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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

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%.

An Aplikasi Sistem Pakar Diagnosa Penyakit Mata Pada Manusia Menggunakan Metode Certainty Factor Berbasis Web Wijaya, Bayu Angga; Tanjung, Juliansyah Putra
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 1 (2020): Article Research, October 2020
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v5i1.10579

Eye is the important senses. If the eye is disrupted then ignore it, it will disturb. In fact, many people delay to checked eye diseases that them suffered, due to the lack of knowledge society, the cost is quite expensive and the imbalance between patients and doctors so that should be queued if will check the eye health. It is necessary for the expert system that can diagnose eye diseases, so a people can checking their eye diseases suffered without have to go to the doctors. This expert system is based on web with the programming language PHP and MySQL database. In the process of withdrawal conclusion, system using the certainty factors method that use a value to assume degree of confidence from an expert to a data. Expert system provides results in the form of the possibility of illness suffered, the value of the percentage of beliefs from the illness and the treatment solution based on the value of confidence that given and system is able to know the type of eye disease experienced by the user based on the symptoms chosen by the user. So, it can help the people to know the eye disease their suffered and the action can be done faster.

Facial Recognition Implementation using Kâ€“NN and PCA Feature Extraction in Attendance System Tanjung, Juliansyah Putra; Wijaya, Bayu Angga
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 1 (2020): Article Research, October 2020
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v5i1.10612

Attendance is the fact that someone is present at an event or goes regularly to an institution, or attendance at an event is the number of people present at that time. The Saifiatul Amaliyah school itself is one of the many schools in Indonesia where the attendance of students or attendance is still done manually. This can cause problems, namely allowing fraud when filling in attendance and errors in data recapitulation. Therefore, in this study a computerized face attendance was created, which was formed using the K-Nearest Neighbor (K-NN) method and combined with the extraction of the Principal Component Analysis (PCA) feature where the attendance process can be done with a person's face. The face attendance system using the K-NN and PCA methods has an accuracy of 82%.

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

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

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

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

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

Designing Applications For Damage Reporting Of Public Facilities Using K-Means Clustering Algorithm Rika Saljuni; Muhammad Sholahuddin; Fanema Putra Hartaret Harefa; Thines Raman; Juliansyah Putra Tanjung; N P Dharshinni
Jurnal Mantik Vol. 6 No. 2 (2022): August: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v6i2.2900

Public facilities are facilities provided for public purposes such as roads, street lighting, bus stops, sidewalks, and pedestrian bridges. The facilities provided are facilities that provide convenience for the community so that they must be maintained properly. Data mining is a process of dredging or collecting important information from large data. The data mining process often uses statistical, mathematical methods, to utilize artificial intelligence technology. The application designed uses 50 datasets which, after normalization, the number of data becomes 350 data, and after preprocessing the data used in the study is 81 data, with 4 attributes and 3 clusters. The results of the data processing resulted in the first data clustering based on the facility attributes produced as many as 29 data, the second data clustering based on the year attribute produced was 12 data, the third data clustering based on the attribute the resulting amount was 40 data

Customer Classification Using Naive Bayes Classifier With Genetic Algorithm Feature Selection Tanjung, Juliansyah Putra; Tampubolon, Fenny Chintya; Panggabean, Ari Wahyuda; Nandrawan, M. Anjas Asmara
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.12182

There is a tendency to decrease the number of speedy customers in the operational area of North Sumatra due to customer dissatisfaction. Termination of employment is carried out by the customer against PT. Telekomunikasi Indonesia, Tbk in North Sumatra. There is no management of customer data classification so that classification information based on certain product purchases cannot be known. Naïve Bayes is a classification algorithm that is easy to use but has weaknesses which result in poor performance, therefore feature selection is needed, the genetic algorithm is an algorithm that is able to select attributes in research, will be selected based on the highest weight so that the accuracy of the prediction results is more optimal. The steps taken in the measurement model using the Naive Bayes Classifier (NBC) approach and the model using the GA-NBC approach obtained accurate results from cross validation measurements, Confusion Matrix, ROC curves for the classification of existing and speedy telephone subscribers. The stages of the Naive Bayes process are: data collection, data preprocessing, processing of the Naive Bayes Classifier algorithm. Then the results are validated and evaluated using the Text Mining Algorithm, and calculating the parameters based on the genetic algorithm. The accuracy produced by the Naive Bayes Classifier model is 85.08%. The accuracy produced by the Naive Bayes Classifier model with the selection of Genetic Algorithm features increased to 89.31%.

Optimizing Gender Classification Accuracy in Facial Images Using Data Augmentation and Inception V-3 Tanjung, Juliansyah Putra; Faldi, Mhd. Rio; Sitompul, Haggai; Ridho, Muhammad; Ambarita, Jojor Putri
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12785

In the digital era, facial recognition technology plays a crucial role in various applications, including gender classification. However, challenges such as variations in expressions and face positions, as well as differences in features between men and women, make this task formidable. This study aims to enhance the accuracy of gender classification using the Inception V-3 method and the Convolutional Neural Network (CNN), along with data augmentation techniques. The Inception V-3 method was chosen for its superiority in accuracy and speed. In contrast, the CNN model was selected in this study as a comparison and due to its algorithmic advantages in learning and extracting high-level features from images, including facial images, which are crucial for tasks such as gender classification. The data augmentation techniques in this study include rescaling, rotation, width and height shifts, shear range, zoom, horizontal flip, and fill method for model accuracy in gender classification with a small dataset. The study results indicate that the Inception V-3 model provides better accuracy (99.31%) in gender classification compared to the CNN model (81.31%). This conclusion underscores that the use of the Inception V-3 method with data augmentation techniques can improve the accuracy of gender classification in facial images.

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