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All Journal CogITo Smart Journal JURNAL PENGABDI MATAPPA: Jurnal Pengabdian Kepada Masyarakat JURNAL PETIK Jurnal Pendidikan dan Konseling Journal of Robotics and Control (JRC) Jurnal Aplikasi dan Inovasi Iptek (JASINTEK) Frontiers Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Pratasik, Stralen
Program Studi Teknik Informatika, Fakultas Teknik, Universitas Negeri Manado
Aerospace Engineering Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Other

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Pengembangan Media Pembelajaran Pengenalan Bahasa Sangihe untuk Anak Sekolah Dasar Berbasis Mobile Alfrina Mewengkang; Stralen Pratasik; RIzaldi Sasela
Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 4 No. 6 (2024): EduTIK : Desember 2024
Publisher : Jurusan PTIK Universitas Negeri Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53682/edutik.v4i1.11178

Abstract

ABSTRAK Tujuan dari penelitian ini adalah untuk merangcamg dan membangun aplikasi pembelajaran berbasis mobile yang kreatif, efektif dan mendukung dalam proses penyampaian pesan kepada peserta didik dalam proses pembelajaran. Penulis membuat aplikasi media pembelajaran ini menggunakan metode MDLC (Multimedia Development Life Cycle) yang memiliki 6 tahapan. Perancangan aplikasi ini menggunakan Adobe Flsh CS6, Adobe Photoshop untuk mengolah gambar, Adobe Audition CS6 untuk mengolah audio, dan Audio Recorder. Hasil dari penelitian meunjukan bahwa pengembangan media pembelajaran pengenalan bahasa sangihe untuk anak sekolah dasar berbasis mobile dapat membantu guru dan orang tua dalam proses penyajian dan dapat menjadi sarana belajar bagi siswa. ABSTRACT The purpose of this study is to design and build a creative, effective and supportive mobile-based learning application in the process of delivering messages to students in the learning process. The author created this learning media application using the MDLC (Multimedia Development Life Cycle) method which has 6 stages. The design of this application uses Adobe Flash CS6, Adobe Photoshop for image processing, Adobe Audition CS6 for audio processing, and Audio Recorder. The results of the study indicate that the development of mobile-based Sangihe language introduction learning media for elementary school children can help teachers and parents in the presentation process and can be a learning tool for students.
The Emerging Role of Artificial Intelligence in Identifying Epileptogenic Zone: A Systematic Literature Review Pamungkas, Yuri; Radiansyah, Riva Satya; Pratasik, Stralen; Krisnanda, Made; Derek, Natan
Journal of Robotics and Control (JRC) Vol. 6 No. 5 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i5.27281

Abstract

Identifying epileptogenic zones (EZs) is a crucial step in the pre-surgical evaluation of drug-resistant epilepsy patients. Conventional methods, including EEG/SEEG visual inspection and neurofunctional imaging, often face challenges in accuracy, reproducibility, and subjectivity. The rapid development of artificial intelligence (AI) technologies in signal processing and neuroscience has enabled their growing use in detecting epileptogenic zones. This systematic review aims to explore recent developments in AI applications for localizing epileptogenic zones, focusing on algorithm types, dataset characteristics, and performance outcomes. A comprehensive literature search was conducted in 2025 across databases such as ScienceDirect, Springer Nature, and IEEE Xplore using relevant keyword combinations. The study selection followed PRISMA guidelines, resulting in 34 scientific articles published between 2020 and 2024. Extracted data included AI methods, algorithm types, dataset modalities, and performance metrics (accuracy, AUC, sensitivity, and F1-score). Results showed that deep learning was the most used approach (44%), followed by machine learning (35%), multi-methods (18%), and knowledge-based systems (3%). CNN and ANN were the most commonly applied algorithms, particularly in scalp EEG and SEEG-based studies. Datasets ranged from public sources (Bonn, CHB-MIT) to high-resolution clinical SEEG recordings. Multimodal and hybrid models demonstrated superior performance, with several studies achieving accuracy rates above 98%. This review confirms that AI (especially deep learning with SEEG and multimodal integration) has strong potential to improve the precision, efficiency, and scalability of EZ detection. To facilitate clinical adoption, future research should focus on standardizing data pipelines, validating AI models in real-world settings, and developing explainable, ethically responsible AI systems.
Co-Authors Adithya Dwi Agus Pramadhani Adriana Oroh Alfrina Mewengkang Arje Cerullo Djamen Armstrong F. Sompotan Baso Mohamad Ahyar Dani Daryos Papaceda Daniel Riano Kaparang Danius Kolibu Derek, Natan Edwin Mangore Henny N. Tambingon Heri Irawan Sandre Jeane Mantiri Julduz Ruland Paus Larry Mandey Made Krisnanda Manampiring, Noni Mandey, Larry J Marcelia Jacob Marchellino Lelet Mariane Pangandaheng Marlin Penina Mamuaja Meike Aneke Supit Meyer Worang Matey Misgter Gidion Maru Mokosuli Yermia Samuel Palilingan, Verry Ronny Pamungkas, Yuri Peggy Veronica Togas Quido Conferti Kainde Quido Conferti Kainde Radiansyah, Riva Satya Raynaldi Ilyas Revolson Mege Rhezza Tezzar Maun Rianto, Indra RIzaldi Sasela Rolef Rumondor Rotty, Viktory Nicodemus Joufree Rudy H. W. Pardanus Sondy C. Kumajas Sri Maulida Tambingon, Henny N Ticoh, J D Trudi Komansilan Verawati I Y Roring Veronika Waroka Walter Balansa Wensi Ronald Lesli Paat