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All Journal Jurnal Ilmu Komputer (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana Jurnal Sains dan Teknologi JUIMA : Jurnal Ilmu Manajemen Jurnal Ilmiah Kursor JUTIK : Jurnal Teknologi Informasi dan Komputer SINTECH (Science and Information Technology) Journal Jurnal RESISTOR (Rekayasa Sistem Komputer) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Bhakti Persada Jurnal Aplikasi IPTEKS Jurnal Pengabdian Mandiri Dharma Jnana Seminar Nasional Aplikasi Iptek (SINAPTEK) Jurnal Pengabdian Informatika (JUPITA) Paradharma: Jurnal Aplikasi IPTEK Jurnal Pengabdian Masyarakat dan Riset Pendidikan Jurnal Nasional Teknologi Informasi dan Aplikasinya
Raharja, Made Agung
Universitas Udayana
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

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Komparasi Ekstraksi Fitur BoW dan TF-IDF untuk Klasifikasi SMS Menggunakan Naive Bayes I Komang Dwiprayoga; Made Agung Raharja
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 2 (2025): JNATIA Vol. 3, No. 2, Februari 2025
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2025.v03.i02.p03

Abstract

Short Message Service (SMS) has become one of the most popular communication media. However, the ease and speed of sending SMS is also utilized by irresponsible parties to send spam messages. These spam messages not only annoy users but can also cause financial losses and theft of personal data. The purpose of this research is to compare feature extraction methods that have the best performance such as TF-IDF and Bag of Word tested with Multinomial Naive Bayes machine learning algorithm. For the first research stage, load dataset, data balancing, data preprocessing, feature extraction, modeling with machine learning algorithms, and then testing and comparing confusion matrix models on each feature extraction. The results of this study show that the use of BoW feature extraction has better performance than the TF-IDF feature extraction model with an accuracy value of 94.44%. 
Analisis dan Klasifikasi Genre Musik Menggunakan Algoritma STFT dan Random Forest Merry Royanti Manalu; Made Agung Raharja
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 3 No. 1 (2024): JNATIA Vol. 3, No. 1, November 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2024.v03.i01.p24

Abstract

This research analyzes the classification of music genres using the Short Time Fourier Transform (STFT) algorithm. The main objective is to identify the effectiveness of STFT, along with the Random Forest classification algorithm, in distinguishing music genres based on their spectral characteristics. The STFT method is utilized to transform audio signals into a spectral representation within a short time window. The extracted spectral features are then fed into the Random Forest classification algorithm to classify different music genres. This research involves the use of representative datasets from various music genres for performance evaluation. Experimental results show that using STFT as a feature and employing the Random Forest classification algorithm in the process are able to provide satisfactory results in distinguishing music genres, with an accuracy of 86%. These findings demonstrate the potential of STFT, in combination with Random Forest, as a useful tool in music analysis and automatic classification of music genres. 
Pengelompokan Lagu Populer untuk Musik Gym Menggunakan Metode K-Means Clustering Pande Nyoman Weda Wesnawa; Made Agung Raharja
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 4 (2024): JNATIA Vol. 2, No. 4, Agustus 2024
Publisher : Informatics Department, 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.p24

Abstract

Music streaming has emerged as the primary mode for individuals to enjoy music while exercising at the gym. Spotify, among the largest music streaming platforms, surveyed 2,000 gym users in the US, revealing that 82% utilize Spotify during workouts. Studies indicate music significantly influences workout quality. This study aims to cluster popular Spotify songs of 2023 using KMeans based on audio attributes like tempo, energy, and danceability. Data sourced from Kaggle's 2023 Spotify dataset underwent preprocessing. Utilizing the Elbow method, optimal cluster count determination yielded two clusters: one apt for gym use and another unsuitable. Out of 954 songs, 72.3% were gym appropriate. Visualizations via pie charts and 3D scatter plots depicted clusters based on BPM, energy, and danceability. Purity evaluation scored 1.0, ensuring accurate cluster formation. This research aids gym proprietors in crafting strategies to select motivating music, enhancing members' workout experiences. 
Analisis Sentimen pada Ulasan Aplikasi myIM3 Menggunakan Multinomial Naive Bayes dengan TF-IDF Ni Komang Ayu Juliana; Made Agung Raharja
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 3 (2024): JNATIA Vol. 2, No. 3, Mei 2024
Publisher : Informatics Department, 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.p25

Abstract

The digital service adoption in Indonesia has emerged as a primary trend to meet the needs of the millennial generation, seeking greater convenience and speed. Amidst this trend, self-service apps like MyIM3 by Indosat Ooredoo Hutchison have become a trusted solution for users to manage their services more efficiently. Sentiment analysis is crucial for understanding user responses to such apps. This study employs the Multinomial Naïve Bayes algorithm with hyperparameter alpha 0.8 and TF-IDF to analyze sen timent towards user reviews on Google Play Store for MyIM3. The dataset, sourced from Kaggle, consists of 8475 reviews, pre-processed and labeled to 8212 reviews. Model evaluation with an 80:20 split reveals an overall accuracy of 89%, with a precision of 86% for negative (0) and 93% for positive (1) labels. The recall for negative is 95% and positive is 81%. Thus, this research contributes to understanding user perspectives on MyIM3 and provides a basis for enhancing the quality of app-based services. 
Implementasi Random Forest Dengan LASSO Dalam Klasifikasi Penyakit Yang Ditularkan Melalui Nyamuk Kadek Dwitya Adhi Pradyto; Made Agung Raharja
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 1 No. 4 (2023): JNATIA Vol. 1, No. 4, Agustus 2023
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2023.v01.i04.p23

Abstract

Several diseases that can attack human health can be transmitted through disease vectors. One of the insects belonging to the disease vector is the mosquito. Diseases that can attack humans due to transmission through mosquitoes include malaria, dengue fever, chikungunya, yellow fever, rift valley fever, and many more. With so many types of diseases that are transmitted by mosquitoes and the symptoms that look quite similar, a classification process is carried out to distinguish the types of diseases. In this study, the classification was carried out using the Random Forest algorithm with the LASSO algorithm for feature selection. It was found that the average accuracy values of the Random Forest before and after carrying out feature selection using LASSO were 88% and 76%, respectively. From the values obtained, it can be concluded that the Random Forest has better performance without feature selection using the LASSO method. 
Implementasi Aplikasi “SahabatAnabul” Berbasis User Centered Design Sebagai Website Pangkalan Informasi Anabul Ni Luh Putu Ayu Siwastuti Cayadewi; Made Agung Raharja
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 1 No. 3 (2023): JNATIA Vol. 1, No. 3, Mei 2023
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2023.v01.i03.p24

Abstract

Currently, many people keep animals in their homes. Most of the animals they have are dogs and cats. Thus, they need a lot of information about the animals they keep. Therefore, to make it easier for people to find information about Anabul, an information website about Anabul was created, starting from the types of Anabul, its characteristics, what Anabul needs, and others. This journal produces a website based SahabatAnabul application. SahabatAnabul is a website to share all information about anabul, especially for dogs and cats. The resulting website can help people who have pets or want to know information about the types of dogs and cat breeds that exist in the world. 
Analisis Sentimen Gambar pada Media Sosial dengan Pendekatan Deep Learning Ronaldito Juan Bantaras T.; Made Agung Raharja
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 1 No. 3 (2023): JNATIA Vol. 1, No. 3, Mei 2023
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JNATIA.2023.v01.i03.p22

Abstract

Sentiment analysis of images on social media using a deep learning approach is an interesting research topic in the field of artificial intelligence. It involves data collection, training deep neural network models, testing and evaluation, and application and analysis on social media. The results of this analysis provide valuable insights to users in understanding user responses to content, detecting evolving sentiment trends, and providing important insights for business purposes and decision-making. Deep learning offers a strong and effective method for understanding emotional expressions within images shared on social media platforms. 
Co-Authors Agus Muliantara Agus Tommy Adi Prawira Kusuma Albert Okario Alit Indrawan, I Gusti Ngurah Anak Agung Istri Ngurah Eka Karyawati Anak Agung Istri Ngurah Eka Karyawati Apsari, Made Sri Ayu Ari Mogi, I Komang Aulia Iefan Datya Ayu Nikki Asvikarani Bayu Prasetiyo Bendesa, Gde Agung Mandala Christian Tonyjanto Cokorda Pramartha Cokorda Pramartha Devan Bramantya Dewantara, I Komang Raka Dewi, Ni Putu Anita Figo Stevhen Hidayat Gde Agung Mandala Bendesa Gerson Feoh Gst. Ayu Vida Mastrika Giri Gusto Gibeon Ginting Haposan Simangunsong Harta, I Gede Bendesa Aria I Dewa Made Bayu Atmaja Darmawan, I Dewa Made Bayu I Gede Acintia Udayana I Gede Arta Wibawa I Gede Santi Astawa I Gede Surya Rahayuda I Gusti Agung Ayu Istri Lestari I Gusti Bgs Darmika Putra I Gusti Ngurah Anom Cahyadi Putra I Gusti Ngurah Bagus Arimbawa I Kadek Gowinda I Ketut Gede Suhartana I Ketut Tangkas Agus Sucita I Komang Agus Ari Negara I Komang Ari Mogi I Komang Dwiprayoga I Komang Widia Pratama I Komang Yosua Triantara I Made Ari Madya Santosa I Made Satria Bimantara I Made Teja Geni Astra I Made Widiartha I Made Widiartha I Nyoman Adiputra I P G. Adiatmika I Putu Gede Hendra Suputra I Putu Gede Maysa Putra I Putu Risky Adi Sanjaya I Putu Satria Dharma Wibawa I Putu Sedana Wijaya I WAYAN SANTIYASA I Wayan Supriana I Wayan Supriana Supriana Ida Ayu Gde Suwiprabayanti Putra Ida Ayu Nyoman Yuliastuti Ida Bagus Gede Dwidasmara Ida Bagus Gede Sarasvananda Ida Bagus Made Mahendra IM Suyana Utama Indra Budi Trisno Jeremi Herodian Abednigo Juliana, Ni Komang Ayu Kadek Andre Suryana Kadek Belvanatha Gargita Satwikananda Kadek Dwitya Adhi Pradyto Kameliya Putri Komang Dean Ananda Komang Dean Nanda Komang Indra Pradnya Lalu Muhamad Waisul Kuroni Made Dwiki Budi Laksana Made Edy Septian Santosa Mas, I Made Treshnanda Merry Royanti Manalu Nararia Ningrat, Putu Vidi Nesa Padmawati Ngurah Agus Sanjaya ER Ngurah Kelvin Febryanta Lila Ananda Ni Kadek Ayu Fitriandayani Ni Komang Ayu Juliana Ni Luh Komang Indira Pramesti Ni Luh Putu Ayu Siwastuti Cayadewi Ni Made Ayu Suandewi Ni Made Rai Nirmala Santhi Ni Putu Anita Dewi Ni Putu Dian Kartika Sari Ni Putu Yeni Astiti Ningsih, Tri Adi Nusan Bagus Wibisana Pande Nyoman Weda Wesnawa Pradyto, Kadek Dwitya Adhi Putra, IGN Anom Cahyadi Putri Noviyanti Putri, Kameliya Putu Bagus Dio Pranata Putu Ode Irfan Ardika Putu Vidi Nararia Ningrat Putu Wida Gunawan Radhitya, Made Leo Ronaldito Juan Bantaras T. Simangunsong, Haposan Siwastuti Cayadewi, Ni Luh Putu Ayu Suryana, Kadek Andre Susy Purnawati T, Ronaldito Juan Bantaras Utari, Ni Nyoman Grisyana Wesnawa, Pande Nyoman Weda Wijayakusuma, I Gusti Ngurah Lanang Yoga, Bhaskara Budi Yohanes Kristianto Kristianto