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All Journal CommIT (Communication & Information Technology) ComTech: Computer, Mathematics and Engineering Applications Engineering, Mathematics and Computer Science Journal (EMACS)
Alexander Agung Santoso Gunawan
Bina Nusantara University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Engineering Industrial & Manufacturing Engineering Mathematics

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 1 
Face Recognition Performance in Facing Pose Variation Alexander Agung Santoso Gunawan; Reza A Prasetyo
CommIT (Communication and Information Technology) Journal Vol. 11 No. 1 (2017): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v11i1.1847

Abstract

There are many real world applications of face recognition which require good performance in uncontrolled environments such as social networking, and environment surveillance. However, many researches of face recognition are done in controlled situations. Compared to the controlled environments, face recognition in uncontrolled environments comprise more variation, for example in the pose, light intensity, and expression. Therefore, face recognition in uncontrolled conditions is more challenging than in controlled settings. In thisresearch, we would like to discuss handling pose variations in face recognition. We address the representation issue us ing multi-pose of face detection based on yaw angle movement of the head as extensions of the existing frontal face recognition by using Principal Component Analysis (PCA). Then, the matching issue is solved by using Euclidean distance. This combination is known as Eigenfaces method. The experiment is done with different yaw angles and different threshold values to get the optimal results. The experimental results show that: (i) the more pose variation of face images used as training data is, the better recognition results are, but it also increases the processing time, and (ii) the lower threshold value is, the harder it recognizes a face image, but it also increases the accuracy.
Color Extraction and Edge Detection of Nutrient Deficiencies in Cucumber Leaves Using Artificial Neural Networks Arie Qur'ania; Prihastuti Harsani; Triastinurmiatiningsih Triastinurmiatiningsih; Lili Ayu Wulandhari; Alexander Agung Santoso Gunawan
CommIT (Communication and Information Technology) Journal Vol. 14 No. 1 (2020): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v14i1.5952

Abstract

The research aims to detect the combined deficiency of two nutrients. Those are nitrogen (N) and phosphorus (P), and phosphorus and potassium (K). Here, it is referred to as nutrient deficiencies of N and P and P and K. The researchers use the characteristics of Red, Green, Blue (RGB) color and Sobel edge detection for leaf shape detection and Artificial Neural Networks (ANN) for the identification process to make the application of nutrient differentiation identification in cucumber. The data of plant images consist of 450 training data and 150 testing data. The results of identifying nutrient deficiencies in plants using backpropagation neural networks are carried out in three tests. First, using RGB color extraction and Sobel edge detection, the researchers show 65.36% accuracy. Second, using RGB color extraction, it has 70.25% accuracy. Last, with Sobel edge detection, it has 59.52% accuracy.
Hubungan Deret Bertingkat Berdasarkan Bilangan Eulerian dengan Operator Beda Alexander Agung Santoso Gunawan
ComTech: Computer, Mathematics and Engineering Applications Vol. 2 No. 1 (2011): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v2i1.2727

Abstract

Rank series defined as: is a generalization of the fixed rank series (the sum of powers), in which its closed solution has been found empirically by Jacob Bernoulli in 1731. This paper will explore the relationship between rank series and differential operator. To see this relationship, examples for the case m = 1.2 and α = 1.2. are provided. 
Discovering the Optimal Number of Crime Cluster Using Elbow, Silhouette, Gap Statistics, and NbClust Methods Noviyanti T. M. Sagala; Alexander Agung Santoso Gunawan
ComTech: Computer, Mathematics and Engineering Applications Vol. 13 No. 1 (2022): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v13i1.7270

Abstract

In recent years, crime has been critical to be analyzed and tracked to identify the trends and associations with crime patterns and activities. Generally, the analysis is conducted to discover the area or location where the crime is high or low by using different clustering methods, including k-means clustering. Even though the k-means algorithm is commonly used in clustering techniques because of its simplicity, convergence speed, and high efficiency, finding the optimal number of clusters is difficult. Determining the correct clusters for crime analysis is critical to enhancing current crime resolution rates, avoiding future incidents, spending less time for new officers, and increasing activity quality. To address the problem of estimating the number of clusters in the crime domain without the interference of humans, the research carried out Elbow, Silhouette, Gap Statistics, and NbClust methods on datasets of Major Crime Indicators (MCI) in 2014−2019. Several stages were performed to process the crime datasets: data understanding, data preparation, cluster modelling, and cluster validation. The first two phases were performed in the R Studio environment and the last two stages in Azure Studio. From the experimental result, Elbow, Silhouette, and NbClust methods suggest a similar number of optimum clusters that is two. After validating the result using the average Silhouette method, the research considers two clusters as the best clusters for the dataset. The visualization result of Silhouette method displays the value of 0,73. Then, the observation of the data is well-grouped. It is placed in the correct group.
Pengembangan Automatic Pet Feeder Mengunakan Platform Blynk Berbasis Mikrokontroller ESP8266 Heri Ngarianto; Alexander Agung Santoso Gunawan
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 2 No. 1 (2020): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v2i1.6260

Abstract

In this paper, automatic pet feeder device was developed. This device is useful for feeding pets at home when all family members are busy working or traveling. The device settings can be controlled by the mobile application automatically, thus the pet owners can ensure the feeding of their pets. The development of this tool is based on an electronic board which uses an ESP8266 microcontroller namely WeMos D1 MINI and is equipped with a Relay Shield to control the amount of food released. In addition, through Blynk platform, the pet owners can control this device remotely. Pet feeding also can be done according to a schedule that can be pre-arranged as needed in realtime.
Semantic Segmentation for Aerial Images: A Literature Review Yongki Christian Sanjaya; Alexander Agung Santoso Gunawan; Edy Irwansyah
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 2 No. 3 (2020): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v2i3.6737

Abstract

Semantic image segmentation is one of the fundamental applications of computer vision which can also be called pixel-level classification. Semantic image segmentation is the process of understanding the role of each pixel in an image. Over time, the model for completing Semantic Image Segmentation has developed very rapidly. Due to this rapid growth, many models related to Semantic Image Segmentation have been produced and have also been used or applied in many domains such as medical areas and intelligent transportation. Therefore, our motivation in making this paper is to contribute to the world of research by conducting a review of Semantic Image Segmentation which aims to provide a big picture related to the latest developments related to Semantic Image Segmentation. In addition, we also provide the results of performance measurements on each of the Semantic Image Segmentation methods that we discussed using the Intersectionover-Union (IoU) method. After that, we provide a comparison for each semantic image segmentation model that we discuss using the results of the IoU and then provide conclusions related to a model that has good performance. We hope this review paper can facilitate researchers in understanding the development of Semantic Image Segmentation in a shorter time, simplify understanding of the latest advancements in Semantic Image Segmentation, and can also be used as a reference for developing new Semantic Image Segmentation models in the future
Damage Classification on Bridges using Backpropagation Neural Network Victoria Ivy Tansil; Novita Hanafiah; Alexander Agung Santoso Gunawan; Dewi Suryani
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 3 No. 2 (2021): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v3i2.7406

Abstract

Bridge structures can be damaged due to various factors such as pressure, vibration, temperature, etc. This study aims to detect damaged on bridges early so that accidents that can occur due to the damaged-on bridge can be avoided. The research method is divided into designing a model, building the model, and evaluating the model. The result of this research is a program that can classify healthy or damaged bridges using vibration data of tested points on bridges.
Prediction of Heart Disease UCI Dataset Using Machine Learning Algorithms Anderies Anderies; Jalaludin Ar Raniry William Tchin; Prambudi Herbowo Putro; Yudha Putra Darmawan; Alexander Agung Santoso Gunawan
Engineering, MAthematics and Computer Science (EMACS) Journal Vol. 4 No. 3 (2022): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v4i3.8683

Abstract

Heart disease is inflammation or damage to the heart and blood vessels over time. the disease can affect anyone of any age, gender, or social status. After many studies trying to overcome and learn about heart disease, in the end, this disease can be detected using machine learning systems. It predicts the likelihood of developing heart disease. The results of this system give the probability of heart disease as a percentage. Data collection using secret data mining. The data assets handled in python programming use two main algorithms for machine learning, the decision tree algorithm, and the Bayes naive algorithm which shows the best of both for heart disease accuracy. The results we get from this study show that the SVM algorithm is the algorithm with the most excellent precision. and the highest accuracy with a score of 85% in predicting heart disease using machine learning algorithms.Heart disease is inflammation or damage to the heart and blood vessels over time. the disease can affect anyone of any age, gender, or social status. After many studies trying to overcome and learn about heart disease, in the end, this disease can be detected using machine learning systems. It predicts the likelihood of developing heart disease. The results of this system give the probability of heart disease as a percentage. Data collection using secret data mining. The data assets handled in python programming use two main algorithms for machine learning, the decision tree algorithm, and the Bayes naive algorithm which shows the best of both for heart disease accuracy. The results we get from this study show that the SVM algorithm is the algorithm with the most excellent precision. and the highest accuracy with a score of 85% in predicting heart disease using machine learning algorithms.
User Experience Analysis of Duolingo Using User Experience Questionnaire Anderies Anderies; Cindy Agustina; Tania Lipiena; Ayunda Raaziqi; Alexander Agung Santoso Gunawan
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 5 No. 3 (2023): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v5i3.9227

Abstract

The internet is one of the vital means for everyone to get various information easily and exact like they’re looking for. The use of internet-based learning that is applied in modern times is very influential in the field of education compared to the past, because it can develop language skills in a country, besides that increasingly sophisticated technology can help students learn in a structured manner. One of the impacts we can see or feel is on the learning process. With the internet, it is so much easier either for the students or the teachers. One of the well-known applications in the world is Duolingo. Duolingo is one of many applications that give so much influence to language learning applications. More than 300 million people already use Duolingo for their learning. The purpose of this experiment is to analyze the User Experience of the Duolingo application. The experimental method was applied using surveys distributed via social media. There are 103 Duolingo users who were willing to take the surveys and answer all of the questions given. The result of the survey showed Novelty’s scale has the lowest mean, and Perspicuity’s scale has the highest. That means some of Duolingo’s users found that the application is less interesting. Hence, that could affect the effectiveness of the application.
Co-Authors Anderies Anderies Arie Qur’ania Ayunda Raaziqi Cindy Agustina Dewi Suryani Edy Irwansyah Heri Ngarianto Jalaludin Ar Raniry William Tchin Lili Ayu Wulandhari Novita Hanafiah Noviyanti T. M. Sagala Prambudi Herbowo Putro Prihastuti Harsani Reza A Prasetyo Tania Lipiena Triastinurmiatiningsih, Triastinurmiatiningsih Victoria Ivy Tansil Yongki Christian Sanjaya Yudha Putra Darmawan