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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING JOURNAL OF SCIENCE AND SOCIAL RESEARCH Journal of Applied Engineering and Technological Science (JAETS) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Pengabdian kepada Masyarakat Nusantara ARSY : Jurnal Aplikasi Riset kepada Masyarakat JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Journal Software, Hardware and Information Technology Journal of Mathematics and Scientific Computing With Applications Jurnal Sains dan Teknologi Jurnal Masyarakat Indonesia Jurnal Pemberdayaan Sosial dan Teknologi Masyarakat Jurnal Ilmu Komputer dan Sistem Informasi Jurnal Tiyasadarma: Jurnal Pengabdian Kepada Masyarakat Advance Sustainable Science, Engineering and Technology (ASSET)
Lubis, Arif Ridho
Politeknik Negeri Medan
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry 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 Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Veterinary

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Management maintenance system for remote control based on microcontroller and virtual private server Idham Kamil; Julham Julham; Muharman Lubis; Arif Ridho Lubis
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 3: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i3.pp1349-1355

Abstract

Open loop shaped control system is a form of system control without any feedback from the system. One example is the on-off condition which functions to connect and disconnect electricity. The condition to be studied is a dc motor that can be set to live and die via internet server-based client service. The server in this system is a virtual private server (VPS) device that will provide a source of service to the client in the form of a collection of information on dc motor conditions. In addition, its function is also to record the working time of the dc motor. So that a schedule can be determined when the dc motor is maintained. While the client is a control unit consisting of a microcontroller device, an ethernet module enc28j60 and a dc motor. In general the working principle of the system is beginning with the user accessing the desired VPS IP address through a web browser application. From the web browser the user chooses a dc motor to be activated. But before the client has been connected to the VPS regularly (every second), the point is to always get the latest dc motor condition information. Then the microcontroller will set the dc motor in active or off condition. The research method used is research and development. The results obtained from this study are that the amount of bandwidth needed for communication between VPS and microcontrollers via the internet network, when the control unit works is 6.02 kbps, while the response time for dc motor is 3.16 seconds and the response time for dc motor 2 is 3.46 seconds.
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.
Dealing with Voters’ Privacy Preferences and Readiness in Electronic Voting Muharman Lubis; Arif Ridho Lubis
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 3: September 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v11.i3.pp994-1003

Abstract

Various countries have been encouraged to adopt electronic voting because it can reduce operational cost and time spent for tabulation process. In the current research, it has been mentioned several problem arised in term of technical aspects, voters’ trust, machine vulnerabilities and privacy right in which experts argued the election system have been compromised. In short term, the certain faction will try to exploit the system weaknesses for their own benefit, while in the long term, it can create public distrust to the government, which decrease the voters turn out, break the participation willingness and downgrade the quality of voting. Thus, the government should deal with previous issues in the election before adopting electronic approach while at the same time align with voters’ expectation to provide better election in serve citizen through comprehensive analysis. This study provide initial step to analyse the readiness of electronic voting from the social perspective in response to how Indonesia view the initiative to adopt new tech in voting system.
Decision Making in the Tea Leaves Diseases Detection Using Mamdani Fuzzy Inference Method Arif Ridho Lubis; Santi Prayudani; Muharman Lubis; Al Khowarizmi
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 3: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i3.pp1273-1281

Abstract

The tea plants (Camellia Sinensis) are small tree species that use leaves and leaf buds to produce tea harvested through a monoculture system. It is an agriculture practice to cultivate one types of crop or livestock, variety or breed on a farm annually. Moreover, the emergence of pests, pathogens and diseases cause serious damages to tea plants significantly to its productivity and quality to optimum worst. All parts of the tea plant such as leaves, stems, roots, flowers and fruits are exposed to these harm lead to loss of yield 7 until 10% per year. The intensity of these attacks vary greatly on particular climate, the degree slope and the plant material used. Therefore, this study analyzes tea leaves as a common part used in recipes to create unique taste and flavor in tea production, especially in agro-industry. The decision making method used is Fuzzy Mamdani Inference as one of model with functional hierarchy with initial input based on established criteria. Fuzzy logic will provide tolerance to the set of value, so that small changes will not result in significant category differences, only affect the membership level on the variable value. Previous method using probabilities have shown 78% tea leaves have been attacked by category C (Gray Blight) while using Mamdani indicated 86% of tea leaves have been infected. In this case, this result pointed out that Fuzzy Mamdani Inferences have more optimal result compare to the previous method.
Pelatihan Penggunaan Media Ajar Di Yayasan Hajjah Siti Syarifah Kecamatan Medan Tembung Hikmah Adwin Adam; Yuyun Yusnida Lase; Yulia Fatmi; Arif Ridho Lubis
ARSY : Jurnal Aplikasi Riset kepada Masyarakat Vol. 3 No. 2 (2023): ARSY : Jurnal Aplikasi Riset kepada Masyarakat
Publisher : Lembaga Riset dan Inovasi Al-Matani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/arsy.v3i2.395

Abstract

Yayasan Pendidikan Islam Hajjah Siti Syarifah terletak di Jln. Kemenangan No. 76-A Medan Tembung Kota Medan. Lembaga pendidikan ini didirikan untuk memberi pelajaran agama Islam tambahan untuk melengkapi pelajarah agama yang diberikan pada sekolah formal. Yayasan Hajjah Siti Syarifah memiliki jenjang Pendidikan dari tingkat RA dan MDA. Proses belajar mengajar di Yayasan Siti Syarifah masih memiliki banyak kendala, salah satu yang menjadi kendala pada saat ini adalah kurangnya pemahaman dan pengetahuan tenaga pendidik dalam menggunakan media ajar, sehingga proses belajar mengajar yang dilaksanakan masih kurang optimal. Dari kendala yang telah penulis uraikan diatas, penulis mencoba memberikan solusi dengan cara memberikan pelatihan mengenai penggunaan media ajar menggunakan canva kepada tenaga pendidik. Dengan adanya pelatihan tersebut diharapkan tenaga pendidik dapat membuat media ajar yang dapat membantu dalam proses pembelajaran, sehingga materi yang disampaikan lebih interaktif. Dari hasil pelatihan yang dilakukan pada Yayasan tersebut dapat ditarik kesimpulan bahwa pelatihan yang diberikan kepada tenaga didik dapat memberikan pengetahuan dan keterampilan yang baru mengenai penggunaan media ajar menggunakan canva dan tanggapan tenaga pendidik terhadap pelatihan yang diberikan sangat positif dan pelaksanaan pelatihan yang diberikan dapat dikategorikan sangat baik.
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.
Improving Text Summarization Quality by Combining T5-Based Models and Convolutional Seq2Seq Models Arif Ridho Lubis; Habibi Ramdani Safitri; Irvan Irvan; Muharman Lubis; Al-Khowarizmi Al-Khowarizmi
Journal of Applied Engineering and Technological Science (JAETS) Vol. 5 No. 1 (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.v5i1.2503

Abstract

In the natural language processing field, there are several sub-fields that are very closely related to information retrieval, such as the automatic text summarization sub-field. obtained from the convolutional T5 and Seq2Seq models in summarizing text on hugging faces found features that can affect text summary such as upper- and lower-case letters which have an impact on changing the understanding of the text of the document. This study uses a combination of parameters such as layer dimensions, learning rate, batch size, and the use of Dropout to avoid model overfitting. The results can be seen by evaluating metrics using ROUGE. This study produces a value of ROUGE-1 on 4 documents that are tested which produces an average of 0.8 which is the optimal value, for ROUGE-2 on 4 documents that are tested which results in an average of 0.83 which is an optimal value while ROUGE-L on 4 documents conducted tests that produce an average of 0.8 which is the optimal value for the summary model.
Deep neural networks approach with transfer learning to detect fake accounts social media on Twitter Arif Ridho Lubis; Santi Prayudani; Muhammad Luthfi Hamzah; Yuyun Yusnida Lase; Muharman Lubis; Al-Khowarizmi Al-Khowarizmi; Gabriel Ardi Hutagalung
Indonesian Journal of Electrical Engineering and Computer Science Vol 33, No 1: January 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v33.i1.pp269-277

Abstract

The massive use of social media makes people take actions that have a negative impact on cyberspace, such as creating fake accounts that aim to commit crimes such as spam and fraud to spread false information. Fake accounts are difficult to detect in the traditional way because fake accounts always use photos, names, and unreal information, there are several criteria that can identify a fake account such as no information, few followers, and minimal activity. In the traditional model, it is difficult to detect fake accounts on many Twitters social media accounts, so the application of the deep learning model with the convolutional neural network (CNN) algorithm and the application of deep learning can help detect fake accounts. This study will use data on Twitter social media so that this research produces good accuracy for the scenarios described at the methodology stage. This research produces an accuracy of 86% for the deep learning model with the CNN algorithm, and with the traditional model, it produces an accuracy of 51% while the use of transfer learning produces an accuracy of 93.9%.
Analyzing Food Prices in North Sumatra Province in 2022 – 2024 Using the Linear Regression Method Faza, Sharfina; Firjatullah, Muhammad; Azhar, Muhammad Fauzan; Hidayatullah, Rafly Artha; Lubis, Arif Ridho
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 1 (2024): Issues July 2024
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i1.11788

Abstract

The food price data used was obtained from literature studies which include main food ingredients such as rice, beef, chicken, eggs, red onions, red chili pepper and cooking oil. The linear regression method is used to model the relationship between the independent variable (year) and the dependent variable (food prices), with the aim of predicting future food prices based on historical data. Although linear regression can provide fairly accurate food price estimates, improvements in prediction models can be achieved by incorporating other analysis methods such as time series analysis or machine learning. The implications of this research highlight the importance of effective policy planning to maintain food price stability and ensure sufficient food availability for the community. Future research could involve more in-depth analysis of the factors influencing food prices, as well as the development of more sophisticated prediction models to support decision-making in agriculture and food. This research aims to analyze food prices in the Northern region of Sumatra Province during the period 2022 to 2024 using the linear regression method. The results show that rice, meat, chicken meat, chicken eggs, shallots, red chilies and edible oils all increased, but only chicken meat, shallots and edible oils also experienced a decrease.
Comparison of ARIMA and LSTM Models in Stock Price Forecasting: A Case Study of GOTO.JK Adam, Hikmah Adwin; Raditiansyah, Farhan; Imani, Muhammad Rayyan; Fawwaz, Mohammad Faris; Julham, Julham; Lubis, Arif Ridho
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 1 (2024): Issues July 2024
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i1.11841

Abstract

The renowned Indonesian company, PT Gojek Tokopedia Tbk, has a significant impact on the Indonesian economy by attracting investors to invest their shares. This study uses stock closing price data to forecast stock prices using ARIMA (AutoRegressive Intergrated Moving Average) and LSTM (Long Short-Term Memory) models, to predict using prediction by dividing the data into groups of 10 or 20 data with data sets to be trained as multiples. The analysis shows that ARIMA is superior to LSTM based on the comparison of average error and average percentage error, where the average error results in LSTM (3.843) and ARIMA (3.259), as well as the average error of LSTM (4.04%) and ARIMA (3.57%). The research supports the conclusion that ARIMA has a better performance in predicting the stock price of PT Gojek Tokopedia Tbk. These results provide important insights for investors and market participants, while the research supports the increased use of seasonal patterns in ARIMA forecasting for more accurate results in the future. Future research is recommended to explore additional factors and optimized models to further improve stock price prediction.
Co-Authors A, Azanuddin Achmad Yani Adam, Hikmah Adwin Adha, Lilis Tiara Al Khowarizmi Ardi Hutagalung, Gabriel Ariyani, Tika Azhar, Muhammad Fauzan Dewi Wulandari Dini Oktarina Dwi Handayani Elviawaty Muisa Zamzami Fatmi, Yulia Fawwaz, Mohammad Faris Faza, Sharfina Firjatullah, Muhammad Gabriel Ardi Hutagalung Gunawan Gunawan Habibi Ramdani Safitri Harefa, Hafid Rahman Haryadi - Hidayatullah, Rafly Artha Hikmah Adwin Adam Hikmah Adwin Adam Husna, Meryatul Ilham Ramadhan Nasution Imani, Muhammad Rayyan Irvan, Irvan Julham Julham Julham Julham Kamil, Idham Lampson Pindahaman Purba Lase, Yuyun Lubis, Fahdi Saidi Luckyhasnita, Andam M.Pd, Akrim Mahyuddin K. M Nasution Mardianto, Willy Mhd Faris Pratama Michael J Watts Mughnyanti, Mayang Muhammad Basri Muhammad Luthfi Hamzah Muhammad Rafif Rasyidi Muharman Lubis Mulhartono, Tomi Muttaqin, Alif Noorachmad Nadi, Farhad Nst, Fifi Anggiani Br Nurhaflah Soraya Nurlinda Opim Salim Sitompul Prayudani, Santi Purba, Lampson Pindahaman Purnamawati, Sarah Putra, Purwa Hasan Raditiansyah, Farhan Rahmadani Rahmadani Rahmadani Rahmadani Rina Anugrahwaty Rinaldy, Muhammad Eri Riza Sulaiman Rizki Syahputra Romi Fadillah Rahmat Salam, Azrizal Sarah Purnamawati Selvida, Desilia Sembiring, Boni Oktaviani Sibarani, Yous Syafli, Sekar Arini Syamsul Arifin Tasril, Virdyra Tessya Fakhta Tri Nasution Yulia Fatmi Yulia Fatmi Yusuf, Kadri Yuyun Yusnida Lase