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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics CESS (Journal of Computer Engineering, System and Science) Jurnal Teknologi Informasi dan Komunikasi InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Abdimas Talenta : Jurnal Pengabdian Kepada Masyarakat Juripol Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Query : Jurnal Sistem Informasi Zero : Jurnal Sains, Matematika, dan Terapan JURIKOM (Jurnal Riset Komputer) Data Science: Journal of Computing and Applied Informatics ComTech: Computer, Mathematics and Engineering Applications Building of Informatics, Technology and Science Jurnal Mantik Indonesian Journal of Education and Mathematical Science International Journal of Advances in Data and Information Systems Randwick International of Social Science Journal Jurnal Scientia Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences Journal of Applied Data Sciences TECHSI - Jurnal Teknik Informatika Prisma Sains: Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) The Indonesian Journal of Computer Science Journal of Digital Market and Digital Currency
Efendi, Syahril
Department Of Computer Science And Information Technology, University Of North Sumatra, Indonesia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Interactive Virtual Reality For Fun Mathematics Learning With Deep Understanding At SMP Swasta Ar-Rahman Percut Herman Mawengkang; Syahril Efendi; Muliawan Firdaus
Jurnal Mantik Vol. 5 No. 3 (2021): November: 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/jurnalmantik.v5i3.1591

Abstract

This partnership program was intended to solve problems that arose at SMP Swasta Ar-Rahman Percut such as the lack of teacher skills in developing learning media to foster students’ motivation and implementing online teaching that optimizes the use of technology. In the context of online learning, such difficulties will increase. The solutions offered were training through workshops on the development of interactive virtual reality (VR) using OpenSpace3D and assistance in the implementation. Five stages: Learn, Teach, Evaluate, Acknowledge, and Fostering in the method were held sequentially. Adult learning principles, focusing on experiential learning and self-motivation, were applied to encourage participants and instructors to actively participate. Results showed that the program has made a significant impact on teachers' skills in developing and implementing interactive VR in fun mathematics online learning.
SISTEM PENDETEKSIAN POLA TAJWID PADA CITRA AL-QUR’AN MENGGUNAKAN ALGORITMA BIDIRECTIONAL ASSOCIATIVE MEMORY Lidya Rosnita; Muhammad Zarlis; Syahril Efendi
TECHSI - Jurnal Teknik Informatika Vol 8, No 2 (2016)
Publisher : Teknik Informatika Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/techsi.v8i2.138

Abstract

Pada umumnya didalam dunia pengolahan citra untuk mengenali sebuah pola dapat diberikan beberapa pelatihan terlebih dahulu. Didalam penelitian ini pendeteksian pola tajwid pada citra Al-Qur’an menggunakan empat pola tajwid iqlab, dengan algoritma Bidirectional Associative Memory yang kemudian akan diukur unjuk kerjanya berdasarkan delapan nilai sensitif yang berbeda. Berdasarkan hasil komplesitas algoritma, sistem pendeteksian pola tajwid pada citra Al-Qur’an menggunakan algoritma Bidirectional Associative Memory memiliki kompleksitas sebesar T(n) =  (n). Hasil penelitian menunjukkan bahwa sistem pendeteksian pola tajwid pada citra Al-Qur’an menggunakan algoritma Bidirectional Associative Memory memiliki kisaran true detection sebesar 72 % hingga 84 %. 
Intelligent evacuation model in disaster mitigation M Safii; Syahril Efendi; Muhammad Zarlis; Herman Mawengkang
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3805

Abstract

Mitigation is a pre-disaster action that aims to prepare for a disaster situation. One of the mitigation activities is evacuation, which aims to reduce disaster-related losses. Because disaster damage cannot be predicted, dealing with evacuation efforts requires a dynamic model. This study will utilize a dynamic model that combines the game theory model for choosing the evacuation location and the open vehicle routing problem (OVRP) model for selecting evacuation routes to create an intelligent system. The game theory model will be used to supplement the selection of alternate evacuation locations by taking into account geographical features that are very uncertain in the event of a crisis. An evacuation route equipped with an OVRP model with the goal of optimizing travel time is required to mobilize disaster-affected people. With the development of the Intelligent Evacuation System idea, combining the two models will create a new model. The simulation model is evaluated using linear, interactive, and discrete optimizer (LINDO), which may minimize the evacuation time from the evacuation route to a safe destination by 50 minutes if there is no contra-flow to allow more people to be evacuated.
Perbandingan Algoritma Prim Dengan Algoritma Floyd-Warshall Dalam Menentukan Rute Terpendek (Shortest Path Problem) Zuhri Ramadhan; Muhammad Zarlis; Syahril Efendi; Andysah Putera Utama Siahaan
JURIKOM (Jurnal Riset Komputer) Vol 5, No 2 (2018): April 2018
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (490.326 KB) | DOI: 10.30865/jurikom.v5i2.625

Abstract

Masalah optimasi menjadi hal yang kompleks dalam mencari jalur atau rute optimal, banyak metode yang menjadi indikator rute optimal salah satunya adalah rute terpendek. Pencarian rute terpendek (shortest path) merupakan salah satu metode untuk menyelesaikan masalah rute perjalanan, metode shortest path problem dapat menggunakan berbagai macam algoritma seperti algoritma prim dan algoritma Floyd-warshall, namun algoritma mana diantara keduanya yang paling optimum dalam menentukan masalah rute terpendek. Dengan proses pencarian menggunakan graf dan dianalisa hasil dengan tabel kebenaran maka akan didapat hasil paling optimum diantara kedua algoritma.
Feature Selection Using Eigen Vector to Improve K-Means Clustering Nugroho Syahputra; Muhammad Zarlis; Syahril Efendi
CESS (Journal of Computer Engineering, System and Science) Vol 7, No 2 (2022): July 2022 - In Process
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v7i2.35449

Abstract

Banyaknya jumlah atribut data set dari proses pengelompokan data dengan K-Means Clustering dapat mempengaruhi besaran jumlah iterasi yang dihasilkan. Pada riset ini, Eigen Vector digunakan untuk melakukan seleksi fitur pada data set. Data set yang telah diseleksi selanjutnya dilakukan proses clustering dengan K-Means Clustering. Data set yang digunakan pada riset ini adalah Wine Quality Dataset yang diperoleh dari UCI Machine Learning Repository, dengan 11 atribut ,4898 records data dan 7 kelas atribut. Hasil dari riset ini menunjukkan bahwa rata-rata jumlah iterasi yang diperoleh dari perbandingan pengujian dengan menggunakan K-Means tanpa seleksi fitur yaitu diperoleh rata-rata sebesar 21 iterasi, sedangkan K-Means dengan seleksi fitur Eigen Vector diperoleh rata-rata sebesar 15 iterasi. Evaluasi clustering dihitung menggunakan Davies-Bouldin Index (DBI). Nilai DBI pada K-Means Clustering tanpa seleksi fitur yaitu sebesar 0.746667, sedangkan pada K-Means Clustering dengan Eigen Vector yang telah diseleksi sebanyak 5 atribut diperoleh nilai rata-rata DBI masing-masing sebesar 0.664316.
Sentiment Analysis of Food Order Tweets to Find Out Demographic Customer Profile Using SVM Syahril Efendi; Poltak Sihombing
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 21 No 3 (2022)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (432.854 KB) | DOI: 10.30812/matrik.v21i3.1898

Abstract

The use of online food ordering through food systems or applications continues to increase, requiring vendors to implement marketing and sales strategies through surveys, feedback. The problems that arise are building a system analysis model from a collection of tweets with hashtags or usernames for ordering food online . The Support Vector Machine (SVM) algorithm is used for text classification. Tweets are collected into data sets, training data, and testing data, then a classification model of the SVM Algorithm is built. Preprocessing data, tweets are cleansing, tokenized, and stopword remove. From the collected tweets, they are grouped into 10 variables to identify demographic profiles. The results of the analysis are classified as positive sentiments, namely residence, price range, using promos, paid types, halal food while negative sentiments are ethnicity, culture, vegetarianism, place. Classification accuracy is important to validate the results of the SVM model. From 500 train data tweet, the resulting classification is 66% positive sentiment and 34% negative sentiment. Overall accuracy model Linier SVM result 83.2% with accuracy 92.55%.
Metode Algoritma Support Vector Machine (SVM) Linier Dalam Memprediksi Kelulusan Mahasiswa Oktaviana Bangun; Herman Mawengkang; Syahril Efendi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4572

Abstract

The accumulation of student databases can occur if students are unable to complete their studies, namely graduating at a predetermined time. Data mining techniques are often used to process student data so that they can produce predictions of student graduation in order to graduate at a predetermined time. One of the data mining techniques that is often used is the Support Vector Machine (SVM) algorithm. This study aims to analyze the performance of the SVM algorithm to produce a predictive model of student graduation in order to graduate at a predetermined time in the Public Health Study Program, Faculty of Public Health, Deli Husada Health Institute. The method used in this study is a linear SVM algorithm starting from data retrieval by selecting the attributes that will be used for the next stage, data processing consists of cleaning data whose contents do not exist and data transformation which is the determination of the category of each data, modeling is done with the SVM algorithm. from training data and testing and evaluation data to validate and measure the accuracy of the model. The test results with the amount of training data as much as 70% and testing data as much as 30% shows that the linear SVM algorithm provides an accuracy value of 90%
Seleksi Fitur Menggunakan Eigen Vector Untuk Peningkatan Kinerja K-Means Clustering Dalam Pengelompokan Data Nugroho Syahputra; Muhammad Zarlis; Syahril Efendi
Building of Informatics, Technology and Science (BITS) Vol 4 No 2 (2022): September 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v4i2.2022

Abstract

The large number of data set attributes from the data grouping process with K-Means Clustering can affect the number of iterations produced. In this research, Eigen Vector is used to perform feature selection on the data set. The selected data set is then clustered using K-Means Clustering. The data set used in this research is the Wine Quality Dataset obtained from the UCI Machine Learning Repository, with 11 attributes, 4898 data records and 7 attribute classes. Then the South German Credit Dataset was obtained from kaggle.com with 20 attributes, 1000 data records and 2 attribute classes. The results of this research indicate that the number of iterations obtained from the comparison of tests using K-Means without feature selection is that in the Wine Quality Dataset, 11 iterations are obtained, and in the South German Credit Dataset, there are 10 iterations. Meanwhile, K-Means with Eigen Vector feature selection obtained the number of iterations in the Wine Quality Dataset with a total of 5 iterations, and in the South German Credit Dataset with a total of 4 iterations. Clustering evaluation was calculated using Sum of Square Error (SSE). The SSE value in K-Means Clustering without feature selection from the Wine Quality Dataset is 678.5735, while in the South German Credit Dataset it is 1534.3167. While the K-Means Clustering with Eigen Vector from the Wine Quality Dataset is 383.0517, and the South German Credit Dataset is 469.0698. From the results of the proposed method is able to reduce the percentage of errors and minimize the number of iterations on K-Means Clustering with feature selection using Eigen Vector
Model Dynamic Facility Location in Post-Disaster Areas in Uncertainty lili Tanti; Syahril Efendi; Maya Silvi Lydia; Herman Mawengkang
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 22 No 1 (2022)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i1.2095

Abstract

Indonesia has many disaster-prone areas, natural disasters that occur in Indonesia in 2021 are 5,402 disasters. For disaster management in post-disaster areas, logistical planning is needed in the distribution of logistical assistance, it is estimated that the logistics costs of disaster assistance reach approximately 80% of the total costs in disaster management so that logistical assistance is an expensive activity of disaster relief. However, so far the process of distributing logistical assistance to disaster posts has not been evenly distributed. One of the causes of the unequal distribution is the inappropriate selection of distribution post locations. The facility location model is dynamic and has the objective function of minimizing the distance between emergency posts and refugee posts in terms of distribution of disaster relief goods in one cluster group. For grouping unsupervised learning data using a machine learning clustering algorithm, k-means. Model validation has been carried out using max run and max optimization 1000 times with results reaching 90%. This proves that the emergency facility location model can be used to determine the location of the emergency center, where the determination of the location of the emergency center has the closest distance to the request point/post shelter for disaster victims
Optimization of Support Vector Machine Algorithm Using Stunting Data Classification Saraswati Yoga Andriyani; Maya Silvi Lydia; Syahril Efendi
Prisma Sains : Jurnal Pengkajian Ilmu dan Pembelajaran Matematika dan IPA IKIP Mataram Vol 11, No 1: January 2023
Publisher : IKIP Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/j-ps.v11i1.6619

Abstract

Several studies from Indonesia reveal that malnutrition and stunting are still severe concerns to be addressed in the future. The complexity of the problem of stunting or nutritional status requires the responsibility of all parties, including science and technology. The issue of monitoring and data collection related to stunting or the nutritional status of children in Indonesia, especially Medan City, North Sumatra Province, is an essential factor in determining the calculations carried out by each Community Health Center with many attributes. Currently, the Support Vector Machine method is a solution to increase government intervention's effectiveness in classifying malnutrition and stunting. However, the Support Vector Machine algorithm still needs to improve, namely the difficulty of selecting the right and optimal features for the attribute weights, causing a low prediction accuracy. Therefore, researchers aim to optimize the Support Vector Machine Algorithm with Particle Swarm Optimization using Linear, Polynomial, Sigmoid, and Radial Basic Function kernels. The results were obtained from research utilizing nutritional status data, that performance in improving the Support Vector Machine algorithm based on Particle Swarm Optimization using four kernel tests, namely Linear, Polynomial, Sigmoid, and Radial Basic Function obtained different results, not all kernels in this study can improve accuracy well. The best performance is using the Radial Basic Function kernel with an Accuracy value of 78%, Precision of 89%, Recall of 66%, and F1-Score of 72%, so it is feasible for accurate information regarding the classification of nutritional status.
Co-Authors Abdulbasah Kamil, Anton Abi Rafdi Ahmad Rozy Ahmadi, Fauzan Nur Al Khowarizmi Aminuyati Andysah Putera Utama Siahaan Arjon Turnip Asrizal Asrizal Badawi, Afif Br Bangun, Desy Milbina Br Ginting, Dewi Sartika Budi K. Hutasuhut Chairil Umri Dadang Priyanto Devi Maiya Sari Nasution Erna Budhiarti Erna Budhiarti Nababan Erna Budhiarti Nababan Fahmi Fahmi Fajar Muhajir Fatma Sari Hutagalung Fauzan Nurahmadi Fauzi Amri Fuzy Yustika Manik, Fuzy Yustika Ginting, Dewi Sartika Br Halim Maulana Hamzani, Fitri Rezky Harahap, Lailan Hariyati Lubis, Hariyati Harumy, T. Henny Febriana Hasibuan, Nisma Novita Hasugian , Paska Marto Hengki Tamando Sihotang Hengki Tamando Sihotang Herianto, Tulus Joseph Herimanto Herimanto herman mawengkang Herman Mawengkang Hotmaida Lestari Siregar Ichsanuddin Hakim Ignazio Ahmad Pasadana Iin Parlina Indah Purnama Sari Indra Edy Syahputra Irzal Sofyan Jaya, Ivan Khowarizmi, Al- Lailan Harahap Lidya Rosnita lili Tanti Lubis, Fahrurrozi M Safii M. Isa Indrawan Mahyuddin K. M Nasution Manurung, Rodiyah Aini Mardiansyah, Heru Marischa Elveny, Marischa Maya Silvi Lydia Mesran, Mesran Mochamad Wahyudi Mohammad Andri Budiman Muhammad Iqbal Muhammad Iqbal Muhammad Riki Atsauri Muhammad Rusdi dan Afritha Amelia - Muhammad Zarlis Muhammad Zarlis Muhammad Zarlis Muhammad Zarlis Muhammad Zarlis Muhammad Zarlis Muhammad Zarlis, Muhammad Muliawan Firdaus Mulkan Azhari Naemah Mubarakah Nainggolan, Pauzi Ibrahim Nugroho Syahputra Oktaviana Bangun Pahala Sirait Poltak Sihombing Poltak Sihombing Poltak Sihombing Poltak Sihombing Poltak Sihombing Poltak Sihombing Prayoga, Nanda Dimas Purwanto Purwanto Rahmad Syah Riah Ukur Ginting Rika Permata Sari Siregar Rizki Suwanda Saib Suwilo Santoso, Zikri Akmal Saraswati Yoga Andriyani Sarif, Muhammad Irfan Sawaluddin Sawaluddin Sembiring, Rahmat W Seniman Seniman Seniman Seniman, Seniman Siagian, Deliyana Simamora, Windi Saputri Solly Aryza Sri Dwi Hastuti Sri Melvani Hardi Suherman Suherman Suherman, Suherman Sutarman Sutarman Sutarman Sutarman Syah, Rahmad B. Y. Syahputra, Indra Edy Syahputra, Muhammad Romi Syahraini, Syahraini Syahriol Sitorus Taufiqurrahman Taufiqurrahman Tulus Tulus Tulus Tulus Vinsensia, Desi Watts, Michael J. Weber, Gerhard Wilhelm yeni absah Yudhistira Yudhistira Yudhistira Zakarias Situmorang Zega, Imanuel Zuhri Ramadhan Zulkarnain Lubis