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All Journal Jurnal Komputer Terapan Journal of Applied Smart Electrical Network and System (JASENS) Proceeding Applied Business and Engineering Conference
Yuli Triyani
Politeknik Caltex Riau
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Penerapan Deep Learning Pada Jenis Penyakit Tanaman Kelapa Sawit Menggunakan Algoritma Convolutional Neural Network Wiwin Styorini; Wahyu Eka Putra; Wahyuni Khabzli; Yuli Triyani
Jurnal Komputer Terapan Vol. 8 No. 2 (2022): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (328.978 KB) | DOI: 10.35143/jkt.v8i2.5522

Abstract

The problem of Plant Destruction Organisms (OPT), especially related to disease, has always been an issue in the management of oil palm plantations. Oil palm has diseases caused by pests and others that can affect the growth and fruiting process. For this reasearch, the aims to identify whether or not oil palm plants are healthy through the color of their leaves, so that it will facilitate the performance of farmers. Deep Learning (DL) is a field of science from machine learning by doing deeper learning for many layers. Convolutional Neural Network (CNN) is one of the DL algorithms designed to process data in two-dimensional form such as images. Therefore, in this study, the CNN method will be applied to classify the health of oil palm plants based on the color of the leaves. The data used are 3000 data with test scenarios for training data and testing data are 90%:10%, 80%:20%, 70%:30% and 65%:35%. Based on the 4 test scenarios, the best accuracy obtained is 99.90% for the scenario of 65% of training data and 35% of testing data. While the lowest level of accuracy is 99.50% for the scenario of 90% training data and 10% testing data.
Aplikasi Penentuan Dosis Kebutuhan Pupuk Nitrogen Berdasarkan BWD Pada Tanaman Padi Yuli Triyani; Andika Djojo Kusuma
Jurnal Komputer Terapan Vol. 8 No. 2 (2022): Jurnal Komputer Terapan
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (353.826 KB) | DOI: 10.35143/jkt.v8i2.5560

Abstract

Nitrogen is one of the nutrients that is needed for vegetative growth of rice, but excessive fertilizer application can damage plants. The balanced application of nitrogen fertilizer to rice plants is a solution to improve the growth of rice plants so that their productivity becomes more optimal. To get the appropriate fertilizer dose, farmers must use the BWD table, but BWD is difficult to obtain, the price is quite expensive, and its use is done manually by comparing the color of the rice leaves with the color of each level in the BWD table. Different perceptions between each use often occur. Therefore, an application for determining the need for nitrogen fertilizer based on BWD was designed for rice plants. This system consists of pre-processing, feature extraction and classification stages. The pre-processing stage is the stage of improving image quality, while the feature extraction stage uses the histogram of s-RGB method to obtain the Mean and Mode values ​​of the color intensity of rice leaves. This system classifies based on the characteristics that have been extracted into 3 classes, namely: 2-3, 3-4, and 4-5 based on the BWD level. Then the system will calculate the dose of nitrogen fertilizer needed based on the input data of GKG and land area. The classification stage uses the K-NN method. Based on the results of training using 210 images and testing 90 images of rice leaves, the best results were obtained using k-NN 3 neighbors with an accuracy of 95.5%, AUC 0.98 and training time 0.8 seconds. So it can be concluded that the classification using k-NN can determine the dose required for rice plants properly.
KLASIFIKASI PENYAKIT DIABETIC RETINOPATHY PADA CITRA FUNDUS BERBASIS DEEP LEARNING vania annisa queentinela; Yuli Triyani
ABEC Indonesia Vol. 9 (2021): 9th Applied Business and Engineering Conference
Publisher : Politeknik Caltex Riau

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Abstract

Diabetic Retinopathy is one of the complications of diabetes and if it is treated too late, the patient will experience permanent blindness. Diabetic Retinopathy cannot be detected directly. This is because the hallmark of Diabetic Retinopathy is on the retina of the eye and can only be detected by an ophthalmoscope which produces an image of the fundus. However, the stage of detecting and classifying the type of Diabetic Retinopathy using an Ophthalmoscope still takes a long time to get results, so a system that can detect Diabetic Retinopathy is needed quickly to detect Diabetic Retinopathy. The Diabetic Retinopathy detection system that will be built is a Deep Learning-based system by detecting the eye fundus image which will go through several stages of process such as preparing data, image training stage and image testing stage. The data set used is from the kaggle.com and strare sites. This system will detect and classify Diabetic Retinopathy based on Deep Learning based on the characteristics of the appearance of mycroaneurysms, hard exudates, soft exudates, and bleeding in the form of dots, lines, and spots on the retina of the eye. The results obtained from the learning process obtained an accuracy of 86.7% and an error of 13.3%. So it can be concluded that the googlenet architecture can classify diabetic retinopathy well.
PERANCANGAN JARINGAN FEMTOCELL PADA JARINGAN LTE MENGGUNAKAN MODEL PROPAGASI COST 231 MULTIWALL Kevin Sean Farrel Manurung; Yuli Triyani
ABEC Indonesia Vol. 9 (2021): 9th Applied Business and Engineering Conference
Publisher : Politeknik Caltex Riau

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Abstract

Caltex Riau Polytechnic Building has 3 floors which are always crowded with students, lecturers and staff every day. The network connection inside the building is not as good as the network connection outside the building because the signal from the BTS is attenuated by the thick walls of the building, causing the signal strength inside the building to be weak. The solution to overcome the problems that occur is to design an indoor Femtocell network on the LTE network in the building. Caltex Riau Polytechnic which can improve signal quality and expand coverage. This design applies the COST 231 Multiwall propagation model which will be simulated using RPS Software. Before designing the femtocell network, calculations are carried out based on Coverage and Capacity to get the required number of FAPs per floor. The simulation results get the average value of RSRP on each floor of the Main Building is -48.55 dBm, -48.81 dBm, -43.18 dBm and the average value of RSRP on each floor of the Multipurpose Building is -45.16 dBm, -51.16 dBm, -42.61 dBm. And the simulation results get the average SINR value on each floor of the Main Building is 20.96 dB, 21.86 dB, 24.46 dB and the average SINR value on each floor of the Multipurpose Building is 16.74 dB, 16.74 dB, 0 dB (because only 1 FAP is placed) The results obtained from the design have met the standard parameters used by the Tri operator.
Rancang Bangun Sistem Monitoring Prototype Mesin Packaging Berbasis PLC Wira Indani; Andrean Wahyudi; Wiwin Styorini; Yuli Triyani
Journal of Applied Smart Electrical Network and Systems Vol 3 No 01 (2022): Vol 3 No 1 : June 2022
Publisher : Indonesian Society of Applied Science (ISAS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jasens.v3i01.304

Abstract

Meningkatnya kebutuhan akan informasi, pemantauan, dan pengendalian sistem monitoring terhadap peralatan, industri, otomotif, dan bahkan farmasi membuat teknologi monitoring secara real time dibutuhkan. Sistem Monitoring dirancang agar mesin produksi dapat dimonitor secara realtime, sehingga pengawasan suatu proses produksi menjadi effisien. Sistem ini memanfaatkan controller PLC (Programmable logic controller) sebagai pengatur dari sistem monitoring yang dikomunikasikan dengan Scada sebagai software untuk memproses data yang dihasilkan dapat ditampilkan melalui PC secara realtime. Implementasi dari Rancang Bangun Sistem Monitoring Prototype Mesin Packaging Berbasis PLC dapat menampilkan data dari waktu permesinan, dari Running time, Minor Stop Time dan Breakdown Time. Dimana klasifikasi waktu tersebut di buat secara otomatis sehingga dapat memudahkan para operator dalam mendata waktu permesinan dan memonitor kegiatan produksi me njadi lebih mudah dan terstruktur. Data dari PLC yang didapatkan akan ditransmisikan kepada Sercer OPC yang terhubung dengan SCADA Intouch Wonderware sebagai display untuk proyek akhir ini. Dari sepuluh kali pengambilan data, waktu running yang diterima dari PLC ke PC (Wonderware) dan sebaliknya dari PC (Wonderware) ke PLC sistem ini merespon dengan baik ketika fungsi dijalankan. Dimana terdapat perbedaan waktu atau delay sebesar 0,27s pada tombol ON dan 0,33s pada tombol OFF dengan penekanan dengan Software dan Hardware. Penekanan dengan software mendapatkan waktu tunda yang lebih besar dikarenakan melewati server dahulu untuk menghubungkan PLC ke PC (Wonderware). Kata kunci: PLC, Scada, Monitoring, Sistem, human error.
Co-Authors Andika Djojo Kusuma Andrean Wahyudi Kevin Sean Farrel Manurung vania annisa queentinela Wahyu Eka Putra Wahyuni Khabzli Wira Indani Wiwin Styorini