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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) Jurnal Penelitian Komunikasi dan Opini Publik Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal TAM (Technology Acceptance Model) INOVTEK Polbeng - Seri Informatika Communications in Science and Technology SENTIA 2015 Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Buletin Ilmiah Sarjana Teknik Elektro INSERT: Information System and Emerging Technology Journal Journal of Electrical, Electronic, Information, and Communication Technology (JEEICT) Jurnal Pengabdian Polinema Kepada Masyarakat Jurnal Abdi Masyarakat Indonesia Jurnal Fortech Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Jurnal Informatika Polinema (JIP) Jurnal TAM (Technology Acceptance Model) Jurnal FORTECH
Faisal Rahutomo
Universitas Sebelas Maret
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Public Health Other

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Comparison of Machine Learning Algorithms with Feature Engineering for Epileptic Seizure Prediction Based on Electroencephalogram (EEG) Signals Ibrahim, Sutrisno; Rahutomo, Faisal; Henda, Reihan; Aljalal, Majid
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i4.13145

Abstract

Epilepsy is a neurological disorder marked by recurrent seizures, which can greatly reduce patients' quality of life. Early and accurate seizure prediction is essential for effective clinical intervention and patient safety. This study proposes and evaluates a seizure prediction system using EEG signals processed through machine learning techniques combined with optimized feature extraction methods. The research contribution is the comprehensive comparative analysis of classifier-feature pairs for identifying the most effective configuration for seizure prediction tasks. Three classifiers—Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost)—were systematically compared, each combined with precisely engineered feature extraction methods, including Common Spatial Pattern (CSP), Discrete Wavelet Transform (DWT), statistical features, and frequency domain features. EEG data from seven patients, totaling approximately 68 hours with 40 seizure events, were obtained from the Children's Hospital Boston database. The results demonstrate that XGBoost with CSP features achieved the highest overall accuracy at 88% and specificity at 88%, while XGBoost with DWT features reached the highest sensitivity at 87%. Additional metrics including F1-score (0.85) and AUC-ROC (0.91) confirmed XGBoost's superior performance. Comparison with five recent studies showed our approach offers a 3-5% improvement in accuracy and sensitivity. These findings highlight the critical impact of both classifier selection and feature engineering in improving EEG-based seizure prediction, with implications for developing real-time monitoring systems despite challenges in clinical implementation due to inter-patient variability.
Rancang Bangun Game Bersepeda Berbasis 3D Map Tersinkronisasi Dengan Sistem Kendali Gyroscope Dan Infrared Latif Priyadi, Abdul; Sutrisno, Sutrisno; Rahutomo, Faisal
Jurnal FORTECH Vol. 3 No. 2 (2022): Jurnal FORTECH
Publisher : FORTEI (Forum Pendidikan Tinggi Teknik Elektro Indonesia)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56795/fortech.v3i2.104

Abstract

Body health must be maintained by every individual from children to the elderly. In order for the health of the body to be in prime condition, it takes sports activity. One of the sports favored by children is cycling. Cycling is a good sport because it moves many parts of the body, from the hands on the wheel and feet to pedaling. However, some children are lazy and get bored easily when exercising because there is no motivation. Some children prefer to spend their time playing games, so a tool is needed as a means of sport that is packaged in a game to make it interesting. To form a game that resembles bicycle movements, tools can be made using ESP8266, MPU6050 as steering wheel control sensors, and infrared sensors as pedal controls. This tool will be input into the game with serial communication via a USB cable. Games with bicycle themes are made using Unity 3D coded with Visual Studio Code. The results of tests carried out using the black box method, tools and games can run according to design, starting from connecting hardware to software, calibrating, controlling games with tools, and running gameplay on games. Testing was also carried out using the User Acceptance Test (UAT) method with 60 respondents and obtained an overall score of 94.11%.
PENERAPAN ALGORITMA WEIGHTED TREE SIMILARITY UNTUK PENCARIAN SEMANTIK Riyanarto Sarno; Faisal Rahutomo
JUTI: Jurnal Ilmiah Teknologi Informasi Vol 7, No 1, Januari 2008
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (612.589 KB) | DOI: 10.12962/j24068535.v7i1.a60

Abstract

Full-text search and metadata-enabled search have weakness in the precision of the searched article. This research offers weighted tree similarity algorithm combined with cosine similarity method to count similarity in semantic search. In this method metadata is constructed based on the tree of labelled node, labelled and weighted branch. The structure of tree metadata is constructed based on semantic information like taxonomi, ontologi, preference, synonim, homonym and stemming. From testing result, the precision of search using weighted tree similarity algorithm is better that full-text search and metadata-enabled search.
Development of Sign Language Interpreter Glove for Speech-Impaired Deaf Individuals with Levenshtein Algorithm as an Autotext Correction System. Febry, Dea Muthia; Kanieza, Ananda Putra; Ramadhan, Gilang Fajar; Aini, Velisa Nur; Rahutomo, Faisal
Journal of Electrical, Electronic, Information, and Communication Technology Vol 6, No 1 (2024): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jeeict.6.1.79652

Abstract

Assistive technology is technology developed in the form of aids, adaptive tools, and rehabilitation for individuals with disabilities to compensate for their lacking abilities. With assistive technology available to people with special needs, it can help them enhance their independent living skills and reduce their dependence on others, including in communication activities. This research aims to assess the feasibility of developing assistive technology for the speech-impaired, specifically a glove that can translate the SIBI letter sequence into words displayed on a 6 x 12 LCD screen and also produce sound output from the sign language hand movements of speech-impaired individuals when using the glove. The research method used in this study is Research and Development (R&D). The calibration test phase indicated that the prototype was in good condition, as evidenced by an accuracy rate above 90% for voltage at 0◦ and 90◦ angles produced by each finger. Consequently, the next calibration phase, which involves translating sensor readings into SIBI letters through digital data values, can be carried out by taking the ADC values of each finger. Subsequently, the glove was tested to read 7 out of 20 alphabets and achieved a success rate of ≥ 90% for 5 alphabets. The lowest success rate was 70% for the letter E. The average success rate for the 7 alphabet experiments was 91.4%. In the field test phase, the glove was tested on a deaf-mute student to form several words, and the output text displayed on the LCD and audio output matched the readings corrected by the auto-text correction system.
Application of LSTM Algorithm to Assist Diagnosis of Epilepsy Based on Electroencephalogram (EEG) Signals Ibrahim, Sutrisno; Zebua, Kaleb Nathan; Rahutomo, Faisal; Naufal, Muhammad Alif Rizky
Journal of Electrical, Electronic, Information, and Communication Technology Vol 7, No 1 (2025): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jeeict.7.1.100360

Abstract

Epilepsy is a common disease that affects the brain's ability and has the potential to destroy the quality of life of sufferers. Diagnosis of epilepsy can be done by clinical testing and by using the electroencephalography (EEG) method. This research aims to apply artificial intelligence to improve the effectiveness and accuracy of EEG signal analysis. Epilepsy diagnosis is done automatically based on trained EEG signal files. This application can be done by applying the Long-Short Term Memory (LSTM) machine learning algorithm for recognizing patterns from brain signals that lead to epilepsy. The development was carried out using the EEG signal dataset from the University of Bonn which consists of 5 data sets. The detection process consists of the stages of data loading, augmentation, filtering, training, and classification. The developed system will be loaded into a GUI to facilitate users. The result of this research is a machine learning model with Long Short-Term Memory (LSTM) algorithm that has an accuracy rate of 91%, validation accuracy of 94% and loss of 0.2. Compared to other machine learning approaches such as SVM, KNN, and ANN, the proposed method achieves higher accuracy without the need for explicit feature extraction, highlighting its effectiveness in time-series signal classification. The model evaluation results show that this research is successful in assisting the detection of epilepsy using EEG signals with a high level of accuracy and efficiency.
Enhancing the Readability of Academic Data for Machine Learning through Preprocessing Techniques Soraya, Anna Mayyah; Rahutomo, Faisal; Anwar, Miftahul
Journal of Electrical, Electronic, Information, and Communication Technology Vol 8, No 1 (2026): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jeeict.8.1.103104

Abstract

Academic data plays a central role in supporting decision-making in educational institutions. However, the successful implementation of machine learning to analyze and make predictions based on academic data highly depends on the quality and readability of the data. To fully harness the potential of machine learning, careful preprocessing of academic data is essential. This research aims to design and implement preprocessing techniques, that is imputation, winsorizing, and dropping data on academic data. To handle missing values, the Multivariate Imputation by Chained Equation method is used with three different algorithms, linear regression, random forest, and KNN, and then the accuracy of these three algorithms in predicting missing values is compared. Additionally, winsorizing method is applied to outliers and data duplication is addressed by dropping duplicate data. Based on the testing results through evaluation metrics, these preprocessing techniques can improve model accuracy by 0.037 for MAE, 0.11 for RMSE, and 0.006 for MSE. The processed data allows the model to function more optimally and produce more reliable results.
Schedule Information System of Medical Profession Program Al Hanif, Zaidan Alvin; Rahutomo, Faisal; Sulistyo, Meiyanto Eko
Journal of Electrical, Electronic, Information, and Communication Technology Vol 8, No 1 (2026): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jeeict.8.1.103117

Abstract

This article details the development of an automated, web-based scheduling system for medical professional education at UNS Surakarta, Indonesia. The goal of this research was: To build an information system assisting UNS medical faculty administrators by automating medical professional education scheduling and enabling fast, precise, and efficient schedule access. This study employed a prototyping method, sequentially defining the objective object (system requirements), designing, implementing, and evaluating the system. The prototyping method focuses on finding the general purpose of the system because this prototype is an initial description of the system for greater continuation. Several diagrams, such as ERD and use case diagrams, describe the system architecture design. The team performed system testing using the black box method to ensure the system functions as designed. The results show that the system can produce clinical clerkship scheduling more quickly and effectively. After testing the black box system, a user acceptance testing test was to be carried out by conducting a satisfaction survey, getting an assessment result of 76.9%. This system's user satisfaction was relatively high.
Antenna Tracker System for Unmanned Aerial Vehicles: A Short Review Yusuf, Hayyan; Rahutomo, Faisal; Sutrisno, Sutrisno
Journal of Electrical, Electronic, Information, and Communication Technology Vol 5, No 2 (2023): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jeeict.5.2.72496

Abstract

Unmanned Aerial Vehicle (UAV) is a modern technology used to perform difficult and dangerous aerial missions that cannot be carried out by manned aerial vehicles. Ground Control Station (GCS) is a system that manages all the parameters of the UAV. GCS and UAV communicate using radio waves through telemetry, which functions to transmit and receive flight data.  Antenna tracker is a device used to connect the GCS (Ground Control Station) and the UAV (Unmanned Aerial Vehicle). The antenna tracker works by performing tracking to direct the antenna towards the UAV. Nowadays, there are various forms of antennas used in telecommunication technology. Each type of antenna has its own radiation characteristics, some are directional, while others are more omnidirectional. Directional antennas are the right choice to be used with an antenna tracker. Generally, directional antennas have a narrow radiation range but a relatively long transmission distance. This paper provides a review of the state-of-the-art in antenna tracker technology for unmanned aerial vehicles (UAVs), with a focus on design, performance, and type of antenna. The study involved a literature search of various databases. The design approaches for antenna tracker systems range from simple single-axis trackers to sophisticated dual-axis trackers with pan-tilt mechanisms, and type of antenna such as helical were explored. The study concludes that antenna trackers have numerous applications in various industries, including military, agriculture, and surveying, and the demand for reliable and accurate antenna trackers is expected to continue to grow with the increasing popularity of UAVs.    
Indonesia Democracy Index (IDI) Forecasting in 2019 using Moving Average and Correlation Between IDI's Aspect Using Pearson Correlation Coefficient Rahutomo, Faisal; Rossiawan Hendra Putra, Dimas; Musthofa, M Bisri; mari, Ngat
Journal of Electrical, Electronic, Information, and Communication Technology Vol 2, No 2 (2020): JOURNAL OF ELECTRICAL, ELECTRONIC, INFORMATION, AND COMMUNICATION TECHNOLOGY
Publisher : Universitas Sebelas Maret (UNS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jeeict.2.2.41361

Abstract

Abstract—This experiment aims to analyze the forecasting of the Indonesian Democracy Index (IDI) in 2019, which uses each province data by the Moving Average method. The parameters used in this experiment refer to data obtained from the Central Statistics Agency (BPS) in 2009-2018. The level of achievement of IDI is measured based on the development and implementation of 3 aspects, 11 variables, and 28 indicators. Experiment purposes to find the average percentage of absolute error MAPE (Mean Absolute Percentage Error) for each province and looks for correlations between the three main aspects of forming IDI namely civil liberties, political rights, and democratic institutions. IDI Indonesia's forecasting results in 2019 the IDI has an average value of 68.28 with a MAPE of 4.78%. The results of the correlation between the three aspects of forming the IDI using the Pearson correlation coefficient resulted in the aspect of civil liberties having no correlation with aspects of political rights or aspects of democratic institutions with Pearson values of -0.05 and -0.19. Whereas aspects of political rights correlate with democratic institutions with Pearson's value of 0.48.Keywords—Forecasting, Indonesian Democracy Index, Moving Average. Pearson Correlation Coefficient
Co-Authors Abdul Latif Priyadi Agustaf Fanisnaini Narolis Ahmad Hafidh Ayatullah Aini, Velisa Nur Aisy Muhammad R Al Hanif, Zaidan Alvin Ali, Muhammad Haidar Aljalal, Majid Annisa Taufika Firdausi Annisa Taufika Firdausi Anwar, Miftahul Ariadi Retno Tri Hayati Ririd Ariyo, Bashiru Olalekan Astiningrum, Mungki Aulia, Indinabilah Bambang Harjito, Bambang Carfin Febriawan Pratama Putra Christine Dewi Christine Kartika Dewi Daffa , Aminuddin Dhebys Suryani Hormansyah, Dhebys Suryani Dhiana Novita Sari Diana Mayangsari Ramadhani Diana Mayangsari Ramadhani Dwi Puspitasari Dyah Ayu Irawati Dyah Ayu Irawati, Dyah Ayu Ekojono Febri Liantoni Febry, Dea Muthia Fidyawan, Miftahul Agtamas Gunawan Budi Prasetyo Hafidh Ayatullah, Ahmad Haris Setiyono Henda, Reihan Ibrahim, Sutrisno Ikawati, Deasy Sandhya Elya Imam Fahrur Rozi Imam Nawawi Imam Nawawi, Imam Indinabilah Aulia Inggrid Yanuar Risca Pratiwi Inggrid Yanuar Risca Pratiwi Irvan Wahyu Nurdian Joko Haryono Josaphat Tetuko Sri Sumantyo Kanieza, Ananda Putra Kharismadita, Paratisa Kurniawan, Muhammad Fachrul Latif Priyadi, Abdul mari, Ngat Meiyanto Eko Sulistyo Meiyanto Eko Sulistyo Meiyanto Eko Sulistyo Meiyanto Eko Sulistyo Mekonnen, Atinkut Molla Miftahul Agtamas Fidyawan Moechammad Sarosa Muhammad Arief Rahman Muhammad Arief Rahman Muhammad Bisri Musthafa Muhammad Elfa Rodhian Putra Muhammad Fachrul Kurniawan Muhammad Hamka Ibrahim Muhammad Hamka Ibrahim Muhammad R, Aisy Muhammad Rifky Prayanta Musthafa, Muhammad Bisri Musthofa, M Bisri Naufal, Muhammad Alif Rizky Ngatmari Ngatmari Ngatmari, Ngatmari Nugraha, Bagus Putra Nur Rochmanshah Nurdian, Irvan Wahyu Pangestu Nur Mirzha Paratisa Kharismadita Pramana Yoga Saputra Pramudita, Muhammad Aisamuddin Eka Putra Prima Arhandi, Putra Prima Putra, Carfin Febriawan Pratama Rahmad, Cahya Rahman, Muhammad Arief Ramadhan, Gilang Fajar Ridwan Rismanto Riyanarto Sarno Rochmanshah, Nur Rohman, Obby Auliyaur Rosa Andrie Asmara Rossiawan Hendra Putra, Dimas Sari, Dhiana Novita Septarina, Amalia Agung Soraya, Anna Mayyah Subuh Pramono Sulistyoningrum, Trie Endah Sutrisno Sutrisno Sutrisno Sutrisno Sutrisno Sutrisno Sutrisno, Sutrisno Ulla Delfana Rosiani Yoppy Yunhasnawa Yushintia Pramitarini Yushintia Pramitarini Yusuf, Hayyan Zanuar Hanif Rachmat Adi Zebua, Kaleb Nathan