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Dinamik
Published by Universitas Stikubank
ISSN : 08549524     EISSN : 26231786     DOI : 10.35315/dinamik.v28i1
Core Subject : Science,
Computer Science & IT
The Jurnal DINAMIK aims to: Promote a comprehensive approach to informatics engineering and management incorporating viewpoints of different applications (computer graphics, computer networks and security, computer vision, computational intelligence, databases, big data, IT project management, and other fields relevant to information technology. Encourage scientists, practicing engineers, and others to conduct research and similar activities.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 12 Documents
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Search results for , issue "Vol 29 No 2 (2024)" : 12 Documents clear
Feature Extraction dengan Gray Level Co-Occurrence Matrix Warna Alami dari Tanaman Ketapang berbasis Geolokasi Ningsih, Dewi Handayani Untari; Zuliarso, Eri; Radyanto, Mohammad Riza; Santoso, Dwi Budi
Dinamik Vol 29 No 2 (2024)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v29i2.9851

Abstract

Ekstraksi fitur, juga dikenal sebagai ekstraksi fitur, adalah proses mengubah data mentah menjadi representasi yang lebih ringkas dan berguna yang dapat digunakan untuk analisis atau pemodelan lebih lanjut. Ini dilakukan pada tahap preprocessing sebelum masuk ke tahap analisis atau pemodelan. Pewarnaan alami dari berbagai tanaman yang menghasilkan warna tanin setelah pencelupan dikumpulkan menjadi satu alur gradasi warna yang berkaitan dengan susunan derajat atau peningkatan, peralihan warna dari satu warna ke warna lain. Variasi dalam alur ini dipengaruhi oleh jenis tanaman, lama pencelupan, jenis fiksasi yang digunakan, jenis kain, dan lokasi tanaman yang dijadikan sampel.Metode ekstraksi fitur dengan Matriks Co-Occurrence Level Gray (GLCM) digunakan untuk mengidentifikasi gradasi warna yang dihasilkan oleh pewarnaan pada daun Ketapang. Proses ekstraksi fitur tekstur dari gambar tanaman Ketapang menggunakan matriks GLCM digunakan untuk menganalisis dan memahami pola warna alami tanaman Ketapang di berbagai lokasi geografis. Untuk menggambarkan dan mengukur pola warna alami tanaman Ketapang, Gray Level Co-occurrence Matrix (GLCM) adalah representasi statistik dari distribusi spasial intensitas piksel dalam citra, yang mengukur frekuensi kemunculan pasangan intensitas piksel yang berdekatan dan memberikan informasi tentang tekstur citra
Prioritizing Pregnant Mother at Risk of Stunting: An Analytic Network Process Approach Cahyono, Taufiq Dwi; Hadikurniawati, Wiwien
Dinamik Vol 29 No 2 (2024)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v29i2.9889

Abstract

Stunting occurs due to malnutrition which inhibits growth in toddlers. Stunting can also be caused by problems during pregnancy. This study aims to identify the risk of stunting during pregnancy and determine pregnant women who are at risk of this condition. By identifying and prioritizing critical factors that contribute to stunting in children under five, this research is expected to assist policy makers in developing effective solutions to reduce stunting rates. Handling the problem of stunting is important for the Government because it relates to the future generation of Golden Indonesia 2045. This study evaluates appropriate actions or therapies to reduce the risk of having children born with the potential to experience stunting. In the process of selecting pregnant women who are at risk of giving birth to children with the risk of stunting, a selection procedure is carried out that considers several factors such as the mother's age, mother's nutritional intake, arm circumference, hemoglobin level, parity, birth spacing, height, and mother's body mass index (BMI). The analytic network process (ANP) approach is used to determine the outcome of the selection process. The ranking is determined based on the calculation of the weighting of the criteria and sub-criteria in the ANP method. Based on the results of calculations using the ANP approach, PM 1 pregnant women get the highest score and are ranked first. These pregnant women are considered to have the highest risk of giving birth to babies with stunting risk.

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