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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 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 Masyarakat Indonesia 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
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 Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences

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Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

 1 
Analysis of Educator Readiness in the Online Teaching Learning Process Using Naïve Bayes Yuyun Yusnida Lase; Yulia Fatmi; Haryadi Haryadi; Arif Ridho Lubis; Santi Prayudani
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.v5i2.5964

Abstract

This study discusses the readiness of educators in the online teaching and learning process. Samples of data were taken randomly as many as 100 (one hundred) people who were carried out using a questionnaire for educators at the junior high school level in the city of Medan. The variables used in the research are human resources, facilities and infrastructure, skills in applying technology, time management in online learning, the assessment process. Data processing and data analysis using nave Bayes algorithm. This algorithm is very well used for the process of classifying large amounts of data. The reason for using the nave Bayes algorithm in processing and analyzing data is because the way this algorithm works uses statistical and probability methods in predicting future results. The results of calculations using the nave Bayes algorithm based on the specified training data show that educators at the junior high school level are ready for the online learning process.
OpenCV Using on a Single Board Computer for Incorrect Facemask-Wearing Detection and Capturing julham -; Meryatul Husna; Arif Ridho Lubis
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.v5i2.6118

Abstract

OpenCV (Open Source Computer Vision Library) is a software library intended for real-time dynamic image processing, created by Intel. In this study, the library will be used to detect the face, nose and mouth. Furthermore, the system is equipped with the knowledge that if the mouth and nose or one of them is detected, then the face has not used the mask correctly and the system records the face. The system is supported by an image capture device in the form of a camcorder, a processing device in the form of a single board computer, such as a Raspberry Pi and a display device in the form of a monitor. And the result is that the system is able to make a decision whether the face is wearing a mask correctly or not. By means of labeling around the face in the form of red angular lines, if not properly use the mask. The success rate is 88.4% using detector parameters, namely: scale factor = 1.1 for all face, nose and mouth object libraries.
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 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 Husna, Meryatul Idham Kamil Ilham Ramadhan Nasution Imani, Muhammad Rayyan Irvan, Irvan Julham Julham Julham Julham Kadri Yusuf Luckyhasnita, Andam M.Pd, Akrim Mahyuddin K. M Nasution Mardianto, Willy Mhd Faris Pratama Michael J Watts Muhammad Basri Muhammad Luthfi Hamzah 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 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 Tessya Fakhta Tri Nasution Yulia Fatmi Yulia Fatmi Yuyun Yusnida Lase