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All Journal CommIT (Communication & Information Technology) Jurnal Teknologi dan Sistem Komputer Journal of Information Technology and Computer Science
Krisnandi, Dikdik
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Diseases Classification for Tea Plant Using Concatenated Convolution Neural Network Krisnandi, Dikdik; Pardede, Hilman F.; Yuwana, R. Sandra; Zilvan, Vicky; Heryana, Ana; Fauziah, Fani; Rahadi, Vitria Puspitasari
CommIT (Communication and Information Technology) Journal Vol 13, No 2 (2019): CommIT Vol. 13 No. 2 Tahun 2019
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v13i2.5886

Abstract

Plant diseases can cause a significant decrease in tea crop production. Early disease detection can help to minimize the loss. For tea plants, experts can identify the diseases by visual inspection on the leaves. However, providing experts to deal with disease identification may be very costly. The machine learning technology can be implemented to provide automatic plant disease detection. Currently, deep learning is state-of-the-art for object identification in computer vision. In this study, the researchers propose the Convolutional Neural Network (CNN) for tea disease detections. The researchers focus on the implementation of concatenated CNN, namely GoogleNet, Xception, and Inception-ResNet-v2, for this task. About 4727 images of tea leaves are collected, comprising of three types of diseases that commonly occur in Indonesia and a healthy class. The experimental results confirm the effectiveness of concatenated CNN for tea disease detections. The accuracy of 89.64% is achieved.
Automatic detection of crop diseases using gamma transformation for feature learning with a deep convolutional autoencoder Zilvan, Vicky; Ramdan, Ade; Supianto, Ahmad Afif; Heryana, Ana; Arisal, Andria; Yuliani, Asri Rizki; Krisnandi, Dikdik; Suryawati, Endang; Suryo Kusumo, Raden Budiarianto; Yuawana, Raden Sandra; Kadar, Jimmy Abdel; Pardede, Hilman F.
Jurnal Teknologi dan Sistem Komputer [IN PRESS] Volume 10, Issue 3, Year 2022 (July 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2022.14250

Abstract

Precision agriculture is a management strategy for sustaining and increasing the production of agricultural commodities. One of its implementations is for crop disease detection. Currently, deep learning methods have become widespread methods for the automatic detection of crop diseases. Most deep learning methods showed better performance when using an original image in raw form as inputs. However, the original image of crop diseases may appear similar between one disease to another.  Therefore, the deep learning methods may misclassify the data. To deal with these, we propose the gamma transformation with a deep convolutional autoencoder to extract good features from the original image data. We use the output of the gamma transformation with a deep convolutional autoencoder as inputs to a classifier for the automatic detection of crop diseases. Our experiments show that the average accuracies of our method improve the performance of crop disease detection compared to only using raw data as inputs.
Analysis of Entrance Test Results Effect on Student's Performance using Multiple Linear Regression Gultom, Dito William Hamonangan; Supianto, Ahmad Afif; Bachtiar, Fitra Abdurrachman; Krisnandi, Dikdik; Kusumo, R. Budiarianto Suryo; Heryana, Ana
Journal of Information Technology and Computer Science Vol. 9 No. 3: December 2024
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.93343

Abstract

The entrance selection test is the starting gate to find out the ability of prospective students. Student performance, measured by the Academic Achievement Index and the length of study, are two factors that measure the quality of a department or faculty. Therefore, it is necessary to analyze the effect of the entrance test results on student performance. The Faculty of Computer Science at Brawijaya University has various types of tests that prospective students must take in order to be accepted into the Computer Science Master's Program. Broadly speaking, these types of tests consist of Interview Tests, Academic Potential Tests, TOEFL Tests, Field Ability Tests, Psychological Tests, and S1 GPA. Regression analysis using the Multiple Linear Regression method is applied in the first 4 semesters of lectures. Tests conducted on the regression model resulted in a Mean Square Error value of 0.0321 in the first semester, 0.0273 in the second semester, 0.015 in the third semester, and 0.031 in the fourth semester, and 1.5301 for the graduation semester. While the K- Fold Cross Validation score resulted in a score of -0.044 in the 1st semester, 0.2838 in the second semester, 0.9037 in the third semester, and 0.9011 in the fourth semester, and -0.2786 in the graduation semester. In addition, dashboard visualization gets an average score of 68.33, which means it can be accepted and can be used by the Academic Team for the Master's Program in Computer Science at Brawijaya University.
Co-Authors Ade Ramdan Ahmad Afif Supianto Andria Arisal, Andria Fani Fauziah, Fani Fitra Abdurrachman Bachtiar Gultom, Dito William Hamonangan Heryana, Ana Kadar, Jimmy Abdel Kusumo, R. Budiarianto Suryo Pardede, Hilman F. Suryawati, Endang Suryo Kusumo, Raden Budiarianto Vicky Zilvan, Vicky Vitria Puspitasari Rahadi Yuawana, Raden Sandra Yuliani, Asri Rizki Yuwana, R. Sandra