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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of ICT Research and Applications Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer JETT (Jurnal Elektro dan Telekomunikasi Terapan) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SMARTICS Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Indonesian Journal of Electrical Engineering and Computer Science Journal of Dinda : Data Science, Information Technology, and Data Analytics eProceedings of Engineering Jurnal INFOTEL Journal of Information Technology and its Utilization
Aji Gautama Putrada
Telkom University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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Journal : Jurnal INFOTEL

 1 
An Evaluation of SVM in Hand Gesture Detection Using IMU-Based Smartwatches for Smart Lighting Control Maya Ameliasari; Aji Gautama Putrada; Rizka Reza Pahlevi
JURNAL INFOTEL Vol 13 No 2 (2021): May 2021
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v13i2.656

Abstract

Hand gesture detection with a smartwatch can be used as a smart lighting control on the internet of things (IoT) environment using machine learning techniques such as support vector machine (SVM). However, several parameters affect the SVM model's performance and need to be evaluated. This study evaluates the parameters in building an SVM model for hand gesture detection in intelligent lighting control. In this study, eight gestures were defined to turn on and off four different lights, and then the data were collected through a smartwatch with an Inertial Measurement Unit (IMU) sensor. Feature selection using Pearson Correlation is then carried out on 36 features extracted from each gesture data. Finally, two sets of gestures were compared to evaluate the effect of gesture selection on model performance. The first set of gestures show that the accuracy of 10 features compared to the accuracy of 36 features is 94% compared to 71%, respectively. Furthermore, the second set of gestures has an accuracy lower than the first set of gestures, which is 64%. Results show that the lower the number of features, the better the accuracy. Then, the set of gestures that are not too distinctive show lower accuracy than the highly distinctive gesture sets. The conclusion is, in implementing gesture detection with SVM, low data dimensions need to be maintained through feature selection methods, and a distinctive set of gesture selection is required for a model with good performance.
Imputasi KNN terhadap Nilai yang Hilang dari Prediksi Durasi Hujan Berbasis Regresi pada Data BMKG Ikke Dian Oktaviani; Aji Gautama Putrada
JURNAL INFOTEL Vol 14 No 4 (2022): November 2022
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v14i4.840

Abstract

The prediction of rain duration based on data from the Meteorology, Climatology, and Geophysics Agency (BMKG) is an important issue but remains an open problem. At the same time, several studies have shown that missing values can cause a decrease in the performance of the model in making predictions. This study proposes k-nearest neighbors (KNN) imputation to overcome the problem of missing values in predicting rain duration. The source of the rain duration prediction dataset is the BMKG data. We compared gradient boosting regression (GBR), adaptive boosting regression (ABR), and linear regression (LR) for the regression model for predicting rain duration. We compared the KNN imputation method with several benchmark methods, including zero imputation, mean imputation, and iterative imputation. Parameters r2, mean squared error (MSE) and mean bias error (MBE) measure the performance of these imputation methods. The test results show that for rain duration prediction using the regression method, GBR shows the best performance, both for train data and test data with r2 = 0.915 and 0.776, respectively. Then our proposed KNN imputation has the best performance for missing value imputation compared to the benchmark imputation method. The prediction values of r2 and MSE when using KNN imputation at Missing Percentage = 90% are 0.71 and 0.36, respectively.
Temporal Sequential-Artificial Neural Network Enhancements for Improved Smart Lighting Control Putrada, Aji Gautama; Abdurohman, Maman; Perdana, Doan; Nuha, Hilal Hudan
JURNAL INFOTEL Vol 16 No 1 (2024): February 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i1.1025

Abstract

Several previous studies have proposed a temporal sequential-artificial neural network (TS-ANN) to convert PIR Sensor movement data into presence data and improve the performance of smart lighting control. However, such a temporal-sequential forecasting concept has a curse of dimensionality problem. This study aims to proposes the application of principal component analysis with TS-ANN (PCA-TS-ANN) as an intelligent method for controlling smart lighting with low dimensions. We have primary data directly from a smart lighting implementation that utilizes PIR sensors. We apply cross-correlation to the original dataset to find the optimum time step. Then we discover the optimum TS-ANN based on selected tuning parameter values through PCC. We then design and compare scenarios involving the combination of TS-ANN and PCA. Finally, we evaluate these scenarios using the metrics Accuracy, Precision, Recall, F1− Score, and Delay. The results of this study are the PCA-TS-ANN model with Accuracy, Precision, Recall, and F1−Score value of 0.9993, 0.9997, 0.9994, and 0.9996 respectively. The PCA method reduces the TS-ANN features from 1200 features to 36 features. The model size has also decreased from 3534kB to 807kB. Our model has a simpler complexity with TS-ANN that the µ ± σ Delay is 0.27±0.06 ms versus 0.34±0.11 ms.
Co-Authors Abdillah, Hilal Nabil Abiyan Bagus Baskoro Aditya Firman Ihsan Adrian Gusti Nurcahyo Agita Rachmad Muzakhir Algi Fajardi Alieja Muhammad Putrada Andrian Rakhmatsyah Angga Anjaini Sundawa Anita Auliani Argo Surya Adi Dewantoro Aziz Nurul Iman Baginda Achmad Fadillah Bambang Setia Nugroho Bayu Kusuma Belva Rabbani Driantama Bramantio Agung Prabowo Calvin M.T Manurung Catur Wirawan W Catur Wirawan Wijiutomo Daniel Arga Amallo Dawani, Febri Dicky Prasetiyo Dita Oktaria Doan Perdana Dodi W. Sudiharto Dodi Wisaksono Sudiharto Dody Qori Utama Endro Ariyanto Erwid Musthofa Jadied Fachrial Akbar Fadhlillah Fadhlillah Fadhlurahman Irwan Fairus Zuhair Azizy Atoir Fakhri Akbar Pratama Farisah Adilia Fauzan Ramadhan Sudarmawan Fauzan, Mohamad Nurkamal Fauzan, Mohamad Nurkamal Fazmah Arif Yulianto Febrina Puspita Utari Fitra Ilham Gabe Dimas Wicaksana Gentur Cipto Tri Atmaja Hamman Aryo Bimmo Hanifa Zahra Dhiah Hilal Hudan Nuha Hirianinda Malsegianty S Ikbar Mahesa Ikke Dian Oktaviani Ikke Dian Oktaviani Ikrimah Muiz Ilham Fadli Surbakti Imas Nur Tiarani Irfan Dwi Wijaya Irfan Nugraha Januar Triandy Nur Elsan Krisna Kristiandi Hartono Kurnia Wisuda Aji Mahmud Dwi Sulistiyo Mahmud Imroba Maman Abdurohman Maman Abdurrahman Mar Ayu Fotina Mas'ud Adhi Saputra Maya Ameliasari Mohamad Nurkamal Fauzan Mohamad Nurkamal Fauzan Mohamad Nurkamal Fauzan Muhamad Nurkamal Fauzan Muhammad Al Makky Muhammad Alkahfi Khuzaimy Abdullah Muhammad Dafa Prima Aji Muhammad Fahmi Nur Fajri Muhammad Ihsan Muhammad Kukuh Alif Lyano Muhammad Shibgah Aulia Muhhamad Affan Hasby Muhhamad Affan Hasby Muhtadu Syukur A Mulia Hanif Nando, Parlin Nando, Parlin Niken Cahyani Novian Anggis Suwastika Nuha, Hilal H Nur Alamsyah NUR ALAMSYAH Nur Alamsyah, Nur Nur Ghaniaviyanto Ramadhan Nurkamal Fauzan, Mohamad Pahlevi , Rizka Reza Pamungkas, Rizaldi Ramdlani Parman Sukarno Prasti Eko Yunanto Putrada, Alieja Muhammad Putri Azanny Raden Muhamad Yuda Pradana Kusumah Rafie Afif Andika Rahmat Suryoputro Rahmat Yasirandi Randy Agustyo Raharjo Reynaldo Lino Haposan Pakpahan Richasdy, Donny Rizki Jamilah Guci Sabrina Adinda Sari Sailellah, Hassan Rizky Putra Seli Suhesti Sena Amarta Sidik Prabowo Siti Amatullah Karimah Subkhan Ibnu Aji Sulthan Kharisma Akmal Syafrial Fachri Pane Syafwan Almadani Azra Syiarul Amrullah, Muhammad Taufik Suyanto Vera Suryani Wanda Firdaus Yahya Ermaya Yuda Prasetia Zidni Fahmi Suryandaru