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All Journal Jurnal Ilmu Komputer Buletin Udayana Mengabdi (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana Jurnal Ilmiah Kursor Jurnal Pengabdian Informatika (JUPITA) Jurnal Nasional Teknologi Informasi dan Aplikasinya
Agus Muliantara
Program Studi Teknik Informatika Fakultas MIPA Universitas Udayana
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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Impelementasi Kriptografi RSA dan XOR Cipher Untuk Enkripsi Citra Digital KTP Gede Krisna Surya Artajaya; Agus Muliantara
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.p01

Abstract

The advancement of technology has led to innovative solutions in administrative sectors, exemplified by the introduction of “KTP Digital”. However, not everyone has adopted “KTP Digital” and is still relying on scanned copies of identity cards that can expose digital image data to security vulnerabilities. This study addresses these vulnerabilities by proposing encryption techniques. Utilizing RSA and XOR Cipher algorithms, this research demonstrates effective encryption and decryption of digital image data. Evaluation metrics, including Peak Signal-toNoise Ratio (PSNR), confirm minimal similarity between plain and cipher images, indicating robust encryption. Specifically, PSNR values for plain vs. cipher images range from 7 to 8 dB, well below 10 dB, indicating a very significant difference. Additionally, high PSNR values between original and decrypted plain images, which is 100 dB, suggest negligible data alteration post decryption confirming that the decryption process successfully restores the image to its original state. 
Memprediksi Kelulusan Mahasiswa Graduate dan Dropout dengan Support Vector Machine dan GridSearchCV Ni Putu Eka Marita Anggarini; Agus Muliantara
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.p04

Abstract

In today's educational landscape, having a model to predict whether a student will graduate or drop out based on their academic statistics is highly beneficial. Such a model allows for early assessment of academic success. Human calculations alone can be time-consuming and often lack accuracy, hence the introduction of machine learning models to address this issue. This research utilizes a dataset comprising undergraduate students from various majors in higher education institutions. The data were collected while the students were still enrolled, with their grades from the first year serving as a key feature. The response variable in the dataset is labeled as either 'dropout' or 'graduate'. We employ Support Vector Machines (SVM) with GridSearchCV optimization to build the predictive model. The goal of this model is to predict a student’s academic success as early as their first-year statistics are available. If a student is predicted to drop out, targeted interventions can be provided to help them overcome challenges, ultimately aiming to improve graduation rates. 
Deteksi Objek pada Citra Menggunakan Model YOLO Intara Pratama Harahap; Agus Muliantara
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.p03

Abstract

Object detection is a crucial task in the field of computer vision and digital image processing, with numerous practical applications. This paper focuses on the implementation of the You Only Look Once (YOLO) model, a deep learning-based approach for object detection. The YOLO model offers several advantages over previous methods, such as simultaneous prediction of bounding boxes and object class probabilities, a relatively simple Convolutional Neural Network (CNN) architecture, and high computational speed, making it suitable for real-time applications. The study utilizes a dataset of 770 images, with 524 for training, 136 for validation, and 110 for testing, specifically focused on detecting various pet animals. The training process involves annotation of the image data, followed by training and validation of the YOLO model. The results demonstrate the model's ability to effectively detect and classify objects, achieving high performance metrics such as precision, recall, and mean Average Precision (mAP) nearing 0.8 towards the end of the training process. Additionally, a confusion matrix is presented, highlighting the model's accuracy in classifying different classes, with the highest accuracy for the 'Cat' class at 95%. The paper concludes by discussing the model's performance and potential areas for improvement. 
Perancangan Guitar Tuning Berbasis Web I Nyoman Dheva Surya; Cokorda Pramartha; Agus Muliantara
Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 2 (2024): JNATIA Vol. 2, No. 2, Februari 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.i02.p07

Abstract

Guitar tuning is a crucial step in preparing for playing, ensuring accurate sound production. In response to this need, our research led to the development of a web-based application dedicated to facilitating precise and straightforward guitar tuning. Leveraging web technology, this application offers users practical tuning experience without the need for additional software installation. By implementing comprehensive tuning features and employing the Fast Fourier Transform (FFT) method, our application guarantees accuracy in detecting and analyzing the spectrum of incoming sound vibrations. This technological approach ensures that users can confidently tune their guitars, avoiding mistuned strings. The decision to create a web-based app aims to optimize device space utilization, as it only requires a standard browser, readily available on all operating systems. In addition to tuning functionality, the application enriches the user experience by providing a collection of guitar chords for each note. This feature enables users to practice and play songs with adjustable transposition and speed. To assess the application's functionality and reliability, we conducted 100 trials using the BlackBox method, focusing on tuning the six guitar strings (e-b-g-d-a-e). The results demonstrated a remarkable 99% accuracy, affirming the system's effectiveness in facilitating precise guitar tuning. In conclusion, our research yields a practical, intuitive, and highly effective web application for users to tune their guitars with confidence and ease. 
Klasifikasi Teks Spam dengan Algoritma Support Vector Machine dan Chi – Square - Perbaikan Tabel Getzbie Alfredo Tpoy; Agus Muliantara
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.p03

Abstract

Spam messages are messages that contain false information, commonly regarding events, banking, insurance, bills, advertisements, and viruses. To address the issue of spam, classification can be performed on the received messages. Classification can be done by separating texts that contain spam messages from texts that contain legitimate (ham) messages. In this study, spam text classification was conducted using the Support Vector Machine algorithm, feature selection using Chi-Square. The Chi-Square feature selection method was performed using percentages of 20%, 40%, 60%, and 80%, with accuracy, precision, recall, and F1-Score as the measured values. The result of study obtained was an accuracy of 98.82% with an F1-Score of 93.05% at a feature selection percentage of 60%, using the RBF kernel. Feature selection with percentages of 20%, 40%, and 80% resulted in accuracies of 97.93%, 98.29%, and 98.02%, respectively. These accuracies were better compared to the accuracy without feature selection, which was 97.57%. 
Prediksi Paket Return Menggunakan Metode Decision Tree Menerapkan Algoritma C4.5 Berbasis Website Oskar Maha Kasi; Agus Muliantara
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.p03

Abstract

The development of e-commerce technology has provided significant benefits in facilitating online transactions. However, one of the problems that is often faced by e-commerce companies is the management of return packages, where customers return goods that have been purchased. In order to optimize the package return management process, an effective prediction method is needed to estimate the possibility of returning packages by customers. With a website-based package return prediction system, e-commerce companies can take appropriate actions to manage return packages more efficiently. For example, they can adjust their inventory strategy, improve product quality, or provide special promotional offers to customers who have a high probability of returning packages. This can help increase customer satisfaction, reduce the cost of managing return packages, and increase the overall operational efficiency of the company. 
Co-Authors Adikara, I Wayan Wikananda Adipurwa Mahandiri, Dewa Nyoman Agung Afandi, M. Faisal Anak Agung Istri Ngurah Eka Karyawati Anak Agung Ketut Ayuningsasi Anggarini, Ni Putu Eka Marita Anggotra, Puspadevi Angreni, Ni Putu Dewi Ari Negara, I Komang Agus Arsa, Dewa Made Sri Artajaya, Gede Krisna Surya Awan, Diky Rizky Bayu Prasetiyo Bayu Prasetiyo Celia Maureen Chandra Christian, Yuriko Cokorda Pramartha Cokorda Pramartha Da Silva, Fransiska Christina Sio Edo Krishnanda Aditya Fransiska Christina Sio Da Silva Gede Arya Kadyanan, I Gusti Agung Gede Krisna Surya Artajaya Getzbie Alfredo Tpoy Gst. Ayu Vida Mastrika Giri Gusto Gibeon Ginting Harahap, Intara Pratama I Gede Acintia Udayana I Gede Ariawan Eka Putra I Gede Surya Rahayuda I Gede Wira Kusuma Jaya I Gusti Agung Gede Arya Kadyanan I Gusti Ngurah Arya Sucipta I Ketut Agus Pranata Muliawan I Komang Agus Ari Negara I Komang Ari Mogi I Made Arthya Andika Putra I Made Surya Adi Palguna I Made Widhi Wirawan I Made Widiartha I Made Widiartha I Nyoman Dheva Surya I Putu Bagus Merta Sentana I Putu Deni Pratama I Putu Diska Fortunawan I Putu Gede Hendra Suputra I Wayan Supriana I Wayan Wikananda Adikara I.B.M. Mahendra I.D.M.B.A. Darmawan I.G.S. Astawa I.M. Widiartha I.P.G.H. Suputra Ida Ayu Gde Suwiprabayanti Putra Ida Bagus Gde Ardita Mahaprawira Ida Bagus Gede Dwidasmara Ida Bagus Gede Dwidasmara Ida Bagus Made Wiguna Tedja Sukmana Intara Pratama Harahap Kasi, Oskar Maha Ketut Ardha Chandra Komang Kartika Noviyanti L.P.I. Harini Lalu Muhamad Waisul Kuroni Luh Arida Ayu Rahning Putri Luh Gede Astuti Made Dwiki Budi Laksana Made Rahayu Setyaningrum Mahandiri, Dewa Nyoman Agung Adipurwa Mahendra, Ida Bagus Michael Senna Saputra Muhammad Caesar Gilang Indrawan Ngurah Agus Sanjaya ER Ni Kadek Dwi Marhaeni Ni Luh Putu Krisna Lestari Ni Made Ayu Suandewi Ni Made Julia Budiantari Ni Putu Eka Marita Anggarini Noviyanti, Putri Nusan Bagus Wibisana Nyoman Putra Prasetya Wardhana Oskar Maha Kasi Palla, Hans Rio Alfredo Pattraksha, Anak Agung Gde Ramananda Kartikeya Putra, Ida Ayu Gde Suwiprabayanti Putrawan, Putu Bagus Kresna Putri Noviyanti Raharja, Made Agung Sandhya Wandhana, Gede Krisnawa Surya, I Nyoman Dheva Suryawan, Albertus Ivan Tpoy, Getzbie Alfredo Ubaidillah, Muhammad Afif Wahyu Ramadhan Wayan Kiki Oktalao Wiraprathama, Alvin