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All Journal Jurnal Keteknikan Pertanian Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Telematika : Jurnal Informatika dan Teknologi Informasi Seminar Nasional Informatika (SEMNASIF) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ILKOM Jurnal Ilmiah Compiler Indonesian Journal of Computer Science

Andiko Putro Suryotomo

Universitas Pembangunan Nasional Veteran Yogyakarta

Author-ID : 2490405

Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering

Published : 9 Documents Claim Missing Document

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Articles

IMPLEMENTASI PENGECEKAN PLAGIARISME PROPOSAL TUGAS AKHIR MAHASISWA TEKNIK INFORMATIKA UPN VETERAN YOGYAKARTA Awang Hendrianto Pratomo; Andiko Putro Suryotomo
Seminar Nasional Informatika (SEMNASIF) Vol 1, No 1 (2020): Peran Digital Society dalam Pemulihan Pasca Pandemi
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar

AbstractThe thesis proposal submission system currently used by the Department of Informatics Engineering at UPN "Veteran" Yogyakarta is still using conventional manual methods, so difficulties are encountered in checking the document's similarity for the student's final project proposal. The Winnowing algorithm can be used to do similarity checking in text-based documents. The Winnowing algorithm works by retrieving text data from the thesis proposal, then carrying out several steps such as preprocessing by finding the smallest hash value of each document used as a comparison to determine the similarity value between documents. The results showed that the smaller n-gram size would get a larger percentage similarity value (76% and 89%). This happens because a smaller n-gram size will result in smaller truncated strings, increasing the probability of finding the same string of characters. The bigger n-gram size will result in more characters than the smaller n-gram size, which causes fewer character strings to be found thus lowering the similarity value (down to 57% and 68%).Keywords: thesis proposal, plagiarism, Winnowing algorithm, hash value, n-gramAbstrakSistem informasi skripsi yang saat ini digunakan oleh program studi Teknik Informatika UPN “Veteran” Yogyakarta masih menggunakan cara konvensional, sehingga ditemui kesulitan dalam proses pemeriksaan kemiripan dokumen yang ada dalam proposal tugas akhir mahasiswa. Pemeriksaan kemiripan dokumen dapat dilakukan dengan menggunakan algoritme Winnowing. Algoritme Winnowing bekerja dengan cara mengembil data teks dari proposal, kemudian dilakukan beberapa tahapan seperti preprocessing kemudian dilakukan dengan mencari nilai hash terkecil dari setiap dokumen yang digunakan sebagai pembanding untuk menentukan nilai kemiripan dokumen tersebut. Hasil penelitian diketahui bahwa pemilihan n-gram yang semakin kecil akan memperoleh nilai persentase kemiripan yang besar (76% dan 89%). Hal ini terjadi karena pada n-gram yang lebih sedikit, string yang dipotong lebih kecil sehingga kemungkinan untuk ditemukannya rangkaian karakter yang sama semakin besar. Semakin besarnya n-gram, maka mengandung karakter yang lebih banyak dibandingkan dengan n-gram yang lebih kecil sehingga menyebabkan rangkaian karakter yang ditemukan semakin berkurang sehingga menurunkan nilai similaritas (hingga 57% dan 68%).Kata kunci: proposal skripsi, plagiarisme, algoritme Winnowing, nilai hash, n-gram

Simulation of oil palm root water uptake by using 2D numerical soil-water flow model Lisma Safitri; Andiko Putro Suryotomo; Satyanto Krido Saptomo
Jurnal Keteknikan Pertanian Vol. 9 No. 1 (2021): Jurnal Keteknikan Pertanian
Publisher : PERTETA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19028/jtep.09.1.31-40

The sustainability in oil palm plantation requires a specific information about the oil palm root water uptake. As an introduction, it is find of interest to simulate the root water uptake and water content pattern of oil palm. The study was performed by applying the 2D simulation soil-water flow model to 17th year old oil palm tree located in Siak, Riau with the loam soil type. The climate data during 22 Nov – 22 Dec 2018 was used to predict the evapotranspiration. The simulation over 30 days based on Richard equation illustrated the root water uptake distribution, water content change, pressure head and flow velocity. The most intensive root water uptake occurred in the upper root zone of oil palm tree as an impact of the higher root density. The significant root water uptake in the upper root zone lead to the decreasing of water content and increasing of pressure head. Consequently, there was a change of water flow direction from the wet area in the downward and sideward do dry root zone as the water supply to the oil palm tree. The validation test showed that the simulation performed well (R2 = 0.724 and RMSE = 0.0066).

Chicken Egg Fertility Identification using FOS and BP-Neural Networks on Image Processing Shoffan Saifullah; Andiko Putro Suryotomo
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 5 (2021): Oktober2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

This article aims to test FOS (first-order statistical) in extracting features of embryonated eggs. This test uses the initial step of image processing to get the best input image in feature extraction. The image processing method starts from the image acquisition process, then improves with image preprocessing and segmentation. Image acquisition in this study uses the concept of egg candling in a dark place captured with a smartphone camera. The acquisition results are improved by image preprocessing using gray scaling, image enhancement (by Histogram Equalization), and segmentation of chicken egg image. The segmentation results were extracted using FOS with five parameters: mean, entropy, variance, skewness, and kurtosis. Based on the calculation of these parameters, it is graphed and shows the difference in patterns between fertile and infertile eggs. However, some eggs have a similar pattern, thus affecting the identification process. The identification process used neural networks by the backpropagation method for training and testing. The training results provide an accuracy value of 100% of all training data; however, 80% of the new test data obtained test results at testing. This test is carried out with 100 data, 50 each for training and test data. Based on the test results, which significantly affect the level of accuracy is the feature extraction method. FOS pattern in detecting the fertility of chicken eggs by BP Neural Network is still categorized as low, so it is necessary to improve methods to get maximum results.

Detection of Chicken Egg Embryos using BW Image Segmentation and Edge Detection Methods Shoffan Saifullah; Andiko Putro Suryotomo; Yuhefizar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 6 (2021): Desember 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

This study aims to identify chicken egg embryos with the concept of image processing. This concept uses input and output in images. Thus the identification process, which was originally carried out using manual observation, was developed by computerization. Digital images are applied in identification by various image preprocessing, image segmentation, and edge detection methods. Based on these three methods, image processing has three processes: image grayscaling (convert to a grayscale image), image adjustment, and image enhancement. Image adjustment aims to clarify the image based on color correction. Meanwhile, image enhancement improves image quality, using histogram equalization (HE) and Contrast Limited Adaptive Histogram Equalization methods (CLAHE). Specifically for the image enhancement method, the CLAHE-HE combination is used for the improvement process. At the end of the process, the method used is edge detection. In this method, there is a comparison of various edge detection operators such as Roberts, Prewitt, Sobel, and canny. The results of edge detection using these four methods have the SSIM value respectively 0.9403; 0.9392; 0.9394; 0.9402. These results indicate that the SSIM values of the four operators have the same or nearly the same value. Thus, the edge detection method can provide good edge detection results and be implemented because the SSIM value is close to 1.00 (more than 0.93). Image segmentation detected object (egg and embryo), and the continued process by edge detection showed clearly edge of egg and embryo.

Identification of chicken egg fertility using SVM classifier based on first-order statistical feature extraction Shoffan Saifullah; Andiko Putro Suryotomo
ILKOM Jurnal Ilmiah Vol 13, No 3 (2021)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v13i3.937.285-293

This study aims to identify chicken eggs fertility using the support vector machine (SVM) classifier method. The classification basis used the first-order statistical (FOS) parameters as feature extraction in the identification process. This research was developed based on the processs identification process, which is still manual (conventional). Although currently there are many technologies in the identification process, they still need development. Thus, this research is one of the developments in the field of image processing technology. The sample data uses datasets from previous studies with a total of 100 egg images. The egg object in the image is a single object. From these data, the classification of each fertile and infertile egg is 50 image data. Chicken egg image data became input in image processing, with the initial process is segmentation. This initial segmentation aims to get the cropped image according to the object. The cropped image is repaired using image preprocessing with grayscaling and image enhancement methods. This method (image enhancement) used two combination methods: contrast limited adaptive histogram equalization (CLAHE) and histogram equalization (HE). The improved image becomes the input for feature extraction using the FOS method. The FOS uses five parameters, namely mean, entropy, variance, skewness, and kurtosis. The five parameters entered into the SVM classifier method to identify the fertility of chicken eggs. The results of these experiments, the method proposed in the identification process has a success percentage of 84.57%. Thus, the implementation of this method can be used as a reference for future research improvements. In addition, it may be possible to use a second-order feature extraction method to improve its accuracy and improve supervised learning for classification.

Fish detection using morphological approach based on K means segmentation Shoffan Saifullah; Andiko Putro Suryotomo; Bambang Yuwono
Compiler Vol 10, No 1 (2021): May
Publisher : Institut Teknologi Dirgantara Adisutjipto

Image segmentation is a concept that is often used for object detection. This detection has difficulty detecting objects with backgrounds that have many colors and even have a color similar to the object being detected. This study aims to detect fish using segmentation, namely segmenting fish images using k-means clustering. The segmentation process is processed by improving the image first. The initial process is preprocessing to improve the image. Preprocessing is done twice, before segmentation using k-means and after. Preprocessing stage 1 using resize and reshape. Whereas after k-means is the contrast-limited adaptive histogram equalization. Preprocessing results are segmented using k-means clustering. The K-means concept classifies images using segments between the object and the background (using k = 8). The final step is the morphological process with open and close operations to obtain fish contours using black and white images based on grayscale images from color images. Based on the experimental results, the process can run well, with the ssim value close to 1, which means that image information does not change. Processed objects provide a clear picture of fish objects so that this k-means segmentation can help detect fish objects.

Input Variable Selection for Oil Palm Plantation Productivity Prediction Model Andiko Putro Suryotomo; Agus Harjoko
Telematika Vol 20, No 1 (2023): Edisi Februari 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i1.9674

Purpose: This study aims to implement and improve a wrapper-type Input Variable Selection (IVS) to the prediction model of oil palm production utilizing oil palm expert knowledge criteria and distance-based data sensitivity criteria in order to measure cost-saving in laboratory leaf and soil sample testing.Methodology: The proposed approach consists of IVS process, searching the best prediction model based on the selected variables, and analyzing the cost-saving in laboratory leaf and soil sample testing.Findings/result: The proposed method managed to effectively choose 7 from 19 variables and achieve 81.47% saving from total laboratory sample testing cost.Value: This result has the potential to help small stakeholder oil palm planter to reduce the cost of laboratory testing without losing important information from their plantation.

Nondestructive Chicken Egg Fertility Detection Using CNN-Transfer Learning Algorithms Shoffan Saifullah; Rafal Drezewski; Anton Yudhana; Andri Pranolo; Wilis Kaswijanti; Andiko Putro Suryotomo; Seno Aji Putra; Alin Khaliduzzaman; Anton Satria Prabuwono; Nathalie Japkowicz
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26722

This study explores the application of CNN-Transfer Learning for nondestructive chicken egg fertility detection. Four models, VGG16, ResNet50, InceptionNet, and MobileNet, were trained and evaluated on a dataset using augmented images. The training results demonstrated that all models achieved high accuracy, indicating their ability to accurately learn and classify chicken eggs’ fertility state. However, when evaluated on the testing set, variations in accuracy and performance were observed. VGG16 achieved a high accuracy of 0.9803 on the testing set but had challenges in accurately detecting fertile eggs, as indicated by a NaN sensitivity value. ResNet50 also achieved an accuracy of 0.98 but struggled to identify fertile and non-fertile eggs, as suggested by NaN values for sensitivity and specificity. However, InceptionNet demonstrated excellent performance, with an accuracy of 0.9804, a sensitivity of 1 for detecting fertile eggs, and a specificity of 0.9615 for identifying non-fertile eggs. MobileNet achieved an accuracy of 0.9804 on the testing set; however, it faced challenges in accurately classifying the fertility status of chicken eggs, as indicated by NaN values for both sensitivity and specificity. While the models showed promise during training, variations in accuracy and performance were observed during testing. InceptionNet exhibited the best overall performance, accurately classifying fertile and non-fertile eggs. Further optimization and fine-tuning of the models are necessary to address the limitations in accurately detecting fertile and non-fertile eggs. This study highlights the potential of CNN-Transfer Learning for nondestructive fertility detection and emphasizes the need for further research to enhance the models’ capabilities and ensure accurate classification.

Implementasi Deep Classifier untuk Diagnosis Penyakit Glaukoma pada Citra Retina Mata Dhimas Arief Dharmawan; Raden Achmad Chairdino Leuveano; Andiko Putro Suryotomo; Michel Pierce Tahya; Sayang Sani
Indonesian Journal of Computer Science Vol. 12 No. 5 (2023): Indonesian Journal of Computer Science (IJCS) Volume 12 Number 5 (2023)
Publisher : STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Penerapan deep learning untuk diagnosis glaukoma dari gambar retina merupakan bidang yang berkembang pesat dalam pencitraan medis. Penelitian ini menyelidiki keampuhan model pembelajaran mendalam dengan menggunakan dua set data uji yang berbeda: DRISHTI-GS dan ORIGA, yang menjelaskan potensi dan tantangan dalam tugas medis yang kritis ini. Dalam kasus dataset DRISHTI-GS, model deep learning menunjukkan kinerja yang bervariasi di seluruh zaman. Epoch awal menunjukkan akurasi yang rendah dan kehilangan yang tinggi, tetapi peningkatan yang signifikan terjadi antara epoch 40 dan 70, mencapai akurasi sekitar 96% pada epoch 100. Hal ini menunjukkan potensi deep learning dalam mendiagnosis glaukoma dari gambar retina DRISHTI-GS. Sebaliknya, dataset ORIGA menunjukkan kemajuan yang lebih konsisten. Model ini terus meningkatkan akurasi, mencapai 97,54% pada epoch 80, dengan penurunan kerugian yang terjadi secara bersamaan, yang mengindikasikan konvergensi yang kuat. Hal ini menggarisbawahi kemahiran model dalam diagnosis dataset ORIGA, menyoroti janji klinisnya. Singkatnya, penelitian ini menunjukkan kelayakan deep learning untuk diagnosis glaukoma dari gambar retina, dengan hasil yang menjanjikan pada dataset DRISHTI-GS dan ORIGA.

Co-Authors Agus Harjoko Alin Khaliduzzaman Andri Pranolo Anton Satria Prabuwono Anton Yudhana Awang Hendrianto Pratomo Bambang Yuwono Dhimas Arief Dharmawan Lisma Safitri Michel Pierce Tahya Nathalie Japkowicz Raden Achmad Chairdino Leuveano Rafal Drezewski Saifullah, Shoffan Satyanto Krido Saptomo Sayang Sani Seno Aji Putra Wilis Kaswijanti Yuhefizar Yuhefizar

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