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All Journal Jurnal Matematika Jurnal Ilmu Komputer (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana E-Jurnal Matematika AdMathEdu : Jurnal Ilmiah Pendidikan Matematika, Ilmu Matematika dan Matematika Terapan PYTHAGORAS: Jurnal Program Studi Pendidikan Matematika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Jurnal Pengabdian Informatika (JUPITA) Prosiding Seminar Nasional Universitas Ma Chung Jurnal Nasional Teknologi Informasi dan Aplikasinya
I Gede Santi Astawa
Program Studi Teknik Informatika, Jurusan Ilmu Komputer. Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Udayana
Humanities Computer Science & IT Education Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Other

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Penerapan K-Means Clustering Pada Klasifikasi Risiko Kesehatan Ibu Hamil Nurtiani, Ni Made Novia; Astawa, I Gede Santi
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 1 (2022): JNATIA Vol. 1, No. 1, November 2022
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

The health condition of pregnant women greatly affects the growth and development of the fetus in the womb. There are many cases of maternal and infant deaths that occur in the world. Both caused by the condition of the mother's health during pregnancy and after childbirth. Several factors affect the health condition of pregnant women, namely age, blood pressure, blood sugar levels in the body of pregnant women, body temperature of pregnant women. This study will apply the K-Means method to classify the health risks of pregnant women. The author will also use the Elbow method to find the right cluster to classify.
Pengklasifikasian Kualitas Pisang dengan Deep Learning CNN Arsitektur VGG16 Junior, Vodka Joe; Santi Astawa, I Gede
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 2 No 3 (2024): JNATIA Vol. 2, No. 3, Mei 2024
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v02.i03.p10

Abstract

Bananas are one of the most popular fruits consumed worldwide, valued for their nutritional benefits and versatility in various dishes. However, ensuring banana quality, including ripeness and integrity, remains crucial in meeting consumer expectations and maintaining supply chain standards. Manual classification of banana quality can be tedious, prompting the need for efficient methods. In this study, we explore the classification of banana quality using Convolutional Neural Network (CNN) with VGG16 architecture and image augmentation. Leveraging previous research and considering the superior performance of VGG16, we gathered data from Kaggle and evaluated our model's accuracy. The implementation yielded promising results, achieving a peak accuracy of 97.50% with 15 epochs and an 80%-20% training-validation data split. This surpasses previous methods, indicating the effectiveness of CNN with VGG16 in banana quality classification. Keywords: Banana quality, Convolutional Neural Network, VGG16 architecture, Image augmentation, Classification accuracy
Implementasi Metode Optimasi Gradient Centralization untuk Pembuatan Model Klasifikasi Citra Pemandangan Alam Renaisan, Pasha; Astawa, I Gede Santi
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 1 (2022): JNATIA Vol. 1, No. 1, November 2022
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

Optimization algorithms are algorithms that are needed to properly train Neural Networks. Optimization algorithms help improve model performance by modifying the attributes of the neural network, such as weights and learning rate to further enchant the model. Gradient Centralization is a new optimization algorithm that optimizes by centralizing gradient vectors to have zero mean. This paper focuses on finding the optimal learning rate for Gradient Centralization and uses that learning rate to create a classification model to classify natural scene images. The optimal learning rate obtained by this research is 2e-5 and the model obtained 84,17% mean recall, 84,39% mean precision, and overall 83,60% accuracy.
Pengujian Fungsionalitas Sistem Pengamanan Digital Watermarking Kartu Indonesia Sehat Menggunakan Algoritma MSB Cahyani, Ni Putu Intan; Santi Astawa, I Gede
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 3 (2023): JNATIA Vol. 1, No. 3, Mei 2023
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

The rapid development of technology makes the dissemination of information also develop, and the use of digital data is increasingly used even in the health sector, one of which is when registering at health institutions online so that when registering it is necessary to scan the Healthy Indonesia Card to register. Sometimes there are onkuns who are not responsible for misusing it to do evil things, so based on these problems, the Healthy Indonesia Card (KIS) that will be used should be inserted with a watermark to minimize unwanted events. Watermark is a method of inserting information into digital data that aims to protect data ownership. One technique that can be used is the steganography technique, which uses the MSB (Most Significant Bit) algorithm. The length of time it takes to insert a watermark on a Healthy Indonesia Card (KIS) depends on how much text will be inserted to become a watermark Keywords: Watermarking, MSB Algoritma, Watermark KIS, Steganography
Ekstraksi Fitur MFCC pada Lagu Gundhul Pacul Sitorus, Roger Julian; Astawa, I Gede Santi
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 4 (2023): JNATIA Vol. 1, No. 4, Agustus 2023
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

MFCC is an effective method in audio feature extraction, including in song and music analysis. This method involves converting the frequency spectrum of the audio signal into the Mel scale, which is more in line with human auditory perception, and then calculating the cepstral coefficient. The results of the MFCC feature extraction on the song "Gundhul Pacul" show the pattern of the song's spectral and rhythmic characteristics. By using the MFCC representation, it is possible to see changes in energy and frequency patterns in audio signals at various time intervals. The results of the MFCC show that there are 13 resulting cepstral coefficients. However, the number of cepstral coefficients can be adjusted depending on the application and specific needs. Keywords: Features Extraction, MFCC, Gundhul Pacul
Hyperparameter Tuning Algoritma KNN Untuk Klasifikasi Kanker Payudara Dengan Grid Search CV Dharma, Nyoman Hendradinata; Astawa, I Gede Santi
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 1 No 1 (2022): JNATIA Vol. 1, No. 1, November 2022
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

One of the deadliest diseases in the world is Breast cancer. Breast cancer is a disease caused by abnormal cells that grow and develop rapidly and malignantly in the human breast and spread quickly to the tissues or organs around the breast. Data from Riskesdas in 2019 stated that in Indonesia, the prevalence of breast cancer was 41.2 per 100,000 Indonesians with an average death rate of 17 per 100,000 Indonesians. Technology nowadays is increasingly advanced and developed which can help people to find out the disease they are suffering from early before carrying out further examinations with the doctor. Breast cancer can be detected early by classifying it with machine learning algorithm. In this research, Breast cancer will be classified using K-Nearest Neighbor algorithm with Grid Search to classify whether a person has breast cancer or not. K-Nearest Neighbor (KNN) is one of the classification algorithms, where classification is carried out on data objects based on learning data whose neighbors are closest to the data object. The performance results of the classification model using K-Nearest Neighbor are 83% accuracy, 73% precision, and 89% recall.
Cover dan Halaman Depan Astawa, I Gede Santi
Jurnal Ilmu Komputer Vol 17 No 2 (2024): Jurnal Ilmu Komputer
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

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c cover dan halaman depan Astawa, I Gede Santi
Jurnal Ilmu Komputer Vol 16 No 2 (2023): Jurnal Ilmu Komputer
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

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Vector-Borne Disease Detection Using Random Forest and BPSO Raharja, Made Agung; Pradyto, Kadek Dwitya Adhi; Wibawa, I Gede Arta; Astawa, I Gede Santi
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.96722

Abstract

Vector-borne diseases such as malaria, dengue fever and yellow fever still pose a serious threat to public health. To distinguish between these diseases, an accurate classification process is required. In this study, Random Forest algorithm is used as a classification method due to its ability to overcome overfitting and provide good accuracy results. However, the large number of features in the data can cause redundancy and decrease the accuracy of the model. Therefore, the Binary Particle Swarm Optimization (BPSO) method is used as a feature selection technique to optimize the performance of Random Forest. The optimization process is also complemented by finding the best parameters using Random Search and Grid Search. Evaluation was conducted on a vector-borne disease dataset with 64 features and 11 disease classes. The results showed that the accuracy of the model increased from 90.48% to 100% after feature selection by BPSO which selected 37 best features, and Random Search proved to be more efficient in computation time than Grid Search. This research shows that the combination of Random Forest and BPSO can improve classification accuracy and efficiency in detecting vector-borne diseases.
Aplikasi Manajemen Karyawan Berbasis Website Sebagai Pengelola Karyawan di CV Tridatu Solution I Nyoman Dwi Pradnyana Putra; I Gede Santi Astawa; I Komang Ari Mogi
Jurnal Pengabdian Informatika Vol. 2 No. 4 (2024): JUPITA Volume 2 Nomor 4, Agustus 2024
Publisher : Jurusan Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana

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Abstract

Pengembangan zaman yang didukung oleh teknologi dan informasi telah memberikan manfaat besar bagi masyarakat, terutama di dunia usaha dan pendidikan. Saat ini, teknologi menjadi kebutuhan penting bagi manusia dan membantu dalam berbagai kegiatan, termasuk di dunia kerja. Kemajuan teknologi yang pesat telah menghasilkan berbagai aplikasi website yang banyak digunakan untuk memenuhi kebutuhan khusus. Dalam konteks perusahaan, aplikasi manajemen karyawan menjadi penting untuk meningkatkan efisiensi dan efektivitas pengelolaan sumber daya manusia. CV Tridatu Solution, sebuah Software House yang berbasis di Bali, adalah salah satu perusahaan yang membutuhkan implementasi aplikasi manajemen karyawan berbasis website. Saat ini, pengelolaan data karyawan di perusahaan tersebut belum terotomatisasi, sehingga memerlukan waktu yang lama untuk mencari dan mengakses informasi yang diperlukan. Dengan mengimplementasikan aplikasi manajemen karyawan berbasis website, CV Tridatu Solution diharapkan dapat meningkatkan pengelolaan karyawan, produktivitas, efisiensi, dan kualitas layanan. Selain itu, aplikasi ini juga dapat menjadi acuan bagi perusahaan lain dalam mengembangkan sistem pengelolaan karyawan yang baik dan efektif.
Co-Authors Adi Pradana, I Gede Surya Agus Juniartha, I Wayan Agus Prayogo Anak Agung Istri Ngurah Eka Karyawati Anak Agung Istri Ngurah Eka Karyawati Apsari, Made Sri Ayu Cahyani, Ni Putu Intan Christirahma, Ratri Desy Cokorda Rai Adi Pramartha Dewa Agung Ayu Mutiara Dewi Dewi, Dewa Agung Ayu Mutiara Dharma, Nyoman Hendradinata G.K. GANDHIADI Gede Gery Sastrawan Gede Maharta Pamuji Gorianto, Frisca Olivia Gotra, Anak Agung Ngurah Mahadana Apta Gst. Ayu Vida Mastrika Giri I Dewa Made Bayu Atmaja Darmawan I Gede Arta Wibawa I Gusti Agung Gede Ary Mahayasa I Gusti Agung Gede Arya Kadyanan I Gusti Ayu Purnami Pinatih I Gusti Ngurah Anom Cahyadi Putra I Gusti Ngurah Bagus Pramana Putra I Kadek Gowinda I Kadek Gowinda I Komang Ari Mogi I Komang Ari Mogi I Made Teja Sarmandana I Made Widiartha I Made Widiartha I Made Widiartha I Nyoman Dwi Pradnyana Putra I Nyoman Restu Muliarta I Putu Ananta Wijaya I Putu Gede Hendra Suputra I Putu Indie Surya Jayadi I Putu Rama Anadya I Putu Ryan Paramaditya I Putu Sedana Wijaya I Wayan Agus Juniartha I WAYAN SANTIYASA I Wayan Supriana Ida Ayu Taria Putri Mahadewi Ida Bagus Gede Bayu Priyanta Ida Bagus Gede Dwidasmara Ida Bagus Made Mahendra Jonas Kuntoro Junior, Vodka Joe Kadek Vincky Sedana Kompiang Gede Sukadharma Luh Arida Ayu Rahning Putri LUH PUTU IDA HARINI Made Darma Yunantara Marselinus Putu Harry Setyawan Ngurah Agus Sanjaya ER Ni Kadek Trisnawati Ni Made Novia Nurtiani Ni Made Rai Nirmala Santhi Ni Putu Intan Cahyani Ni Wayan Yulia Damayanti Nurtiani, Ni Made Novia Nyoman Hendradinata Dharma Pasha Renaisan Pradyto, Kadek Dwitya Adhi Prashanti, Ni Putu Vidya Vira Prawira, Agus Prebiana, Kiki Dwi Putra, Putu Pasek Wahyu Chandra Putri Cahyaning Putu Bayu Baskara Raharja, Made Agung Rasita Natasya Br Sitepu Renaisan, Pasha Restu Muliarta, I Nyoman Roger Julian Sitorus Saputra, I Wayan Wirahadi Sitorus, Roger Julian Suwitra, I Made Pradnyanandana Suwitra Theresia Seftiani Girsang Trisnawati, Ni Kadek Valerie Laurent Wijana, Sawendo Eko Wijaya, I Putu Ananta Yowe, Samson Cornelius Gele