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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 (JUISI) 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 Smatika Jurnal : STIKI Informatika Jurnal
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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Journal : knowledge engineering and data science

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Automatic 3D Cranial Landmark Positioning based onSurface Curvature Feature using Machine Learning Suputra, Putu Hendra; Sensusiati, Anggraini Dwi; Artaria, Myrtati Dyah; Verkerke, Gijsbertus Jacob; Yuniarno, Eko Mulyanto; Purnama, I Ketut Eddy
Knowledge Engineering and Data Science
Publisher : citeus

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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.
Prediction of Audit Findings Using Deep Learning with Financial and Non-Financial Data: A Case Study in Province X Setiawan, Fery Yohan; Yuniarno, Eko Mulyanto; Rachmadi, Reza Fuad
Knowledge Engineering and Data Science
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The implementation of the audit from the local government financial statements by The Audit Board of The Republic of Indonesia (BPK RI), especially for the Province X representative, are frequently faced by the various limitations, one of them being the required audit time. At this moment, the BPK RI representative of Province X doesn’t have the tools that are able to help the accurate of sample determination for the pick test, which resulted in this study proposing the application of multi-label classification to predict the findings of financial statement (Laporan Keuangan, LK) audits based on financial and non financial ratio. The multi-label classification approach used is a traditional approach and deep learning. The model selection was based on model performance evaluation, using metrics such as accuracy, hamming loss, average precision, average recall, and F1 Score, resulting in the best model being DNN. The DNN model achieved an accuracy of 0.7728, a Hamming loss of 0.1750, an average precision of 0.8393, an average recall of 0.9120, and an F1 Score of 0.8740. The DNN model can be used to predict audit findings in determining the audit sample, thereby minimizing the limitations, particularly time constraints, often encountered during LK audits.
Deep Learning for Multi-Structured Javanese Gamelan Note Generator Kurniawati, Arik; Yuniarno, Eko Mulyanto; Suprapto, Yoyon Kusnendar
Knowledge Engineering and Data Science
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Javanese gamelan, a traditional Indonesian musical style, has several song structures called gendhing. Gendhing (songs) are written in conventional notation and require gamelan musicians to recognize patterns in the structure of each song. Usually, previous research on gendhing focuses on artistic and ethnomusicological perspectives, but this study is to explore the correlation between gendhing as traditional music in Indonesia and deep learning technology that replaces the task of gamelan composers. This research proposes CNN-LSTM to generate notation of ricikan struktural instruments as an accompaniment to Javanese gamelan music compositions based on balungan notation, rhythm, song structure, and gatra information. This proposed method (CNN-LSTM) is compared with LSTM and CNN. The musical data in this study is represented using numerical notation for the main melody in balungan notation. The experimental results showed that the CNN-LSTM model showed better performance compared to the LSTM and CNN models, with accuracy values of 91.9%, 91.5%, and 91.2% for CNN-LSTM, LSTM, and CNN, respectively. And the value of note distance for the Sampak song structure is 4 for the CNN-LSTM model, 8 for the LSTM model, and 12 for the CNN model. The smaller the note distance, the closer it is to the original notation provided by the gamelan composer. This study provides relevance for novice gamelan musicians who are interested in learning karawitan, especially in understanding ricikan struktural music notation and gamelan art in composing musical compositions of a song.
Co-Authors Aditya Nur Ikhsan Soewidiatmaka Agung Dewa Bagus Soetiono Agung Wicaksono Agustinus Bimo Gumelar Ahmad Zaini Alan Luthfi Ali Sofyan Kholimi 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 Laras Suciningtyas 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 Alwali Muhammad Fadli Fakih Muhammad Reza Pahlawan Muhammad Zulfikar Alfathan Rachmatullah Muhtadin Mulyanto, Edy Myrtati Dyah Artaria Myrtati Dyah Artaria, Myrtati Dyah Nasrulloh, Muhammad Nova Eka Budiyanta Nugroho, Vidityar Adith Oddy Virgantara Putra Pramunanto, Eko Pramunanto, Eko Priambodo, Joko Putu Hendra Suputra R Dimas Adityo Rachmadi, Reza Fuad 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, Fery Yohan Setiawan, Rachmad Setijadi, Eko Sevito Fernanda Pambudi Soetiono, Agung Dewa Bagus 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 Umi Laili Yuhana Verkerke, Gijsbertus Jacob Wicaksono, Alif Aditya Willy Achmat Fauzi Wisnu Widiarto Wiwik Anggraeni Yose Rizal Yose Rizal Yoyon K. Suprapto Yoyon K. Suprapto Yoyon Kusnendar Suprapto Zaini, Ahmad