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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal RESISTOR (Rekayasa Sistem Komputer) Jurnal Pengabdian Informatika (JUPITA) Jurnal Nasional Teknologi Informasi dan Aplikasinya
Giri, Gst. Ayu Vida Mastrika
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Humanities Computer Science & IT Electrical & Electronics Engineering Energy Engineering Other

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Implementasi Sistem Cerdas Menggunakan Case Base Reasoning Sebagai Rujukan Terpadu Penerima Bantuan Kemiskinan di Kabupaten Tabanan Supriana, I Wayan; Giri, Gst. Ayu Vida Mastrika; Bimantara, I Made Satria
Jurnal RESISTOR (Rekayasa Sistem Komputer) Vol. 5 No. 2 (2022): Jurnal RESISTOR Edisi Oktober 2022
Publisher : Prahasta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31598/jurnalresistor.v5i2.1097

Abstract

Strategi dan inovasi mempercepat penanggulangan kemiskinan pemerintah Kabupaten Tabanan semakin digalakkan, tahun 2020 diperkirakan persentase kemiskinan mengalami peningkatan karena banyak sektor parisiwata dan sektor industri lainnya terdampak covid-19. Sampai saat ini distribusi program-program pengentasan kemiskinan berpusat pada database terpadu, sementara dilapangan terdapat banyak kendala. Identifikasi rumah tangga miskin perlu ditingkatkan sehingga dapat menentukan jenis bantuan utama yang dibutuhkan berdasarkan komponen kriteria yang sudah dipenuhi. Melalui penelitian ini dikembangkan aplikasi berupa sistem cerdas yang dapat menentukan bantuan prioritas rumah tangga miskin. Sistem yang dikembangkan menggunakan metode case base reasoning yaitu identifikasi rumah tangga sasaran didasari oleh penalaran berbasis kasus. Model penilaian menggunakan 23 fitur identifikasi rumah tangga miskin dan 18 fitur bantuan kemiskinan. Berdasarkan penelitian yang sudah dilakukan, model CBR dengan kluster K-Means lebih baik dibandingkan CBR tanpa kluster. Komposisi data training 80% dan data testing 20%, sistem CBR dengan indexing K-mean memiliki akurasi sebesar 0.48% dan tanpa indexing sebesar 0.46%
Klasifikasi Kualitas Buah dengan Menggunakan Convolutional Neural Network (Studi Kasus: Dataset Fresh and Rotten Classification) Dwijayana, I Gede Diva; Mahendra, I Putu Fajar Tapa; Simarmata, Ivan Luis; Giri, Gst. Ayu Vida Mastrika
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 2 No 2 (2024): JNATIA Vol. 2, No. 2, Februari 2024
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

This research aims to develop a deep learning model for fruit quality classification using Convolutional Neural Network (CNN) with the Fresh and Rotten Classification dataset. Two CNN models are compared, with the first model serving as the baseline and the second model resulting from parameter tuning based on the first model. The results indicate that increasing the number of epochs improves the model accuracy, as evidenced by the first model achieving 91% accuracy with 10 epochs and 93% accuracy with 15 epochs. Similar patterns are observed in the second model, with 87% accuracy at 10 epochs and 90% accuracy at 15 epochs. Despite the second model involving the addition of layers and parameters, its accuracy tends to be lower compared to the first model. The research emphasizes that increasing the number of epochs enhances model performance, while adding layers does not always lead to significant improvements, depending on the model's complexity and dataset characteristics. The first model, trained with 15 epochs, demonstrates the highest accuracy, approaching results from similar previous studies. This evaluation provides valuable insights for developing a CNN-based fruit classification model on the Fresh and Rotten Classification dataset. Keywords: Fruit Classification, Rotten, Fresh, Convolutional Neural Network, Accuracy, Epochs
Musical Instrument Classification using Audio Features and Convolutional Neural Network Giri, Gst. Ayu Vida Mastrika; Radhitya, Made Leo
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.8058

Abstract

This research classifies acoustic instruments using Convolutional Neural Network (CNN). We utilize a dataset from Kaggle containing audio recordings of piano, violin, drums, and guitar. The training set consists of 700 guitar, percussion, violin, and 528 piano samples. The test set contains 80 samples of each instrument. Features such as Mel spectrograms, MFCCs, and other spectral and non-spectral characteristics are extracted using the Librosa package. Three feature sets"”spectral-only, non-spectral-only, and a combined set"”are employed to evaluate the efficacy of CNN models. Various CNN configurations are tested by adjusting the number of convolutional filters, learning rates, and epochs. The combined feature set achieves the highest performance, with a validation accuracy of 71.8% and a training accuracy of 76.9%. In comparison, non-spectral features achieve a validation accuracy of 68.4%, and spectral-only features achieve 69.3%. These findings highlight the benefits of using a comprehensive feature set for accurate classification.
Application of Gated Recurrent Unit in Electroencephalogram (EEG)-Based Mental State Classification Giri, Gst. Ayu Vida Mastrika; Sanjaya ER, Ngurah Agus; Suhartana, I Ketut Gede
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8825

Abstract

The classification of mental states based on electroencephalogram (EEG) recordings has recently gained significant interest in cognitive monitoring and human-computer interaction fields. Due to high signal variability and sensitivity to noise, correct classification is still tricky, even with advances in the analysis of EEG signals. Among deep learning models, Gated Recurrent Unit (GRU) models have established great potential for sequential EEG data analysis. The applications of the GRUs are less reviewed in tasks concerning classification cases of mental states compared to hybrid and convolutional models. Based on this paper, we will propose a method for developing a model based on the GRU network trained with raw EEG data in the classification tasks of mental states of concentration and relaxed conditions. We analyzed 400 EEG recordings taken from 10 subjects within a controlled environment and collected using the Muse EEG Headband. The mean, standard deviation, skewness, kurtosis, power spectral density, zero-crossing rate, and root mean square were extracted as statistical features from the raw EEG data. After parameter tuning, the GRU-based model achieved an excellent average accuracy value of 95.94% and also yielded precision, recall, and F1-scores within the range of 0.95 to 0.97 over 5-fold cross-validation. This shows that GRU works well in classifying mental states based on the EEG data.
Analisis Keamanan Aplikasi Mobile Denpasar Prama Sewaka Adiriyanto, Shiennyta Florensia; Wibawa, I Gede Arta; Giri, Gst. Ayu Vida Mastrika
Jurnal Pengabdian Informatika Vol. 3 No. 1 (2024): JUPITA Volume 3 Nomor 1, November 2024
Publisher : Jurusan Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana

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Abstract

Denpasar Prama Sewaka is a mobile application that integrates all the mobile applications owned by the Denpasar City Government. This mobile application is still in the development stage and, as such, it may have various security vulnerabilities that need to be addressed. This is typically due to the primary focus being on functionality and development rather than security. Mobile applications in the development phase tend to have common vulnerabilities that frequently occur, such as weak encryption, poor input validation, and insecurity related to network resources. Therefore, penetration testing of the Denpasar Prama Sewaka mobile application is essential to mitigate potential security risks. To prioritize the security of the code and manifest of this application, MobSF on Linux will be utilized to conduct the assessment.
Pengembangan Desain UI/UX dan Pemodelan Sistem Manajemen Konten untuk Situs Web Perjalanan I Komang Gede Apriana, I Komang Gede Apriana; Wibawa, I Gede Arta; Giri, Gst. Ayu Vida Mastrika
Jurnal Pengabdian Informatika Vol. 3 No. 2 (2025): JUPITA Volume 3 Nomor 2, Februari 2025
Publisher : Jurusan Informatika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Udayana

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Abstract

Artikel ini menyajikan pengembangan untuk mendesain UI/UX dan memodelkan sistem manajemen konten untuk situs web perjalanan yang menampilkan informasi tentang destinasi wisata dan aktifitas yang ada di Indonesia, khususnya di Pulau Bali. Lingkup kegiatan meliputi analisis kebutuhan pengguna, desain antarmuka, pengembangan prototipe, dan evaluasi. Metode yang digunakan adalah metode desain iteratif yang melibatkan pengguna dalam setiap tahap. Hasil utama dari kegiatan ini adalah sebuah prototipe situs web perjalanan yang memiliki UI/UX yang menarik, mudah digunakan, dan sesuai dengan konten yang disajikan. Prototipe ini juga dilengkapi dengan sistem manajemen konten yang memungkinkan pengelola situs untuk menambah, mengubah, atau menghapus konten secara dinamis. Simpulan utama dari kegiatan ini adalah bahwa desain UI/UX dan sistem manajemen konten yang baik dapat meningkatkan kualitas dan daya tarik situs web perjalanan serta memudahkan pengelolaan konten. Keberartian dari kegiatan ini adalah memberikan kontribusi bagi pengembangan industri pariwisata di Indonesia melalui penyediaan informasi yang akurat, relevan, dan menarik bagi wisatawan.
Co-Authors Adiriyanto, Shiennyta Florensia Dwijayana, I Gede Diva I Gede Arta Wibawa I Ketut Gede Suhartana I Komang Gede Apriana, I Komang Gede Apriana I Made Satria Bimantara I Wayan Supriana Mahendra, I Putu Fajar Tapa Ngurah Agus Sanjaya ER Radhitya, Made Leo Simarmata, Ivan Luis