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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknik ITS Semantik TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor Journal of Urban Society´s Arts Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Seminar Nasional Informatika (SEMNASIF) Register: Jurnal Ilmiah Teknologi Sistem Informasi SMATIKA Journal of Animation & Games Studies EMITTER International Journal of Engineering Technology Jurnal Teknologi dan Sistem Komputer Journal of Information Technology and Computer Science Knowledge Engineering and Data Science Berkala Arkeologi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control SENTIA 2015 Conference on Innovation and Application of Science and Technology (CIASTECH) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Mnemonic Jurnal Informatika dan Sistem Informasi Journal of Innovation Information Technology and Application (JINITA) JAREE (Journal on Advanced Research in Electrical Engineering) JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Sewagati Berkala Arkeologi
Eko Mulyanto Yuniarno
Dapartemen Teknik Komputer Iinstitut Teknologi Sepuluh Nopember
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Deteksi Gerak Otot Frontalis Berbasis Citra 3 Dimensi Menggunakan Gray Level Co-Occurrence Matrix (GLCM) Hardianto Wibowo; Mauridhi Hery Purnomo; Eko Mulyanto Yuniarno
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 1, No 2, August-2016
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (707.04 KB) | DOI: 10.22219/kinetik.v1i2.25

Abstract

Ekspresi wajah atau mimik merupakan salah satu dari hasil gerak otot pada wajah. Dalam kamus besar bahasa Indonesia, ekspresi merupakan pengungkapan atau proses menyatakan, yaitu memperlihatkan atau menyatakan maksud, gagasan perasaan dan lain sebagainya. Ekspresi wajah atau mimik dipengaruhi oleh saraf tujuh atau nervuse facialis. Facial Action Coding System (FACS) standardiasi ekspresi dalam format pergerakan enam ekspresi dasar, yaitu bahagia, sedih, terkejut, takut, marah dan jijik. Dalam otot, bahwa setiap otot yang bergerak pasti terjadi kontraksi, dan pada saat terjadi kontraksi, otot akan mengembang atau menggelembung. Otot dibagai menjadi tiga bagian, yaitu origo dan insersio sebagai ujung otot dan belly sebagai titik tengah otot, jadi setiap terjadi gerakkan maka otot bagian belly akan mengembang atau menggelembung. Teknik pengambilan data yaitu dengan merekam data dalam bentuk 3D, setiap terjadi kontraksi maka otot bagian belly akan menggelembung dan data inilah yang akan diolah dan dibandingkan. Dari pengolahan data ini akan didapat kekuatan maksimum kontraksi yang akan dipakai sebagai acuan untuk besaran pergeseran otot khususnya pada otot frontalis. Dalam deteksi pergerakan akan menggunakan metode Gray Level Co-occurrence Matrix (GLCM), dan akan didapatkan pula besaran pergeseran otot secara maksimal. Dari hasil pengujian didapatkan nilai pergeseran pergerakan otot sebesar 2.928.
Penentuan Pola Kunjungan Wisatawan Ke Berbagai Objek Wisata Di Pulau Ambon Menggunakan Frequent Pattern Growth Muhammad Fadli Fakih; Eko Mulyanto Yuniarno; Supeno Mardi Susiki N
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol 2, No 3, August-2017
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (535.978 KB) | DOI: 10.22219/kinetik.v2i3.46

Abstract

Untuk menyusun perencanaan pengembangan objek daya tarik wisata di pulau Ambon khususnya penentuan pola perjalanan wisatawan diperlukan data dan Metode yang relevan, salah satu metode penentuan pola kunjungan wisata adalah Metode Frequent Pattern Growth yang terbukti dapat digunakan untuk melihat pola kecenderungan. Dari 505 sampel data wisatawan Gabungan yang terdiri dari 331 wisatawan Domestik dan 174 wisatawan mancanegara yang berkunjung ke 18 objek daya tarik wisata di pulau Ambon dengan minimum support 10 % dan minimum confidence 70 % didapatkan 12 pencarian pola kunjungan wisatawan yakni pola kunjungan wisatawan domestik, Mancanegara dan Gabungan  ke seluruh ODTW, ODTW Pantai, ODTW Sejarah dan ODTW Alam. Dari masing–masing item, diambil 10 pola berdasarkan tingkat confidence tertinggi untuk dijadikan bahan rekomendasi bagi dinas terkait menyangkut penentuan pola kunjungan wisatawan di pulau Ambon.
Multiple Face Tracking using Kalman and Hungarian Algorithm to Reduce Face Recognition Computational Cost Willy Achmat Fauzi; Supeno M Susiki Nugroho; Eko Mulyanto Yuniarno; Wiwik Anggraeni; Mauridhi Hery Purnomo
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 5, No 1 (2021): April
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jaree.v5i1.191

Abstract

Currently, research in face recognition systems mainly utilized deep learning to achieve high accuracy. Using deep learning as the base platform, per frame image processing to detect and recognize faces is computationally expensive, especially for video surveillance systems using large numbers of mounted cameras simultaneously streaming video data to the system. The idea behind this research is that the system does not need to recognize every occurrence of faces in every frame. We used MobileNet SSD to detect the face, Kalman filter to predict face location in the next frame when detection fails, and Hungarian algorithm to maintain the identity of each face. Based on the result, using our algorithm 87.832 face that must be recognized is reduced to only 204 faces, and run at the real-time scenario. This method is proven to be used in surveillance systems by reducing the computational cost.Keywords: Hungarian algorithm, Kalman filter, multiple face tracking, video surveillance system.
Prediction of Students' Ability to Difficulty Level of Problem Based on Linear Method Hervit Ananta Vidada; Eko Mulyanto Yuniarno; Supeno Mardi Susiki Nugroho; Umi Laili Yuhana
JEEE-U (Journal of Electrical and Electronic Engineering-UMSIDA) Vol 5 No 2 (2021): October
Publisher : Muhammadiyah University, Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/jeeeu.v5i2.1459

Abstract

Knowing the ability of students is something that is important to formulate exam questions correctly, namely questions with the appropriate level of difficulty. However, in general, exam questions are prepared with the assumption that students' abilities are the same, so the results obtained do not reflect the actual abilities of students. This study focuses on predicting the ability of grade 6 students in mathematics. The data was obtained from 400 exam questions with 8 materials done by 23 students. Students' ability categories are grouped into 3, namely high ability, medium ability, and low ability. The difficulty of the questions is grouped into difficult questions, medium questions, and easy questions based on the assessments of 5 different class teachers. Our research uses the linear regression method and successfully shows that there is a close relationship between students' abilities and the level of difficulty of the questions. The difficulty level of the questions contributed 63% to the students' abilities. The standard error of 0.04905 means that the regression model is the right model in determining students' abilities.
Kombinasi Fitur Multispektrum Hilbert dan Cochleagram untuk Identifikasi Emosi Wicara Agustinus Bimo Gumelar; Eko Mulyanto Yuniarno; Wiwik Anggraeni; Indar Sugiarto; Andreas Agung Kristanto; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 2: Mei 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1364.227 KB) | DOI: 10.22146/jnteti.v9i2.166

Abstract

In social behavior of human interaction, human voice becomes one of the means of channeling mental states' emotional expression. Human voice is a vocal-processesed speech, arranged with word sequences, producing the speech pattern which able to channel the speakers' psychological condition. This pattern provides special characteristics that can be developed along with biometric identification process. Spectrum image visualization techniques are employed to sufficiently represent speech signal. This study aims to identify the emotion types in the human voice using a feature combination multi-spectrum Hilbert and cochleagram. The Hilbert spectrum represents the Hilbert-Huang Transformation(HHT)results for processing a non-linear, non-stationary instantaneous speech emotional signals with intrinsic mode functions. Through imitating the functions of the outer and middle ear elements, emotional speech impulses are broken down into frequencies that typically vary from the effects of their expression in the form of the cochlea continuum. The two inputs in the form of speech spectrum are processed using Convolutional Neural Networks(CNN) which best known for recognizing image data because it represents the mechanism of human retina and also Long Short-Term Memory(LSTM)method. Based on the results of this experiments using three public datasets of speech emotions, which each of them has similar eight emotional classes, this experiment obtained an accuracy of 90.97% with CNN and 80.62% with LSTM.
Convolutional Neural Network untuk Pendeteksian Patah Tulang Femur pada Citra Ultrasonik B–Mode Rika Rokhana; Joko Priambodo; Tita Karlita; I Made Gede Sunarya; Eko Mulyanto Yuniarno; I Ketut Eddy Purnama; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 8 No 1: Februari 2019
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1222.712 KB)

Abstract

The bone fracture detection using X–rays or CT–scan produces accurate images but has harmful effect radiation. This paper presented the use of ultrasonic waves (US) as an alternative to substitute those two instruments. This study used femur bovine and chicken bones in conditions with and without meat. The fractures are artificially made on transverse and oblique patterns. The scanning US probe produces two-dimensional (2D) B–mode images. Fracture detection is done using five variations of the Convolutional Neural Network (CNN) architectural design, i.e., CNN1–CNN5. The results showed that the CNN4 is the best design of bone contour recognition and bone fracture classification compared to the other tested designs, with 95.3% accuracy, 95% sensitivity, and 96% specificity. The comparison with the Support Vector Machine (SVM) and k-NN classification methods indicate that CNN has superior performance in accuracy, sensitivity, and specificity.
Deteksi Region of Interest Tulang pada Citra B-mode secara Otomatis Menggunakan Region Proposal Networks Tita Karlita; I Made Gede Sunarya; Joko Priambodo; Rika Rokhana; Eko Mulyanto Yuniarno; I Ketut Eddy Purnama; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 8 No 1: Februari 2019
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1632.87 KB)

Abstract

Bone imaging using ultrasound is a safe technique since it does not involve ionizing radiation and non-invasive. However, bone detection and localization to find its region of interest (RoI) is a challenging task because b-mode ultrasound images are characterized by high level of noise and reverberation artifacts. The image quality is user-dependent and the boundary between tissues is blurry, which makes it challenging to interpret images. In this paper, the deep learning approach using Region Proposal Networks was implemented to detect bone’s RoI in b-mode images. The Faster Region-based Convolutional Neural Network model was fine-tuned to detect and determine the bone location in b-mode images automatically. To evaluate the results, in-vivo experiments were carried out using human arm specimens. A total of 1,066 b-mode bone images from six different subjects were used in the training phase and testing phase. The proposed method was successful in determining the bone RoI with the value of the mAP, the accuracy of detection, and the accuracy of localization of 0.87, 98.33%, and 95.99% respectively.
Penggunaan Teknik Fotogrametri Dalam Rekonstruksi Pahatan Pada Batu Prasasti Goenawan A Sambodo; Yoyon K. Suprapto; Eko Mulyanto Yuniarno
Berkala Arkeologi Vol 40 No 2 (2020)
Publisher : Balai Arkeologi Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1162.459 KB) | DOI: 10.30883/jba.v40i2.597

Abstract

This research discusses and applies photogrammetry techniques to determine the depth of the script carvings on some worn-out stone inscriptions so images of scripts can be more readable. Inscriptions are the backbone of ancient Indonesian historical writings. Unfortunately the significance of many such ancient inscriptions can not yet be used optimally since many inscriptions are found in a state of having poor legibility, and this due both to natural as well as human factors. To this day, photogrammetry techniques have not been widely used by Indonesian researchers in order to help analyze existing cultural heritage objects, especially stone inscriptions. In addition to previous photogrammetric techniques reviews, this article also brings forward my experiment on the photogrammetric techniques, especially those directly related to the stone inscriptions. The reconstruction was not intended to interpret the meaning of the scripts, but rather to give epigraphists a new insight into other ways of clarifying worn-out scripts.
Automatic 3D Cranial Landmark Positioning based on Surface Curvature Feature using Machine Learning Putu Hendra Suputra; Anggraini Dwi Sensusiati; Myrtati Dyah Artaria; Gijsbertus Jacob Verkerke; Eko Mulyanto Yuniarno; I Ketut Eddy Purnama
Knowledge Engineering and Data Science Vol 5, No 1 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v5i12022p27-40

Abstract

Cranial anthropometric reference points (landmarks) play an important role in craniofacial reconstruction and identification. Knowledge to detect the position of landmarks is critical. This work aims to locate landmarks automatically. Landmarks positioning using Surface Curvature Feature (SCF) is inspired by conventional methods of finding landmarks based on morphometrical features. Each cranial landmark has a unique shape. With the appropriate 3D descriptors, the computer can draw associations between shapes and landmarks using machine learning. The challenge in classification and detection in three-dimensional space is to determine the model and data representation. Using three-dimensional raw data in machine learning is a serious volumetric issue. This work uses the Surface Curvature Feature as a three-dimensional descriptor. It extracts the local surface curvature shape into a projection sequential value (depth). A machine learning method is developed to determine the position of landmarks based on local surface shape characteristics. Classification is carried out from the top-n prediction probabilities for each landmark class, from a set of predictions, then filtered to get pinpoint accuracy. The landmark prediction points are hypothetically clustered in a particular area, so a cluster-based filter is appropriate to isolate them. The learning model successfully detected the landmarks, with the average distance between the prediction points and the ground truth being 0.0326 normalized units. The cluster-based filter is implemented to increase accuracy compared to the ground truth. Thus, SCF is suitable as a 3D descriptor of cranial landmarks.
Menghitung Luas Bangun Datar pada Papan Tulis Menggunakan Yolo Alan Luthfi; Eko Mulyanto Yuniarno; Supeno Mardi Susiki Nugroho
Jurnal Teknik ITS Vol 11, No 3 (2022)
Publisher : Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23373539.v11i3.92620

Abstract

pembelajaran revolusioner kedua setelah papan tulis hitam tradisional, karena papan tulis pintar yang bisa disematkan dalam ruang kelas modern bisa menggerakan sekolah ke arah mode operasi digital yang lebih terintegrasi. Pada papan tulis pintar harus memiliki fitur yang dapat membedakan papan tulis pintar dengan papan tulis biasa, karena papan tulis pintar memiliki fitur-fitur atau kegunaan lebih superior daripada papan tulis biasa. Oleh karena itu diperlukan pengembangan fttur pada papan tulis pintar. Tujuan penelitian adaah membuat program yang dapat mendeteksi bangun datar dan parameternya lalu menghitung luas bangun datar pada papan tulis pintar. Metode yang akan digunakan adalah dengan menggunakan YOLO sebagai framework pengerjaan dalam pembuatan program deteksi bangun datar dan parameternya.
Co-Authors Aditya Nur Ikhsan Soewidiatmaka Agung Dewa Bagus Soetiono Agung Wicaksono Agustinus Bimo Gumelar Ahmad Zaini Alan Luthfi Ali Sofyan Kholimi Alwali, Muhammad Anang Kukuh Adisusilo Andreas Agung Kristanto, Andreas Agung Ardyono Priyadi Arief Kurniawan Arik Kurniawati Aris Widayati Atyantagratia Vidyasmara Daryanto Bambang Purwantana Beny Yulkurniawan Victorio Nasution Beny Yulkurniawan Victorio Nasution Boedinoegroho, Hanny Citra Ratih Prameswari Diah Puspito Wulandari Endang Setyati Endang Sri Rahayu Enggartiasto Faudi Ristyawan Esther Irawati Setiawan Evi Septiana Pane, Evi Septiana F.X. Ferdinandus Fakih, Muhammad Fadli Fandiantoro, Dion Hayu Farah Zakiyah Rahmanti Farodisa, Annida Miftakhul Feby Artwodini Muqtadiroh Fresy Nugroho Gijsbertus Jacob Verkerke Gijsbertus Jacob Verkerke Goenawan A Sambodo Gunawan Gunawan Gunawan Hardianto Wibowo Harfianti, Nadya Putri Herman Thuan Herman Thuan To Saurik Hermawan, Norma Hervit Ananta Vidada Hutama Harsono, Nathanael I Ketut Eddy Purnama I Made Gede Sunarya Imam Robandi Indar Sugiarto Isa Hafidz Ismoyo Sunu Jaya Pranata Joan Santoso Joko Priambodo Khairunnas Khairunnas Koeshardianto, Meidya Kurniawan, Arief Lailatul Husniah Lutfi Ananditya Septiandi Masy Ari Ulinuha Matahari Bhakti Nendya Matahari Bhakti Nendya, Matahari Bhakti Mauridhi Hery Purnomo Mauridhi Hery Purnomo Mauridhi Hery Purnomo Moch. Iskandar Riansyah Mochamad Hariadi Mochamad Yusuf Alsagaff Muhammad Fadli Fakih Muhammad Reza Pahlawan Muhammad Zulfikar Alfathan Rachmatullah Muhtadin Mulyanto, Edy Myrtati Dyah Artaria Nasrulloh, Muhammad Nova Eka Budiyanta Nugroho, Vidityar Adith Oddy Virgantara Putra Pambudi, Sevito Fernanda Pramunanto, Eko Pramunanto, Eko Priambodo, Joko Putu Hendra Suputra R Dimas Adityo Radi Radi Rafly Azmi Ulya, Amik Ragil Bintang Brilyan Rahman, Muhammad Daffa Abiyyu Reza Fuad Rachmadi Rika Rokhana Rika Rokhana Riris Diana Rachmayanti Rokhana, Rika S. Suprapto Saiful Yahya Sambodo, Goenawan A Samuel Gandang Gunanto Sensusiati, Anggraini Dwi Setiawan, Rachmad Setijadi, Eko Soetiono, Agung Dewa Bagus Suciningtyas, Laras Sugiyanto - Sulistyono, Marcelinus Yosep Teguh Supeno M Susiki Nugroho Supeno Mardi Susiki Supeno Mardi Susiki Supeno Mardi Susiki N Supeno Mardi Susiki Nugroho, Supeno Mardi Surya Sumpeno Surya Sumpeno Susiki N, Supeno Mardi Syauqi Sabili Tita Karlita Tita Karlita Tita Karlita Tri Arief Sardjono Tsuyoshi Usagawa, Tsuyoshi Wicaksono, Alif Aditya Willy Achmat Fauzi Wisnu Widiarto Wiwik Anggraeni Yose Rizal Yose Rizal Yoyon K. Suprapto Yoyon K. Suprapto Yoyon Kusnendar Suprapto Yuhana, Umi Laili Zaini, Ahmad