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All Journal Berkala Ilmiah Pendidikan Biologi (BioEdu) Journal of Electronics, Electromedical Engineering, and Medical Informatics
Ahmad Badruzzaman
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media

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PROFIL MISKONSEPSI SISWA PADA MATERI SISTEM ENDOKRIN Ahmad Badruzzaman; Raharjo Raharjo
Berkala Ilmiah Pendidikan Biologi (BioEdu) Vol 8 No 2 (2019)
Publisher : Program Studi Pendidikan Biologi, FMIPA, Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penggunaan istilah asing masih banyak ditemukan di materi sistem endokrin, selain itu materi ini bersifatabstrak  karena  tidak  dapat  diamati  secara  langsung  oleh  siswa.  Kondisi  tersebut  mendorong  terjadinyamiskonsepsi  pada siswa.  Tujuan  penelitian  ini  adalah  mendeskripsikan  profil  miskonsepsi  siswa  padamateri  sistem endokrin dan mendeskripsikan  faktor penyebab miskonsepsinya.  Jenis penelitian  ini adalahdeskriptif  evaluatif. Metode  yang  digunakan  dalam  penelitian  ini  adalah  metode  tes  objektif  dan  metodewawancara.  Instrumen  penelitian  berupa  instrumen  tes  diagnostik  bersifat  objektif  dengan  21  butir  soaltentang  sistem  endokrin  menggunakan  teknik Certainty  of  Response  Index (CRI)  termodifikasi,  yangberfungsi  untuk mengetahui  letak miskonsepsi  siswa, dan  instrumen wawancara  untuk mengetahui  faktorpenyebab  miskonsepsi.  Butir  soal  yang  dikembangkan  berjenis multiple  choice  test dan true  false  test.Setiap  butir  soal  terdapat  opsi  jawaban,  opsi  alasan,  dan  skala  keyakinan  (0-5).  Data  yang  didapatkanberupa  nilai  persentase  pemahaman  siswa  pada  materi  sistem  endokrin  dan  data  hasil  wawancara.Analisis  data  dilakukan  secara  deskriptif  yang  mengacu  pada  data  hasil  tes  diagnostik  dan  hasilwawancara.  Kategori  pemahaman  siswa  dibagi  menjadi,  paham  konsep  dengan  baik  (P);  Paham  konsepnamun  ragu  (PR);  Miskonsepsi  (MK);  Tidak  tahu  konsep  (TK).  Hasil  penelitian  menunjukkan  bahwasiswa mengalami tidak tahu konsep (TK) 44,67%, paham konsep dengan baik (P) 14,29%, paham konsepnamun  ragu (PR)  18,37%,  dan  miskonsepsi  (MK)  22,68%.  Faktor  penyebab  miskonsepsi  berasal  darisiswa  sendiri,  kurangnya  informasi  mengenai  sistem  endokrin,  guru, dan  metode  pembelajaran.Berdasarkan hasil penelitian, kategori miskonsepsi termasuk dalam kriteria rendah.Kata Kunci: certainty of response index (CRI), miskonsepsi, sistem endokrinAbstract
A Comparative Study of Convolutional Neural Network in Detecting Blast Cells for Diagnose Acute Myeloid Leukemia Ahmad Badruzzaman; Aniati Murni Arymurhty
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 1 (2024): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i1.354

Abstract

Understanding blood plays a crucial role in obtaining information for monitoring health conditions and diagnosis of hematologic diseases such as acute myeloid leukemia. It is characterized by irregular expansion of immature white blood cells called blast cells in the blood and bone marrow. To diagnose acute myeloid leukemia, a sample of bone marrow is necessary to be examined under a microscope through bone marrow examination. As for minimizing human subjectivity and automating medical screening, this study performed image classification for detecting blast cells in leukocytes from microscopic images. We compared a well-established convolutional neural network architecture such as ResNet, ResNeXt, and EfficientNetV2. The model’s performance assessment was done by two evaluation levels which are at a macro level and per class level. The experiment results show ResNet architecture with 18 layers (ResNet 18) outperforms the remaining models at both levels. Furthermore, as the architecture utilizes residual learning, ResNet and ResNeXt models converge faster than EfficientNetV2 at the training phase. In addition, ResNet architecture with 50 layers (ResNet 50) outperforms the remaining models specifically at blast cell identification in case of medical screening. Therefore, this study concludes that ResNet 50 is the best model to detect blast cells under this condition. However, EfficientNetV2 shows a promising potential at a macro level to classify leukocytes in general. We expect this study to become a preliminary study to develop a convolution neural network architecture specifically to detect blast cells in leukocytes.
Co-Authors Aniati Murni Arymurhty Raharjo Raharjo