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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Priviet Social Sciences Journal Jurnal Citra Multidisiplin Cadika Journal.
Fuadillah, Erry
UIN Sunan Gunung Djati Bandung
Humanities Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Identifikasi EEG Epilepsi Menggunakan Wavelet dan Learning Vector Quantization Erry Fuadillah; Esmeralda C Djamal; Agus Komarudin
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2018
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Epilepsi merupakan salah satu penyakit neurologis kronis yang dapat menyerang sekitar 50 juta orang di semua usia. Di Indonesia terdapat lebih dari 1.400.000 kasus Epilepsi setiap tahun dengan 70.000 pertambahan kasus setiap tahunnya. Sekitar 40-50% terjadi pada anak-anak. Salah satu pemeriksaan Epilepsi menggunakan Elektroensephalogram (EEG) yang mengidentifikasikan frekuensi 2,8-5,4 Hz, dan loncatan amplitudo atau berbentuk spike. Agar pemeriksaan cukup akurat, sinyal EEG dalam domain waktu perlu diproses dalam domain lain untuk identifikasi adanya Epilepsi. Pada penelitian ini telah dibangun sistem identifikasi Epilepsi menggunakan transformasi Wavelet dan Learning Vector Quantization (LVQ). Pembelajaran dan pengujian menggunakan set data EEG dari University of Bonn. Data terdiri atas empat kondisi, yaitu orang normal mata terbuka (Z), orang normal mata tertutup (O), penderita Epilepsi saat serangan (S), dan penderita Epilepsi saat tidak terjadi serangan (F). Sinyal EEG direkam dengan frekuensi sampling 173,5 Hz selama 23,6 detik sehingga setiap set data mempunyai 4097 titik. Wavelet untuk mengekstraksi sinyal EEG yang mempunyai frekuensi sampling menjadi 2,8-5,4 Hz. Sistem identifikasi menggunakan LVQ dengan fitur spektral daya pada frekuensi 2,8-5,4 Hz dan nilai absolut dari amplitudo rata-rata setiap seperempat detik. Sehingga diperoleh 220 fitur. Sistem telah diuji menggunakan data S dan Z dengan akurasi 67% terhadap data non latih. Penggunaan Wavelet dapat meningkatkan akurasi dari 67% menjadi 72%. Penambahan fitur rata-rata amplitudo dapat meningkatkan akurasi menjadi 94%. Sistem juga telah diuji menggunakan set data ZO dan FS dengan hasil 73% tanpa ekstraksi Wavelet, 65% dengan ekstraksi Wavelet dan 75% untuk Wavelet dengan fitur lengkap. Sistem identifikasi juga telah diuji terhadap data latih dengan akurasi 100%.
The application of computational thinking and experiential learning concepts to improve algorithm skills among Junior High School Students of Salman Al Farisi Bandung Fuadillah, Erry
Priviet Social Sciences Journal Vol. 6 No. 1 (2026): January 2026
Publisher : Privietlab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55942/pssj.v6i1.1100

Abstract

Computational thinking (CT) is a 21st-century skill that is currently receiving widespread attention in many developed countries, where it has been incorporated into primary and secondary school curricula. Developing this skill requires a learning model that provides students with direct experience; one such model is experiential learning. This model emphasises that real-life experiences are the primary source of knowledge formation and computational thinking skills. This study aims to apply the concept of computational thinking to programming algorithms for junior high school students. This was achieved by comparing the learning outcomes of experimental classes that implemented an experiential learning model with computational thinking with those of a control class that used conventional methods. The results of the analysis showed that the average student learning outcome value in the experimental class was 87.826, compared to 81.36363 in the control class. Based on the t-test, the calculated t-value of 1.33676 is smaller than the t-table value of 1.68107, so H₀ is accepted and H₁ is rejected. Therefore, there is no significant difference in learning outcomes between the two groups. However, applying computational thinking through experiential learning models shows a positive upward trend in student learning outcomes and provides a more meaningful learning experience for understanding programming algorithm concepts.
Comprehensive Review: Transforming Self-Education through Automatic Question Generation Technology Erry Fuadillah; Lala Septem Riza; Rani Megasari
Master Journal of Future Education Vol. 2 No. 1 (2025): Oktober
Publisher : CV. Master Literasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63461/cadikajournal.v21.296

Abstract

Automatic question generator (AQG) technology is a system developed to create questions automatically from input in the form of text, images, and videos. AQG has been developed using various approaches such as natural language processing (NLP), statistical approaches, and other machine approaches. AQG has a very important role in the world of education, especially in independent education, because it can be used as a good evaluation medium for students. Utilizing AQG in independent education gives students full control to determine their learning. AQG turns learning into a more interactive experience by generating questions that can trigger critical thinking and problem-solving skills. AQG technology developed in independent learning will encourage students to respond actively to the material and understand concepts more deeply. Approximately 60% of research related to AQG has been conducted for assessment, 18% for knowledge acquisition, and the remainder for validation and other purposes. This research was conducted by conducting a comprehensive review of 63 articles related to AQG in education.
The application of computational thinking and experiential learning concepts to improve algorithm skills among Junior High School Students of Salman Al Farisi Bandung Erry Fuadillah
Priviet Social Sciences Journal Vol. 6 No. 1 (2026): January 2026
Publisher : Privietlab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55942/pssj.v6i1.1100

Abstract

Computational thinking (CT) is a 21st-century skill that is currently receiving widespread attention in many developed countries, where it has been incorporated into primary and secondary school curricula. Developing this skill requires a learning model that provides students with direct experience; one such model is experiential learning. This model emphasises that real-life experiences are the primary source of knowledge formation and computational thinking skills. This study aims to apply the concept of computational thinking to programming algorithms for junior high school students. This was achieved by comparing the learning outcomes of experimental classes that implemented an experiential learning model with computational thinking with those of a control class that used conventional methods. The results of the analysis showed that the average student learning outcome value in the experimental class was 87.826, compared to 81.36363 in the control class. Based on the t-test, the calculated t-value of 1.33676 is smaller than the t-table value of 1.68107, so H₀ is accepted and H₁ is rejected. Therefore, there is no significant difference in learning outcomes between the two groups. However, applying computational thinking through experiential learning models shows a positive upward trend in student learning outcomes and provides a more meaningful learning experience for understanding programming algorithm concepts.
PENGEMBANGAN SISTEM OTOMATISASI PENOMORAN SURAT KELUAR BERBASIS GOOGLE SHEETS DAN APPS SCRIPT UNTUK MENINGKATKAN EFISIENSI LAYANAN ADMINISTRASI Erry Fuadillah; Galih Prawijaya
Jurnal Citra Multidisiplin Vol. 1 No. 5 (2026): Jurnal Citra Multidisiplin
Publisher : STKIP Citra Bakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38048/jcm.v1i5.6933

Abstract

Penelitian ini bertujuan untuk mengembangkan dan mengimplementasikan sistem otomatisasi penomoran surat keluar berbasis Google Sheets dan Google Apps Script guna meningkatkan efisiensi dan konsistensi layanan administrasi di lingkungan perguruan tinggi. Sistem dirancang untuk mengintegrasikan proses permintaan surat melalui Google Form, pengelolaan basis data pada Google Sheets, serta mekanisme auto-generate nomor surat yang terhubung dengan pembuatan dokumen secara otomatis. Pengujian dilakukan pada periode Oktober–Desember 2025 dengan total 1.328 surat keluar, terdiri atas 843 surat (63,5%) yang diproses melalui sistem otomatis dan 485 surat (36,5%) yang dikelola secara manual untuk kebutuhan administratif fleksibel tingkat fakultas. Hasil validasi terhadap 843 surat otomatis menunjukkan tidak ditemukan duplikasi nomor, tidak terdapat ketidaksesuaian format kode surat, serta seluruh nomor tersusun secara berurutan sesuai tanggal penerbitan. Meskipun terdapat 195 selisih urutan nomor, hal tersebut disebabkan oleh penggunaan nomor bersama pada mekanisme manual dan tidak memengaruhi integritas sistem. Temuan ini menunjukkan bahwa sistem yang dikembangkan mampu menjaga konsistensi dan keunikan nomor surat, meningkatkan efisiensi pengelolaan administrasi, serta mendukung tata kelola persuratan yang lebih terstruktur dan akuntabel di lingkungan perguruan tinggi.
Co-Authors Esmeralda C Djamal Galih Prawijaya Komarudin, Agus Lala Septem Riza Rani Megasari