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All Journal Jurnal Sains dan Teknologi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Pilar Nusa Mandiri Jurnal Ekobistek Sebatik MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Informasi dan Pendidikan Indonesian Journal of Electrical Engineering and Computer Science Community Development Journal: Jurnal Pengabdian Masyarakat Majalah Ilmiah UPI YPTK Journal of Computer Scine and Information Technology Bulletin of Computer Science Research Jurnal Ipteks Terapan : research of applied science and education Jurnal Komtekinfo SATIN - Sains dan Teknologi Informasi Journal Of Informatics And Busisnes Jurnal Teknik dan teknologi tepat guna The Indonesian Journal of Computer Science Jurnal TAM (Technology Acceptance Model)
Syafri Arlis
Universitas Putra Indonesia YPTK
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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Development of Mastoid Air Cell System Extraction Method on Temporal CT-scan Image Syafri Arlis; Sarjon Defit; Sumijan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 6 No 3 (2022): Juni 2022
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (480.37 KB) | DOI: 10.29207/resti.v6i3.4090

Abstract

Mastoiditis is disease that to infection of the mastoid bone cavity that affects the size of the air cell system of the temporal bone. Visually, the information temporal CT image mastoid bone has can assist medical experts in viewing the mastoid air cell system (MACS), but the fact that medical personnel are experiencing difficulties in determining the size MACS is due to the many different characteristics and objects overlap, so that in the measurement of the area, precise and accurate results have not been obtained. This study aims to separate the object of the MACS with the development of extraction. The proposed method uses Morphology and Regionprops operations. The dataset used in the testing process is 347 of 5 patients indicated for Mastoiditis. The results obtained can calculate the area of MACS for each test image. Based on image testing, the area of the smallest MACS in this study was 0.589 cm2 and the largest was 6.183 cm2. This, the smaller the size of the MACS indicates the severity of infection, so this study can help medical personnel make decisions and take appropriate treatment actions.
Machine Learning Analisis Klasifikasi dalam Penentuan Status Gizi Anak Yanto, Musli; Febri Hadi; Syafri Arlis
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5278

Abstract

Malnutrition is one of the problems that occurs in children due to a lack of nutritional intake. Indonesia contributed 36%, making it the fifth country with the largest cases of malnutrition in the world. On this basis, a solution is needed to reduce the growth rate of malnutrition cases. This research aims to carry out classification analysis to determine nutritional status by optimizing machine learning (ML) performance. The ML classification analysis process will later utilize the performance of the artificial neural network (ANN) method with the Multilayer Perceptron (MLP) algorithm. ML performance can be optimized using the Pearson’s correlation (PC) method to produce optimal classification analysis patterns. This research data set uses child nutrition case data from 576 patients sourced from the M. Djamil Padang Province Regional General Hospital (RSUP). The data set is divided into 417 training data and 159 test data. On the basis of the tests that have been carried out, the performance of the PC method can provide precise and accurate analysis patterns. This analysis pattern has also been able to provide a fairly good level of accuracy, namely 95%. Not only that, this research is also able to present analysis patterns with the best ANN architectural model in classifying nutritional status. Based on the overall results, this research can be used as an alternative solution to the treatment of nutritional problems in children.
Co-Authors Agung Ramadhanu Andrian, Reski Aswandi, Nopan Candra Putra Darma Syahrullah Ekajaya Dila, Rahmah Dodi Guswandi Doli Raharjo, Tio Edwar, Eggy Febyanti Fajrul Islami Febri Hadi Feri Pratama Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo, Gunadi Guslendra Guslendra Hanum, Luthfia Harkamsyah Andrianof Hendrik, Billy Johan Harlan Jufriadif Na`am, Jufriadif Larissa Navia Rani Larissa Naviarani M.Iqbal, M.Iqbal Mardison Mardison Marfalino, Hari Muhammad, Abulwafa Musli Yanto Nabila Frisca Oktavia Nur, Rofil M Nurcahyo, Yuhandri Olivia, Ladyka Febby Permana, Nabilah Putri Prayuda, Muhammad Hozi Purnama, Ayu Widya Putra, Deri Marse Putra, Muhammad Reza Ramadhanu, Agung Ridwan, Ridwan Rini Sovia Rofil M. Nur Roza, Yesi Betriana Rusydi, Rezki Sahari Sahari Sahari Sahari Sahari Sarjon Defit Sarjon Defit SIGIT SANJAYA, SIGIT Sofika Enggari Sumijan Sumijan Sumijan Sumijan Sumijan, Sumijan Sumijan Supperianto, Bambang Syahputra, Afriadi Utama Putra, Kharisma Wahyu, Fungki Winda Sutra Amanda Yanto, Musli Yuhandri Yuhandri Yunus Yuhandri, Y Yulasmi