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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Zero : Jurnal Sains, Matematika, dan Terapan Jusikom: Jurnal Sistem Informasi Ilmu Komputer JURNAL MAHAJANA INFORMASI INFOKUM Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Jurnal Pengabdian UNDIKMA Prioritas : Jurnal Pengabdian Kepada Masyarakat Jurnal ABDIMAS MUTIARA The Indonesian Journal of Computer Science JURNAL TEKNOLOGI, KESEHATAN DAN ILMU SOSIAL
Amir Saleh
Politeknik Negeri Medan
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Design of Simulation Definite Integral Application learning Using Trapezoid Method based on VB.Net N. PRIYA DHARSHINNI; Amir Saleh; Fadhillah Azmi; I Fawwaz
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 4, No 1 (2020): ---> EDISI JULI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (365.427 KB) | DOI: 10.31289/jite.v4i1.3880

Abstract

The definite integral is one of the subjects that is difficult for students to understand because the process of calculating definite integral of functions is quite complicated and long because it requires mastery of some integrating rules so an interactive learning simulation application is needed to make it easier for students to calculate definite integral of functions and the depiction of the area the curve. One method for calculating definite integrals is the trapezoid method. The trapezoid method works by dividing the boundary into 2 intervals namely x = x0 to x = x1. Simulation media application learning will be designed with the VB.Net programming language. This simulation media learning starts with reading and checking data input. The process is continued by displaying the depiction of the input curve and ending with calculating the area of the curve. Simulation media learning provides a facility to store the input data, the results of the calculation of the area and the image of the curve function in the image format of * .bmp. In this media, the media and material expert’s the results of the average are produced by 88.68% included into media category is very valid media and the results of pre-test and post-test trials showed an increase with an average value of 48.3 for pre-test and 87 for the post-test of the passing grade requirement of 70.Keywords: Definite Integral, Trapezoid Method,VB.Net, Media Validation.
Face Identification on Login Security Using Algorithm Combination of Viola-Jones and Cosine Similarity FADHILLAH AZMI; Amir Saleh; N P Dharshinni
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 4, No 1 (2020): ---> EDISI JULI
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (328.953 KB) | DOI: 10.31289/jite.v4i1.3885

Abstract

Data security by using an alphanumeric combination password is no longer used, so it needs to be added security that is difficult to be manipulated by certain people. One type of security is the type of biometrics technology using face recognition which has different characteristics by combining the Viola-Jones algorithm to detect facial features, GLCM (Gray Level Co-occurrence Matrix) for extracting the texture characteristics of an image, and Cosine Similarity for the measurement of the proximity of the data (image matching). The image will be detected using the Viola-Jones algorithm to get face, eyes, nose, and mouth. The image detection results will be calculated the value of the texture characteristics with the GLCM (Gray Level Cooccurrence Matrix) algorithm. Image matching using cosine similarity will determine or match the data stored in the database with new image input until identification results are obtained. The results obtained in this study get the level of accuracy of the identification of the three algorithms by 77.20% with the amount of data that was correctly identified as many as 386 out of 500 images.Keywords: Security, face recognition, Viola-Jones, Cosine Similarity.
Identifikasi Tanaman Herbal Berdasarkan Citra Daun Menggunakan Cosine Similarity dan Features Extraction Alexander F K Sibero; Amir Saleh
JURNAL MAHAJANA INFORMASI Vol 5 No 1 (2020): JURNAL MAHAJANA INFORMASI
Publisher : Universitas Sari Mutiara Indonesia Medan

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Abstract

Tanaman herbal merupakan tanaman yang dimanfaatkan sebagai obat-obatan untuk berbagai jenis penyakit. Tanaman ini banyak terdapat di daerah hutan atau lingkungan hijau yang keberadaannya tak jarang luput dari manusia. Bahkan beberapa orang sulit untuk membedakan mana tumbuhan herbal dan mana yang bukan. Langkah yang terbaik sebelum menggunakan tumbuhan tersebut adalah dengan melakukan identifikasi terhadap daun tanaman herbal, karena terdapat juga tumbuhan yang hampir mirip dengan tanaman herbal ternyata adalah tanaman yang mengandung racun. Identifikasi yang dilakukan dengan cara mengambil citra dari daun tersebut dan diolah menggunakan komputer dengan metode identifikasi seperti, cosine similarity. Sebelum diklasifikasi dengan metode cosine similarity, terlebih dahulu citra akan diolah dan diektraksi ciri menggunakan ciri tekstur dengan GLCM dan ciri morfologi dengan moment invariant. Hal ini dilakukan untuk mengetahui ciri-ciri dari masing-masing citra daun untuk proses identifikasi. Berdasarkan pengujian yang dilakukan menggunakan metode yang diusulkan tersebut diperoleh akurasi rata-rata identifikasi dengan ketepatan sebesar 89,57%.
FACE IMAGE RETRIEVAL SYSTEM USING COMBINATION METHOD OF SELF ORGANIZING MAP AND NORMALIZED CROSS CORRELATION Amir Saleh; Diky Suryandy; Jesron Nainggolan
INFOKUM Vol. 9 No. 2, June (2021): Data Mining, Image Processing and artificial intelligence
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (438.05 KB)

Abstract

Content based image retrieval (CBIR) is one method in computer vision that is widely applied in various fields of life. In this study, two algorithms will be combined, namely self organizing map (SOM) and normalized cross correlation (NCC) to test the method in the face image retrieval system. The SOM algorithm is used to perform learning on the system created and the NCC method is used to calculate the proximity value between the input image and the image contained in the database to be displayed as the result of image retrieval. The test results in the proposed research show good results with an accuracy rate of face image retrieval of 93.62%. This percentage is higher than using the usual SOM method with an accuracy rate of face image retrieval of 91.62%.
Iris Recognition Using Hybrid Self-Organizing Map Classifier and Daugman’s Algorithm Amir Saleh; Yusuf Roni Laia; Fransiskus Gowasa; Victor Daniel Sihombing
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4441

Abstract

One of the neural network algorithms that can be used in iris recognition is self-organizing map (SOM). This algorithm has a weakness in determining the initial weight of the network, which is generally carried out randomly, which can result in a decrease in accuracy when an incorrect determination is made. The solution that is often used is to apply a hybrid process in determining the initial weight of the SOM network. This study takes an approach using the cosine similarity equation to determine the initial weight of the network SOM in order to increase recognition accuracy. In addition, the localization process needs to be carried out to limit the area of the iris image being studied so that it is easy for the recognition process to be carried out. The method proposed in this study for iris recognition, namely hybrid SOM and Daugman’s algorithm, has been tested on several people by capturing the iris of the eye using a digital camera. The captured eyes have been localized first using the Daugman’s algorithm, and then the image features were extracted using the GLCM and LBP methods. In the final stage of the study, an iris recognition comparison test was performed, and the results obtained an accuracy of 85.50% using the proposed method and an accuracy of 73.50% without performing a hybrid process on the SOM network.
A Hybrid RBF Neural Network and FCM Clustering for Diabetes Prediction Dataset Muhammad Khalil Gibran; Amir Saleh
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 4, No 2 (2023)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v4i2.15573

Abstract

This study aims to predict diabetes by combining the Radial Basis Function Neural Network (RBFNN) and Fuzzy C-Means (FCM) clustering methods. Diabetes prediction is an important part of research in an effort to prevent, manage, and reduce this type of disease. The FCM clustering method is used to group diabetes data into groups that have similar characteristics and obtain the final centroid. Then, the RBFNN method is used to build a predictive model using the center of each group as a reference point in the RBF function based on the centroid generated from the FCM clustering method. This step allows for modeling the non-linear relationship between health attributes and diabetes risk in more detail. In this study, the dataset obtained used input parameters regarding health data and risk factors for the disease. The goal of combining these methods is to develop a predictive model that can help identify individuals at high risk of developing diabetes. This hybrid approach has the potential to improve the effectiveness and accuracy of diabetes prediction. From the tests carried out, the proposed method obtained an accuracy of 92%, a precision of 90%, a recall of 92%, and an F1-score of 91%. By combining the clustering power of FCM clustering with RBF's ability to model non-linear relationships, this hybrid approach can make a good contribution to diabetes prediction and assist in efforts to prevent and control this disease.
Implementation of Recommendation Systems in Determining Learning Strategies Using the Naïve Bayes Classifier Algorithm Saleh, Amir; Dharshinni, NP; Perangin-Angin, Despaleri; Azmi, Fadhillah; Sarif, Muhammad Irfan
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.v8i1.11954

Abstract

Recommendation systems are widely used in various fields of life to provide suggestions for a product, service, or piece of information to someone where there is an object to choose from. The recommendation system can also be applied in the field of education, especially in improving the quality of learning that occurs in schools. In this study, developing and implementing a recommendation system was used to determine the learning strategy applied in class. The system is very necessary in order to obtain effective and efficient learning in accordance with the desired learning style of students. In addition, learning that leads to students' desire to learn can make it easier for teachers to achieve predetermined learning goals. In this study, collaborative filtering techniques based on the Naive Bayes algorithm were used to determine the learning strategy. Before carrying out the recommendation process, datasets will be collected first, which are obtained from student responses through the questionnaires provided. This data will be used as training data to obtain recommendations on learning strategies that will be applied by the teacher in the classroom. After the training data is collected, the teacher will provide a response, and the results obtained will be used as testing data. From the results of implementing a recommendation system that has been built using the Naïve Bayes algorithm, the accuracy obtained is 90.91% in determining learning strategies that are appropriate to student learning styles.
Herbal Plant Classification Using Multi-Feature Extraction and Multilayer Perceptron Simanjuntak, Englis Franata; Sipahutar, Yohannes Saputra; Pasaribu, Martin Josua; Saleh, Amir
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v5i2.20835

Abstract

Herbal plants used for medicine have prompted many researchers in the field of computer science to develop an efficient way to identify these plants through their leaves. This study will propose artificial neural networks, such as Multilayer Perceptron (MLP), to classify herbal plants. This method is used with feature extraction methods like the Gray Level Co-occurrence Matrix (GLCM), Hue Saturation Value (HSV), and Histogram of Oriented Gradients (HOG) to find out about the leaves' texture, color, and histogram. The dataset used was taken directly with a digital camera from various types of herbal plants that people usually see in everyday life. The dataset, which consisted of 450 images, was classified into nine classes. The entire dataset will be processed using a combined feature extraction method before the MLP method is used for clustering. This method is used to better understand the diversity of herbal plants and improve classification accuracy. The experimental results show that the combination of the feature extraction method and the MLP algorithm can achieve the highest accuracy of 95.56% in identifying various types of plants. This research provides significant benefits and contributes to the development of an herbal plant recognition system capable of accurate classification.
Image Segmentation Using Hybrid Clustering Algorithms for Machine Learning-Based Skin Cancer Identification Maulana, Riza; Interiesta, Diva Cahaya; Sofy, Annisa Kurnia; Maulana, Ilham Habib; Saleh, Amir
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 5, No 2 (2024)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v5i2.21016

Abstract

Early identification of skin cancer is crucial to increasing the chances of a cure and reducing mortality rates. This research aims to develop a method for identifying skin cancer using image processing techniques, specifically the hybrid clustering method. This method integrates machine learning with fuzzy c-means clustering (FCM) and hierarchical clustering (HC) segmentation techniques to segment skin cancer more accurately. Hybrid clustering is used to separate suspicious areas in skin images, resulting in more precise segmentation compared to conventional methods. The segmentation results are then used as input for various machine learning methods that are trained to recognize patterns in identifying types of skin cancer. Tests were carried out using data obtained from the Kaggle Dataset, and the results showed that the proposed method was able to achieve a high level of accuracy in identifying skin cancer. After segmentation, the ensemble learning method yielded the best identification results. The Random Forest algorithm, which is applied to process and analyze features from skin images, shows higher performance compared to other machine learning methods. Tests show that the Random Forest method with the proposed segmentation achieves an accuracy level of up to 89%, while other machine learning methods such as K-Nearest Neighbor only achieve an accuracy level of around 86%. This research makes an important contribution to the development of efficient and reliable diagnostic tools for skin cancer identification, with appropriate segmentation methods proven to increase accuracy.
KNN Algorithm for Identification of Tomato Disease Based on Image Segmentation Using Enhanced K-Means Clustering Nasution, Amir Saleh; Alvin, Alvin; Siregar, Ana Tince; Sinaga, Monica Sari
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 3, August 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i3.1486

Abstract

Image segmentation is an important process in identifying tomato diseases. The technique that is often used in this segmentation is k-means clustering. One of the main problems in this technique is the case of local minima, where the cluster that is formed is not suitable due to the incorrect selection of the initial centroid. In image data, this case will have an impact on poor segmentation results because it can erase parts that are actually important to be lost or there is still background in the recognition process, which has an impact on decreasing accuracy results. In this research, a method for image segmentation will be proposed using the k-means clustering algorithm, which has been added with the cosine similarity method as the proposed contribution. The use of the cosine method will determine the initial centroid by calculating the level of similarity of each image feature based on color and dividing them into several categories (low, medium, and high values). Based on the results obtained, the proposed algorithm is able to segment and distinguish between leaf and background images with good results, with the kNN reaching a value of 94.90% for accuracy, 99.50% for sensitivity, and 93.75% for specificity. The results obtained using the kNN method with k-means segmentation obtained a value of 92.46% for accuracy, 96.30% for sensitivity, and 91.50% for specificity. The results obtained using the kNN method without segmentation obtained a value of 90.22% for accuracy, 93.30% for sensitivity, and 89.45% for specificity.
Co-Authors Achmad Ridwan Alexander F.K. Sibero Alvin Alvin, Alvin Azanuddin Azmi, Fadhillah Despaleri Perangin-Angin Dharshinni, NP Diky Suryandy Fadhillah Azmi FADHILLAH AZMI Fadhillah Azmi Fadhillah Azmi, Fadhillah Fawwaz, Insidini Fransiskus Gowasa Gibran, M Khalil Gibran, M. Khalil I Fawwaz Indra, Evta Interiesta, Diva Cahaya Jesron Nainggolan M Khalil Gibran M. Khalil Gibran Manurung, Immanuel H G Maulana, Ilham Habib MAULANA, RIZA Mawaddah Harahap, Mawaddah Mega Puspita Sari Miftahul Jannah Muhammad Khalil Gibran Muhammad Riki Atsauri N P Dharshinni N P Dharshinni N. PRIYA DHARSHINNI Nanda Novita Nanda Novita Nanda Novita, Nanda Pasaribu, Martin Josua Perangin-angin, Despaleri Regina Puspa Sari Damanik, Almerinda Rina Anugrahwaty, Rina Sarif, Muhammad Irfan Sibero, Alexander F K Simanjuntak, Englis Franata Sinaga, Monica Sari Sinaga, Nicholas Ferdinan Sipahutar, Yohannes Saputra Siregar, Ana Tince Siregar, Yunita Sari Sitompul, Boy Arnol Sofy, Annisa Kurnia Victor Daniel Sihombing Wijaya Laia, Laksana Febri Yusuf Roni Laia