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All Journal International Journal of Electrical and Computer Engineering TEKNIK INFORMATIKA Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmu Komputer dan Agri-Informatika Telematika : Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) CESS (Journal of Computer Engineering, System and Science) Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Petir Jurnal Ilmu Komputer dan Bisnis Indonesian Journal of Multidisciplinary Science Jurnal Sains Terapan : Wahana Informasi dan Alih Teknologi Pertanian Journal of Applied Agricultural Science and Technology Journal of Internet and Software Engineering Journal of Technology and System Information Scientific Journal of Informatics
Shelvie Nidya Neyman
Department Of Computer Science, IPB University, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Public Health Social Sciences Veterinary

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Neural Network Innovation for Analyzing Physiological Changes in Cattle Within Modern Transportation Systems Dhika, Harry; Buono, Agus; Neyman, Shelvie Nidya; Astuti, Dewi Apri
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 16 No. 2 (2025): JURNAL SIMETRIS VOLUME 16 NO 2 TAHUN 2025
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v16i2.16007

Abstract

The distribution of cattle before Eid al-Adha often leads to transport-induced stress, negatively affecting livestock performance and economic value. This study aims to develop a predictive model of post-transport cattle performance using Artificial Neural Networks (ANN). The dataset includes physiological parameters (rectal temperature, heart rate, and respiration) and blood metabolites (glucose and creatinine) collected before and after transportation. Data augmentation and feature selection were applied using Pearson correlation to address class imbalance. The ANN model was tuned with regularisation and dropout techniques to prevent overfitting. Evaluation results show that the model achieved 91% accuracy, with F1-scores of 0.90 (Increase), 0.97 (Stable), and 0.87 (Decrease). These findings demonstrate that ANN can capture complex patterns of physiological conditions in cattle and provide reliable predictions. This model has the potential to serve as the basis for developing an early warning system to minimize the risk of performance decline in cattle due to transport stress more adaptively and efficiently.
Pengembangan Model LMS Berbasis Serverless untuk Mengatasi Masalah Kinerja di Lingkungan Padat Pengguna Utama, Muhammad Jaka; Neyman, Shelvie Nidya; Priandana, Karlisa
Jurnal Ilmu Komputer dan Agri-Informatika Vol. 12 No. 2 (2025)
Publisher : Sekolah Sains Data, Matematika, dan Informatika. Institut Pertanian Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jika.12.2.142-154

Abstract

Moodle adalah salah satu aplikasi Learning Management System (LMS) yang berperan penting dalam pembelajaran secara daring. Salah satu faktor penting yang perlu diperhatikan dari LMS Moodle adalah kinerja di lingkungan padat pengguna. Padat pengguna mengacu pada situasi di mana jumlah pengguna atau peserta dalam sistem melebihi kapasitas yang dapat ditangani oleh sistem tersebut. Penelitian ini bertujuan untuk menghasilkan suatu pilihan model arsitektur LMS Moodle agar dapat menangani masalah kinerja dalam lingkungan padat pengguna. Pendekatan yang digunakan adalah arsitektur hybrid yang menggabungkan teknologi serverless pada komponen database, penyimpanan data, dan session handler, serta container di atas virtual machine (VM) dengan layanan IaaS pada core system dengan dukungan load balancing dan auto scaling. Penelitian dilakukan melalui empat tahap, yaitu identifikasi masalah, perancangan, implementasi, dan analisis model LMS. Hasil evaluasi menunjukkan bahwa model LMS yang dikembangkan mampu menangani hingga 1500 pengguna bersamaan tanpa penurunan kinerja signifikan, dengan response time di bawah 2500 ms dan failure request di bawah 1%. Pengujian lanjutan dengan konfigurasi batas minimum resource memungkinkan sistem melayani hingga 10.000 pengguna secara simultan. Skor benchmark plugin Moodle menunjukkan performa optimal pada seluruh aspek. Model ini terbukti dapat meningkatkan kehandalan dan skalabilitas LMS di lingkungan padat pengguna.
A Computer Vision Approach for Bali Cattle Morphometric Measurement Using Multi-Threshold Segmentation and FIS–CF-Based Classification Arnaldy, Defiana; Kudang Boro Seminar; Muladno; Heru Sukoco; Shelvie Nidya Neyman
JURNAL TEKNIK INFORMATIKA Vol. 19 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v19i1.47324

Abstract

Manual morphometric measurement of livestock is time-consuming, stressful to animals, and poses safety risks to handlers. This study presents a computer vision-based system for automatically measuring three key morphometric parameters of Bali cattle—withers height (WH), body length (BL), and chest girth (CG)—in accordance with the Indonesian National Standard (SNI). Images were captured from side and rear perspectives and processed using threshold-based image segmentation in the HSV color space to isolate the cattle contour. Pixel-to-centimeter calibration was performed using a fixed reference marker placed at a known distance of 1.5–2.0 m from the camera. The extracted morphometric values were subsequently fed into a Fuzzy Inference System with Certainty Factor (FIS-CF) for cattle grading and classification. Threshold values ranging from 0.5 to 0.9 were evaluated against manual ground-truth measurements using MAE, RMSE, MAPE, and R². The optimal threshold of 0.9 achieved MAPE values of 9.85% (WH), 6.04% (BL), and 11.49% (CG), representing up to 52% improvement over the lowest threshold. Although R² values remain negative due to limited sample size and non-linear pixel-to-metric variance, a consistent upward trend toward zero confirms measurement improvement with higher thresholds. The proposed method offers a practical, non-invasive alternative to manual measurement, with potential application in precision livestock farming and automated cattle grading systems.
Co-Authors A'isy, Nabil Arif Adi, Divo Wibowo Adzimi, Sista Naelly Affriza Brilyan Relo Pambudi Agus Putra Agus Buono Ahmad Rizaqu Muttaqi Al Habsy, Muhamad Alexandi, Muhammad Findi Alfasih, Hafiz Agi Alghifari, Muhammad Ali Djamhuri Ali Imran Alita Wulan Dini Ansyafa, Khairunnissa Zahran Athallah, Zaki Rafi Benhard Sitohang Defiana Arnaldy DEWI APRI ASTUTI Eliyah Acantha Manapa Sampetoding Erydhani, Gama Batara Ezer, Angga Eben Fadhil, Muhamad Fajarudin, Muhammad Febrika Ardy, Lutfi Aziz Halim, Kevin Viriya Harahap, Handika Saputra Harry Dhika, Harry Hartono Wijaya, Sony Hendra Rahmawan Hendra Rahmawan Herawan, Yoga Heru Sukoco Hesmi Aria Yanti I Nyoman Prama Pradnyana Imaduddin, Zaki Imas Sukaesih Sitanggang Irawan, Ferdiansyah Irman Hermadi Istiqomah, Iklima Karlisa Priandana Kudang Boro Seminar Kurniawan, Hafiz Abdul Fatah Lindayati Lindayati Lindayati Lindayati Lubis, Mohammad Hafiz Fachrezi Luki Abdullah Manurung, Cindy Arditha Claudia Marimin Marimin Mayangkara, Mahatmadi Ariq mufti, abdul Muhamad Syukur Muhammad Fikri Isnaini Muladno - Nugroho, Arga Agung Pohan, Muhammad Reza Zulva Rahman, Allegra Alif Ramadhan, Fauzan Naufal Gibran Ruswandi, Keinanjung Safitrah, Tiara Setiawan, Aep Sinaga, Antonio Banggas Gregory Sipahutar, Luat Paska Sitorus, David Zico Rafael Sri Nurdiati Sri Wahjuni Sugi Guritman Suraswati, Indah Tambunan, Muhammad Rafid Habibi Toto Haryanto Utama, Muhammad Jaka Verry Riyanto Wahjuni, Sri Wijaya, Sony H Yan Mitha Djaksana Yanto, Nur Witdi