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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 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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Unlocking Early Detection and Intervention Potential: Analyzing Visual Evoked Potentials (VEPs) in Adolescents/Teenagers with Narcotics Abuse Tendencies from the TelUnisba Neuropsychology EEG Dataset (TUNDA) Wijayanto, Inung; Sulistyo, Tobias Mikha; Nur Pratama, Yohanes Juan; Safitri, Ayu Sekar; Rahmaniar, Thalita Dewi; Sa’idah, Sofia; Hadiyoso, Sugondo; Wibowo, Raiyan Adi; Kurnia Ismanto, Rima Ananda; Putri, Athaliqa Ananda; Khasanah, Andhita Nurul; Diliana, Faizza Haya; Azzahra, Salwa; Gadama, Melsan; Utami, Ayu Tuty
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.476

Abstract

Narcotics abuse has extensive negative impacts on individuals, families, and society, including physical harm to organs and mental health disorders. Addressing teenage narcotics problems requires collaborative efforts involving educational institutions, families, and psychologists. Currently, narcotics has increasingly targeted teenagers, becoming a serious issue that demands special attention in prevention and treatment. Handling narcotic problems at the adolescent level necessitates close collaboration among educational institutions, families, and the community, including psychologists. Emphasizing the importance of early detection and prevention, this study proposes a method to detect the possibility of narcotic abuse in adolescents using the Go/No-Go Association Task (GNAT) test designed by psychologists. The study introduced the TelUnisba Neuropsychology EEG Dataset (TUNDA), an open EEG dataset with data on the emotional and habitual aspects of drug abuse in Indonesia, classified into "normal" and "risk" by psychologists. The processed EEG signal is the visual evoked potential (VEP) within 1000 milliseconds following the visual stimulus onset. The data is classified as “slow” and “fast” based on respondent's responses using MobileNetV2 architecture. Results showed MobileNetV2 achieved the highest accuracy for both normal and risk categories, with accuracies of 0.86 and 0.85 respectively. This study obtained ethical clearance and received funding support from Telkom University and Universitas Islam Bandung, with technical assistance from the Smart Data Sensing Laboratory. The authors declare no conflicts of interest related to this study.
Klasifikasi Sinyal EKG menggunakan Ciri Statistik dan Parameter Hjorth dengan SVM dan k-NN WIJAYANTO, INUNG; HUMAIRANI, ANNISA; RIZAL, ACHMAD; HADIYOSO, SUGONDO
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 10, No 1: Published January 2022
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v10i1.132

Abstract

ABSTRAKSinyal elektrokardiogram (EKG) dapat dianalisis dengan memperhatikan bentuk, durasi, dan irama. Pada penelitian ini, dikembangkan sebuah metode ekstraksi ciri sinyal EKG dengan menggunakan parameter Hjorth dan ciri statistik. Kedua parameter tersebut diaplikasikan untuk mengekstrak ciri-ciri dari rekaman suara sinyal EKG. Terdapat tiga kondisi rekaman sinyal EKG yang menjadi masukan dari sistem, kondisi normal, atrial fibrillation (AF), dan congestive heart failure (CHF). Set ciri rekaman EKG yang didapatkan kemudian diklasifikasikan dengan menggunakan metode support vector machine (SVM) dan k-Nearest Neighbor (k-NN) untuk dibandingkan performansinya. Hasil pengujian menggunakan semua ciri sebagai prediktor menunjukkan bahwa usulan sistem mampu memberikan akurasi sebesar 100%. Sementara itu pada skenario reduksi ciri dimana hanya dua ciri yaitu skewness dan complexity, performansi sistem tidak berkurang. Komparasi dengan beberapa studi sebelumnya menunjukkan bahwa usulan metode lebih unggul dalam hal akurasi deteksi dan jumlah ciri yang digunakan.Kata kunci: EKG, atrial fibrillation, congestive heart failure, Hjorth, SVM, k-NN ABSTRACTAn electrocardiogram (ECG) signal can be analyzed by paying attention to its shape, duration, and rhythm. In this study, feature extraction for ECG signals is applied using the Hjorth parameter and statistical characteristics. These two parameters are applied to extract the characteristics of the ECG signal sound recording. There are three conditions of ECG signal recording that are used as input for the system. They are normal conditions, atrial fibrillation (AF), and congestive heart failure (CHF). The set of ECG recording features are classified using the support vector machine (SVM) and k-Nearest Neighbor (k-NN) methods. The test results using all features show that the proposed system can achieve 100% of accuracy. On the other hand, by reducing the feature using only skewness and complexity, the system’s performance is not reduced. Comparative studies with several previous studies show that the proposed method is superior in detection accuracy and the number of features used.Keywords: ECG, atrial fibrillation, congestive heart failure, Hjorth, SVM, k-NN
Implementation of Ensemble Machine Learning with Voting Classifier for Reliable Tuberculosis Detection Using Chest X-ray Images with Imbalance Dataset Jauhari, Muhammad I; Wirakusuma, Muhammad P.; Sidqi, Anka; Putra, I Gusti Ngurah R. A.; Wijayanto, Inung; Rizal, Achmad; Hadiyoso, Sugondo
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.472

Abstract

Tuberculosis (TB) is an infectious disease caused by bacteria. Tuberculosis is spread through the air and saliva that contain mycobacterium tuberculosis. If not treated immediately, it can spread to other vital organs, such as the heart and liver, and can even lead to death. In this study, we developed a severe tuberculosis detection system using the Tuberculosis (TB) dataset with simple computation. We used 4200 data points (3500 Normal and 700 TB). In other words, this research aimed to create lightweight computation with Machine Learning (Voting Classifier in Ensemble Learning) as the classifier using Imbalance data. Initial experiments used single machine learning with the best-performing models, Support Vector Machine (SVM), and Random Forest as classifiers. With an accuracy of 98.6% and 98%, they were combined using Ensemble Learning without feature extraction; the accuracy, AUC, Recall, Precision, and F1-score using the voting classifier were 99.1%, 99.3%, 99%, 98%, and 98%, respectively.
Application of Hybrid Metaheuristic Algorithms for Feature Selection in Event-Related Potential Classification in Problematic Gamers Using Electroencephalograph Signal Wijayanto, Inung; Hadiyoso, Sugondo; Safitri, Ayu Sekar; Rahmaniar, Thalita Dewi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 2 (2025): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i2.638

Abstract

Online games have become a popular form of entertainment, particularly for relieving stress, and the rise in online gaming has led to an increase in problematic gaming behaviors. Excessive use of the internet for gaming has raised concerns about its neurophysiological impact, particularly on cognitive and emotional functions. Electroencephalogram signal and Event-Related Potential analysis are valuable tools for monitoring these effects. Given the vast amount of features that can be extracted from EEG signals, it is crucial to apply efficient feature selection methods to identify the most informative ones. This study utilizes the Go/No-Go Association Task combined with the recording of 16-channel EEG signals, chosen as the data-recording method to observe the response of individuals who are problematic online gamers to several stimulus themes. In this context, metaheuristic algorithms like Genetic Algorithm, Ant Colony Optimization, and Particle Swarm Optimization are employed to enhance feature selection. A hybrid approach, combining one of these methods with Binary Stochastic Fractal Search is proposed to improve classification accuracy and optimize feature selection. The results demonstrate that the hybridization of the best algorithm with B-SFS successfully selects the optimal features, achieving perfect classification performance, with an accuracy, sensitivity, and specificity of 1.00 for all respondents. This emphasizes the effectiveness of B-SFS, particularly its diffusion process, where Gaussian distribution facilitates the search for the best solution, thereby improving the reliability of feature selection for detecting problematic gaming behavior.
Wrapper Feature Subset Selection for Feature Extraction of Bonang Barung Single Tone Convertion Into Numeric Notation Wijayanto, Inung; Hakim, Nurina Listya; Rizal, Achmad
JMECS (Journal of Measurements, Electronics, Communications, and Systems) Vol. 1 No. 1 (2015): JMECS
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/jmecs.v1i1.1477

Abstract

Several researches have been done to study the characteristics of the bonang barung, one of Javanese Gamelan music instrument. One of them is convertion of bonang barung single tone to numeric notation using Harmonic FFT as feature extraction and Backpropagation Artificial Neural Network (ANN) for classification. The tone detection accuracy result from previous research is 70,74%. In this research we try to improve the detection result by searching the dominant features using Wrapper Feature Subset Selection (WFSS). Sequential forward selection (SFS) and sequential backward selection (SBS) are used as searching algorithm. The input of the system is a song recorded from a bonang barung then the detected tone is converted into numeric notation. From the experiment, WFSS-SFS produced 6 features with 86,4% accuracy while WFSS-SBS give a better result, it produced 13 features with 92,9% accuracy of tone detection.
Penerapan Sensor Akselerometer dan Giroskop untuk Membedakan Pola Berjalan Militer dan Non-Militer Meidatomo , Muhammad Haykal; Wijayanto, Inung; Hadiyoso, Sugondo
eProceedings of Engineering Vol. 12 No. 3 (2025): Juni 2025
Publisher : eProceedings of Engineering

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Abstract

Mengkaji pola gerakan berjalan (GAIT)menggunakan perangkat sensor yang menggabungkanakselerometer dan giroskop pada prajurit militer. Pola gerakanyang khusus, seperti cara berjalan tegap, memainkan peranvital dalam membedakan antara prajurit TNI dan individupada umumnya. Dalam penelitian ini, sensor MPU6050dikenakan pada perangkat wearable berbasis ESP32 untukmerekam gerakan tiga dimensi (x, y, z) yang dihasilkan olehakselerometer dan giroskop ketika subjek berjalan. Data yangdiperoleh kemudian dikirimkan melalui Bluetooth Low Energy(BLE) untuk dianalisis lebih lanjut. Tujuan dari penelitian iniadalah untuk mengidentifikasi perbedaan pola gerakan antaraprajurit militer dan individu dengan tingkat kebugaran fisikyang lebih rendah. Klasifikasi dilakukan dengan membagi duakategori berdasarkan gaya berjalan: tegap (militer) dan lemas(individu dengan kondisi fisik tidak optimal). Analisa statistikdan variabel gerakan digunakan guna mendalami perbedaangerak berjalan. Temuan yang dihasilkan diharapkan dapatmenunjang pengembangan sistem otomatis untuk mengenaliprajurit militer berdasarkan pola gerakan berjalan mereka.Kata kunci—GAIT, BLE, akselerometer, MPU6050, ESP32,militer
Implementasi Sensor Matrix Force Sensing Resistor (FSR) untuk Mendeteksi Kelainan Telapak Kaki Subiakto, Septiaini Dela; Wijayanto, Inung; Saidah, Sofia
eProceedings of Engineering Vol. 11 No. 4 (2024): Agustus 2024
Publisher : eProceedings of Engineering

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Abstract

Lengkungan pada telapak kaki yang berupa celah antara bagian dalam dari kaki dan permukaan disebut arkus. Terdapat tiga jenis arkus yaitu normal, tinggi, dan datar. Dari ketiga jenis arkus tersebut, dua diantaranya merupakan kelainan pada telapak kaki (tinggi dan datar). Kelainan ini berpengaruh pada calon anggota Kepolisian Republik Indonesia (POLRI) da- lam proses pemeriksaan fisik. Saat ini, pemeriksaan fisik tersebut masih menggunakan cara konvensional dengan menggunakan penggaris untuk mengukur panjang dan lebar telapak kaki. Untuk mengatasi masalah tersebut, penelitian ini merancang sebuah hardware, yaitu sistem pendeteksi telapak kaki bernama <Flatyfoot=. Sistem ini menggunakan komponen sensor matrix FSR, shift register dan analog, mikrokontroler ESP32, dan baja ringan. Kata Kunci-Arkus, Hardware, Mikrokontroler ESP32, PO- LRI, Sensor Matrix FSR
Implemetasi Convolutional Neural Network (CNN) dalam Mendeteksi dan Mendiagnosis Kelainan Telapak Kaki Alrizqi, Naufal Dwi; Wijayanto, Inung; Saidah, Sofia
eProceedings of Engineering Vol. 11 No. 4 (2024): Agustus 2024
Publisher : eProceedings of Engineering

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Abstract

Arkus adalah lengkungan pada telapak kaki berupa celah antara bagian dalam dari kaki dan permukaan. Ada tiga jenis arkus yaitu normal, tinggi, dan datar, dengan dua terakhir dianggap sebagai kelainan kaki. Kelainan ini menjadi fokus dalam penilaian calon anggota Kepolisian Republik Indonesia (POLRI). Saat ini, pengukuran arkus dilakukan secara manual dengan penggaris, yang sering kali tidak akurat. Untuk mengatasi masalah ini, kami merancang sistem pendeteksi telapak kaki bernama "Flatyfoot". Sistem ini, yang berbasis Deep Learning dan menggunakan arsitektur Convolutional Neural Network (CNN) ResNet152 V2, dapat mengidentifikasi dua jenis telapak kaki (normal dan tidak normal) dengan akurasi tinggi. Dengan demikian, proses seleksi POLRI dapat menjadi lebih akurat dan efisien. Penelitian kami menunjukkan bahwa sistem ini memiliki performa 84,44%, menandakan bahwa model dapat berjalan sesuai rancangan. Kata Kunci4Flatfoot, POLRI, Deep Learning, Convolutional Neural Network (CNN), Deteksi Kelainan Telapak Kaki
Desain dan Implementasi User Interface Sistem Deteksi Kelainan Telapak Kaki Fardiyanti, Defitriana; Wijayanto, Inung; Saidah, Sofia
eProceedings of Engineering Vol. 11 No. 4 (2024): Agustus 2024
Publisher : eProceedings of Engineering

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Abstract

Arkus adalah lengkungan pada telapak kaki berupa celah antara bagian dalam dari kaki dan permukaan. Ada tiga jenis arkus yaitu normal, tinggi, dan datar, dengan dua terakhir dianggap sebagai kelainan kaki. Kelainan ini menjadi fokus dalam penilaian calon anggota Kepolisian Republik Indonesia (POLRI). Saat ini, pengukuran arkus dilakukan secara manual dengan penggaris, yang sering kali tidak akurat. Untuk mengatasi masalah ini, kami merancang sistem pendeteksi telapak kaki bernama "Flatyfoot". Sistem ini, yang berbasis Deep Learning dan menggunakan arsitektur CNN ResNet152 V2, dapat mengidentifikasi dua jenis telapak kaki (normal dan tidak normal) dengan akurasi tinggi. Dengan demikian, proses seleksi POLRI dapat menjadi lebih akurat dan efisien. Penelitian kami menunjukkan bahwa sistem ini memiliki performa 84,44%, menandakan bahwa model dapat berjalan sesuai rancangan. Kata Kunci: Flatfoot, POLRI, Deep Learning, Convolutional Neural Network
Security Surveillance System Area Tower Bts Berbasis Iot Pada Perusahaan Pt Dayamitra Telekomunikasi Lubis, Rahayu; Wijayanto, Inung; Darlis, Denny
eProceedings of Engineering Vol. 11 No. 4 (2024): Agustus 2024
Publisher : eProceedings of Engineering

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Abstract

Base Transceiver Station (BTS) adalah infrastruktur telekomunikasi yang memungkinkan komunikasi nirkabel antara perangkat telekomunikasi dan jaringan operator. Meskipun penting, perangkat di BTS sering menjadi target pencurian, terutama baterai di shelter dan kabel ground di luar shelter, yang mengakibatkan penurunan kualitas jaringan dan kerugian bagi provider. Untuk mengatasi masalah ini, diperlukan sistem surveillance yang dapat dipantau dari jarak jauh. Sistem ini mencakup kamera CCTV untuk memantau area site dan RFID untuk akses petugas ke area site, serta website yang menampilkan informasi yang dikirim oleh perangkat menggunakan metode komunikasi MQTT dan HTTP, dengan data tersimpan dalam database. Selain website, informasi juga dapat dilihat melalui Telegram sebagai silent notification yang mengirimkan capture image saat CCTV mendeteksi objek. Hasil pengujian menunjukkan bahwa kamera CCTV mampu mengambil gambar dan melakukan live streaming dengan protokol RTSP/IP Cam, dengan tingkat accuracy object detection sebesar 76% (pagi), 83% (siang), 89% (sore), dan 87% (malam); precision 93% (pagi), 88% (siang), 95% (sore), dan 93% (malam); serta sensitivity 78% (pagi), 88% (siang), 91% (sore), dan 93% (malam). RFID-RC522 High Frequency bekerja dengan baik dalam mendeteksi UID dan username tag serta mengendalikan status doorlock, dengan hanya tiga kegagalan dari lima belas percobaan karena koneksi WiFi. Pengujian komunikasi menggunakan protokol HTTP dan MQTT menunjukkan bahwa komunikasi antara hardware dan software berjalan dengan baik. Kamera CCTV terhubung ke website selama 5 detik, sedangkan RFID membutuhkan waktu selama 10 detik. Website dapat menerima dan menampilkan data yang dikirimkan oleh alat dengan baik. Kata kunci : Surveillance System, CCTV, RFID, HTTP, BTS
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 Azahra, Yasmin Azis, Qitfirul Abdul Azriel Gilbert Samuel Rogito Azzahra, Salwa Bagus Tri Astadi Balova , Fathrurrizqa Bambang Hidayat Bara, Alfianto Teofilus Bayu Erviga Yulanda Setiawan Budhi Irawan Daivalana Mahadika Priatama Denny Darlis 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 Hakim, Nurina Listya Hendriadi Mukri HUMAIRANI, ANNISA Hurianti Vidyaningtyas I Nyoman Apraz Ramatryana Ilham Fadhlurrohman Ilva Herdayanti Indah Ratu Aulia Indra Bari Yulio 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 Jehan Pratama Herdaning Karina Permatasari Khairul Sani Kurnia Ismanto, Rima Ananda Leanna Vidya Yovita Lokahita, Lulu Luthfi Muhammad Pahlevi M. Fadhil Abdullah Meidatomo , Muhammad Haykal Meidi Mahendra Rahmatullah Melati Wahyutami Milan Adila Amalia Mohamad Ilham Abdurrahman 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 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 Satrio Nur Adhi Gyat Sa’idah, Sofia 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 Yasmin Azahra Yoza Radyaputra YULI SUN HARIYANI Zulfikar F.M. Ramli