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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 Scientific Journal of Informatics JURNAL AGRILAND Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Abdimas Talenta : Jurnal Pengabdian Kepada Masyarakat Data Science: Journal of Computing and Applied Informatics CCIT (Creative Communication and Innovative Technology) Journal Journal of Information System, Applied, Management, Accounting and Research Jurnal Informatika Kaputama (JIK) Jurnal Pengabdian Al-Ikhlas Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science Journal of Research in Mathematics Trends and Technology Narra J Jurnal EduHealth
Mahyuddin K. M Nasution
Faculty Of Computer Science And Information Technology, Universitas Sumatera Utara, Medan, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Public Health Social Sciences Veterinary

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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

Abstract

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.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1212.692 KB) | DOI: 10.32734/jocai.v2.i2-324

Abstract

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 effect of the TF-IDF algorithm in times series in forecasting word on social media Arif Ridho Lubis; Mahyuddin K. M. Nasution; Opim Salim Sitompul; Elviawaty Muisa Zamzami
Indonesian Journal of Electrical Engineering and Computer Science Vol 22, No 2: May 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v22.i2.pp976-984

Abstract

Forecasting is one of the main topics in data mining or machine learning in which forecasting, a group of data used, has a label class or target. Thus, many algorithms for solving forecasting problems are categorized as supervised learning with the aim of conducting training. In this case, the things that were supervised were the label or target data playing a role as a 'supervisor' who supervise the training process in achieving a certain level of accuracy or precision. Time series is a method that is generally used to forecast based on time and can forecast words in social media. In this study had conducted the word forecasting on twitter with 1734 tweets which were interpreted as weighted documents using the TF-IDF algorithm with a frequency that often comes out in tweets so the TF-IDF value is getting smaller and vice versa. After getting the word weight value of the tweets, a time series forecast was performed with the test data of 1734 tweets that the results referred to 1203 categories of Slack words and 531 verb tweets as training data resulting in good accuracy. The division of word forecasting was classified into two groups i.e. inactive users and active users. The results obtained were processed with a MAPE calculation process of 50% for inactive users and 0.1980198% for active users.
Pemanfaatan Website Sebagai Sarana Informasi dan Promosi Desa Liang Muda Mahyuddin K.M. Nasution; Ivan Jaya; Sri Melvani Hardi; Pauzi Ibrahim Nainggolan
JURNAL PENGABDIAN AL-IKHLAS UNIVERSITAS ISLAM KALIMANTAN MUHAMMAD ARSYAD AL BANJARY Vol 8, No 2 (2022): AL-IKHLAS JURNAL PENGABDIAN
Publisher : Universitas Islam kalimantan MAB

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31602/jpaiuniska.v8i2.6579

Abstract

Teknologi informasi merupakan suatu proses mengolah data dengan cara yang bervariasi atau memperoleh informasi dengan kualitas yang baik. Informasi yang memiliki relevansi dan bernilai strategis serta akurat dibutuhkan dan digunakan di berbagai jenis keperluan baik untuk kebutuhan pribadi dan keperluan usaha atau bisnis serta di pemerintahan. Salah satu produk dari teknologi informasi adalah sistem informasi berbasis website. Pemanfaatan website menjadi salah satu cara untuk mempromosikan produk dan potensi dari suatu daerah atau desa. Dengan website yang dapat diakses secara daring, informasi yang disebarkan dapat menjangkau seluruh lapisan masyarakat di berbagai belahan dunia terkait dengan produk dan potensi yang ada pada desa. Berdasarkan hal tersebut, desa Liang Muda yang terletak di kecamatan STM Hulu kabupaten Deli Serdang provinsi Sumatera Utara, perlu memanfaatkan penggunaan website sebagai media informasi dan promosi untuk masyarakat di desa Liang Muda dan juga untuk masyarakat di luar dari desa Liang Muda baik di dalam negeri maupun di luar negeri. Kegiatan ini dapat meningkatkan sumber daya manusia di desa Liang Muda dalam pemanfaatan website dan juga memajukan dan meningkatkan perekonomian desa secara berkala.
Twitter Data Analysis and Text Normalization in Collecting Standard Word Arif Ridho Lubis; Mahyuddin K M Nasution
Journal of Applied Engineering and Technological Science (JAETS) Vol. 4 No. 2 (2023): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v4i2.1991

Abstract

is one of the most important data sources in social data analysis. However, the text contained on Twitter is often unstructured, resulting in difficulties in collecting standard words. Therefore, in this research, we analyze Twitter data and normalize text to produce standard words that can be used in social data analysis. The purpose of this research is to improve the quality of data collection on standard words on social media from Twitter and facilitate the analysis of social data that is more accurate and valid. The method used is natural language processing techniques using classification algorithms and text normalization techniques. The result of this study is a set of standard words that can be used for social data analysis with a total of 11430 words, then 4075 words with structural or formal words and 7355 informal words. Informal words are corrected by trusted sources to create a corpus of formal and informal words obtained from social media tweet data @fullSenyum. The contribution to this research is that the method developed can improve the quality of social data collection from Twitter by ensuring the words used are standard and accurate and the text normalization method used in this study can be used as a reference for text normalization in other social data, thus facilitating collection. and better-quality social data analysis. This research can assist researchers or practitioners in understanding natural language processing techniques and their application in social data analysis. This research is expected to assist in collecting social data more effectively and efficiently.
KEAMANAN INFORMASI DATA PRIBADI PADA MEDIA SOSIAL Mesra Betty Yel; Mahyuddin K. M Nasution
Jurnal Informatika Kaputama (JIK) Vol 6 No 1 (2022): Volume 6, Nomor 1, Januari 2022
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59697/jik.v6i1.144

Abstract

Perkembangan teknologi informasi dan internet saat ini telah mengubah cara manusia dalam melakukan komunikasi. Salah satunya adalah perkembangan media sosial, media sosial sudah menjadi bagian dari kehidupan untuk memperoleh, membagikan dan menyebarluaskan informasi. Semakin berkembangnya media sosial maka masalah keamanan informasi dan privasi juga menjadi hal yang penting saat ini. Media sosial sebagai salah satu sumber bocornya informasi rahasia sudah menjadi hal yang umum saat ini. Tanpa disadari, banyak data mengenai privasi seseorang yang telah bocor di internet. Data privasi yang tersebar bisa disebabkan oleh kelalaian maupun penyedia layanan. Keamanan sistem informasi merupakan aset yang harus dilindungi keamanannya. Keamanan secara umum diartikan sebagai “quality or state of being secure to be free from danger”. Metode penelitian dilakukan adalah menggunakan metode blended. Penelitian ini dilakukan dengan cara mencari dan serta memahami literatur atau yang berhubungan keamanan informasi pada media social dan penelitian pustaka. Enam poin utama yang harus dipertimbangkan saat menggunakan sistem aplikasi online terkait privasi data yaitu keamanan dan data perlindungan, kesadaran pengguna, pengaturan kontrol, manajemen risiko, transparansi, dan etika. Perlu dibangun kepercayaan ke dalam rancangan layanan Internet, baik melalui kegiatan rancang bangun pengelolaan suatu sistem yang lebih mengedepankan user priority. Memungkinkan, user diberikan pilihan mekanisme kontrol terhadap perlu tidaknya dalam mengungkapkan informasi pribadi dan penggunaannya.
Development of teaching materials based on learning video using movavi education set at Yayasan Perguruan Tunas Karya Batang Kuis Ivan Jaya; Mahyuddin K. M. Nasution
ABDIMAS TALENTA: Jurnal Pengabdian Kepada Masyarakat Vol. 6 No. 1 (2021): ABDIMAS TALENTA : Jurnal Pengabdian Kepada Masyarakat
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (604.02 KB) | DOI: 10.32734/abdimastalenta.v6i1.5131

Abstract

The COVID-19 pandemic that has recently hit various countries including Indonesia has resulted in major changes in various fields, including in the development of the education sector. The teaching and learning process has turned from face-to-face into an online method. However, there are several obstacles experienced by schools that implement an online learning system, one of them was the ability of teachers who do not understand various learning application platforms. In addition, the material provided by the teacher is not maximally acceptable to students because most teachers provide learning material from the pages of textbooks or teacher writings (scans, photos, or presentation files). For this reason, it is necessary to have variations in the provision of teaching materials to students by making interesting and creative learning videos using the Movavi Education Set. With learning videos, students can do lessons at home, repeat it, and can ask the teacher some points from it if they don't understand. By using Movavi Education Set, teachers are also free to be creative in making learning videos that can be shared through commonly used communication applications such as e-mail, WhatsApp, line, google classroom and other applications.
Performance Analysis Of The Combination Of Blum Blum Shub And Rc5 Algorithm In Message Security Rambe, Basyit Mubarroq; Nababan, Erna Budhiarti; Nasution, Mahyuddin KM
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 7 No. 2 (2024): Vol. 7 No. 2 (2024): Issues January 2024
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v7i2.10937

Abstract

This research aims to enhance message security in the RC5 algorithm by integrating it with the Blum Blum Shub (BBS) algorithm. The rapid growth in data and information exchange, driven by advancements in information and communication technology, demands robust security against attacks such as eavesdropping, interruption, and data modification. Cryptography, particularly with symmetric and asymmetric keys, becomes a solution to maintain message confidentiality. The RC (Rivest Cipher) algorithm, specifically RC5, has become a popular choice in network applications due to its speed and variable key length complexity. This study attempts to improve the quality of encryption keys by integrating the Blum Blum Shub (BBS) method, a mathematical random number generator algorithm. RC5 and BBS are used together to secure messages, producing ciphertext that is difficult to predict and smaller in file size compared to the standard RC5 method. The test results show that the processing speed is independent of the number of characters in the plaintext, while the encrypted file size resulting from the RC5-BBS combination is more efficient than using the default RC5. In conclusion, integrating BBS into RC5 can enhance the security and efficiency of the encryption algorithm, with the potential for widespread application in cryptography-based data security
A Mathematical Model of Diet Menu Problem Based on Boolean Linear Programming Approach Harahap, Latifah Hanum; Nasution, Mahyuddin K. M.; Sawaluddin, Sawaluddin
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12592

Abstract

This study aims to model the diet menu problem based on a Boolean Linear Programming approach. A balanced diet is the key to a healthy lifestyle. A balanced diet is a diet that combines foodstuffs in the right amount of food components in one menu (dishes using certain recipes). When you have an unbalanced diet, your body will not get the right amount of nutrients. This is what causes the importance of managing the diet menu. Because of that, a diet menu problem model was formed based on the Boolean Linear Programming approach to cover a varied range of daily diet menu management and meet daily nutritional needs while minimizing costs. The stages of establishing the diet menu problem model are carried out by determining the notations, parameters, variables, objective functions, and some constraints related to the diet menu.
Knowledge, attitude, and practices of midwives on premature rupture of membranes (PROM): A cross-sectional study in Samosir and Toba, Indonesia Lumbanraja, Sarma N.; Tobing, Immanuel DL.; Santosa, Heru; Nasution, Mahyuddin KM.; Aritonang, Evawany Y.; Ichwan, Muhammad; Imelda, Fatwa; Siahaan, Andre MP.
Narra J Vol. 4 No. 1 (2024): April 2024
Publisher : Narra Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52225/narra.v4i1.335

Abstract

Indonesia has a significantly higher maternal mortality ratio (MMR) than other countries in Southeast Asia, and infection is one of the most common causes of maternal deaths, of which premature rupture of membranes (PROM) can be the consequence of the infections. In primary healthcare settings, midwives play an important role in identifying and managing PROM appropriately; however, studies on their knowledge, attitudes, and practices related to PROM are limited. The aim of this study was to determine the midwives' knowledge, attitude and healthcare practice on PROM in Indonesia. A cross-sectional study was conducted among midwives at primary healthcare facilities in Samosir and Toba Regency, North Sumatra, Indonesia, from July to November 2022. The knowledge, attitude and practice towards PROM were assessed. Results showed that 57.5% of midwives had poor knowledge and 35.1% had poor attitude levels. There were 4.9% of midwives referred the patients immediately to the hospital. Our data indicated that aged 31−40 or 41−50 years, having a lower than bachelor degree and having a higher monthly number of referred PROM patients were significantly associated with poor knowledge compared to younger, having a bachelor degree, and lower monthly referral patient number, respectively. Similarly, younger, having higher degree and a having lower monthly referral number of PROM cases were associated with higher chances of having a sufficient-good attitude towards PROM. This study highlights that a significant percentage of midwives had poor levels of knowledge and attitude, and age, educational level and monthly referral number of PROM cases were associated with the level of knowledge and attitude.
Co-Authors Al Khowarizmi Albadri, Aldhi Alexander F.K. Sibero Amelia Oktarini Siregar Ananda, Rana Fathinah Aritonang, Evawany Y. Azmi, Zulfian - Betty Yel, Mesra Efendi, Syahril Elveny, Marischa Elviawaty Muisa Zamzami Erna Budhiarti Nababan Habib Satria Harahap, Latifah Hanum Herman Mawengkang Heru Santosa Imelda, Fatwa Ivan Jaya Ivan Jaya Lubis, Arif Ridho Lumbanraja, Sarma N. Marischa Elveny, Marischa Muhammad Ichwan Muhammad Zarlis Muhammad Zarlis, Muhammad Nainggolan, Pauzi Ibrahim Opim Salim Sitompul Rahmad Syah Rahman Aulia, Rahman Rambe, Basyit Mubarroq Sawaluddin Sawaluddin, Sawaluddin Siahaan, Andre MP. Sri Melvani Hardi Sutarman Sutarman Syah, Rahmad B. Y. Syah, Rahmad Bayu Syamsafitri Syamsafitri Tobing, Immanuel DL. Verdi Yasin Y. Syah, Rahmad B. Zarlis, M Zarlis, M Zulfian Azmi