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All Journal Indonesian Journal of Data and Science TEPIAN Buletin Poltanesa Adopsi Teknologi dan Sistem Informasi International Journal of Artificial Intelligence in Medical Issues
Rosmasari
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Agriculture, Biological Sciences & Forestry Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Education Health Professions Mathematics Medicine & Pharmacology Public Health

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Pengujian Kinerja Web Server Atas Penyedia Layanan Elastic Cloud Compute (EC2) Pada Amazon Web Services (AWS) Fandy; Rosmasari; Putra, Gubtha Mahendra
Adopsi Teknologi dan Sistem Informasi (ATASI) Vol. 1 No. 1 (2022): Adopsi Teknologi dan Sistem Informasi (ATASI)
Publisher : Mulawarman University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/atasi.v1i1.45

Abstract

Pembuatan Web Server yang tangguh dalam segi kehandalan, kecepatan maupun performa pada Web Server menjadi hal yang wajib dalam menangani semua permintaan user / visitor. Banyak pilihan server yang dapat dijadikan sebuah Web Server salah satunya Virtual Private Server (VPS). Virtual Private Server (VPS) adalah sebuah Server yang dibagi menjadi beberapa bagian yang mempunyai system operation sendiri. Provider VPS yang menjadi favorit dan sering menjadi rekomendasi pilihan layanan VPS adalah Amazon Web Services. Keunggulan dari kedua provider tersebut adalah memiliki Enterprise SSD atau penggunaan SSD (Solid State Drive) dalam hardware penyimpanannya. Berbagai pendapat datang mengenai kinerja atau performa dari provider tersebut mengenai layanan VPS mana yang cocok dijadikan sebagai sebuah Web Server yang tangguh. Munculah gagasan untuk dilakukan sebuah pengujian Load Testing pada Web Server yang dibangun atas layanan elastic cloud compute pada Amazon Web Services, beberapa parameter pengujian seperti Throughput, Response Time, Latency dan Resource Utilization digunakan sebagai penilaian kinerja atau performa dari Web Server Amazon Web Services. Pengujian juga menggunakan aplikasi Performance Testing dari Apache JMeter. Berdasarkan hasil pengujian pada Amazon Web Services dengan jenis pengujian Load Testing dan tipe pengujian HTTP Request, Web Server yang dibangun pada layanan ELASTIC CLOUD COMPUTE memiliki performa atau kinerja Web Server lebih baik dari pemyedia layanan lain
Sugeno Fuzzy Logic for IoT-based Chicken Farm Drinking Water Quality Monitoring Rosmasari; Nur Rahmad , Didi; Prafanto, Anton; Khoirunnita, Aulia; Jamil, Muh
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.229

Abstract

Introduction: The quality of drinking water plays a crucial role in the health and productivity of broiler chickens. In Indonesia, many poultry farms still rely on manual water testing using litmus paper, which may yield inaccurate results. This study aims to develop an Internet of Things (IoT)-based system integrated with the Sugeno fuzzy logic method to monitor and assess the quality of drinking water for broiler chickens in real time. Methods: An IoT prototype was developed using an ESP32 microcontroller, pH and turbidity sensors, and a cloud-based mobile application. Water quality data from 1,975 samples were collected over three days from a broiler farm in East Kalimantan using water sourced from a former mining lake. The system applies the Sugeno fuzzy inference system with 15 expert-defined rules to classify water quality into four categories: Very Good, Good, Bad, and Very Bad. Performance was evaluated using a Confusion Matrix. Results: The system achieved a classification accuracy of 96.76%, precision of 97.52%, recall of 98.79%, and F1-score of 98.15%. The results demonstrate the system's effectiveness in identifying water quality, with the majority of predictions falling into the correct class. The system also successfully transmitted real-time data to an Android application for monitoring purposes. Conclusions: The integration of IoT and Sugeno fuzzy logic provides a reliable, accurate, and scalable solution for real-time water quality monitoring in poultry farming. This system enhances decision-making for farmers, supports animal welfare, and can be further developed to include additional environmental parameters for broader livestock health monitoring
Classification of Multi-Region Bone Fractures from X-ray Images Using Transfer Learning with ResNet18 Alex, Rasni; Rosmasari
International Journal of Artificial Intelligence in Medical Issues Vol. 3 No. 1 (2025): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v3i1.249

Abstract

Fracture detection in radiographic images is a critical task in orthopaedic diagnostics, often requiring timely and accurate interpretation by medical professionals. However, manual evaluation of X-rays is time-consuming and prone to subjective bias. This study proposes an automated deep learning approach for binary classification of bone fractures using a pre-trained ResNet18 architecture. The model was trained and validated on a multi-region X-ray dataset consisting of 10,580 images categorized into fractured and non-fractured classes. To improve generalization, data augmentation techniques such as rotation and horizontal flipping were applied during pre-processing. The final model achieved a validation accuracy of 97.59%, with high true positive and true negative rates as confirmed by the confusion matrix analysis. The results demonstrate the effectiveness of transfer learning in handling radiographic image classification tasks while maintaining computational efficiency. This research contributes to the development of reliable and scalable computer-aided diagnostic tools that can support clinical decision-making, especially in environments with limited resources.
Predicting Thyroid Cancer Recurrence After Radioactive Iodine Therapy Using Random Forest and Neural Network Models Rosmasari
International Journal of Artificial Intelligence in Medical Issues Vol. 3 No. 1 (2025): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v3i1.250

Abstract

Thyroid cancer recurrence following Radioactive Iodine (RAI) therapy remains a clinical concern, necessitating accurate and timely risk prediction to guide post-treatment management. This study aims to evaluate the effectiveness of machine learning models—Random Forest and Neural Networks—in predicting recurrence using a structured clinical dataset consisting of 383 patient records and 13 diagnostic and pathological attributes. All categorical features were encoded ordinally, and the dataset was partitioned into training and testing sets with appropriate normalization for neural network processing. Both models were evaluated using standard metrics including accuracy, precision, recall, and F1-score. The Random Forest model achieved an accuracy of 97.39%, outperforming the Neural Network which recorded 93.04%. Moreover, Random Forest showed better recall in detecting recurrence cases, making it a more suitable model for clinical application. These results demonstrate that machine learning, particularly ensemble-based methods, can offer a practical and interpretable solution for recurrence prediction, supporting data-driven decision-making in thyroid cancer follow-up care.
Decision Support System on Grass Selection for Gardening Creation Using Multi-Objective Optimization on The Basis of Ratio Analysis (MOORA) Astuti, Indah Fitri; Najib, Naufal; Rosmasari; Dedy Cahyadi; Alex, Rasni; Kridalaksana, Awang Harsa
TEPIAN Vol. 4 No. 4 (2023): December 2023
Publisher : Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tepian.v4i4.2961

Abstract

Grass is a one-piece plant with thin, tapering leaves that grow from the base of the stem. Grass is one of the most common types of plants on earth, but not all types are the same. Some need regular care, while others can grow without ever being touched. There are various types of grass, but only a few types of grass are suitable as ornamental plants. Currently, garden construction service providers still use a manual system to determine the type of grass, so errors can occur, resulting in damage to the garden and a decrease in the artistic value and selling price of the grass. A technology can help determine the type of grass for making a garden, namely a decision support system. This system was built on a web basis using the Multi-Objective Optimization on the Basis of Ratio Analysis method to recommend types of grass for making gardens. This research uses 17 types of grass as alternative data and five selection criteria, namely leaf shape, growth rate, weather resistance, leaf color, care, leaf texture and price per meter. This method's research shows Japanese grass as the best alternative with the highest optimization value, namely -0.02499. The results of system validation using the Confusion Matrix method to compare system calculations with manual calculations obtained an accuracy value of 71%.
Geolocation untuk Lahan Kelapa Sawit Berbasis Android Astuti, Indah Fitri; Ahmad, Faqih Nur; Cahyadi, Dedy; Rosmasari; Kridalaksana, Awang Harsa; Andrea, Reza
Poltanesa Vol 23 No 1 (2022): Juni 2022
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v23i1.1288

Abstract

Kelapa sawit adalah salah satu spesies dengan jenis monokotil dan berakar serabut. Flora ini masuk ke Famili Arecaceae dan ordo Arecales. Perkebunan kelapa sawit termasuk tanaman yang dikelola oleh perusahaan-perusahaan komersial. Luas lahan yang dibutuhkan untuk tiap usaha saqit biasanya sangat besar, agar mampu menghasilkan produk yang menguntungkan pemilik dan karyawannya. Secara umum, produk kelapa sawit menjangkau banyak jenis, dari penghasil minyak sayur, minyak untuk dunia industri atau bahan bakar. Lahan tersebut biasanya memiliki banyak cabang sehingga banyak menyesatkan. Lokasi kebun yang diteliti berada di desa Bukit Pertama Kabupaten Kutai Timur Provinsi Kalimantan Timur. Penelitian ini bertujuan membantu orang yang berkepentingan namun tidak terlalu mengenal daerah tersebut dengan baik, juga untuk mengetahui luas lahan dan jumlah pokok sawit. Sistem menampilkan beberapa lahan yang saat ini terjangkau oleh sinyal internet, sehingga daerah yang lokasinya masih terlalu jauh dari lokasi yang diteliti atau pun yang kurang/tidak terjangkau sinyal belum akan dapat diakses. Geolocation berbasis android digunakan untuk mempermudah pembangunan sistem dan penggunaan sistem. Fitur yang disediakan adalah lahan, lokasi dan bantuan. Fitur lahan akan memberikan rincian informasi berupa luas, tahun tanam, jumlah pokok, dan tahun penanaman. Studi ini menghasilkan sistem geolocation pemetaan lahan sawit yang dapat diakses melalui aplikasi di telepon genggam.
Co-Authors Ahmad, Faqih Nur Awang Harsa Kridalaksana Dedy Cahyadi Dedy Cahyadi, Dedy Fandy Indah Fitri Astuti, Indah Fitri Jamil, Muh Khoirunnita, Aulia Najib, Naufal Nur Rahmad , Didi Prafanto, Anton Putra, Gubtha Mahendra Rasni Alex Reza Andrea