Journal of Informatics Information System Software Engineering and Applications (INISTA)最新文献

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Automatic Birdsong Splitting and Syllabic Analysis of Jalak Suren Jalak Suren 的自动鸟鸣声分割和音节分析

Journal of Informatics Information System Software Engineering and Applications (INISTA) Pub Date : 2023-07-10 DOI: 10.20895/inista.v5i2.1091

Agi Prasetiadi, Julian Saputra

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引用次数: 0

Implementasi Metode Waterfall Pada Perancangan Aplikasi Rekam Medis Berbasis Website dan Whatshap Gateway 采用瀑布方法设计基于网站的医疗记录应用程序和Whatshap Gateway

Journal of Informatics Information System Software Engineering and Applications (INISTA) Pub Date : 2022-11-30 DOI: 10.20895/inista.v5i1.852

I. Yunita, Agus Pramono, Retno Waluyo, Suliswaningsih

{"title":"Implementasi Metode Waterfall Pada Perancangan Aplikasi Rekam Medis Berbasis Website dan Whatshap Gateway","authors":"I. Yunita, Agus Pramono, Retno Waluyo, Suliswaningsih","doi":"10.20895/inista.v5i1.852","DOIUrl":"https://doi.org/10.20895/inista.v5i1.852","url":null,"abstract":"Rekam medis menjadi sebuah dokumen wajib harus dimiliki oleh tempat pelayanan kesehatan. Dengan adanya rekam medis menjadi pertimbangan seorang doketer untuk melakukan tindakan medis kepada pasien. Dokter XYZ membuka praktek di Patikraja, Banyumas. Rekam medis yang ada selama ini masih menggunakan kertas sehingga terdapat beberapa permasalahan yang dialami, diantaranya data rekam medis hilang serta rusak, data rekam medis lebih dari satu dan pasien tidak datang saat jadwal chek up. Untuk itu diperlukan pemanfaat teknologi untuk mengatasi permasalahan tersebut. Oleh karena itu tujuan peneltian ini akan membuat aplikasi rekam medis berbasis website dan whatshap gateway. Sedangkan metode yang digunakan untuk pengembangan sistem yaitu metode waterfall. Penelitian ini menyimpulkan metode waterfall dapat digunakan untuk membuat aplikasi rekam medis berbasis website dan whatshap gateway, setelah dilakukan pengujian black box secara kesuluruan kebutuhan fungsional perangkat lunak sudah berfungsi sesuai keinginan pengguna. Dengan adanya aplikasi ini mempermudah proses administrasi dan pengarsipan rekam medis secara digital.","PeriodicalId":264049,"journal":{"name":"Journal of Informatics Information System Software Engineering and Applications (INISTA)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122756841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analisis Segmentasi Sel Darah Merah berbasis Mask-RCNN 放大红细胞片段分析

Journal of Informatics Information System Software Engineering and Applications (INISTA) Pub Date : 2022-11-30 DOI: 10.20895/inista.v5i1.766

Dyah Aruming Tyas, Triana Ratnaningsih

{"title":"Analisis Segmentasi Sel Darah Merah berbasis Mask-RCNN","authors":"Dyah Aruming Tyas, Triana Ratnaningsih","doi":"10.20895/inista.v5i1.766","DOIUrl":"https://doi.org/10.20895/inista.v5i1.766","url":null,"abstract":"Pengembangan Computer-aided diagnosis (CAD) pada bidang patologi klinik memiliki tantangan tersendiri. CAD pada bidang patologi klinik diharapkan dapat membantu proses pengamatan laboratorium. Salah satu tantangan pengembangan CAD tersebut adalah pada proses segmentasi sel darah merah. Segmentasi sel darah merah yang menempel biasanya menimbulkan kesalahan segmentasi berupa bentuk sel tidak utuh atau sel sama sekali tidak tersegmentasi. Kesalahan segmentasi akan berakibat pada kesalahan pengenalan jenis sel darah sehingga diperlukan metode yang tepat untuk proses segmentasi. Oleh sebab itu, penelitian ini berfokus untuk menganalisis hasil segmentasi sel darah merah yang diperoleh menggunakan arsitektur model Mask-RCNN. Variasi parameter detection min confidence dilakukan untuk melihat dampaknya pada hasil segmentasi. Berdasarkan hasil penelitian diperoleh bahwa akurasi hasil segmentasi terbaik adalah 91,24% yang berasal dari model Mask-RCNN dengan nilai parameter detection min confidence = 0,7. Pada model tersebut, baik sel darah merah tunggal ataupun sel darah merah yang saling menempel dapat disegmentasi dengan baik.","PeriodicalId":264049,"journal":{"name":"Journal of Informatics Information System Software Engineering and Applications (INISTA)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125240899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1