Article Per Year (5 Year)
p-Index From 2019 - 2024
3.167
P-Index
This Author published in this journals
All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Majalah Ilmiah Teknologi Elektro Techno.Com: Jurnal Teknologi Informasi Seminar Nasional Informatika (SEMNASIF) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Network Engineering Research Operation [NERO] SINTECH (Science and Information Technology) Journal Jurnal Ilmiah Teknologi dan Komputer (JITTER) International Journal of Artificial Intelligence in Medical Issues
I Made Sunia Raharja
Program Studi Teknologi Informasi Universitas Udayana Bukit Jimbaran, Bali, Indonesia
Computer Science & IT Control & Systems Engineering Dentistry Electrical & Electronics Engineering Engineering Health Professions Medicine & Pharmacology Public Health

Published : 23 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 23 Documents
Search

 1   2   3 
Rancang Bangun Sistem Informasi Pemantau Kesehatan Balita Menggunakan Sistem Inferensi Fuzzy Boy Jehezkiel Kamanang Mahar; I Made Sunia Raharja; Gusti Agung Ayu Putri
JITTER : Jurnal Ilmiah Teknologi dan Komputer Vol 4 No 2 (2023): Jurnal Jitter Vol. 4, No. 2, August 2023.
Publisher : Program Studi Teknologi Informasi, Fakultas Teknik, Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JTRTI.2023.v04.i02.p10

Abstract

Proper use of Information Technology in the health sector will significantly help the management of information become easier to done, business processes become more effective than before, and most importantly, services for the community can be done more efficiently, better and more accurately. The Indonesian government is eager to become an independent country, and of course, this can be achieved with the optimal next generation of the nation. However, the facts on the ground cause nutritional problems, and malnutrition occurs in Indonesia. Cases of hunger and poor nutrition are not monitored because the current medical record application has not been able to provide information on the nutritional status of children under five automatically. The impact is that many problems about malnutrition and malnutrition are not well monitored given the complete medical record data they have. This study aims to discuss and develop the Toddler Health Monitoring module, which is an additional component in the application of medical records. Fuzzy Tsukamoto's inference method is used to create a reasoning engine that can determine the nutritional health status of toddlers because this method is flexible and improves data on output, it is easier, can be accepted by many people, and it is more suitable to understand by the humans.
Analisis Genre Film Berdasarkan Data Subtitle Nathania Novenrodumetasa; I Made Agus Dwi Suarjaya; I Made Sunia Raharja
JITTER : Jurnal Ilmiah Teknologi dan Komputer Vol 4 No 2 (2023): Jurnal Jitter Vol. 4, No. 2, August 2023.
Publisher : Program Studi Teknologi Informasi, Fakultas Teknik, Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JTRTI.2023.v04.i02.p23

Abstract

Film merupakan sekumpulan gambar yang mengalami banyak pemrosesan editing untuk mencapai hasil yang memuaskan. Film memiliki genre yang berfungsi untuk membedakan jenis film dan banyak film yang ternyata tidak sesuai dengan genre yang ada pada film tersebut. Berdasarkan masalah yang disebutkan tujuan dilakukan penelitian ini yaitu untuk menganalisis genre film berdasarkan data subtitle dari film. Tahap-tahap yang dilakukan dalam penelitian ini yaitu pengumpulan data subtitle, penggabungan kata menjadi kalimat, proses labelling, preprocessing, klasifikasi dan visualisasi. Bahasa pemrograman yang digunakan adalah Python, algoritma yang digunakan untuk klasifikasi adalah Naïve Bayes Classifier dan Random Forest dan visualisasi menggunakan Tableau. Dari penelitian ini diperoleh hasil akurasi menggunakan algoritma Random Forest lebih tinggi dibandingkan dengan Naïve Bayes, dimana hasil akurasi algoritma Random Forest adalah 0.841, sedangkan hasil akurasi algoritma Naïve Bayes adalah 0.682.
Performance Metrics of AdaBoost and Random Forest in Multi-Class Eye Disease Identification: An Imbalanced Dataset Approach Thomas Edyson Tarigan; Erma Susanti; M. Ikbal Siami; Ika Arfiani; Agus Aan Jiwa Permana; I Made Sunia Raharja
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 2 (2023): 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.v1i2.98

Abstract

This study presents a comprehensive evaluation of AdaBoost and Random Forest Classifier algorithms in the classification of eye diseases, focusing on a challenging scenario involving an imbalanced dataset. Eye diseases, particularly Cataract, Diabetic Retinopathy, Glaucoma, and Normal eye conditions, pose significant diagnostic challenges, and the advent of machine learning offers promising avenues for enhancing diagnostic accuracy. Our research utilizes a dataset preprocessed with Canny edge detection for image segmentation and Hu Moments for feature extraction, providing a robust foundation for the comparative analysis. The performance of the algorithms is assessed using a 5-fold cross-validation approach, with accuracy, precision, recall, and F1-score as the key metrics. The results indicate that the Random Forest Classifier outperforms AdaBoost across these metrics, albeit with moderate overall performance. This finding underscores the potential and limitations of using advanced machine learning techniques for medical image analysis, particularly in the context of imbalanced datasets. The study contributes to the field by providing insights into the effectiveness of different machine learning algorithms in handling the complexities of medical image classification. For future research, it recommends exploring a diverse range of image processing techniques, delving into other sophisticated machine learning models, and extending the study to encompass a wider array of eye diseases. These findings have practical implications in guiding the selection of machine learning tools for medical diagnostics and highlight the need for continuous improvement in automated systems for enhanced patient care.
Co-Authors Agus Aan Jiwa Permana Anak Agung Ketut Agung Cahyawan Wiranatha Ayu Krisnasari Ni Komang Azhari SN Boy Jehezkiel Kamanang Mahar Dwi Putra Gita Dwi Putra Githa Erma Susanti G M Arya Sasmita Gede Haris Premana Wibawa Gusti Agung Ayu Putri I Kadek Suabdinegara I ketut Gede Darma Putra I Made Adhiarta Wikantyasa I Made Agus Dwi Suarjaya I Made Budi Setiawan I Made Sukarsa I Putu Astya Prayudha I Putu Wira Cahaya Pratama Yudha Ida Ayu Shinta Dewi Paramitha Ika Arfiani Kadek Alit Sudiyana Kristiani Br Munthe M Ikbal Siami Nathania Novenrodumetasa Ni Komang Sutiari Ni Putu Ari Sinta Dewi Ni Putu Chendy Widya Santi Ni Putu Erika Sari Bintari Ni Putu Juli Astuti Ni Wayan Wisswani Putu Adhika Dharmesta Putu Wahyu Novian Martika Thomas Edyson Tarigan