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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Perfecting a Video Game with Game Metrics International Journal of Advances in Intelligent Informatics InfoTekJar (Jurnal Nasional Informatika dan Teknologi Jaringan) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Data Science: Journal of Computing and Applied Informatics Jurnal Mantik Indonesian Journal of Electrical Engineering and Computer Science

Opim Salim Sitompul

Universitas Sumatera Utara

Author-ID : 2405303

Computer Science & IT Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media

Published : 12 Documents Claim Missing Document

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Articles

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Advertisement billboard detection and geotagging system with inductive transfer learning in deep convolutional neural network Romi Fadillah Rahmat; Dennis Dennis; Opim Salim Sitompul; Sarah Purnamawati; Rahmat Budiarto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 5: October 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i5.11276

In this paper, we propose an approach to detect and geotag advertisement billboard in real-time condition. Our approach is using AlexNet’s Deep Convolutional Neural Network (DCNN) as a pre-trained neural network with 1000 categories for image classification. To improve the performance of the pre-trained neural network, we retrain the network by adding more advertisement billboard images using inductive transfer learning approach. Then, we fine-tuned the output layer into advertisement billboard related categories. Furthermore, the detected advertisement billboard images will be geotagged by inserting Exif metadata into the image file. Experimental results show that the approach achieves 92.7% training accuracy for advertisement billboard detection, while for overall testing results it will give 71,86% testing accuracy.

File Reconstruction in Digital Forensic Opim Salim Sitompul; Andrew Handoko; Romi Fadillah Rahmat
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 2: April 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i2.8230

File recovery is one of the stages in computer forensic investigative process to identify an acquired file to be used as digital evident. The recovery is performed on files that have been deleted from a file system. However, in order to recover a deleted file, some considerations should be taken. A deleted file is potentially modified from its original condition because another file might either partly or entirely overriding the file content. A typical approach in recovering deleted file is to apply Boyer-Moore algorithm that has rather high time complexity in terms of string searching. Therefore, a better string matching approach for recovering deleted file is required. We propose Aho-Corasick parsing technique to read file attributes from the master file table (MFT) in order to examine the file condition. If the file was deleted, then the parser search the file content in order to reconstruct the file. Experiments were conducted using several file modifications, such as 0% (unmodified), 18.98%, 32.21% and 9.77%. From the experimental results we found that the file reconstruction process on the file system was performed successfully. The average successful rate for the file recovery from four experiments on each modification was 87.50% and for the string matching process average time on searching file names was 0.32 second.

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.

Enhancing Performance of Parallel Self-Organizing Map on Large Dataset with Dynamic Parallel and Hyper-Q Alexander F.K. Sibero; Opim Salim Sitompul; Mahyuddin K.M. Nasution
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

Self-Organizing Map (SOM) is an unsupervised artificial neural network algorithm. Even though this algorithm is known to be an appealing clustering method,many efforts to improve its performance are still pursued in various research works. In order to gain faster computation time, for instance, running SOM in parallel had been focused in many previous research works. Utilization of the Graphics Processing Unit (GPU) as a parallel calculation engine is also continuously improved. However, total computation time in parallel SOM is still not optimal on processing large dataset. In this research, we propose a combination of Dynamic Parallel and Hyper-Q to further improve the performance of parallel SOM in terms of faster computing time. Dynamic Parallel and Hyper-Q are utilized on the process of calculating distance and searching best-matching unit (BMU), while updating weight and its neighbors are performed using Hyper-Q only. Result of this study indicates an increase in SOM parallel performance up to two times faster compared to those without using Dynamic Parallel and Hyper-Q.

The Flowshop Scheduling Makespan by the ACO-GA Algorithm: The Flowshop Scheduling Makespan by the ACO-GA Algorithm Jonas Franky R Panggabean; Opim Salim Sitompul; Erna Budhiarti Nababan
Jurnal Mantik Vol. 3 No. 4 (2020): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (431.808 KB)

Flow shop scheduling could be a scheduling model where all jobs that are processed flow within the same direction / path. the matter is usually faced if n jobs are processed on m machines, where what must be done first and what allocates jobs on the machine in order that a scheduled production process are obtained. To validate this algorithm a computational test was done employing a dataset of 60 examples from the Taillard Benchmark. HS algorithm with a comparison of two constructive heuristics from the literature, namely the NEH heuristic and stochastic greedy heuristic (SG). The average results obtained for dataset sizes are 20 x 5 to 50 x 10, that the ACO-GA algorithm has smaller makespan compared to the opposite two algorithms, except for large dataset sizes the ACO-GA algorithm has larger makespan compared to the 2 algorithms above with difference of 1.4 units of your time

The feature extraction for classifying words on social media with the Naïve Bayes algorithm Arif Ridho Lubis; Mahyuddin Khairuddin Matyuso Nasution; Opim Salim Sitompul; Elviawaty Muisa Zamzami
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp1041-1048

To classify Naïve Bayes classification (NBC), however, it is necessary to have a previous pre-processing and feature extraction. Generally, pre-processing eliminates unnecessary words while feature extraction processes these words. This paper focuses on feature extraction in which calculations and searches are used by applying word2vec while in frequency using term frequency-Inverse document frequency (TF-IDF). The process of classifying words on Twitter with 1734 tweets which are defined as a document to weight the calculation of frequency with TF-IDF with words that often come out in tweet, the value of TF-IDF decreases and vice versa. Following the achievement of the weight value of the word in the tweet, the classification is carried out using Naïve Bayes with 1734 test data, yielding an accuracy of 88.8% in the Slack word category tweet and while in the tweet category of verb 78.79%. It can be concluded that the data in the form of words available on twitter can be classified and those that refer to slack words and verbs with a fairly good level of accuracy. so that it manifests from the habit of twitter social media user.

Goal Programming Method in Optimizing Course Student Admission, Operational Costs and Profits Muhammad Khahfi Zuhanda; Saib Suwilo; Opim Salim Sitompul; Mardingsih Mardingsih
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.v5i2.6072

In today's business competition, educational institutions or courses require optimizing profits. However, this is not easy because, in its implementation, there are many priority objective functions that must be fulfilled. In this case, the course institution has different class programs, namely literacy, numeracy, and math olympiad programs, with various operational costs per program type and course fees per program type. This problem requires setting priorities because of the limited number of revenues, operating costs, and profit targets. In this paper, the researcher uses Winter's Exponential Smoothing forecasting model to determine the number of students, operating costs, and profits in the following year. Then the researcher analyzes the planning with the goal programming method to minimize the deviation of the multi-objective programming. This research shows that the number of student admissions who can meet market demand for January 2021 decreased by 5.41%, in February decreased by 3.20%, in March decreased by 1.29%, April remained constant, in May increased by 1.44%, June increased by 2.16%, July increased 2.82%, August increased 2.78%, September increased 3.47%, October increased 3.22%, November an increase of 3.33%, and for December an increase of 2.69%. The total operational cost that does not exceed the target limit is Rp. 30,475,0000 for one year. The total profit has reached the target to be achieved, which is Rp. 322,150,000 for one year.

Biased support vector machine and weighted-smote in handling class imbalance problem Hartono Hartono; Opim Salim Sitompul; Tulus Tulus; Erna Budhiarti Nababan
International Journal of Advances in Intelligent Informatics Vol 4, No 1 (2018): March 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v4i1.146

Class imbalance occurs when instances in a class are much higher than in other classes. This machine learning major problem can affect the predicted accuracy. Support Vector Machine (SVM) is robust and precise method in handling class imbalance problem but weak in the bias data distribution, Biased Support Vector Machine (BSVM) became popular choice to solve the problem. BSVM provide better control sensitivity yet lack accuracy compared to general SVM. This study proposes the integration of BSVM and SMOTEBoost to handle class imbalance problem. Non Support Vector (NSV) sets from negative samples and Support Vector (SV) sets from positive samples will undergo a Weighted-SMOTE process. The results indicate that implementation of Biased Support Vector Machine and Weighted-SMOTE achieve better accuracy and sensitivity.

Kombinasi K-Nearest Neighbor (KNN) dan Relief-F untuk Meningkatkan Akurasi Pada Klasifikasi Data Rahmad Nurhadi Yusra; Opim Salim Sitompul; Sawaluddin Sawaluddin
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 6, No 1 (2021): InfoTekJar September
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v6i1.4106

Dalam penelitian ini, penulis mengusulkan proses peningkatan akurasi pada K-Nearest Neighbor (KNN) dengan kombinasi seleksi fitur menggunakan metode Relief-F. Adapun penyebab kurang maksimalnya akurasi pada K-Nearest Neighbor dibandingkan dengan metode klasifikasi lainnya disebabkan oleh pengaruh atribut yang kurang signifikan dan persentase pengaruh yang cenderung rendah dari suatu data dalam menentukan kelas pada data baru. Metode Relief-F digunakan untuk melakukan seleksi pada atribut yang korelasinya kurang baik dari data yang diujikan. Pengujian dari metode yang diusulkan yaitu membandingkan akurasi yang diperoleh dari metode KNN tanpa menggunakan seleksi fitur dengan KNN menggunakan seleksi fitur Relief-F. Hasil pengujian yang diperoleh yaitu metode yang diusulkan mampu meningkatkan akurasi klasifikasi dari KNN dengan peningkatan yang diperoleh yaitu sebesar 10.32% setelah dibandingkan dengan pengujian KNN tanpa seleksi fitur.

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.

Co-Authors Afriandy Sharif Alexander F.K. Sibero Andrew Handoko Arif Ridho Lubis Dennis Dennis Elviawaty Muisa Zamzami Erna Budhiarti Nababan Goklas Tomu Simbolon Hartono Hartono Jonas Franky R Panggabean Mahyuddin K. M. Nasution, Mahyuddin K. M. Mardingsih Mardingsih Muhammad Khahfi Zuhanda Rahmad Nurhadi Yusra Romi Fadillah Rahmat Saib Suwilo Sarah Purnamawati Sawaluddin Sawaluddin Tulus Tulus Yundari, Yundari Yuni Cancer

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