Cholesterol Imagedoi:10.34820/FK2/RYQ5ENTelkom University Dataverse2022-09-173Novamizanti, Ledya, 2022, "Cholesterol Image", https://doi.org/10.34820/FK2/RYQ5EN, Telkom University Dataverse, V3Cholesterol Imagedoi:10.34820/FK2/RYQ5ENNovamizanti, LedyaTelkom University DataverseNovamizanti, LedyaNurbani, Cita AisahAndana, Shafira NurNovamizanti, Ledya2022-09-17Medicine, Health and Life Scienceseye image, cholesterol levelData berupa gambar mata yang diambil menggunakan kamera digital. Kategori gambar, terdiri atas tiga kelas, yaitu: normal, beresiko kolesterol, dan kolesterol tinggi. The data are eye images taken using a digital camera. The image category consists of three classes, namely: normal, cholesterol risk, and high cholesterolCC0 WaiverS. N. Andana, L. Novamizanti, I. N. A. Ramatryana. Measurement of Cholesterol Conditions of Eye Image using Fuzzy Local Binary Pattern (FLBP) and Linear Regression. IEEE International Conference on Signals and Systems (ICSigSys). 2019.C. A. Nurbani, L. Novamizanti, I. N. A. Ramatryana, N. P. D. P. Wardana. Measurement of Cholesterol Levels through Eye based on Co-Occurrence Matrix on Android. IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob), 2019.M. A. Siddik, L. Novamizanti, I. N. A. Ramatryana. Deteksi Level Kolesterol melalui Citra Mata Berbasis HOG dan ANN. ELKOMIKA, Vol. 7, No. 2, Mei 2019, Halaman 284 - 296. doi: http://dx.doi.org/10.26760/elkomika.v7i2.284G. B. Y. Mukti, L. Novamizanti, P. A. Fadhila. Measurement of Cholesterol Level through Eye Image using Local Binary Pattern Histogram. TEST Engineering & Management. Volume 83. Page Number: 12553 - 12560. March - April 2020D. Sari, J. Raharjo, & L. Novamizanti, L. (2020, December). Cholesterol Level Detection Through Eye Image Using Fractal and Decision Tree. In IOP Conference Series: Materials Science and Engineering (Vol. 982, No. 1, p. 012010). IOP Publishing.J. Raharjo, L. Novamizanti, I. N. A. Ramatryana, Cholesterol Level Measurement Through Iris Image Using Gray Level Co-Occurrence Matrix And Linear Regression. ARPN Journal of Engineering and Applied Sciences. VOL. 14, NO. 21, November 2019beresiko-200-24-kanan.bmpimage/bmpberesiko-200-24-kiri.bmpimage/bmpberesiko-203-16-kanan.bmpimage/bmpberesiko-203-16-kiri.bmpimage/bmpberesiko-203-22-kanan.bmpimage/bmpberesiko-203-22-kiri.bmpimage/bmpberesiko-203-3-kanan.bmpimage/bmpberesiko-203-3-kiri.bmpimage/bmpberesiko-203-8-kanan.bmpimage/bmpberesiko-205-18-kanan.bmpimage/bmpberesiko-205-18-kiri.bmpimage/bmpberesiko-207-2-kanan.bmpimage/bmpberesiko-207-2-kiri.bmpimage/bmpberesiko-207-6-kanan.bmpimage/bmpberesiko-207-6-kiri.bmpimage/bmpberesiko-209-17-kanan.bmpimage/bmpberesiko-209-17-kiri.bmpimage/bmpberesiko-215-15-kanan.bmpimage/bmpberesiko-215-15-kiri.bmpimage/bmpkolesterol-246-5-kanan.bmpimage/bmpkolesterol-246-5-kiri.bmpimage/bmpkolesterol-247-14-kanan.bmpimage/bmpkolesterol-247-14-kiri.bmpimage/bmpkolesterol-250-10-kanan.bmpimage/bmpkolesterol-250-10-kiri.bmpimage/bmpkolesterol-255-23-kanan.bmpimage/bmpkolesterol-255-23-kiri.bmpimage/bmpkolesterol-259-7-kanan.bmpimage/bmpkolesterol-259-7-kiri.bmpimage/bmpnormal-149-21-kanan.bmpimage/bmpnormal-149-21-kiri.bmpimage/bmpnormal-180-20-kanan.bmpimage/bmpnormal-180-20-kiri.bmpimage/bmpnormal-186-26-kanan.bmpimage/bmpnormal-186-26-kiri.bmpimage/bmpnormal-187-25-kanan.bmpimage/bmp