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All Journal Jurnal Ilmu Komputer JURNAL REKAYASA DAN MANAJEMEN AGROINDUSTRI E-Journal of Tourism (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana Jurnal Buana Informatika Jurnal Teknologi Informasi dan Ilmu Komputer JUTIK : Jurnal Teknologi Informasi dan Komputer JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal Jurnal WIDYA LAKSMI (Jurnal Pengabdian Kepada Masyarakat) Jurnal Pengabdian Informatika (JUPITA) Jurnal Nasional Teknologi Informasi dan Aplikasinya
Ngurah Agus Sanjaya ER
Universitas Udayana
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Analisis Sentimen Twitter Pengaruh Tokoh Politik dengan Menggunakan Metode K-Nearest Neighbor Palguna, I Made Surya Adi; Sanjaya ER, Ngurah Agus
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

Understanding public sentiment towards political figures is crucial for gauging their influence and impact. This study employs sentiment analysis to analyze Twitter data and assess the influence of political figures. Sentiment analysis, a computational exploration of expressed opinions, emotions, and sentience, allows insights into public perception on a large scale. This work leverages the K-Nearest Neighbor (KNN) algorithm, which classifies data based on its similarity to existing data points. Tweets undergo preprocessing, followed by TF-IDF weighting for keyword importance and cosine similarity calculations for comparing tweets to a labeled training dataset. By analyzing the nearest neighbors, sentiment values are assigned. The KNN model achieved an accuracy of 89%, a precision of 85%, and a recall of 88%, demonstrating its effectiveness in assessing sentiment and influence through Twitter data. This research contributes to the field of political communication by offering a robust method for analyzing public opinion and gauging the influence of political figures on social media platforms. Keywords: Sentiment Analysis, K-Nearest Neighbor, TF-IDF, Cosine Similarity
Klasifikasi Kategori Cerita Pendek Menggunakan Support Vector Machine Afandi, M Faisal; ER, Ngurah Agus Sanjaya
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 2 No 1 (2023): JNATIA Vol. 2, No. 1, November 2023
Publisher : Informatics Study Program, Faculty of Mathematics and Natural Sciences, Udayana University

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Abstract

Short stories are fascinating literary works to read because they present concise narratives that don't require readers to spend a lot of time to complete a story. Although the stories are short, determining the story category still requires careful reading to understand the content. However, it can become challenging when there is a large number of stories to be classified. Therefore, this research aims to develop a system that can automatically classify short story texts. The method used in this research is SVM (Support Vector Machine). The research is conducted to assist in automatically classifying short stories and create a system that bridges people to enjoying written works while enhancing literacy. The data used consists of short stories in the categories of romance, horror, and religion. The best-performing model is obtained through the training and validation process using new data. The results of testing the SVM method with a 70:30 data scenario, and hyperparameter C=10, gamma = 0.1 with kernel rbf or gamma = scale with kernel linear, yield an accuracy of 96% with a precision of 96.72%, recall of 96.36%, and an f1-score of 96.40%. Keywords: Cerita Pendek, Teks Klasifikasi, TF-IDF, Support Vector Machine
Penyusunan Sistem Rekomendasi Produk Diecast Mobil Dengan Metode Content-Based Filtering (CBF) Aditya Nugraha, Anak Agung; Sanjaya ER, Ngurah Agus
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 growing popularity of diecast car collections has created a demand for efficient recommendation systems to assist collectors in discovering new products. This study focuses on the development of a content-based filtering (CBF) recommendation system for diecast car products. The system employs the TF-IDF (Term Frequency-Inverse Document Frequency) and cosine similarity techniques to calculate the relevance between products and user preferences. By analyzing the textual features of diecast car products, such as brand, model, and specifications, the CBF system generates personalized recommendations based on similarity scores. The evaluation of the system's performance demonstrates its effectiveness in providing accurate and relevant recommendations, which enhance the user experience and facilitate the exploration of the diecast car market. Keywords: Content-Based Filtering, Diecast cars, Recommendation System, TF-IDF, Cosine Similarity
Klasifikasi Emosi Lirik Lagu dengan Long Short Term Memory dan Word2Vec Fortunawan, I Putu Diska; ER, Ngurah Agus Sanjaya
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

This research focuses on the classification of emotions in song lyrics using LSTM (Long Short-Term Memory) and Word2Vec embedding. Emotion classification in lyrics plays a crucial role in music recommendation systems, sentiment analysis, and understanding the affective aspects of music. The study explores the effectiveness of LSTM, a type of recurrent neural network (RNN), in capturing the sequential dependencies and patterns in lyrics, combined with Word2Vec embedding to represent the semantic meaning of words.The dataset consists of a collection of song lyrics labeled with 2 emotions. The lyrics are preprocessed and convertedinto word vectors using the Word2Vec model. The LSTM model is then trained on the preprocessed lyrics data, aiming to predict the corresponding emotion category for a given set of lyrics. Experimental results demonstrate that the proposed approach achieves a maximum accuracy of 72.8% in classifying emotions in song lyrics. The LSTM model leverages the sequential information in the lyrics to capture the emotional context effectively. The Word2Vec embedding enhances the representation of words, allowing the model to understand the semantic relationships between words and better discriminate between different emotional categories. Keywords: TextProcessing, Classification, LSTM, Word2Vec
Pengamanan Data Tekstual dengan Kombinasi Vigenere Cipher dan Caesar Cipher Pertiwi, Luh Arimas; Sanjaya ER, Ngurah Agus
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

Problems in data security is an important aspect in maintaining data storage, especially data stored in digital form. This is due to very rapid progress in the field of computer science with the open-system concept that has been widely used, so that this can make it easier for someone to destroy data, especially data stored in digital form without having to be known by the data custodian. In this case the researcher found a problem in using one algorithm, namely the Caesar cipher in data security, where there is a Brute force attack that tries all possible key combinations to crack a password. In the context of the Caesar Cipher, brute force can be used to try all possible shifts of letters and find a key that produces a plausible decrypted text. This study aims to maximize the security of textual data by combining two algorithms in it, in which the algorithm used is the Vigenere Cipher and the Caesar Cipher. The result of this research is that textual data that is secured becomes more difficult to understand by third parties who may want to manipulate data. Keywords : Textual Data, Vigenere Cipher, Caesar Cipher
Pemilihan Topik Skripsi Menggunakan LDA Candra Mahatagandha, Pijar; Sanjaya ER, Ngurah Agus
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

Writing is one of the most important activities to express ideas in visual form. However, many students have difficulty in writing research. The difficulty experienced by students lies in choosing a topic that will be developed in the background. The topics needed in research writing are topics that are currently being discussed by the community so that initial observations are needed to get the appropriate topic. However, not infrequently the observation stage will take a long time, so a solution is offered by building an LDA model to assist students in choosing a thesis topic. This study uses an unsupervised learning algorithm, namely LDA or (Latent Dirichlet Allocation) for topic modeling. The best model produced is the LDA model with a combination of 40 topics, alpha 0.4, beta symmetric, and corpus tf-idf with a coherence score of 0.43 and a perplexity of 199.09.
Analisis Sentimen dengan Logistic Regression untuk Deteksi Kata pada Livin’ by Mandiri Nirmala, Ni Made Gita Satviki; Sanjaya ER, Ngurah Agus
Jurnal Nasional Teknologi Informasi dan Aplikasnya Vol 2 No 4 (2024): JNATIA Vol. 2, No. 4, Agustus 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.i04.p25

Abstract

Livin by Mandiri is one of the most frequently used mobile banking. To find out the quality of the application, you can carry out sentiment analysis from reviews. The data taken from the Google Play Store was 6334 data from January 2022 to December 2022. The training data and test data used had a ratio of 80:20. This data goes through preprocessing and then TF-IDF weighting is carried out. After that, the analysis used logistic regression which produced 91.5% with C = 0.75. As well as getting negative sentiment results, namely precision 89%, recall 95%, f1-score 92%. Meanwhile, positive sentiment produces 94% precision, 88% recall, 91% f1-score. There is a word detection program that can help search for keywords including positive sentiment or negative sentiment from the Livin by Mandiri application. Keywords: Livin by Mandiri, Logistic Regression, Mobile Banking, Sentiment Analysis, TF-IDF, Word Detection
Pengembangan Prototipe Aplikasi Chatbot Pengenalan Makanan Nusantara Sebagai Asisten Traveller Indonesia Adu, Enga Prinda; Pramartha, Cokorda Rai Adi; Sanjaya ER, Ngurah Agus
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

Indonesia merupakan negara dengan kepulauan terbesar didunia. Hal ini membuat Indonesia kaya akan budaya dan bahasa. Salah satu ciri khas yang mengindentikkan setiap daerah yang terdapat diIndonesia adalah makanan khas daerah. Makanan khas daerah ini, sangat berkaitan erat dengan suatu daerah yang di wariskan dari generasi ke generasi sebagai tradisi dan sudah menjadi bagian dari budaya masyarakat setempat. Namun, seiring berjalannya waktu makanan khas darerah atau makanan nusantara ini semakin tidak populer dan kalah oleh pengaruh makanan luar yang berasal dari negara lain. Kurangnya pengenalan dan apresiasi terhadap tradisi kebudayaan makanan nusantara dan kurangnya media yang memperkenalkan makanan nusantara ini yang menjadi faktor utama. Oleh karena itu, dalam memperluas pengenalan dan memperkaya wawasan akan makanan nusantara kepada masyarakat Indonesia maka dibuatlah sebuah (aplikasi makanan nusantara) untuk dapat memudahkan masyarakat Indonesia dalam mengetahui makanan khas setiap daerah dan tetap dilestarikan. Penelitian ini menghasilkan aplikasi makanan nusantara berbasis web. Aplikasi yang dihasilkan dapat membantu para traveller maupun warga lokal untuk mengetahui setiap makanan khas daerah yang dikunjungi berdasarkan nama, lokasi terdekat dan menampilkan informasi mengenai makanan khas daerah apa yang di inginkan.
Perlindungan pada Citra Motif Kain Songket dengan Teknik Watermarking Menggunakan RSA Encryption dan MSB Steganography Gemuh Raharja RL, I Wayan Gede; Agus Sanjaya ER, Ngurah
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.p26

Abstract

This research develops a watermarking steganography technique using the MSB method to protect songket cloth motifs. In the MSB-based steganography method for embedding watermarks in images, the MSB transformation is used to replace data bits in image segments with secret data bits. The embedded watermark functions as an identification mark that is difficult to remove or change without destroying the authenticity of the original motif. Accuracy testing using PSNR and MSE produced an average PSNR of 75.177 dB and MSE of 0.0018, which shows that this technique is effective in maintaining the authenticity and integrity of Songket cloth motifs. Keywords: Image Processing, MSB, RSA, Watermarking, Songket
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.
Co-Authors Abimanyu, Cokorda Gde Aditya Nugraha, Anak Agung Adu, Enga Prinda Afandi, M Faisal Agus Muliantara Agus Muliantara Albertus Ivan Suryawan Anak Agung Aditya Nugraha Anak Agung Istri Ngurah Eka Karyawati Anak Agung Sinta Trisnajayanti Anggita S, Ni Putu Ayu Sherly Arimbawa, I Gede Ayu Kadek Nadya Oktaviana Budiantari, Ni Made Julia Candra Mahatagandha, Pijar Cokorda Gde Abimanyu Cokorda Pramartha Cokorda Rai Adi Pramartha Darmayasa, I Nengah Oka Farin Istighfarizky Firman Ali Eka Atmojo Fortunawan, I Putu Diska Gede Krisna Surya Artajaya Gede Sukadarmika Gemuh Raharja RL, I Wayan Gede Giri, Gst. Ayu Vida Mastrika Gst. Ayu Vida Mastrika Giri Gusti Ayu Vida Mastrika Giri Gusti Ayu Vidjaretha Wardana Gusto Gibeon Ginting Hairul Lana HARI MULYAWAN I Dewa Made Bayu Atmaja Darmawan I Dewa Made Candra Wiguna Marcelino I Dewa Made Candra Wiguna Marcelino I Gede Arta Wibawa I Gede Santi Astawa I Gede Wira Kusuma Jaya I Gst. Bgs. Arya Yudiastina I Gusti Agung Gede Arya Kadyanan I Gusti Ngurah Anom Cahyadi Putra I Kadek Agus Andika Putra I Kadek Gowinda I Ketut Gede Suhartana I Ketut Satriawan I Komang Ari Mogi I Komang Surya Adinandika I Made Ady Wirawan I Made Ari Widiarsana I Made Widiartha I Made Widiartha I Nengah Oka Darmayasa I Putu Diska Fortunawan I Putu Edy Suardiyana Putra I Putu Gede Hendra Suputra I WAYAN SANTIYASA I Wayan Sugiana Ida Bagus Gede Dwidasmara Ida Bagus Gede Dwidasmara Ida Bagus Made Mahendra Ida Bagus Made Surya Widnyana Karel Leo Rivaldo Kurniadi, Kenny Luh Arida Ayu Rahning Putri Luh Gede Astuti Luh Gede Astuti Luh Gede Tresna Dewi Luh Putu Eka Nadya Wati LUH PUTU IDA HARINI Made Agus Hendrayana Made Darma Yunantara Made Hanindia Prami Swari Made Widiartha Negara, I Made Wahyu Guna Ni Luh Komang Indira Pramesti Ni Made Alisya Putri Hapsari Ni Made Ary Esta Dewi Wirastuti Ni Made Dian Kurniasari Ni Made Julia Budiantari Ni Putu Ambalika Dewi Ni Putu Intan Cahyani Ni Putu Vina Amandari Nirmala, Ni Made Gita Satviki Palguna, I Made Surya Adi Palla, Hans Rio Alfredo Pertiwi, Luh Arimas Pijar Candra Mahatagandha Pradana, I Putu Aditya Pradiptha, I Gde Made Hendra Putra, Gede Bagus Prawira Putri, Riana Pramesti Raharja, Made Agung Riana Pramesti Putri Safira Sinta Wahyuni, Ni Made Suryawan, Albertus Ivan Wayan Citra Wulan Sucipta Putri Yasa, I Gede Cahya Purnama