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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TEKNIK Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Padjadjaran Journal of Dentistry JETT (Jurnal Elektro dan Telekomunikasi Terapan) International Journal of Artificial Intelligence Research Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JPEK (Jurnal Pendidikan Ekonomi dan Kewirausahaan) To Maega: Jurnal Pengabdian Masyarakat Journal of Electronics, Electromedical Engineering, and Medical Informatics JMECS (Journal of Measurements, Electronics, Communications, and Systems) Indonesian Journal of Electrical Engineering and Computer Science Community Development Journal: Jurnal Pengabdian Masyarakat Media Ilmiah Akuntansi eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT) Journal of Computer Engineering: Progress, Application and Technology Al-Mubtadi : Jurnal Pendidikan Guru Madrasah Ibtidaiyah
Hilman Fauzi, Hilman
Universitas Telkom
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Analisa Metode Pengukuran Berat Badan Manusia Dengan Pengolahan Citra Fauzi, Hilman; Rahman, Fadlur; Azhar, Tauhid Nur; Ayudina, Nasya; Dwiatmaja, Ratri
TEKNIK Vol 38, No 1 (2017): (Juli 2017)
Publisher : Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (256.783 KB) | DOI: 10.14710/teknik.v38i1.12663

Abstract

Body weight is one of the most important parameters to determine the condition of a person's body. To find out information about the weight is generally done by using a measurement scales. However, there are several methods that can be done to determine a person's weight, one of which is by using image processing. Through this study, we tried to decipher the possibility of weight calculation using image processing with various mathematical approach based on a calculation of body surface area (BSA) and the volume of the ellipse for the human body. We process the image in the form of digital photos to generate information on the person's weight on the photo. Furthermore, we did investigate the possibility, calculation, and analysis of the accuracy of the system. To determine the performance of the system that we made, we did the comparison calculation results with body weight results of the scales. As a result, we conclude that the weight calculation method is feasible through image processing with various conditions and restrictions, it is confirmed by the results of the analysis and the accuracy of our calculation system is 95% at a distance of 470 cm between the camera and the object.
Study of Neuromarketing: Visual Influence with Decision Making on Impulse Buying Januar, Rifat; Fauzi, Hilman; Ariyanti, Maya; Heris, Faradisya
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 6, No. 4, November 2021
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v6i4.1334

Abstract

Marketing trends have been increasing in the last few decades. Products need good branding and the right marketing strategy. Various marketing methods have been widely done, and one of them is with the study of neuroscience, especially neuromarketing. Neuromarketing is used to seek the influence of marketing stimuli on consumers and objective data through advances in neurology by utilizing human senses such as restraint, smell, taste, and touch. Measurements of neuromarketing responses to the brain can use electroencephalography signals (EEG). Measurement is done with the visual stimulus of consumers when making decisions. To analyze consumer interests, the majority still using qualitative methods, but it is still considered less effective due to many uncertain factors. In this study, neuromarketing responses were measured to the human brain using (EEG) signal analysis. Data collection was conducted on 11 respondents with a stimulus in the form of different product colors and was affected by changes in light intensity. For pre-processing used bandpass filters to get beta signals in the absence of noise. Then the data will be processed using Fast Fourier Transform (FFT) and energy extraction as characteristic extraction and classification of Support Vector Machines (SVM) in the signal pattern recognition process. The results of testing the best feature combination parameters showed an accuracy value of 72% with a combination of magnitude and phase features. By using the range of phase feature values obtained an accuracy of 67% for signal pattern recognition respondents.
Sistem Deteksi Pra-Kanker Serviks dengan Pengolahan Citra Hasil Inspeksi Visual Asam Asetat Hilman Fauzi; Galih Surya; Rita Magdalena; Ali Budi Harsono; Tauhid Nur Azhar
Techno.Com Vol 20, No 2 (2021): Mei 2021
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v20i2.4285

Abstract

Kanker serviks merupakan penyakit mematikan nomor satu di Indonesia dengan angka kematian tertinggi pada wanita. Berbagai upaya untuk mengurangi angka kematian wanita Indonesia akibat kanker serviks telah banyak dilakukan, salah satunya dengan melakukan screening kanker menggunakan tes inspeksi visual asam asetat (tes IVA). Tes ini merupakan upaya screening untuk mengetahui pra-cancer atau invasive cancer pada kanker serviks dengan memunculkan Acetowhite Epithelium Zone (AEZ) yang dapat dikategorikan sebagai lesi IVA positif maupun lesi jinak. Umumnya, AEZ dapat dilihat dengan kasat mata yang memerlukan keahlian khusus sehingga hasil pengamatannya akan bersifat subjektif dan bergantung pada pengalaman operator. Selain itu, utilitas pemeriksaan kanker serviks ini pun dinilai terbatas dikarenakan sedikitnya jumlah operator ahli yang terlatih. Pada penelitian ini, lesi pra-kanker serviks dikuantifikasi dengan pengolahan citra digital. Citra yang digunakan adalah citra hasil inspeksi visual asam asetat atau citra area mulut rahim yang telah diolesi oleh asam asetat dan dinyatakan terdapat sambungan skuamosa kolumnar (SSK) positif. Kuantifikasi citra lesi pra-kanker serviks dilakukan dengan menggunakan metode standarisasi karakter warna citra pada RGB dan HSV. Pengujian system deteksi lesi pra-kanker serviks diukur dengan menggunakan parameter akurasi, sensitivitas dan spesifisitas terhadap pengaruh tingkat kecerahan dan mean filter. Melalui penelitian ini didapatkan klasifikasi citra tes IVA beserta area lesi IVA positif yang optimal dengan tingkat akurasi 81%, nilai sensitivitas 78% dan nilai spesifisitas 84%. Performa system sangat dipengaruhi oleh ketajaman dan efek pencahayaan pada citra, baik itu intensitas cahaya, efek bayangan, maupun efek pantulan cahaya.
Energy extraction method for EEG channel selection Hilman Fauzi; M. Abdullah Azzam; Mohd. Ibrahim Shapiai; Masaki Kyoso; Uswah Khairuddin; Tadayasu Komura
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 5: October 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i5.12805

Abstract

Channel selection is an improvement technique to optimize EEG-based BCI performance. In previous studies, many channel selection methods—mostly based on spatial information of signals—have been introduced. One of these channel selection techniques is the energy calculation method. In this paper, we introduce an energy optimization calculation method, called the energy extraction method. Energy extraction is an extension of the energy calculation method, and is divided into two steps. The first step is energy calculation and the second is energy selection. In the energy calculation step, l2-norm is used to calculate channel energy, while in the energy selection method we propose three techniques: “high value” (HV), “close to mean” (CM), and “automatic”. All proposed framework schemes for energy extraction are applied in two types of datasets. Two classes of datasets i.e. motor movement (hand and foot movement) and motor imagery (imagination of left and right hand movement) were used. The system used a Common Spatial Pattern (CSP) method to extract EEG signal features and k-NN as a classification method to classify the signal features with k = 3. Based on the test results, all schemes for the proposed energy extraction method yielded improved BCI performance of up to 58%. In summary, the energy extraction approach using the CM energy selection method was found to be the best channel selection technique.
Defining Common Inter-Session and Inter-Subject EEG Channels Using Spatial Selection Method Hilman Fauzi; Tadayasu Komura; Masaki Kyoso; Mohd. Ibrahim Shapiai; Yasmin Mumtaz
International Journal of Artificial Intelligence Research Vol 6, No 2 (2022): Desember 2022
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (373.968 KB) | DOI: 10.29099/ijair.v6i2.284

Abstract

Redundancy of information on brain signals can lead to reduce brain-computer interface (BCI) performance in applications. To overcome this, EEG channel selection is performed to reduce and/or eliminate a number of channels with irrelevant information. In the previous studies, there is energy calculation methods that have been proposed to perform EEG channel selection to improve BCI performance in classifying the brain command of motor imagery stimulation. In this study, channel selection scheme on motor movement signal will be experimented by using spatial selection method. This study performs the common active channel mechanism that divided into two parts: 1) common active channels between sessions, which known as common Inter-session channels and common active channels. These two techniques can be used by all subjects to interpret motor movement type known as common Inter-subject channels. In order to validate the performance of the proposed framework, CSP (common spatial pattern) is used as a feature extraction method and k-NN with k = 3 as the classification method. The obtained results shows that the proposed channel selection technique is able to choose common active channels in five combination numbers on Inter-sessions and Inter-subjects of the acquired EEG signals. Both types of common active channels are proven to improve BCI performance with an accuracy increase of up to 66%.
Pelatihan Implementasi Alat Ukur Hemoglobin Non-Invasif (HbEy) di Palang Merah Indonesia (PMI) Kabupaten Indramayu Rustam Rustam; Hilman Fauzi Tresna Sania Putra; Yulinda Eliskar
To Maega : Jurnal Pengabdian Masyarakat Vol 4, No 1 (2021): Februari 2021
Publisher : Universitas Andi Djemma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35914/tomaega.v4i1.531

Abstract

AbstrakHbEY merupakan salah satu produk hasil riset tim dosen dari Kelompok Keahlian Pengolahan Sinyal Informasi (KK PSI) Telkom University. Aplikasi ini berfungsi untuk mengukur kadar hemoglobin darah secara non-invasif. HbEY memiliki kelebihan yaitu mudah dan nyaman untuk digunakan, serta harga yang terjangkau jika dibandingkan dengan alat yang saat ini banyak digunakan di instansi pemerintahan, salah satunya di Palang Merah Indonesia (PMI). Kenyamanan dan kemudahan penggunaan HbEy berdasarkan pada fakta bahwa HbEy mengukur hemoglobin (Hb) darah secara non-invasif dengan hanya melakukan instalasi aplikasi smartphone android. Pada kegiatan pengabdian masyarakat ini, sosialisasi dan implementasi aplikasi HbEY dilakukan pada masyarakat, pengurus, dan volunteer PMI Kabupaten Indramayu. Hasil dari kegiatan ini menunjukkan aplikasi HbEY dapat menjadi salah satu alternatif alat ukur kadar Hb non-invasif yang dengan mudah, murah, dan nyaman untuk digunakan. Senada dengan itu, HbEy mampu untuk menyajikan informasi terkait kadar normal atau anemia calon pendonor. Sehingga HbEy bisa menjadi acuan awal untuk mengambil keputusan apakah calon pendonor layak atau tidak.    Kata Kunci: HbEY, Produk Riset, Hemoglobin, PMI Kabupaten Indramayu AbstractHbEY is one of the products of research by a team of lecturers from the Information Signal Processing Research Group, Telkom University. This application serves to measure blood hemoglobin levels non-invasively. HbEY has the advantages of being easy and comfortable to use, and an affordable price when compared to tools that are currently widely used in government agencies like Indonesian Red Cross. The convenience and ease of use of HbEy are based on the fact that HbEy measures blood hemoglobin (Hb) non-invasively only by installing an android smartphone application. In this community service activity, socialization and implementation of the HbEY application were carried out to the community, administrators, and PMI Indramayu Regency volunteers. The results show that HbEY can be an alternative non-invasive Hb measurement tool that is easy, cheap, and comfortable to use. Likewise, HbEy is able to present information related to normal or anemia levels of prospective donors. So that HbEy can be an initial reference for making decisions regarding whether prospective donors are eligible to donate blood or not.Keywords: HbEy, Research Product, Hemoglobin, PMI Indramayu Regency
SISTEM DETEKSI GLAUKOMA PADA FOTO FUNDUS RESOLUSI TINGGI Hilman Fauzi; Faidil Hadi
Jurnal Elektro dan Telekomunikasi Terapan (e-Journal) Vol 2 No 2: JETT Desember (2015)
Publisher : Direktorat Penelitian dan Pengabdian Masyarakat, Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (307.491 KB) | DOI: 10.25124/jett.v2i2.105

Abstract

Glaukoma adalah peradangan pada optic mata yang ditandai dengan kemunduran progresif dari kepala saraf optik dan luas pandang. Peradangan ini disebabkan oleh tidak seimbangnya proses produksi cairan dan pembuangannya pada bola mata sehingga menyebabkan tekanan cairan bola mata menjadi tinggi. Kemunduran progresif pada luas pandang bersifat permanen dan tidak dapat disembuhkan, sehingga pendeteksian dini sangat perlu dilakukan sebelum kerusakan menjadi semakin parah. Deteksi glaukoma dapat dilakukan dengan beragam cara, salah satunya adalah dengan melihat ukuran optik disk pada foto fundus digital. Namun, hasil identifikasi foto fundus secara manual dapat menghasilkan diagnosis yang kurang tepat. Pada penelitian ini dilakukan proses simulasi dan analisis suatu sistem yang dapat membantu praktisi kesehatan mendeteksi ukuran optik disk pada foto fundus sehingga dapat mendiagnosis dengan cepat dan akurat. Berdasarkan hasil dari simulasi, akurasi sistem mencapai 76% dengan waktu komputasi 1.5 detik.
ANALISIS KALKULASI BODY MASS INDEX DENGAN PENGOLAHAN CITRA DIGITAL BERBASIS APLIKASI ANDROID Hilman Fauzi; Nasya Ayudina Darsono; Bambang Hidayat .
Jurnal Elektro dan Telekomunikasi Terapan (e-Journal) Vol 5 No 2: JETT Desember 2018
Publisher : Direktorat Penelitian dan Pengabdian Masyarakat, Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (573.546 KB) | DOI: 10.25124/jett.v5i2.1395

Abstract

In 2000 WHO estimated that over 700 million adults will be overwight in 2015 and there’s an increase in the prevalence of obesity up to 50% by 2025 in developed countries. To reduce this risk, there is a standard formula which was lauched by World Health Organization called Body Mass Index (BMI). BMI is an index which referred a standard posture of normal and abnormal healthy human. This index is generated by formula that involves the calculation of proposed human posture approach. In this study, BMI can be calculated by image processing in android application. This proposed method use spatial techniques which digital image will be cropped to get RoI as width and height of a person. Then, the pixels will be processed with normalization to get height and width of RoI pixels calculation for BMI using an elliptical cylinder formula to get the Body Surface Area (BSA) and height pixel after normalization. As the results of the application system design, the BMI performance using digital image processing has an optimal accuracy in up to 91% from 480x640 of camera resolution.
Vascular dementia classification based on hilbert huang transform as feature extractor Wan Siti Nur Shafiqa Wan Musa; Mohd Ibrahim Shapiai; Hilman Fauzi; Aznida Firzah Abdul Aziz
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 2: February 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i2.pp968-974

Abstract

Impairment of cognitive and working memory after stroke was common. Vascular dementia (VaD) was a prevalent type of dementia that was caused by an impaired blood supply to the brain because of a series of small strokes. Electroencephalogram (EEG) gives information about brain status and activity, so it had a lot of potential to be used in diagnosing people with dementia. Since the EEG signal is extremely non-linear and non-stationary data, traditional Fourier analysis such as Fast Fourier Transform (FFT) that broadens sinusoidal signals cannot describe the amplitude contribution of each frequency value in specific time. Meanwhile, Hilbert Huang Transform (HHT) was based on the characteristic local time scale of the signal, it can efficiently obtain instantaneous frequency and instantaneous amplitude for nonstationary and nonlinear data. In this paper, HHT was employed as feature extraction method to extract the energy features of frequency bands from post stroke patients and healthy subjects. The extracted features were fed into extreme learning machine (ELM) for classifying post stroke patient with VaD and healthy subjects. The results of classification accuracy using HHT as feature extractor and FFT as feature extractor were compared. The mean accuracy of classification using HHT was 59.14%, respectively, while mean accuracy of classification using FFT was 94.4%, respectively, in classifying post stroke patient with VaD and healthy subjects.
Classification of Gender Individual Identification Using Local Binary Pattern on Palatine Rugae Image Hilman Fauzi; Cynthia Erika; Sofia Sa'adiah; Fahmi Oscandar
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 8, No 3 (2022): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v8i3.23636

Abstract

Major disasters caused many casualties with the condition of the damaged bodies. It causes the individual identification process to be ineffective through biometric characteristics (such as lips and fingerprints). However, the palatine rugae can carry the individual identification process. Palatine rugae have unique and individual characteristics and are more resistant to trauma because of their internal location. In this study, an individual identification system is proposed to identify gender using the image of palatine rugae. The proposed system is developed by several algorithms and methods, such as Local Binary Pattern (LBP) as the feature extraction method and K-Nearest Neighbor (KNN) as the classification method. Based on the result of the system performed test, the proposed system can identify the gender of an individual by the combination of recognized palatine rugae patterns. The system achieved an accuracy test result of 100% with a specific configuration of LBP and KNN. The research contribution in this study is to develop the individual gender identification system, which proceeds with the palatine rugae pattern image with unique biometric characteristics as an input. The system applied several methods and algorithms, such as Geometric Active Contour (GAC) as a segmentation algorithm, Local Binary Pattern (LBP) as a feature extraction method, and K Nearest Neighbor (KNN) as a classification method.
Co-Authors Achmad Rizal Achmad Rizal Adzra, Faaiq Ammaria Alvin Oktarianto Anak Agung Gede Mahendra Kusuma Andi Zahra Bunga Zana Andria Puja Pratama Ayudina, Nasya Azhar, Tauhid Nur Aznida Firzah Abdul Aziz Bagas Farhan Hadyantoro Bambang Hidayat Bambang Hidayat . Barri, Hablul Bayu Angga Medica Firmanda BIRU, BANYU Boby Irfanudin Anwar Cynthia Erika Dayan Aldina Dendi Gusnadi Denta Rahmadani Dewa Nyoman Indra Dharma, Budi Dwi Sukma Bestry Fahmi Oscandar Fahmi Oscandar, Fahmi Faidil Hadi Fathurrachman, Dhia Firdaus Fauzi, Adryan Favian Dewanta Fina Maharani Fitra Ayu Larasati Galih Surya Gede Hari Yogiswara Gelar Budiman HARSONO, ALI BUDI Hasibuan, DR David H.M. Haya, Allika Fadia Heris, Faradisya Hutagalung, Yessica Maria I Wayan Agus Sugiarsa Irfan Darmawan Iwan Iwut Tritoasmoro Jangkung Raharjo Januar, Rifat Kinantan, Muhammad Rafi Kurnia Sri Yunita Kurnia Sri Yunita La Ode Agus Salim M. Abdullah Azzam M.Fajar Zulvan Nugraha Marliyah, Marliyah Marpaung, bintangsahala0203 Marpaung, Dhea Romantika Marpaung, DR Annaria Magdalena Mas Sarwoko Suraatmadja Masaki Kyoso Maya Ariyanti Mazaya 'Aqila Misbakhul Munir Mochamad Dandi Mohd Ibrahim Shapiai Mohd. Ibrahim Shapiai Muhammad Hablul Barri Muhammad Ilham Muhammad Zuhairi Nasya Ayudina Darsono Naufal Reza Alfiandy Nur Ibrahim Octavian Putera Kesuma Sugeng Oktiandi Nugroho Wasktio Pratiwi, Daulika Putra Fajar Alam Qisthi Nur Rahmah Raditiana Patmasari Rahman, Fadlur Rahmat Widadi Raihan Nur Fadhlillah Ramanta Limantara Sidam Ramdhan Nugraha Rasta, John Ratri Dwi Atmaja Revydo Bima Anshori Revydo Bima Ansori Reza Armanda Lubis Rio Fa, Farrel Rita Magdalena Rustam Rustam Rustam Saepulloh Saepulloh Said, Ziani Salsabila, Afap Saputri, Ikra Yuni SENJAYA, ARIO Siadari, Thomhert S. Sofia Sa'adiah SOFIA SAIDAH Sophya Hadini Marpaung Suci Dwi Yanti Sugondo Hadiyoso Suhardjo Suhardjo Syamsul Rizal Syatta, Hurin Tadayasu Komura Tauhid Nur Azhar Thomhert Siadari Thomhert Suprapto Siadari Tsani, Fajri Twinarya Bagus Wibawa Uswah Khairuddin Utari Nur Ramadhani Yora Vany Octaviany Vany Octaviany Venia Oktafiani Wan Siti Nur Shafiqa Wan Musa Yasi Oktodiranto Yasman, Fudhla Ramadhana Yasmin Mumtaz Yulinda Eliskar Ziani Said