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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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Pengembangan Sistem Ekstraksi Metadata Artikel ilmiah secara Otomatis Rahutomo, Faisal; Irawati, Dyah Ayu; Pramudita, Muhammad Aisamuddin Eka
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 2: April 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2241.739 KB) | DOI: 10.25126/jtiik.2019621227

Abstract

Pengarsipan artikel ilmiah di Jurusan Teknologi Informasi Politeknik Negeri Malang menggunakan platform Open Journal Systems (OJS). Pengarsipan tersebut melalui tahapan penulisan metadata artikel ilmiah yang dilakukan satu-persatu. Metadata artikel ilmiah ini berupa judul, penulis, instansi, surel penulis, abstrak, kata kunci, dan daftar pustaka. Diperlukan waktu yang cukup lama untuk memasukkan metadata artikel ilmiah dalam OJS karena prosedur dalam OJS itu sendiri. Untuk itu penelitian ini mengusulkan sebuah sistem tambahan OJS yang bisa menyediakan metadata artikel ilmiah tersebut secara otomatis. Sistem dibangun menggunakan pendekatan rule-based text parsing. Dalam metode tersebut disusun beberapa aturan untuk mengambil teks yang diperlukan oleh isian metadata OJS yang mewakili sebuah artikel ilmiah. Artikel ilmiah diunggah ke dalam sistem tambahan tersebut untuk menghasilkan metadata-nya secara otomatis. Metadata tersebut selanjutnya disimpan dalam format XML. Pada sistem OJS terdapat perangkat native XML plugin yang bisa melakukan export – import metadata suatu artikel ilmiah untuk OJS. Dari hasil pengujian, sistem bisa memudahkan pengarsipan artikel ilmiah lebih cepat 13 kali dibanding pengisian metadata secara manual.AbstractDepartment of information technology, State Polytechnic of Malang archives its scientific article with Open Journal Systems (OJS) platform. Archiving in OJS needs to write the scientific article metadata manually through a form. Metadata of this scientific article includes title, author, agency, writer e-mail, abstract, keywords and bibliography. Inserting scientific articles metadata in OJS manually takes quite a long time because of the procedure in OJS itself. Highlighting this problem, this research proposes a text processing add-on system for OJS that able to extract the scientific article's metadata automatically. The system is built with rule-based text parsing method. In this method, the authors composed some rules to obtain the metadata of scientific article. Scientific articles were uploaded into the system to capture the metadata of the scientific article automatically. The metadata was then stored in XML. In OJS add-on there is native XML plugin tool that able to export - import the scientific article metadata for OJS. The experimental results show the system able to facilitate the archiving of scientific articles 13 times faster.
Pemanfaatan Data PDDIKTI sebagai Pendukung Keputusan Manajemen Perguruan Tinggi Ngatmari, Ngatmari; Musthafa, Muhammad Bisri; Rahmad, Cahya; Asmara, Rosa Andrie; Rahutomo, Faisal
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 3: Juni 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020722585

Abstract

Pangkalan Data Pendidikan Tinggi (PDDIKTI) merupakan sebuah sistem penyimpan data yang dikelola Pusat Data dan Informasi (Pusdatin) Kementrian Ristek dan Pendidikan Tinggi. Data yang tersedia di PDDIKTI merupakan data yang akurat, karena proses pelaporan data akademik secara berkala dua kali setiap. Data yang telah berlimpah tersebut, tentu sangat disayangkan jika tidak digunakan untuk keperluan yang lebih bermanfaat, misal untuk mengetahui pola akademik kelulusan mahasiswa dan prestasi akademik mahasiswa. Untuk memperoleh informasi-informasi penting tersebut bisa dilakukan dengan cara penggalian informasi (knowledge discovery). Teknik dalam memberikan solusi masalah tersebut adalah teknik klasifikasi untuk membantu pengambilan keputusan, misalkan Decission Tree (C4.5, ID3, CHAID, rule induction) dan teknik peramalan (forecasting) menggunakan metode simple moving average (SMA). Tujuan dari penambangan data PDDIKTI adalah untuk melakukan deteksi dini terhadap mahasiswa, sehingga dosen bisa memberikan masukan-masukan ketika mahasiswa tersebut telah diklasifikan sebagai mahasiswa yang lulus tidak tepat waktu serta memprediksi jumlah mahasiswa yang akan masuk pada perguran tinggi pada salah satu prodi X, sehingga manajemen baik tingkat program studi maupun universitas bisa melakukan langkah-langkah yang dianggap penting guna meningkatkan jumlah mahasiswa. Pengujian pada 2.601 record akademik mahasiswa dengan atribut ipk_sem1, ipk_sem2, ipk_sem3, ipk_sem4, pekerjaan_ortu, ket_lulus, rerata_ipk, penghasilan_ayah, untuk klasifikasi kelulusan mahasiswa menghasilkan nilai accuracy 86,54 % nilai precission 93,37% dan nilai recall 89,27% serta pengujian prediksi jumlah peminat program studi  diperoleh nilai accuracy 78,25 % dan MAPE sebesar 21,75 %.Abstract The Higher Education Database (PDDIKTI) is a data storage system managed by the Center for Data and Information (Pusdatin) of the Ministry of Research and Technology and Higher Education. The data available at PDDIKTI is accurate data, because the process of reporting academic data regularly twice each. The abundant data is certainly unfortunate if not used for more useful purposes, for example to find out the academic patterns of student graduation and student academic achievement. To obtain important information can be done by extracting information (knowledge discovery). Techniques in providing solutions to these problems are classification techniques to assist decision making, for example Decission Tree (C4.5, ID3, CHAID, rule induction) and forecasting techniques using simple moving average (SMA) methods. The purpose of PDDIKTI data mining is to conduct early detection of students, so that lecturers can provide input when the students have been classified as students who graduate not on time and predict the number of students who will enter the tertiary institutions in one of the X study programs, so that management both the level of study program and university can take steps that are considered important to increase the number of students. Tests on 2601 student academic records with the attributes ipk_sem1, ipk_sem2, ipk_sem3, ipk_sem4, occupation_ortu, graduated, average_ipk, income_ayah, for the graduation classification of students resulted in an accuracy value of 86.54% a value of 93.37% and a recall value of 89.27% and a test of 89.27% and a test of graduation prediction of the number of study program enthusiasts obtained an accuracy value of 78.25% and MAPE of 21.75%.
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%.
Co-Authors Abdul Latif Priyadi Agustaf Fanisnaini Narolis Ahmad Hafidh Ayatullah Aisy Muhammad R Ali, Muhammad Haidar Aljalal, Majid Annisa Taufika Firdausi Annisa Taufika Firdausi 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 Dimas Rossiawan Hendra Putra Dwi Puspitasari Dyah Ayu Irawati Dyah Ayu Irawati, Dyah Ayu Ekojono Febri Liantoni 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 Kharismadita, Paratisa Kurniawan, Muhammad Fachrul Latif Priyadi, Abdul M Bisri Musthofa 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 Ngat mari 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 Ridwan Rismanto Riyanarto Sarno Rochmanshah, Nur Rohman, Obby Auliyaur Rosa Andrie Asmara Sari, Dhiana Novita Septarina, Amalia Agung Subuh Pramono Sulistyoningrum, Trie Endah Sutrisno Sutrisno Sutrisno Sutrisno Sutrisno Sutrisno Sutrisno, Sutrisno Ulla Delfana Rosiani Yoppy Yunhasnawa Yushintia Pramitarini Yushintia Pramitarini Zanuar Hanif Rachmat Adi