Chintya Taufik Ismail Simanjuntak Thines Raman Tresya Noviania Pasaribu