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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) 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
Computer Science & IT Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media

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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.
Analysis Of Variation In The Number Of MFCC Features In Contrast To LSTM In The Classification Of English Accent Sounds Afriandy Sharif; Opim Salim Sitompul; Erna Budhiarti Nababan
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 6 No. 2 (2023): Issues January 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i2.8566

Abstract

Various studies have been carried out to classify English accents using traditional classifiers and modern classifiers. In general, research on voice classification and voice recognition that has been done previously uses the MFCC method as voice feature extraction. The stages in this study began with importing datasets, data preprocessing of datasets, then performing MFCC feature extraction, conducting model training, testing model accuracy and displaying a confusion matrix on model accuracy. After that, an analysis of the classification has been carried out. The overall results of the 10 tests on the test set show the highest accuracy value for feature 17 value of 64.96% in the test results obtained some important information, including; The test results on the MFCC coefficient values of twelve to twenty show overfitting. This is shown in the model training process which repeatedly produces high accuracy but produces low accuracy in the classification testing process. The feature assignment on MFCC shows that the higher the feature value assignment on MFCC causes a very large sound feature dimension. With the large number of features obtained, the MFCC method has a weakness in determining the number of features.
Architectural design of an internet of things-based framework for road bike speed optimization Tigor Hamonangan Nasution; Opim Salim Sitompul; Fahmi Fahmi; Muhammad Anggia Muchtar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2125-2140

Abstract

This research aims to develop an internet of things (IoT) system framework to predict cyclists’ optimal speed in road cycling using multisensor data and machine learning. The primary issue raised is the lack of an intelligent system capable of integrating physiological, performance, and environmental data in real-time speeds for cyclists. The designed framework consists of four functional layers: data acquisition layer; data processing and feature layer; predictive modeling layer; and recommendations and output layer. Modeling is carried out using gradient boosting regression (GBR), performed end-to-end with validation on real cyclist activity data. The test results demonstrate that the system can provide precise optimal speed estimates and offer pacing zone recommendations that positively impact athlete performance strategies. This research contributes novelty in the form of an adaptive multivariate prediction approach and a modular IoT architecture design that can be implemented on cloud and edge platforms.
Co-Authors Afriandy Sharif Alexander F.K. Sibero Andrew Handoko Dennis Dennis Elviawaty Muisa Zamzami Erna Budhiarti Nababan Fahmi Fahmi Goklas Tomu Simbolon Hartono Hartono Jonas Franky R Panggabean Lubis, Arif Ridho Mahyuddin K. M Nasution Mardingsih Mardingsih Muhammad Anggia Muchtar Muhammad Khahfi Zuhanda Rahmad Nurhadi Yusra Romi Fadillah Rahmat Saib Suwilo Sarah Purnamawati Sawaluddin Nasution Tigor Hamonangan Nasution Tulus Tulus Yundari, Yundari Yuni Cancer