Penerapan Random Forest untuk Mengukur Tingkat Keparahan Penyakit pada Daun Apel

Ratnawati, Luthfiana; Sulistyaningrum, Dwi Ratna

doi:10.12962/j23373520.v8i2.48517

Cited by 13 publications

(13 citation statements)

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“…The random forest algorithm is generally widely used in previous studies including to measure the severity of disease in apple leaf images (Ratnawati & Sulistyaningrum, 2019). In addition, this algorithm is also better than the xgboost algorithm in overcoming the imbalance class in the classification of hepatitis C disease levels (Syukron et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

The Effectiveness Analysis of Random Forest Algorithms With Smote Technique in Predicting Lung Cancer Risk

Yulianti

Rahmawati

Mardiana³

2022

J. Ris. Inform.

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When compared with other types of cancer, most of the population with cancer die from lung cancer.A person needs to do a screening test through X-rays, CT scans, and MRI to detect the disease. However, before carrying out the process, the doctor will ordinarily investigate a medical history and physical examination first to study the symptoms and possible risk factors for lung cancer. The lung cancer data set has a class imbalance that affects the performance of the random forest algorithm in predicting the risk of lung cancer. This study aims to employ the SMOTE technique to the random forest algorithm to increase accuracy in predicting lung cancer risk. In this research, data processing and analysis use the Python programming language. The test results show an accuracy value of 88% with an AUC value of 0.93. When employing the random forest method to forecast lung cancer risk, the SMOTE technique is useful in dealing with class imbalances in the data set.

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Section: Introductionmentioning

confidence: 99%

The Effectiveness Analysis of Random Forest Algorithms With Smote Technique in Predicting Lung Cancer Risk

Yulianti

Rahmawati

Mardiana³

2022

J. Ris. Inform.

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“…Digital image processing is generally defined as two-dimensional image processing with a computer (Wibisono & Mujiyono, 2018) to obtain more perfect results so that the information contained in the image can be seen more clearly (Suta et al, 2019). The image processing process begins with input in the form of images and produces output in the form of processed images (Ratnawati & Sulistyaningrum, 2019).…”

Section: Literature Review Image Processingmentioning

confidence: 99%

“…Image segmentation is an image processing process that aims to separate the object region from the background region so that objects are easily analyzed in order to identify objects that involve a lot of visual perception (Ratnawati & Sulistyaningrum, 2019) where each region has similar attributes (Andika & Hafiz, 2018).…”

Section: Image Segmentationmentioning

confidence: 99%

“…The main purpose of feature extraction is to characterize the object you want to identify from an image by using measures that have very similar values (Andika & Hafiz, 2018) to obtain the most relevant information from the original data and represent that information in a lower dimensional space (Ratnawati & Sulistyaningrum, 2019). Shape features, geometry features, size features, texture features and color features are features that are often extracted.…”

Section: Feature Extractionmentioning

confidence: 99%

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Classification of Banana Ripeness Based on Color and Texture Characteristics

Kahfi

Hasan

Hasanah

2023

CNAHPC

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Banana is one of the most consumed fruits globally and is a rich source of vitamins, minerals and carbohydrates. With the many benefits that bananas have, many farmers cultivate this fruit. The problem that occurs when the harvest is produced on a large scale is the process of selecting bananas that are still unripe or ripe. Usually farmers carry out the selection process manually by visually identifying ripeness based on the color of the fruit skin. However, direct observation has several drawbacks such as subjectivity, takes a long time and is inaccurate. For this reason, we need a system that can help determine the maturity level of bananas automatically through a series of banana image processing processes. One way that can be used to determine the maturity level of bananas is by looking at the color and texture of the bananas. This study aims to classify the maturity level of bananas based on the color and texture characteristics of the banana image using the Gray Level Co-occurrence Matrix and K-Nearest Neighbor methods for the classification process. Based on the results of the research analysis that has been carried out, using the parameter k which has a value of 3 obtains very high accuracy.

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“…Apple trees that produced also decreased from 100,000 trees to 79,400 producing trees in the Pujon Subdistrict (BPS-Statistics Indonesia, 2019). Several cases led to a decrease in the number plants that produce apples besides climate and the growing number of old plants, too because apple plants are attacked by pests and diseases, one of which is rotten apples (Ratnawati & Sulistyaningrum, 2019). In apples, fruit rot disease is mainly caused by the infection of pathogenic fungus Colletotrichum spp.…”

Section: Introductionmentioning

confidence: 99%

Morphological and molecular identification as well pathogenicity of the causal agents of fruit rot disease of apple manalagi (Malus Sylvestris) in Pujon, East Java

Triasih

Abadi

Muhibbudin

et al. 2023

J Trop Plant Pests Dis

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Apple fruit rot disease is one of the major apple diseases in Indonesia. It has been caused by several species of pathogenic within the genus Colletotrichum. This research aims to identify species of the pathogen causing apple fruit rot disease in Pujon, East Java. Suspected fungal isolates were identified based on morphological (macroscopic and microscopic) and molecular characteristics. Based on the morphological observation, five isolates were identified as Colletotrichum spp. These isolates have similar morphological characteristics such as white-greyish colony color, texture colony velvety, zonation conidia concentric, round cylindrical conidial end, and conidia with 10.4–12.8 ?m in length and width 3.1–3.52 ?m, respectively. Isolates M1 showed the highest pathogenicity, therefore selected for molecular identification. Molecular identification was conducted using ITS1 and ITS4 primer, AM1 was identified as C. gloeosporioides with 99.57% similarity to C. gloeosporioides JX-19 variety from China.

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Penerapan Random Forest untuk Mengukur Tingkat Keparahan Penyakit pada Daun Apel

Cited by 13 publications

References 0 publications

The Effectiveness Analysis of Random Forest Algorithms With Smote Technique in Predicting Lung Cancer Risk

The Effectiveness Analysis of Random Forest Algorithms With Smote Technique in Predicting Lung Cancer Risk

Classification of Banana Ripeness Based on Color and Texture Characteristics

Morphological and molecular identification as well pathogenicity of the causal agents of fruit rot disease of apple manalagi (Malus Sylvestris) in Pujon, East Java

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