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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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Implementasi Perancangan dan Pemeliharaan Jaringan Internet Menuju Smart School pada MA Raden Fattah Ahmad Taufiq Akbar; Bagus Muhammad Akbar; Shoffan Saifullah; Andiko Putro Suryotomo; Rochmat Husaini; Hari Prapcoyo
Masyarakat Berkarya : Jurnal Pengabdian dan Perubahan Sosial Vol. 2 No. 1 (2025): Februari : Masyarakat Berkarya : Jurnal Pengabdian dan Perubahan Sosial
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/karya.v2i1.1079

Abstract

Internet Network is one of the fields in informatics and electronics engineering which is now growing rapidly due to the issue of the industrial revolution 4.0 which is increasingly closely related to Cloud computing technology and the Internet of Things. Without resources and knowledge about computer networks, the Internet of things and Cloud computing are quite impossible to design. Computer networks give birth to internet access which is very much needed by every agency and even the entire community in the world. Especially in educational institutions such as Madrasah Aliyah (MA) Raden Fatah, which is located in Kalasan, Yogyakarta when in the era of the Covid-19 pandemic, it faces the challenge of disruption from offline learning to online learning. To answer the demands of the times, MA Raden Fattah is very enthusiastic in developing its institution towards a quality smart school. The network infrastructure available at MA Raden Fattah has not been optimized, so through this service, network design and management are carried out so that the need for access points that help students and teachers can be met. This service has succeeded in increasing the number of access points, optimizing the management of internet network resources at MA Raden Fattah, and improving the quality of teaching and learning services at the institution
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 (IJCS)
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.
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.
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 Hari Prapcoyo 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 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