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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 SocietyÂ´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

Author-ID : 425596

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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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

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

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.

3D Visualization for Lung Surface Images of Covid-19 Patients based on U-Net CNN Segmentation FX Ferdinandus; Esther Irawati Setiawan; Eko Mulyanto Yuniarno; Mauridhi Hery Purnomo
EMITTER International Journal of Engineering Technology Vol 10 No 2 (2022)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v10i2.709

The Covid-19 infection challenges medical staff to make rapid diagnoses of patients. In just a few days, the Covid-19 virus infection could affect the performance of the lungs. On the other hand, semantic segmentation using the Convolutional Neural Network (CNN) on Lung CT-scan images had attracted the attention of researchers for several years, even before the Covid-19 pandemic. Ground Glass Opacity (GGO), in the form of white patches caused by Covid-19 infection, is detected inside the patient’s lung area and occasionally at the edge of the lung, but no research has specifically paid attention to the edges of the lungs. This study proposes to display a 3D visualization of the lung surface of Covid-19 patients based on CT-scan image segmentation using U-Net architecture with a training dataset from typical lung images. Then the resulting CNN model is used to segment the lungs of Covid-19 patients. The segmentation results are selected as some slices to be reconstructed into a 3D lung shape and displayed in 3D animation. Visualizing the results of this segmentation can help medical staff diagnose the lungs of Covid-19 patients, especially on the surface of the lungs of patients with GGO at the edges. From the lung segmentation experiment results on ten patients in the Zenodo dataset, we have a Mean-IoU score = of 76.86%, while the visualization results show that 7 out of 10 patients (70%) have eroded lung surfaces. It can be seen clearly through 3D visualization.

Automatic note generator for Javanese gamelan music accompaniment using deep learning Arik Kurniawati; Eko Mulyanto Yuniarno; Yoyon Kusnendar Suprapto; Aditya Nur Ikhsan Soewidiatmaka
International Journal of Advances in Intelligent Informatics Vol 9, No 2 (2023): July 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i2.1031

Javanese gamelan is a traditional form of music from Indonesia with a variety of styles and patterns. One of these patterns is the harmony music of the Bonang Barung and Bonang Penerus instruments. When playing gamelan, the resulting patterns can vary based on the music’s rhythm or dynamics, which can be challenging for novice players unfamiliar with the gamelan rules and notation system, which only provides melodic notes. Unlike in modern music, where harmony notes are often the same for all instruments, harmony music in Javanese gamelan is vital in establishing the character of a song. With technological advancements, musical composition can be generated automatically without human participation, which has become a trend in music generation research. This study proposes a method to generate musical accompaniment notes for harmony music using a bidirectional long-term memory (BiLSTM) network and compares it with recurrent neural network (RNN) and long-term memory (LSTM) models that use numerical notation to represent musical data, making it easier to learn the variations of harmony music in Javanese gamelan. This method replaces the gamelan composer in completing the notation for all the instruments in a song. To evaluate the generated harmonic music, note distance, dynamic time warping (DTW), and cross-correlation techniques were used to measure the distance between the system-generated results and the gamelan composer's creations. In addition, audio features were extracted and used to visualize the audio. The experimental results show that all models produced better accuracy results when using all features of the song, reaching a value of around 90%, compared to using only 2 features (rhythm and note of melody), which reached 65-70%. Furthermore, the BiLSTM model produced musical harmonies that were more similar to the original music (+93%) than those generated by the LSTM (+92%) and RNN (+90%). This study can be applied to performing Javanese gamelan music.

Self-Training Naive Bayes Berbasis Word2Vec untuk Kategorisasi Berita Bahasa Indonesia Joan Santoso; Agung Dewa Bagus Soetiono; Gunawan; Endang Setyati; Eko Mulyanto Yuniarno; Mochamad Hariadi; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 2: Mei 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1455.318 KB)

News as one kind of information that is needed in daily life has been available on the internet. News website often categorizes their articles to each topic to help users access the news more easily. Document classification has widely used to do this automatically. The current availability of labeled training data is insufficient for the machine to create a good model. The problem in data annotation is that it requires a considerable cost and time to get sufficient quantity of labeled training data. A semi-supervised algorithm is proposed to solve this problem by using labeled and unlabeled data to create classification model. This paper proposes semi-supervised learning news classification system using Self-Training Naive Bayes algorithm. The feature that is used in text classification is Word2Vec Skip-Gram Model. This model is widely used in computational linguistics or text mining research as one of the methods in word representation. Word2Vec is used as a feature because it can bring the semantic meaning of the word in this classification task. The data used in this paper consists of 29,587 news documents from Indonesian online news websites. The Self-Training Naive Bayes algorithm achieved the highest F1-Score of 94.17%.

Metode Kalibrasi Probe Ultrasonik dari Phantom Kawat Tunggal Menggunakan Algoritma Levenberg-Marquardt Tri Arief Sardjono; Eko Mulyanto Yuniarno; I Made Gede Sunarya; I Ketut Eddy Purnama; Mauridhi Hery Purnomo; Norma Hermawan
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 12 No 3: Agustus 2023
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jnteti.v12i3.6282

A freehand three-dimensional (3D) ultrasound system is a method of acquiring images using a 3D ultrasound probe or conventional two-dimensional (2D) ultrasound probe to give a 3D visualization of an object inside the body. Ultrasounds are used extensively in clinical applications since they are advantageous in that they do not bring dangerous radiation effects and have a low cost. However, a probe calibration method is needed to transform the coordinate position into a 3D visualization display, especially for image-guided intervention. The current ultrasound probe calibration system usually uses the numerical regression method for the N-wire phantom, which has problems in accuracy and reliability due to nonlinear point scattered ultrasound image data. Hence, a method for ultrasound probe positional calibration of single-wire phantom using the Levenberg-Marquardt algorithm (LMA) was proposed to overcome this weakness. This experiment consisted of an optical tracking system setup, a 2D ultrasound probe with marker, an ultrasound machine, and a single-wire object in a water container equipped with a marker. The position and orientation of the marker in a 2D ultrasound probe and the marker in the water container were tracked using the optical tracking system. A 2D ultrasound probe was equipped with a marker connected wirelessly using an optical tracking system to capture the single-wire object. The resulting sequences of 2D ultrasound images were reconstructed and visualized into 3D ultrasound images using three transformations, ultrasound beam to ultrasound probe’s marker, single-wire phantom position to container’s marker, and the 3D visualization transformation. The LMA was used to determine the best optimization parameters for determining the exact position and representing that 3D visualization. The experiment result showed that the lowest mean square error (MSE), rotation error, and translation error were 0.45 mm, 0.25°, and 0.3828 mm, respectively.

Detection of multi-class arrhythmia using heuristic and deep neural network on edge device Arief Kurniawan; Eko Mulyanto Yuniarno; Eko Setijadi; Mochamad Yusuf Alsagaff; Gijsbertus Jacob Verkerke; I Ketut Eddy Purnama
International Journal of Advances in Intelligent Informatics Vol 9, No 3 (2023): November 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i3.1061

Heart disease is a heart condition that sometimes causes a person to die suddenly. One indication is a rhythm disorder known as arrhythmia. Multi-class Arrhythmia Detection has followed: QRS complex detection procedure and arrhythmia classification based on the QRS complex morphology. We proposed an edge device that detects QRS complexes based on variance analysis (QVAT) and the arrhythmia classification based on the QRS complex spectrogram. The classifier uses two-dimensional convolutional neural network (2D CNN) deep learning. We use a single board computer and neural network compute stick to implement the edge device. The outcomes are a prototype device cardiologists use as a supporting tool for analysing ECG signals, and patients can also use it for self-tests to figure out their heart health. To evaluate the performance of our edge device, we tested using the MIT-BIH database because other methods also use the data. The QVAT sensitivity and predictive positive are 99.81% and 99.90%, respectively. Our classifier's accuracy, sensitivity, predictive positive, specificity, and F1-score are 99.82%, 99.55%, 99.55%, 99.89%, and 99.55%, respectively. The experiment result of arrhythmia classification shows that our method outperforms the others. Still, for r-peak detection, the QVAT implemented in an edge device is comparable to the other methods. In future work, we can improve the performance of r-peak detection using the double-check algorithm in QVAT and cross-check the QRS complex detection by adding 1 class to the classifier, namely the non-QRS class.

APPLICATION OF PHOTOGRAMMETRY TECHNIQUES IN RECONSTRUCTING THE CARVING ON STONE INSCRIPTIONS: 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 : BRIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | 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. Abstrak Penelitian ini membahas dan menguji teknik fotogrametri untuk mengetahui kedalaman pahatan pada batu prasasti sehingga rekonstruksi dapat dilakukan guna memperjelas bekas pahatan aksara yang ada. Diharapkan akan dapat memunculkan gambar pahatan dengan lebih jelas sehingga pada akhirnya pahatan tersebut dapat dibaca. Prasasti adalah tulang punggung penulisan sejarah kuna Indonesia. Arti penting prasasti ini belum dapat digunakan dengan maksimal karena banyak prasasti yang ditemukan dalam keadaan aus pahatannya, baik karena kerusakan yang disebabkan oleh faktor alam maupun manusia. Teknik Fotogrametri belum banyak digunakan oleh peneliti dari Indonesia untuk membantu menganalisis benda cagar budaya yang ada terutama prasasti. Selain mengulas beberapa teknik fotogrametri dikemukan pula percobaan teknik fotogrametri yang telah dilakukan khususnya yang berhubungan langsung dengan prasasti. Rekonstruksi yang dilakukan tidak dimaksudkan untuk membaca dan mengartikan hasil pembacaan aksara prasasti, akan tetapi lebih kepada memberi pandangan baru dan potensi penggunaan teknik fotogrametri bagi para ahli epigrafi tentang cara lain dalam memperjelas bekas pahatan yang telah aus.

Modified Deep Pattern Classifier on Indonesian Traditional Dance Spatio-Temporal Data Mulyanto, Edy; Yuniarno, Eko Mulyanto; Hafidz, Isa; Budiyanta, Nova Eka; Priyadi, Ardyono; Hery Purnomo, Mauridhi
EMITTER International Journal of Engineering Technology Vol 11 No 2 (2023)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v11i2.832

Traditional dances, like those of Indonesia, have complex and unique patterns requiring accurate cultural preservation and documentation classification. However, traditional dance classification methods often rely on manual analysis and subjective judgment, which leads to inconsistencies and limitations. This research explores a modified deep pattern classifier of traditional dance movements in videos, including Gambyong, Remo, and Topeng, using a Convolutional Neural Network (CNN). Evaluation model's performance using a testing spatio-temporal dataset in Indonesian traditional dance videos is performed. The videos are processed through frame-level segmentation, enabling the CNN to capture nuances in posture, footwork, and facial expressions exhibited by dancers. Then, the obtained confusion matrix enables the calculation of performance metrics such as accuracy, precision, sensitivity, and F1-score. The results showcase a high accuracy of 97.5%, indicating the reliable classification of the dataset. Furthermore, future research directions are suggested, including investigating advanced CNN architectures, incorporating temporal information through recurrent neural networks, exploring transfer learning techniques, and integrating user feedback for iterative refinement of the model. The proposed method has the potential to advance dance analysis and find applications in dance education, choreography, and cultural preservation.

DESAIN SERIOUS GAME SOSIALISASI BENCANA BERBASIS MODEL TEORI AKTIFITAS Nugroho, Fresy; Mulyanto Yuniarno, Eko; Hariadi, Mochamad
Jurnal Mnemonic Vol 2 No 1 (2019): Mnemonic Vol. 2 No. 1
Publisher : Teknik Informatika, Institut Teknologi Nasional malang

Sosialisasi bencana masih mengalami beberapa kendala antara lain kesulitan bila dilakukan secara offfline, mengingat untuk sosialisasi offline membutuhkan waktu khusus, memerlukan persiapan yang lama dan memerlukan alokasi dana khusus. Makalah ini memberikan sebuah pendekatan baru berupa sosialisasi bencana dalam bentuk serious game. Sehingga dinamakan serious game sosialisasi bencana. Dalam mendesain serious game sosialisasi bencana, menggunakan model teori aktivitas utnuk serious game dan tahapan sosialisasi bencana sebagaimana di isyaratkan dalam undang-undang tentang penanggulangan bencana dan diuraikan pusat vulkanologi dan mitigasi bencana geologi. Untuk membuat sebuah serious game dibutuhkan pula metode finite state machine untuk memudahkan mesin mengatur permainan. Visualisasi juga di rancang untuk menunjukkan capaian yang diperoleh pemain agar lebih menarik.

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 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 Yoyon K. Suprapto Yoyon Kusnendar Suprapto Yuhana, Umi Laili Zaini, Ahmad

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