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All Journal JSM (Jurnal SIFO Mikroskil) Perfecting a Video Game with Game Metrics SAMUDERA International Journal of Advances in Intelligent Informatics Jurnal Teknologi Informasi dan Komunikasi InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Abdimas Talenta : Jurnal Pengabdian Kepada Masyarakat Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika Jurnal Inotera Data Science: Journal of Computing and Applied Informatics Jurnal Mantik Indonesian Journal of Computer Science

Erna Budhiarti Nababan

Universitas Sumatera Utara

Author-ID : 327305

Aerospace Engineering Agriculture, Biological Sciences & Forestry Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences

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Genetic Algorithms Dynamic Population Size with Cloning in Solving Traveling Salesman Problem Erna Budhiarti Nababan; Opim Salim Sitompul; Yuni Cancer
Data Science: Journal of Computing and Applied Informatics Vol. 2 No. 2 (2018): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Population size of classical genetic algorithm is determined constantly. Its size remains constant over the run. For more complex problems, larger population sizes need to be avoided from early convergence to produce local optimum. Objective of this research is to evaluate population resizing i.e. dynamic population sizing for Genetic Algorithm (GA) using cloning strategy. We compare performance of proposed method and traditional GA employed to Travelling Salesman Problem (TSP) of A280.tsp taken from TSPLIB. Result shown that GA with dynamic population size exceed computational time of traditional GA.

Data Security Using Multi-bit LSB and Modified Vernam Cipher Goklas Tomu Simbolon; Opim Salim Sitompul; Erna Budhiarti Nababan
Data Science: Journal of Computing and Applied Informatics Vol. 3 No. 2 (2019): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Data security is one of the most important aspects of today's information era. Some methods are used to secure important data from hackers. The LSB is a steganographic algorithm that is often used to store data in the last bit. In order to improve the security, we combine steganography with cryptography enables. In this research LSB is modified using the multi-bit LSB model. Modifications are made to the bits of each character, the rotation by a certain amount can randomize the plaintext content before cryptographic algorithm, Vernam is performed. The bit on LSB can be inserted data as much as 1, 2, 3 or 4 - bit information. The calculation results of MSE and PSNR values indicate that the use of 1-bit LSB is superior to that of 2-, 3-, or 4-bit LSB.

Detection of the Use of Mask to Prevent the Spread of COVID-19 Using SVM, Haar Cascade Classifier, and Robot Arm Andini Pratiwi; Erna Budhiarti Nababan; Amalia
Data Science: Journal of Computing and Applied Informatics Vol. 6 No. 2 (2022): Data Science: Journal of Computing and Applied Informatics (JoCAI)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jocai.v6.i2-9289

In the effort to hold up the case spread of COVID-19’s growth rate by implementing health protocols such as the use of masks, supervision is needed especially for the people who have not or still have problems to wearing masks. In this research, the system utilizes the robotic power to identify visitors whether they are wearing masks or not, and automatically distribute masks if the user is detected as not wearing a mask. The user face detection process uses the Haar Cascade Classifier algorithm and SVM (Support Vector Machine) to classify users who wear masks or not. For the user who is detected as not wearing masks, myCobot-Pi with the support of suction pump will distribute masks to users. The use of myCobot-Pi as a raspberry pi based robotic arm allows the application of the system on devices that are minimal in terms of specifications and size. Through trials by taking 41 examples of detection cases, 29 cases were found that managed to detect the correct use of masks. In addition, in this study we use PP sheet plastic protector to replace the packaging of the mask because it can be carried by the suction pump properly.

Analysis Of Variation In The Number Of MFCC Features In Contrast To LSTM In The Classification Of English Accent Sounds Afriandy Sharif; Opim Salim Sitompul; Erna Budhiarti Nababan
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 6 No. 2 (2023): Issues January 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i2.8566

Various studies have been carried out to classify English accents using traditional classifiers and modern classifiers. In general, research on voice classification and voice recognition that has been done previously uses the MFCC method as voice feature extraction. The stages in this study began with importing datasets, data preprocessing of datasets, then performing MFCC feature extraction, conducting model training, testing model accuracy and displaying a confusion matrix on model accuracy. After that, an analysis of the classification has been carried out. The overall results of the 10 tests on the test set show the highest accuracy value for feature 17 value of 64.96% in the test results obtained some important information, including; The test results on the MFCC coefficient values of twelve to twenty show overfitting. This is shown in the model training process which repeatedly produces high accuracy but produces low accuracy in the classification testing process. The feature assignment on MFCC shows that the higher the feature value assignment on MFCC causes a very large sound feature dimension. With the large number of features obtained, the MFCC method has a weakness in determining the number of features.

Reduksi Dimensi pada Klasifikasi Data Microarray Menggunakan Minimum Redundancy Maximum Relevance dan Random Forest : The Dimensional Reduction in Microarray Data Classification Using Minimum Redundancy Maximum Relevance and Random Forest Lailan Harahap; Erna Budhiarti Nababan; Syahril Efendi
Indonesian Journal of Computer Science Vol. 12 No. 1 (2023): Indonesian Journal of Computer Science Volume 12. No. 1 (2023)
Publisher : STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i1.3133

Di Indonesia prevalensi kanker pada data Riskesdes tahun 2018 terdapat 1,79 per 1.000 penduduk mengidap penyakit kanker. Akibat tingginya prevalensi kanker maka diperlukan pendeteksian kanker sejak dini. Salah satu cara mendeteksi kanker yaitu dengan teknologi microarray dimana teknologi ini dapat memantau ribuan ekpresi gen secara bersamaan dalam satu percobaan. Namun, data microarray memiliki dimensi yang besar sehingga diperlukan proses reduksi dimensi data microarray pada penyakit prostate cancer da gastric cancer agar dapat menghilangkan atribut yang redundansi dan meningkatkan akurasi pada klasifikasi. Reduksi dilakukan menggunakan MRMR (FCQ dan FCD) dengan k 10,20,30,40,50,60,70,80,90 dan 100. Klasifikasi dilakukan menggunakan RF dengan membentuk 100 tree. Hasil akurasi terbaik pada klasifikasi data prostate cancer yaitu dengan FCQ 100% pada k=10, tanpa reduksi 95% dan akurasi terendah dengan FCD 52% pada k=90. Sedangkan hasil akurasi terbaik klasifikasi data gastric cancer yaitu dengan FCQ dan FCD 100% pada semua k dan akurasi terendah yaitu tanpa reduksi 83%.

Co-Authors ,, Amrullah Afriandy Sharif Amalia Andini Pratiwi Dedy Arisandi Eka Irawan Elviawaty Muisa Zamzami Ermawati Ermawati Ferawaty - Goklas Tomu Simbolon Hardisal, Hardisal Hartono Hartono Hayanti, Septi Herman Mawengkang Herman Mawengkang Jonas Franky R Panggabean Jonatan Sianturi Julpan Julpan Junidar, Junidar, Kelvin Onggrono Lailan Harahap Lismardiana Lismardiana M. Zarlis Muhammad Zarlis Muhammad Zarlis Nasution, Mahyuddin KM Opim Salim Sitompul Opim Salim Sitompul Rahmad Syah Rahmi Suliani Lubis Romi Fadillah Rahmat Rosmelda Ginting Rudy Chandra Sawaluddin Sawaluddin Seniman Sianturi, Jonatan Sinuraya, Junus Syahril Efendi Tulus Tulus Windi Saputri Simamora Yuni Cancer

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