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All Journal Mechatronics, Electrical Power, and Vehicular Technology CommIT (Communication & Information Technology) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Teknologi dan Sistem Komputer Journal of Information Technology and Computer Science Jurnal Elektronika dan Telekomunikasi
Heryana, Ana
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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.
Distracted driver behavior recognition using modified capsule networks Kadar, Jimmy Abdel; Dewi, Margareta Aprilia Kusuma; Suryawati, Endang; Heryana, Ana; Zilfan, Vicky; Kusumo, Budiarianto Suryo; Yuwana, Raden Sandra; Supianto, Ahmad Afif; Pratiwi, Hasih; Pardede, Hilman Ferdinandus
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 14, No 2 (2023)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2023.v14.177-185

Abstract

Human activity recognition (HAR) is an increasingly active study field within the computer vision community. In HAR, driver behavior can be detected to ensure safe travel. Detect driver behaviors using a capsule network with leave-one-subject-out validation. The study was done using CapsNet with leave-one-subject-out validation to identify driving habits. The proposed method in this study consists of two parts, namely encoder and decoder. The encoder used in this study modifies Sabour’s capsule network architecture by adding a convolution layer before going to the primary capsule layer. The proposed method is evaluated using a primary dataset with 10 classes and 300 images for each class. The dataset is split based on hold-out validation and leave-one-subject-out validation. The resulting models were then compared to conventional CNN architecture. The objective of the research is to identify driving behavior. In this study, the proposed method results an accuracy rate of 97.83 % in the split dataset using hold-out validation. However, the accuracy decreased by 53.11 % when the proposed method was used on a split dataset using leave-one-subject-out validation. This is because the proposed method extracts all features including the attributes of each participant contained in the input image (user-independent). Thus, the resulting model in this study tends to overfit.
Two-Stage Object Detection for Autonomous Vehicles With VGG-16 Based Faster R-CNN Dewi, Arnetta Listiana; Pardede, Hilman F.; Suryawati, Endang; Pratiwi, Hasih; Heryana, Ana; Yuliani, Asri R; Ramdan, Ade
Jurnal Elektronika dan Telekomunikasi Vol 24, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.551

Abstract

The implementation of object detection for autonomous vehicles is essential as it is necessary to identify common object on the street so proper response could be designed. While single stage object may be smaller in computations, two-stage object detection is preferred due to the ability to localize the object. In this paper, we propose to use Faster R-CNN with VGG-16 backbone for detections of object on the street. We evaluate the method with open image subset by selecting objects that are common on street. We explore several hyper-parameters setup such as learning rate and the number of ROI regions to find the optimum set-up. We found that the use of learning rate 10-6 with Adam optimizer to be the optimum value for this task. We also found that increasing the number of ROI may benefit the performance. This shows that there is potential for getting a higher mAP with increase the amount of RoI.
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.
Klasifikasi Siswa Slow Learner untuk Mendukung Sekolah dalam Meningkatkan Pemahaman Siswa Menggunakan Algoritma Naïve Bayes Wicaksono, Abdul Harris; Supianto, Ahmad Afif; Wijoyo, Satrio Hadi; Krisnandi, Didik; Heryana, Ana
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 3: Juni 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022935609

Abstract

Tidak semua siswa sekolah bisa menangkap materi dengan kemampuan yang sama dikarenakan tingkat kecerdasan dan kemampuan belajar setiap anak berbeda - beda. Ada siswa yang kemampuan belajarnya rendah sehingga lambat dalam memahami materi yang biasa disebut sebagai slow learner. Siswa slow learner ini perlu perlakuan yg khusus supaya dapat memahami materi seperti siswa lainnya. Siswa slow learner yang tidak terdeteksi dapat memperlambat kegiatan belajar mengajar karena guru harus mengulang kembali menjelaskan materi untuk membuat siswa memahami materi tersebut. Penelitian ini bertujuan untuk mengklasifikasikan siswa slow learner dan non slow learner dan menghasilkan visualisasi dashboard yang dapat digunakan untuk membantu sekolah. Penelitian ini mengangkat studi kasus siswa kelas XI dan XII SMA Tunas Luhur yang berjumlah 89 siswa.  Penelitian ini menggunakan algoritma naive bayes untuk klasifikasi dan cross validation 10 folds sebagai metode pengujian. Hasil pengujian didapatkan nilai akurasi 0.92857, precison  0.94736, recall 0.97297 , dan F-measure 0.96 serta hasil pengujian visualisasi dashboard menggunakan kuesioner System Usability Scale yang menghasilkan skor 71.75 atau acceptable. Algoritma naïve bayes  berhasil mengklasifikasikan siswa slow learner dan non slow learner dengan baik, dan visualisasi dashboard bisa diterima dengan baik oleh pihak sekolah. AbstractNot all school students can capture material with the same abilities because each child's level of intelligence and learning ability are different. There are students whose learning ability is low so that it is slow in understanding the material commonly referred to as slow learner. These slow learner students need special treatment in order to understand the material like other students. Undetectable slow learner students can slow down teaching and learning activities because teachers have to redo explain the material to make students understand the material. This study aims to classify slow learner and non slow learner students and produce dashboard visualizations that can be used to help schools. This study raised the case study of grade XI and XII students of Tunas Luhur High School which amounted to 89 students.  The study used naive bayes algorithms for classification and cross validation of 10 folds as a testing method. The test result obtained an accuracy score of 0.92857, precison of 0.94736, recall of 0.97297 , and F-measure of 0.96 and dashboard visualization test results using the System Usability Scale questionnaire which resulted in a score of 71.75 or acceptable. Bayes' naïve algorithm successfully classifies slow learner and non-slow learner students well, and dashboard visualization is well received by the school.
Co-Authors Ade Ramdan Ahmad Afif Supianto Andria Arisal, Andria Dewi, Arnetta Listiana Dewi, Margareta Aprilia Kusuma Fani Fauziah, Fani Fitra Abdurrachman Bachtiar Gultom, Dito William Hamonangan Kadar, Jimmy Abdel Krisnandi, Didik Krisnandi, Dikdik Kusumo, Budiarianto Suryo Kusumo, R. Budiarianto Suryo Pardede, Hilman F. Pardede, Hilman Ferdinandus pratiwi, hasih Satrio Hadi Wijoyo Suryawati, Endang Suryo Kusumo, Raden Budiarianto Vicky Zilvan, Vicky Vitria Puspitasari Rahadi Wicaksono, Abdul Harris Yuawana, Raden Sandra Yuliani, Asri R Yuliani, Asri Rizki Yuwana, R. Sandra Yuwana, Raden Sandra Zilfan, Vicky