Article Per Year (5 Year)
p-Index From 2019 - 2024
0.444
P-Index
This Author published in this journals
All Journal Komunikasi Fisika Indonesia
Dewi Anjarwati Mahmudah
Universitas Riau
Earth & Planetary Sciences Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Physics

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
ANALISA SENSITIVITAS SENSOR TGS PADA HIDUNG ELEKTRONIK UNTUK IDENTIFIKASI GANODERMA DI BAGIAN AKAR KELAPA SAWIT Mhd Feri Desfri; Minarni Minarni; Dewi Laila Sari; Dewi Anjarwati Mahmudah; Ihsan Okta Harmailil; Irfan Cahyadi
Komunikasi Fisika Indonesia Vol 19, No 1 (2022)
Publisher : Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/jkfi.19.1.1-6

Abstract

Palm oil is one of the main commodities for Indonesia. It is important to identify the disease-causing the decline in productivity. Root rot disease that causes total damage to oil palm plants due to fungal infection G. boninense sp has volatile organic compounds that can be detected using an electronic nose. The electronic nose system is designed with 6 sensor arrays, namely TGS 2612, TGS 822, TGS 2611, TGS 2610, TGS 813, and TGS 2620 which are sensitive to certain VOC compounds. The sample used was infected and uninfected oil palm seedlings aged 4 months. The detection process is carried out on plant roots. Python program is used as a data acquisition system in voltage retrieval. The obtained voltage is processed and further analyzed using a trapezoidal area to determine the sensor response in the identification of Ganoderma. The results of processing using a trapezoidal plane show that TGS 2611 has a very good response. The TGS 2611 sensor has a higher trapezoidal area in identifying oil palm plants that are attacked by Ganoderma with 4 classifications, namely healthy, moderate, sick, and severe.
ANOTASI CITRA BERBASIS PYTHON UNTUK RANCANG BANGUN PERANGKAT LUNAK DETEKSI OBJEK PADA TANDAN BUAH SEGAR KELAPA SAWIT CACAT Minarni Shiddiq; Muhammad Ikhsan Hamid; Vicky Vernando Dasta; Yohanes Dwi Saputra; Dewi Anjarwati Mahmudah; Dinda Kamia Evkha Putri; Annisya Madani; Ihsan Okta Harmailil
Indonesian Physics Communication Vol 20, No 2 (2023)
Publisher : Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/jkfi.20.2.135-140

Abstract

Object detection can determine the existence of an object, scope and image. Object detection begins with the introduction of an object. This method can be used to automate the process of sorting and grading oil palm fresh fruit bunches (FFB) at palm oil mills, which are still done manually. Image annotations are needed in building the software so that the software can identify object features in an image, especially imager in video frames. This study aims to annotate images of oil palm FFB into 2 categories, namely normal palm and abnormal palm. This category is the standard regulation of the Minister of Agriculture No. 14 of 2013. Image acquisition is carried out by varying the position of each oil palm FFB with the top and bottom position of the fruit which is then augmented 4 times which function to multiply the image data model to be annotated. Annotation is done using the python program application, namely Labelimg. The amount of image data that has been annotated is 200 images consisting of 100 normal palm images and 100 abnormal palm images.
Co-Authors Annisya Madani Dewi Laila Sari Dinda Kamia Evkha Putri Ihsan Okta Harmailil Irfan Cahyadi Mhd Feri Desfri Minarni Minarni Muhammad Ikhsan Hamid Vicky Vernando Dasta Yohanes Dwi Saputra