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All Journal International Journal of Electrical and Computer Engineering International Journal of Reconfigurable and Embedded Systems (IJRES) Lontar Komputer: Jurnal Ilmiah Teknologi Informasi ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Proceeding of the Electrical Engineering Computer Science and Informatics JOIV : International Journal on Informatics Visualization Jurnal Abdimasa TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Journal of Electronics, Electromedical Engineering, and Medical Informatics JMECS (Journal of Measurements, Electronics, Communications, and Systems) eProceedings of Engineering
Inung Wijayanto
Universitas Telkom
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Other

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Pengembangan Sistem Analisis Fungsi Kognitif Berbasis Website Untuk Evaluasi Pengaruh Aromaterapi Melalui Data Eeg Prakoso, Mochamad Rafi Alfian; Firdaus, Alvaro Ahmad; Goenadiningrat, Jeahan Fitria; Wijayanto, Inung; Prasasti, Anggunmeka Luhur
eProceedings of Engineering Vol. 12 No. 4 (2025): Agustus 2025
Publisher : eProceedings of Engineering

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Abstract

Kemajuan teknologi digital telah berdampak negatif terhadap kemampuan atensi berkelanjutan, yang berimplikasi pada penurunan produktivitas di ranah akademik maupun profesional. Penelitian ini merancang sistem terpadu berbasis web dan EEG guna mengukur pengaruh aromaterapi terhadap fungsi kognitif, melalui integrasi aplikasi SART yang mencatat respons secara akurat dan modul EEG yang menganalisis sinyal otak untuk mengidentifikasi komponen ERP sebagai indikator atensi. Proses validasi melalui User Acceptance Testing membuktikan bahwa seluruh aspek sistem berfungsi dengan tingkat keberhasilan 100%. Hasil pengujian menunjukkan bahwa kelompok kontrol memiliki tingkat deteksi ERP (P200, P300, P500) yang lebih tinggi dibandingkan kelompok eksperimen, khususnya penurunan signifikan komponen P200 pada kanal AF7. Walaupun akurasi kedua kelompok hampir serupa, kelompok eksperimen menunjukkan waktu respons lebih cepat, namun disertai dengan tingkat kesalahan yang lebih besar. Oleh karena itu, aromaterapi lemongrass dalam konteks studi ini tidak terbukti mampu meningkatkan kemampuan atensi berkelanjutan secara signifikan. Kata kunci— Aromaterapi, Sustained Attention, EEG, ERP, SART
Sistem Stimulasi Dan Analisis Sinyal Eeg Dalam Eksperimen Neuropsikologi Berbasis Video Lokahita, Lulu; Bara, Alfianto Teofilus; Alif, M.Nurfadli; Putri, Indah Amalia; Wijayanto, Inung
eProceedings of Engineering Vol. 12 No. 4 (2025): Agustus 2025
Publisher : eProceedings of Engineering

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Abstract

Masalah kesehatan mental di kalangan mahasiswa semakin mengkhawatirkan, sebagaimana dibuktikan oleh lonjakan jumlah kasus gangguan kecemasan yang mencapai lebih dari seribu mahasiswa per tahun di lingkungan universitas, yang sebagian besar dipicu oleh tekanan akademik, emosional, dan sosial. Penelitian ini bertujuan untuk mengembangkan sistem terpadu yang menggabungkan stimulus visual berupa video dengan perekaman dan analisis sinyal EEG guna mengeksplorasi respons otak terhadap rangsangan emosional. Sistem ini terdiri atas dua komponen utama, yaitu aplikasi EEG Analyzer berbasis Windows yang menampilkan video stimulus dan mencatat data responden, serta sistem klasifikasi sinyal EEG yang menggunakan perangkat Muse dan platform MATLAB untuk memproses sinyal menggunakan filtering Butterworth dan transformasi Fourier (FFT). Eksperimen dilakukan terhadap sepuluh responden dengan bantuan psikolog, yang menghasilkan klasifikasi sinyal dominan berupa gelombang delta dan gamma. Hasil menunjukkan bahwa 60% responden mengalami aktivitas gelombang gamma yang mengindikasikan fokus tinggi, sementara 40% menunjukkan dominasi delta yang berkaitan dengan relaksasi. Evaluasi usability aplikasi melalui kuisioner juga menunjukkan tingkat kepuasan tinggi dari 30 responden. Sistem ini berpotensi menjadi sarana observasi awal kondisi mental berbasis neuropsikologi yang non- invasif dan adaptif terhadap lingkungan akademik. Kata kunci— EEG Analyzer, Gelombang Otak, Video stimulus,FFT,EEG,Neuropsikologi
Analysis of Emission Reduction in Indonesia's Power Generation Sector for the Centennial Milestone using Grammatical Evolution and ARIMA Raharjo, Jangkung; Wijayanto, Inung; Nur Ikhsan, Rifki Rahman; Indra Wijaya, Igpo; Nugroho, Bambang Setia; Rokhmat, Mamat
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.3067

Abstract

This study examines the Indonesian government's commitment to reducing electricity production, a crucial element in achieving sustainable energy. Historically, Indonesia depends on non-renewable energy sources, including coal and oil. Indonesia is presently transitioning to cleaner energy alternatives. This policy is done to align with the objective of global sustainability. This pivotal action by the Indonesian government aims to accelerate the adoption of low-carbon technology by society. Through careful planning, Indonesia aims to establish a sustainable and resilient energy framework that addresses both current and future environmental challenges. The active participation of both the state and private sectors is crucial to support this transition. For instance, investment in research and development of sustainable technology by the private sector can accelerate the improvement or creation of a more sustainable energy framework. Innovative technologies, such as solar, hydropower, and wind, can significantly contribute to reducing carbon footprints. This study conducted an extensive observation and evaluation of the contribution of Indonesia's power generation sector to achieving net-zero emissions. This study utilizes the Autoregressive Integrated Moving Average (ARIMA) and Grammatical Evolution (GE) to predict the overall electrical capacity trajectory leading up to Indonesia's Centennial in 2045. By utilizing the exponential grammar, GE outperforms ARIMA in predicting energy forecasts. This research sheds light on Indonesia's transformative efforts, contributing to a broader understanding of how to cultivate a sustainable and environmentally responsible energy future.
Comparison of Deep Learning Methods for Sleep Apnea Detection Using Spectrogram-Transformed ECG Signals Hadiyoso, Sugondo; Wijayanto, Inung; Sekar Safitri, Ayu; Dewi Rahmaniar, Thalita; Rizal, Achmad; Lata Tripathi, Suman
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 4 (2025): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i4.967

Abstract

Sleep apnea is a sleep disorder that occurs when breathing is repeatedly interrupted during sleep. This condition can lead to various serious health problems if left untreated, such as high blood pressure, poor sleep quality, and difficulty concentrating. Sufferers often do not realize they have sleep apnea because it occurs during sleep. Generally, diagnosis is made through interviews with the patient and their family to identify common symptoms such as snoring, and then confirmed through physical examination and Polysomnography (PSG). Since sleep apnea is related to respiratory activity that correlates with changes in cardiac activity, electrocardiogram (ECG) examination during sleep serves as an alternative diagnostic method. Therefore, this study presents a comparative analysis of deep learning models for detecting sleep apnea from spectrogram-based ECG representations. The raw ECG signals were transformed into spectrograms and then saved as images for classification into normal and abnormal categories. Deep learning (DL) methods were applied for the classification of normal and sleep apnea ECGs. EfficientNet, MobileNetV2, DenseNet, AlexNet, and VGG16 were used to evaluate the performance of the proposed method and identify the best-performing model. The evaluation results show that EfficientNet achieved the highest performance with an accuracy of 91.01%, precision of 90.70%, recall of 95.76%, and an F1-score of 92.61%. EfficientNet outperformed the other evaluated models in this study. By utilizing a spectrogram-based approach combined with a scalable architecture, the method demonstrates competitive accuracy for sleep apnea detection. Exploring other approaches to further improve accuracy remains an interesting direction for future research
Implementasi Skala Lab Sistem E-Health Untuk Distribusi Informasi Kesehatan Deteksi Epilepsi Aulia, Indah Ratu; Yovita, Leanna Vidya; Wijayanto, Inung
eProceedings of Engineering Vol. 10 No. 5 (2023): Oktober 2023
Publisher : eProceedings of Engineering

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Abstract

Epilepsi didefinisikan sebagai kumpulan gejaladan tanda-tanda klinis yang muncul disebabkan gangguanfungsi otak secara intermiten, yang terjadi akibat lepas muatanlistrik abnormal atau berlebihan dari neuron neuron secaraparoksismal dengan berbagai macam etimologi. Salah satupemeriksaan yang membantu diagnosis penderita epilepsiadalah rekaman electroencephalogram (EEG). Namun, prosesini membutuhkan banyak waktu untuk melakukan koordinasidata terkait epilepsi. Pada penelitian ini diimplementasikansistem yang bekerja dengan cara mengolah sinyal-sinyal yangmuncul melalui proses EEG. Sistem ini berbasis website yangmembantu mendistribusikan informasi kepada dokter agardapat langsung mengambil tindakan sehingga penderitaepilepsi mendapat pelayanan dengan cepat. Sistem e-healthdibangun berbasis website menggunakan framework Laraveldan Bootsrap. Pada website tersebut terdapat fitur-fiturdiantaranya menu akses data pasien, data EEG, serta notapasien. Proses akses website dalam jaringan internetdiamankan menggunakan rules firewall yang diatur sesuaikebutuhan. Sistem keamanan berbasis firewall dengan toolsOPNSense. Dalam pengolahan sinyal EEG untuk pengolahansinyal mengunakan klasifikasi SVM dimana SVM inimenentukan sinyal EEG normal atau seizure yang nantinyadiolah di MATLAB. Penggabungan antar website denganpengolahan sinyal dilakukan dengan menggunakan mysql.Dalam penelitian ini pengujian website dilakukanmenggunakan blackbox testing dan pengujian firewalldilakukan menggunakan LOIC.Kata kunci— Epilepsi, Sinyal EEG, , Website, Firewall
An Efficient Unknown Detection Approach for RFID Data Stream Management System Yaacob, Siti Salwani; Mahdin, Hairulnizam; Wijayanto, Inung; Aamir, Muhammad; Jaya, M. Izham; Mohd Radzuan, Nabilah Filzah; Mubarak-Ali, Al-Fahim
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.2824

Abstract

The presence of unknown RFID tags can occur when new, unread tagged objects are introduced into the system, either accidentally or intentionally. Additionally, unknown tags can result from tag duplication errors, where multiple tags have the same identifier, or tag malfunctions, where a tag fails to transmit its identifier correctly. This research addresses the critical issue of detecting unknown tags, focusing on optimizing processing time and energy efficiency in terms of memory usage when detecting these tags. A novel algorithm called SWOR (Sliding Window XOR-based Detection) is introduced, specifically designed to identify unknown tags within RFID data streams. SWOR utilizes a sliding window mechanism combined with an XOR filter, enabling efficient detection of unknown tags while reducing unnecessary processing, which can lead to prolonged processing times, high memory consumption, and scalability issues. Experimental results demonstrate that SWOR decreases execution time by an average of 27% across various tests, outperforming existing approaches in terms of processing time for RFID event streams. The materials and methods employed include comprehensive simulations and real-world RFID data streams to validate the algorithm's effectiveness. This study highlights the potential for significant improvements in RFID system efficiency and paves the way for future research in optimizing RFID tag detection methodologies. The implications for further research include exploring the integration of SWOR with other RFID system components and examining its performance in diverse operational environments. This research contributes to the development of more robust and efficient RFID systems, thereby enhancing their reliability and scalability for various future applications.
Perancangan Sistem Eye Tracking untuk Analisis Pola Pergerakan Mata Dalam Menilai Respons Emosional Terhadap Stimulus Video Hafizha, Izzul Fiqri; Wijayanto, Inung; Saftari, Liana Nafisa
eProceedings of Engineering Vol. 12 No. 5 (2025): Oktober 2025
Publisher : eProceedings of Engineering

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Abstract

Adegan mencekam dan netral dapat memicu perubahan emosi yang berbeda, dengan stres sebagai respons fisiologis dan psikologis terhadap tekanan yang melebihi kapasitas adaptasi individu. Penelitian ini bertujuan untuk merancang sistem eye tracking yang menganalisis pola pergerakan mata sebagai indikator perubahan respons terkait stres. Data dikumpulkan dengan merekam wajah subjek saat menonton video menegangkan. Pola pergerakan mata kemudian dianalisis menggunakan Machine Learning. Hasil penelitian menunjukkan perbedaan signifikan pada total amplitudo saccade antara adegan menegangkan dan non-menegangkan, dengan p-value 0,018 (p < 0,05). Ini menunjukkan bahwa total amplitudo saccade efektif sebagai indikator perubahan emosional akibat stimulus menegangkan, dan dapat diandalkan untuk menilai perubahan respons psikologis, khususnya terkait stres. Sistem eye tracking yang dikembangkan memberikan wawasan lebih dalam tentang hubungan antara stimulus dan perubahan emosi, yang dapat digunakan untuk mengukur respons psikologis terhadap stimulus yang berbeda. Perancangan ini juga dapat digunakan dalam membuka potensi penggunaan teknologi ini dalam studi-studi lebih lanjut terkait respons emosional dan stres. Kata kunci— Perubahan emosi, eye tracking, Artificial Intelligence, Stimulus Video, Amplitudo Saccade, Paired Test
Co-Authors Achmad Muzahid Achmad Rizal Achmad Rizal Adisaputra, Rangga Adnan Azhary Ahmad Hilmi Ahmad Muammar Agusti Akbar Budi Wikanta Aldo Setiawan Alif, M.Nurfadli Alrizqi, Naufal Dwi Ana Durrotul Isma Andhita Nurul Khasanah Andi Muhammad Wahyu Safaat Angga Rusdinar ANGGUNMEKA LUHUR PRASASTI Atiffan Ramadhiat Aulia, Indah Ratu Azahra, Yasmin Azis, Qitfirul Abdul Azriel Gilbert Samuel Rogito Azzahra, Salwa Bagus Tri Astadi Balova , Fathrurrizqa Bambang Hidayat Bambang Setia Nugroho Bara, Alfianto Teofilus Bayu Erviga Yulanda Setiawan Budhi Irawan Daivalana Mahadika Priatama Denny Darlis Dewi Rahmaniar, Thalita Didin Bramastya Diliana, Faizza Haya Eko Susatio Elia Kurniawati Fardiyanti, Defitriana Fathrurrizqa Balova Faturachman Faturachman Fauzia Anis Sekar Ningrum Firdaus, Alvaro Ahmad Firmanda Robi Fitriah Halimah Gadama, Melsan Gelar Budiman Gemilang Kurniawan Soejantono Goenadiningrat, Jeahan Fitria Hafizha, Izzul Fiqri Hairulnizam Mahdin, Hairulnizam Hakim, Nurina Listya Hendriadi Mukri HUMAIRANI, ANNISA Hurianti Vidyaningtyas I Nyoman Apraz Ramatryana Ilham Fadhlurrohman Ilva Herdayanti Indra Bari Yulio Indra Wijaya, Igpo Indrarini Dyah Irawati Iqbal Eshar Dwi Pourindra Iqbal Surya Adi Permana Irsyad Abdul Basit Iwan Iwut Tritoasmoro Jangkung Raharjo Jannah, Sabila Hayyinun Jasmine, Diva Dhila Jauhari, Muhammad I Jaya, M. Izham Jehan Pratama Herdaning Karina Permatasari Khairul Sani Kurnia Ismanto, Rima Ananda Lata Tripathi, Suman Leanna Vidya Yovita Lokahita, Lulu Luthfi Muhammad Pahlevi M. Fadhil Abdullah Mamat Rokhmat Meidatomo , Muhammad Haykal Meidi Mahendra Rahmatullah Melati Wahyutami Milan Adila Amalia Mohamad Ilham Abdurrahman Mohd Radzuan, Nabilah Filzah Mubarak-Ali, Al-Fahim Muhammad Aamir Muhammad Adnan Muhammad Ary Murti Muhammad Ridho Putra Muzahid, Achmad Nadya Silva Arline Nasution, Seri Wahyuni Nizhar Arya Hamitha Novian Permana Nur Afifah Nur Ibrahim Nur Ikhsan, Rifki Rahman Nur Pratama, Yohanes Juan Nurina Listya Hakim Olivia Rossiana Pahira, Ela Diranda Pandu Jati Utomo Pelita Santi Permana, Andri Satia Prakoso, Mochamad Rafi Alfian Prasetio Nugroho Putra, I Gusti Ngurah R. A. Putri, Athaliqa Ananda Putri, Indah Amalia R. Dhenake Aghni Bunga R. S. Deanto R. Yunendah Nur Fu’adah Raditiana Patmasari Rahayu Lubis Rahmaniar, Thalita Dewi Ramdani, Ahmad Zaky Rani Harnila Ratri Dwi Atmaja Rayani Budi Andhini Rayyan Budhiarta Reny Yuliani Arnis Revi Febriana Simanjuntak Rita Magdalena Rita Purnamasari Rivan Radian Suryadi Rizal Fachrudin Maulana Rizky Gilang Gumilar Rogito, Azriel Gilbert Samuel Sa'idah, Sofia Safitri, Ayu Sekar Saftari, Liana Nafisa Satrio Nur Adhi Gyat Sa’idah, Sofia Sekar Safitri, Ayu Sidqi, Anka Siti Nur Fatihah Sofia Sa’idah SOFIA SAIDAH Subiakto, Septiaini Dela Suci Aulia Sugondo Hadiyoso Sulistyo, Tobias Mikha Sunarso Sunarso Syahnas, Aulia Teguh Musaharpa Gunawan Triadi Triadi Unang Sunarya Utami, Ayu Tuty Varian Mohammad Sutama Wahyu Lukman Hasan Wibowo, Raiyan Adi Wirakusuma, Muhammad P. Y. P. Gautama Yaacob, Siti Salwani Yasmin Azahra Yoza Radyaputra YULI SUN HARIYANI Zulfikar F.M. Ramli