Article Per Year (5 Year)
p-Index From 2021 - 2026
0.92
P-Index
This Author published in this journals
All Journal JUTIK : Jurnal Teknologi Informasi dan Komputer METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA
Ni Gusti Ayu Putu Harry Saptarini
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Social Sciences

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Food Journal: An application for allergy early detection Ciptayani, Putu Indah; Ni Gusti Ayu Putu Harry Saptarini; Ni Wayan Wisswani
Matrix : Jurnal Manajemen Teknologi dan Informatika Vol. 14 No. 3 (2024): Jurnal Manajemen Teknologi dan Informatika
Publisher : Unit Publikasi Ilmiah, P3M, Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/matrix.v14i3.128-134

Abstract

Allergies are now increasingly common, due to public awareness and lifestyle changes. Although antibody tests and skin pricks can detect allergies, their accuracy is not always 100%, causing some cases of false negatives. Observing the reactions caused by food is an alternative approach, but people often forget the food they consume, especially if the allergic reaction is indirect. Therefore, tools are needed to record foods and reactions, allowing traceability to undetected allergy triggers. This study is applied research that aims to develop a prototype application for recording daily food and tracking the history of eating activities based on the allergy symptoms entered. The software development method used is the Agile method with the Scrum framework. The Scrum framework is used considering that the development of this application requires speed in its provision, and is susceptible to change, so flexibility in development is an absolute must. The software testing was conducted by User Acceptance Testing (UAT) to ensure it meets the user’s needs and requirements. The UAT results are ease of use 3.8, functionality 3.99, user interface (UI) design 3.8, utility 4, and support and help 4. UAT results indicate that the ease of use and UI design have the worst scores and need to be improved, while the utility and support have the best results.
PERANCANGAN SISTEM PENDETEKSI TEMPAT PARKIR MOBIL MENGGUNAKAN METODE YOU ONLY LOOK ONCE (YOLO) DI POLITEKNIK NEGERI BALI I Wayan Candra Winetra; I Putu Astya Prayudha; Ni Gusti Ayu Putu Harry Saptarini
Jurnal Teknologi Informasi dan Komputer Vol. 10 No. 4 (2024): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi Desember 2024
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v10i4.3577

Abstract

Manajemen parkir di Politeknik Negeri Bali (PNB) yang dilakukan secara manual seringkalimengakibatkan kemacetan dan pemanfaatan lahan parkir yang tidak efisien. Penelitian ini mengatasitantangan tersebut dengan mengembangkan sistem deteksi ruang parkir mobil secara real-time. Sistem inimenggunakan algoritma You Only Look Once (YOLO), sebuah metode deteksi objek mutakhir, untukmengidentifikasi secara akurat ruang parkir yang tersedia di lingkungan kampus PNB. Denganmemberikan informasi parkir secara real-time kepada pengguna, sistem ini bertujuan untukmenyederhanakan prosedur parkir, mengurangi kemacetan, dan meningkatkan efisiensi keseluruhankampus. Hasil penelitian ini menunjukkan kelayakan dan efektivitas sistem yang diusulkan dalammeningkatkan manajemen parkir di PNB
Food Journal: An application for allergy early detection Ciptayani, Putu Indah; Ni Gusti Ayu Putu Harry Saptarini; Ni Wayan Wisswani
Matrix : Jurnal Manajemen Teknologi dan Informatika Vol. 14 No. 3 (2024): Matrix: Jurnal Manajemen Teknologi dan Informatika
Publisher : Unit Publikasi Ilmiah, P3M, Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/matrix.v14i3.128-134

Abstract

Allergies are now increasingly common, due to public awareness and lifestyle changes. Although antibody tests and skin pricks can detect allergies, their accuracy is not always 100%, causing some cases of false negatives. Observing the reactions caused by food is an alternative approach, but people often forget the food they consume, especially if the allergic reaction is indirect. Therefore, tools are needed to record foods and reactions, allowing traceability to undetected allergy triggers. This study is applied research that aims to develop a prototype application for recording daily food and tracking the history of eating activities based on the allergy symptoms entered. The software development method used is the Agile method with the Scrum framework. The Scrum framework is used considering that the development of this application requires speed in its provision, and is susceptible to change, so flexibility in development is an absolute must. The software testing was conducted by User Acceptance Testing (UAT) to ensure it meets the user’s needs and requirements. The UAT results are ease of use 3.8, functionality 3.99, user interface (UI) design 3.8, utility 4, and support and help 4. UAT results indicate that the ease of use and UI design have the worst scores and need to be improved, while the utility and support have the best results.
Perancangan Sistem Peringatan Dini Bencana Berbasis Website Terintegrasi BMKG dan Notifikasi Whatsapp Nuarta, I Wayan; I Putu Astya Prayudha; Ni Gusti Ayu Putu Harry Saptarini; Gde Brahupadhya Subiksa
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp7-17

Abstract

Indonesia has a high level of disaster risk due to its geological conditions. Although the BMKG has provided early warning information, its dissemination has not been fully received by the public in a timely manner. Meanwhile, the widespread use of WhatsApp in Indonesia offers an opportunity to utilize it as a medium for delivering disaster-related information. This study aims to design a website-based disaster early warning system integrated with BMKG data and WhatsApp notifications based on provincial regions. The system was developed using the Waterfall method with PHP, MySQL, and a WhatsApp gateway service, utilizing earthquake and weather data that are updated automatically. The results indicate that the system operates according to functional requirements. WhatsApp notifications were successfully delivered with a 95% success rate and an average delivery time of 2.6 seconds. Black box testing showed 100% valid results, while the System Usability Scale evaluation achieved a score of 82.5, indicating a very good level of usability.
Evaluasi Model Convolutional Neural Network (CNN) dalam Klasifikasi Penyakit Daun Jagung Berbasis Web Menggunakan Citra Digital Santika, I Gusti Ngurah Arya; Ni Gusti Ayu Putu Harry Saptarini; I Putu Astya Prayudha; Gde Brahupadhya Subiksa
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp98-106

Abstract

Corn leaf diseases such as blight and rust can reduce crop yields if they are not detected at an early stage. In Penatahan Village, the process of identifying these diseases is still carried out manually through visual observation, which may lead to misidentification due to the similarity of symptoms between different diseases. Therefore, a technology-based system is needed to assist the identification process in a more objective and efficient manner. This study aims to classify corn leaf diseases using the Convolutional Neural Network (CNN) method based on digital leaf images. The dataset used consists of 319 images categorized into three classes: healthy, blight, and rust, with 80% of the data used for training and 20% for validation. The model was developed using a transfer learning approach with the MobileNetV2 architecture and evaluated using a confusion matrix. The experimental results indicate that the model achieved an accuracy of 92.19%, indicating that the CNN method is capable of effectively classifying corn leaf diseases. The developed system can be utilized as a tool to assist in the rapid and objective identification of corn leaf diseases.
Co-Authors Gde Brahupadhya Subiksa I Putu Astya Prayudha I Wayan Candra Winetra Ni Wayan Wisswani Nuarta, I Wayan Putu Indah Ciptayani Santika, I Gusti Ngurah Arya