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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 Jurnal Migasian The Indonesian Journal of Computer Science Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial
Andiko Putro Suryotomo
Jurusan Informatika, Universitas Pembangunan Nasional Veteran Yogyakarta
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Law, Crime, Criminology & Criminal Justice Public Health

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Performance Analysis of FastAPI Framework on Lost Circulation Handling Management Application in Oil Well Drilling Suryotomo, Andiko Putro; Akbar, Bagus Muhammad; Husaini, Rochmat
Telematika Vol 21 No 1 (2024): Edisi Pertama 2024
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v21i1.13259

Abstract

Purpose: This study aims to conduct a load testing using JMeter and then analyze the performance of the FastAPI framework on the backend of the lost circulation handling management application in oil well drilling. The developed API receives input in the form of drilling parameter data (daily drilling report) from drilling engineers to be processed by a machine learning model (prediction and classification) through the FastAPI framework. The developed API returns processing data in JSON format.Methodology: Performance measurement is done by conducting load testing simulations using the help of JMeter software. Load testing scenarios are created by varying the number of users and ramp-up time, as well as the method of loading the machine learning model used (normal or preemptive loading). The parameters measured in the test scenario are average execution time, maximum execution time, error percentage, and request throughput.Findings: Load testing on a FastAPI-developed API demonstrated that for compute-heavy tasks like machine learning inference, increasing the number of processor cores and using preemptive model loading led to significantly better performance improvements than changes in processor clock speed or switching from HDD to SSD. Even when simulating a higher user load than initially expected (up to 250 users/threads), FastAPI maintained good response times and a low error rate, remaining below 20%.Originality/value/state of the art: This study result is an information about the performance of the FastAPI framework in the application of lost circulation handling management in oil well drilling in the deployment phase, not only up to the model testing phase as in previous studies. 
Nondestructive Chicken Egg Fertility Detection Using CNN-Transfer Learning Algorithms Saifullah, Shoffan; Drezewski, Rafal; Yudhana, Anton; Pranolo, Andri; Kaswijanti, Wilis; Suryotomo, Andiko Putro; Putra, Seno Aji; Khaliduzzaman, Alin; Prabuwono, Anton Satria; Japkowicz, Nathalie
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

Abstract

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.
Input Variable Selection for Oil Palm Plantation Productivity Prediction Model Suryotomo, Andiko Putro; Harjoko, Agus
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

Abstract

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

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i5.3378

Abstract

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.
Identification of chicken egg fertility using SVM classifier based on first-order statistical feature extraction Saifullah, Shoffan; Suryotomo, Andiko Putro
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

Abstract

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.
Co-Authors Agus Harjoko Agus Harjoko Ahmad Taufiq Akbar Akbar, Bagus Muhammad Alin Khaliduzzaman Andri Pranolo Anton Satria Prabuwono Anton Satria Prabuwono Anton Yudhana Awang Hendrianto Pratomo Bambang Yuwono Dharmawan, Dhimas Arief Drezewski, Rafal Husaini, Rochmat Japkowicz, Nathalie Kabul Pratiknyo, Avianto Kaswijanti, Wilis Khaliduzzaman, Alin Leuveano, Raden Achmad Chairdino Lisma Safitri lukmana, allen haryanto Michel Pierce Tahya Nathalie Japkowicz Prapcoyo, Hari Putra, Seno Aji Raden Achmad Chairdino Leuveano Rafal Drezewski Ragil Putradianto, Ristiyan Rochmat Husaini Saifullah, Shoffan Sani, Sayang Satyanto Krido Saptomo Sayang Sani Seno Aji Putra Tahya, Michel Pierce Wilis Kaswijanti Yuhefizar Yuhefizar