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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Journal of Computer System and Informatics (JoSYC)
Kusrini Kusrini
Universitas Amikom Yogyakarta, Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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Kebakaran Hutan Implementasi Metode CLARA Clustering Untuk Pengelompokan Data Potensi Kebakaran Hutan/Lahan Berdasarkan Persebaran Titik Panas (Hotspot) Enni Lidrawati; Saiful Bahri; Umam Faqih Zubaedi; Vinnesa Patricia Carolina; Kusrini Kusrini; Dina Maulina
Journal of Computer System and Informatics (JoSYC) Vol 3 No 4 (2022): August 2022
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v3i4.2006

Abstract

Forest or land fires are events that often occur in various countries in the world that require serious handling from all parties because they have an impact on all lines of life. Therefore, early treatment is needed, one of which is by grouping fire-prone areas using hotspot data. Hotspots can be obtained by satellite in this study taking hotspots from NASA satellites. The data used are latitude, longitude, brightness and confidence. The method used is the Clara Clustering method because this method has the advantage of being resistant to outliers and can be used in large amounts of data. This study concludes that the Clara method can process 16,579 hotspot data with the best Shilhoutte Coefficient value of 0.89 with 2 clusters while the potential possessed by cluster 1 is included in high potential with an average brightness of 3670K and a confidence value of 1 or hight. Meanwhile, cluster 2 has moderate potential with an average brightness of 3490K and a confidence value of 3 or nominal.
Deteksi Hama Pada Daun Apel Menggunakan Algoritma Convolutional Neural Network Dede Husen; Kusrini Kusrini; Kusnawi Kusnawi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4667

Abstract

Today the need for fruit consumption is increasing along with the increasing human population and awareness of the consumption of nutritious foods, apples are one of the most consumed fruits by humans worldwide. According to data quoted from the Indonesian National Statistics Center in 2021, apple production in 2021 decreased from the previous year from 519,531 tons to 509,544 tons. One of the causes of the decline in apple production is the presence of pests on the apple plant. At least there are several types of pests that can be identified on apple leaves, namely Apple Scrub (Venturia inaequalis), Apple Black Root (Botryosphaeria) and Apple Cedar/Rust (Gymnosporangium juniperi virginianae). The research stage begins with conducting several literature studies regarding related research, then formulating and validating the problem and starting to collect data from the Kaggle public dataset. Then in the experimental stage, the author divides the dataset into three parts with a percentage of 80% training data, 10% validation data and 10% testing data. The image classification method used is the Convolutional Neural Network (CNN) algorithm to create a model that can classify image data, the process of implementing the author uses the python programming language to build the model. The author conducted several experiments by making changes to several model parameters that affect the accuracy of the model. To evaluate the performance and accuracy of the model using a confusion matrix. The results of the study indicate that image size, data augmentation and the number of epochs greatly affect the accuracy of the model, from the test results the CNN model with the best accuracy is the model with the image size parameter 256x256, horizontal flip, vertical flip and random rotation data augmentation and the number of the 60th epoch has the highest accuracy rate of 99.66%. The results of this study are expected to be implemented in an application that can be used directly by farmers in detecting pests on apple plants quickly and accurately.
Analisis Perbandingan Kinerja Algoritma Klasifikasi dengan Menggunakan Metode K-Fold Cross Validation Ritham Tuntun; Kusrini Kusrini; Kusnawi Kusnawi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 6, No 4 (2022): Oktober 2022
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v6i4.4681

Abstract

This study aims to compare the performance of two classification data mining algorithms, namely the K-Nearest Neighbor algorithm, and C4.5 using the K-fold cross validation method. The data used in this study are iris public data with a total of 150 data and 3 label target classes, namely iris-setosa, iris-versicolor, and iris-virginica. The training data used is 97% or 145 data from 150 data, and the testing data used is 3% or 5 data, and the number of K in the K-fold cross validation is 30 or 30 times the experimental stage. The results showed that the performance of the K-Nearest Neighbor algorithm was 95.33%, recall was 95.33%, and precision was 96.27%. While the C4.5 algorithm obtained an accuracy of 96.00%, recall of 94.44%, and precision of 93.52%.
Co-Authors Dede Husen Dina Maulina Enni Lidrawati Kusnawi Kusnawi Ritham Tuntun Saiful Bahri Umam Faqih Zubaedi Vinnesa Patricia Carolina