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All Journal Jurnal Material dan Energi Indonesia Journal Of Informatics And Busisnes
MARIA OKTAVIANI
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Computer Science & IT Economics, Econometrics & Finance Energy Materials Science & Nanotechnology

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KARAKTERISTIK NANOPARTIKEL ZnO: STUDI EFEK PELARUT PADA PROSES HIDROTHERMAL TOGAR SARAGI; YONATAN R PURBA; SATRIA AUFFA D U; MARIA OKTAVIANI; TUTI SUSILAWATI; RISDIANA RISDIANA; AYI BAHTIAR
Jurnal Material dan Energi Indonesia Vol 6, No 01 (2016)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (356.403 KB) | DOI: 10.24198/jmei.v6i01.9366

Abstract

Telah berhasil disintesis nanopartikel ZnO (ZnO-NP) pada pelarut yang berbeda dengan metode hidrotermal. Bahan dasar yang digunakan adalah zinc acetate dihydrate (Zn(CH3COO)2.2H2O, Merck, 99 %), sodium hydroxide (NaOH, Merck), dengan pelarut 2-propanol (Sigma Aldrich, 99%) dan ethanol. Karakterisasi optik, morfologi dan struktur kristal nanopartikel ZnO masing-masing dilakukan melalui pengukuran UV-Vis, TEM dan XRD. Dari hasil pengukuran UV-Vis diperoleh bahwa band gap ZnO-NP pada pelarut 2-propanol memiliki energi band gap yang lebih besar dibandingkan dengan sampel pada pelarut ethanol. Dari hasil pengukuran TEM diperoleh bahwa morfologi nanopartikel ZnO pada pelarut 2-propanol memiliki bentuk nano-rod (20 nm ´ 9 nm), sedangkan nanopartikel ZnO pada pelarut etanol lebih cenderung oval (26 nm ´ 15 nm). Karakteristik kristal nanopartikel ZnO pada kedua pelarut memiliki memiliki struktur kristal hexagonal wurtzite.
Penerapan Algoritma C4.5 Dalam Klasifikasi Penyakit Diabetes Menggunakan Dataset Pima Indians Fakhri Hamdani; Muhammad Ari Shidqi; Arip Rahman; Piero Ariessandy; Dwi Saputra; Gregorius Waek; Maria Oktaviani
Journal Of Informatics And Busisnes Vol. 3 No. 4 (2026): Januari - Maret
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jibs.v3i4.4039

Abstract

The increasing number of diabetes mellitus sufferers makes early identification a crucial step to reduce the risk of complications. Utilizing health data through a data mining-based approach offers opportunities to assist in more systematic disease analysis and classification. This research focuses on the application of the Decision Tree C4.5 algorithm to classify diabetes using the Pima Indians Diabetes dataset. The data used consisted of 768 female patients with eight medical attributes related to health conditions, such as glucose levels, body mass index, blood pressure, age, and number of pregnancies. The research process included data processing, model development, and evaluation of the classification results using the CRISP-DM workflow. The results showed that the classification model built using the C4.5 algorithm achieved an accuracy of 77.04%. The resulting decision tree structure demonstrated that the glucose level attribute played a dominant role in determining the classification results. These findings demonstrate that the Decision Tree C4.5 algorithm can be utilized as a fairly effective approach to assist in the initial classification of diabetes based on medical data.
Co-Authors Arip Rahman Ayi Bahtiar Dwi Saputra Fakhri Hamdani Gregorius Waek Muhammad Ari Shidqi Piero Ariessandy RISDIANA RISDIANA SATRIA AUFFA D U Togar Saragi, Togar TUTI SUSILAWATI YONATAN R PURBA