Identification of Pineapple Disease Based on Image Using Neural Network Self-Organizing Map (SOM) Model

Ahmad, Imam; Rahmanto, Yuri; Borman, Rohmat Indra; Rossi, Farli; Jusman, Yessi; Alexander, Allan Desi

doi:10.1109/ice3is56585.2022.10010110

Cited by 14 publications

(12 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To answer research questions, a systematic approach is needed to plan, implement and evaluate. This research approach involves certain stages to achieve the research objectives [10]. So, so that the research can proceed in accordance with the research objectives, the research stages are arranged as shown in Figure 1.…”

Section: Methodsmentioning

confidence: 99%

Expert System for Diagnosing Behavioral Disorders Using the Dempster-Shafer Theory Algorithm

Mayatopani

2024

teknokom

View full text Add to dashboard Cite

Behavioral disorders, especially in children, can have a significant impact on their development in various domains, such as social, emotional, and academic. The diagnostic process for this disorder can be complicated due to overlapping symptoms and the possibility of complex causal factors. Early identification and appropriate treatment of behavioral disorders is essential to prevent more serious impacts on an individual's psychological well-being. Lack of knowledge and difficulty in accessing psychiatrists to find out about behavioral disorders in children are factors that result in this problem requiring a solution. The aim of this research is to create an expert system that utilizes the Dempster-Shafer Theory algorithm to detect behavioral disorders, thus simplifying the diagnosis process and ensuring accurate results. The Dempster-Shafer theory, as an inference engine, can overcome uncertainty by combining several sources of evidence or data that may overlap or be incomplete, resulting in a stronger conclusion. The main feature of this expert system is its ability to carry out diagnoses based on symptoms and display diagnosis results, disease descriptions, and treatment options. Test accuracy produces a value of 90%, which shows that the Dempster-Shafer Theory approach can diagnose behavioral disorders effectively.

show abstract

Section: Methodsmentioning

confidence: 99%

Expert System for Diagnosing Behavioral Disorders Using the Dempster-Shafer Theory Algorithm

Mayatopani

2024

teknokom

View full text Add to dashboard Cite

show abstract

“…Datasets in digital image processing refer to collections of image data that are used to train and test algorithms or models for various image processing tasks (Muraina, 2022). The goal of this is to provide enough variation in image conditions so that the model can learn and work well on new data that has never been seen before (Ahmad et al, 2022). The dataset used in this research contains images of potato leaves collected directly using a camera with the same light level.…”

Section: Collecting Datasetsmentioning

confidence: 99%

Identification of Potato Leaf Diseases Using Artificial Neural Networks With Extreme Learning Machine Algorithm

Erkamim,

Septarini,

Tonggiroh

et al. 2024

pilar

View full text Add to dashboard Cite

Potato plants have an important role in providing a source of carbohydrates for society. However, potato production is often threatened by various plant diseases, such as leaf disease, which can cause a decrease in yields. Identification of diseases on potato leaves is currently mostly done by farmers manually, so it is not always efficient and accurate. So the aim of this research is to identify diseases on potato leaves with artificial neural networks using the ELM (Extreme Learning Machine) approach and the GLCM (Gray Level Co-Occurrence Matrix) method for feature extraction. The GLCM approach functions to obtain texture features on objects by measuring how often certain pairs of pixel intensities appear together at various distances and directions in the image. Meanwhile, the ELM algorithm is used for image identification by adopting a one-time training method without iteration, which involves randomly determining weights and biases in hidden layers, thus allowing training to be carried out quickly and efficiently. Evaluation of the model by looking for the level of accuracy produces a value of 84.667%. The results show that the model developed is capable of accurate identification.

show abstract

“…Pada Tabel 1, menunjukkan bahwa nilai rata-rata dari hasil usability testing ini mencapai 88%. Selanjutnya, hasil dari usability testing tersebut diinterpretasikan dengan menggunakan kriteria berikut: "Baik" jika nilai berada dalam rentang 76% sampai dengan 100%; "Cukup" jika nilai berada dalam rentang 56% sampai dengan 75%; "Kurang Baik" jika nilai berada dalam rentang 40% sampai dengan 55%, dan "Tidak Baik" jika nilai kurang dari 40% [20]. Dengan demikian, kesimpulan yang dapat diambil adalah bahwa sistem pendukung keputusan ini masuk dalam kategori "Baik".…”

Section: Gambar 5 Grafik Persentase Pengujian Usabilityunclassified

Sistem Pendukung Keputusan Pemilihan Vendor It Maintenance Menggunakan Pendekatan Waspas

Fatmayati,

Riyanto,

Rahmadian

et al. 2024

teknokom

View full text Add to dashboard Cite

IT maintenance is important for a company because these activities are carried out to maintain and support the optimal performance of a company's IT systems and infrastructure. Generally, selecting an IT maintenance vendor is done by collecting vendor data and then evaluating it based on the desired criteria. This, of course, results in the time it takes to make a choice and makes it difficult to determine the best option. If you choose the wrong IT maintenance vendor, it will result in disrupting the company's stability. This research was conducted with the aim of developing a decision support system that can be used to determine the IT maintenance vendor that best suits their needs and preferences using the WASPAS (Weighted Aggregated Sum Product Assessment) approach. This method is used to determine the best option through weighted addition and multiplication, producing a final value that reflects the extent to which each option can meet the specified criteria. From the existing case studies, the best alternative was obtained, namely: Microsis (A4) got a preference value of 0.8769, followed by ICT Canopy (A1) with a preference value of 0.8613, Pillar IT (A2) with a preference value of 0.8408, Indocom Niaga (A5) with a preference value of 0.8180, and Sasana Digital (A3) with a preference value of 0.7389. The usability test carried out received a score of 88%, which indicates that the system is suitable for use.

show abstract

Identification of Pineapple Disease Based on Image Using Neural Network Self-Organizing Map (SOM) Model

Cited by 14 publications

References 25 publications

Expert System for Diagnosing Behavioral Disorders Using the Dempster-Shafer Theory Algorithm

Expert System for Diagnosing Behavioral Disorders Using the Dempster-Shafer Theory Algorithm

Identification of Potato Leaf Diseases Using Artificial Neural Networks With Extreme Learning Machine Algorithm

Sistem Pendukung Keputusan Pemilihan Vendor It Maintenance Menggunakan Pendekatan Waspas

Contact Info

Product

Resources

About