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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Ilmu Komputer (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana Jurnal Sains dan Teknologi Jurnal Buana Informatika Jurnal Ilmiah Kursor Jurnal RESISTOR (Rekayasa Sistem Komputer) Jurnal Pengabdian Informatika (JUPITA) Jurnal Nasional Teknologi Informasi dan Aplikasinya
I Dewa Made Bayu Atmaja Darmawan, I Dewa Made Bayu
Program Studi Teknik Informatika Fakultas Matematika Dan Ilmu Pengetahuan Alam Universitas Udayana
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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Journal : Jurnal Ilmu Komputer

 1 
PERBANDINGAN METODE ZCR DAN AUTOCORRELATION UNTUK MENGHITUNG FREKUENSI PADA GAMBELAN GENDER WAYANG I Dewa Made Bayu Atmaja Darmawan
Jurnal Ilmu Komputer Vol 8 No 2: September 2015
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (332.485 KB)

Abstract

Suatu sinyal suara khususnya instrumen gamelan gender memiliki frekuensi yang berbeda disetiap bilahnya. Frekuensi ini membedakan tinggi rendahnya nada yang dihasilkan. Frekuensi dihitung untuk mengetahui karakteristik dari suatu bilah gender wayang yang merupakan bagian dari tahap pelarasan. Pada tulisan ini dilakukan pengujian terhadap dua metode yang umum digunakan yaitu ZCR dan Autocorrelation untuk menghitung frekuensi bilah gender wayang. Suara yang dihasilkan oleh gender wayang memiliki sinyal yang kompleks dan inhamoni. Inharmoni berarti sinyal ini disusun berdasarkan beberapa frekuensi dimana frekuensi-frekuensi tersebut tidak kelipatan dari frekuensi dasarnya. Pada pengujian yang dilakukan diketahui ZCR dan Autocorrelation mampu untuk menghitung frekuensi dari sinyal murni yang dibangkitkan berdasarkan frekuensi setiap bilah gender wayang. Namun, hanya Autocorrelation saja yang mampu untuk menghitung frekuensi yang baik dari sinyal bilah gender wayang dengan memiliki rata-rata selisih yang kecil, yaitu sebesar 3,3953 Hz.
ANALISIS DAN PERBANDINGAN TEKNIK WATERMARKING CITRA DIGITAL MENGGUNAKAN METODE BLOCK BASED DCT DAN LSB I Dewa Made Bayu Atmaja Darmawan
Jurnal Ilmu Komputer Vol 7 No 1: April 2014
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2004.577 KB)

Abstract

Watermarking merupakan aplikasi dari steganografi yang berusaha menyisipkan pesan pada suatu media digital. Namun, berbeda dengan steganografi, media penampung yang digunakan untuk menyimpan informasi adalah objek yang ingin diamankan. Selain daripada itu, informasi yang disembunyikan harus terjaga keutuhannya. Beberapa upaya telah dilakukan untuk melakukan penyisipan informasi pada suatu media penampung, khususnya pada kasus media citra digital. Teknik yang digunakan bekerja pada domain spasial yaitu LSB dan spectral yaitu menggunakan block-based DCT. Tulisan ini akan memberikan evaluasi dan perbandingan terhadap beberapa teknik watermarking tersebut dengan melakukan pengujian terhadap transparansi persepsi (imperceptibility), robustness, dan kapasitas. Hasil pengujian memperlihatkan metode block-based DCT lebih baik dibandingkan dengan LSB pada pengujian Imperceptibility. Sedangkan, pada pengujian robustness penggunaan metode LSB dapat lebih menjaga watermark yang disisipkan.
Implementation of Face Recognition for Attendance Recording in Online Learning Darmawan, I Dewa Made Bayu Atmaja
Jurnal Ilmu Komputer Vol 16 No 2 (2023): Jurnal Ilmu Komputer
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIK.2023.v16.i02.p06

Abstract

The utilization of facial recognition technology has become increasingly imperative within the realm of online learning. The current study introduces a novel system that utilizes face recognition technology to record attendance in online learning environments. The attendance system necessitates students to activate an attendance button, whereby their attendance is subsequently documented through facial recognition technology. The system recognizes students as present solely based on facial recognition. The system stores the duration of online learning activities in a database. Implementing machine learning methodologies, specifically face detection algorithms, improves precision and efficacy in administering student attendance in online education. The system utilizes Haar cascades in OpenCV to detect faces, extract features such as eyes, nose, and mouth, and classify them using LBPH. Through extensive experiments, an accuracy rate of 93.55% was achieved. The study demonstrates the effectiveness of the combined approach, showcasing the potential of Haar cascades and LBPH in face recognition tasks. The present study makes a valuable contribution to the domains of computer vision and educational technology by offering a pragmatic remedy for attendance tracking in virtual learning settings.
Utilizing Machine Learning Techniques for Learning Analytics: A Case Study of Moodle LMS Activity Log Analysis Darmawan, I Dewa Made Bayu Atmaja
Jurnal Ilmu Komputer Vol 17 No 1 (2024): Jurnal Ilmu Komputer
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIK.2024.v17.i01.p05

Abstract

Learning analytics collects data, analyzes, and interprets the learning process that has taken place. The output of this method can be used to improve the quality of teaching or learning. Moodle is a popular learning management system (LMS) used for online learning. Various learning activities carried out by students are recorded in the activity log. This paper shows the potential of using machine learning methods to analyze activity logs taken from Moodle LMS. The sample used in this study refers to implementing the Digital Society course, which students from different fields of science attend. This paper describes using supervised and unsupervised learning on activity log data taken from the Moodle LMS. The variables used as datasets include the frequency of activity reading pdf material, scores, videos, forums, quizzes, and graduation status. The supervised learning model that was built succeeded in obtaining an accuracy of 100% in the application of logistic regression and Naïve Bayes Classification. Unsupervised learning clustered all the data and showed the cluster related to the frequency of online learning activities and students' assessment success status.
Co-Authors Alim Ikegami Anak Agung Gde Ramananda Kartikeya Pattraksha Anak Agung Istri Ngurah Eka Karyawati Ari Mogi, I Komang Arvanchrist Charlie Wijaya Brata, I Putu Bayu Wira Budhiono, Sofia Shieldy Christian, Yuriko Dharmajaya, Gede Putra Elia, Benny I Gede Alanda Indra Kusuma I Gede Angga Narotama I Gede Arta Wibawa I Gede Erwin Winata Pratama I Gusti Agung Gede Arya Kadyanan I Gusti Ayu Nyoman Tri Wahyuni Mahayani I Ketut Gede Suhartana I Ketut Gede Suhartana I Komang Ari Mogi I Komang Ari Mogi, I Komang Ari I Komang Tryana Mertayasa I Made Anditya Mahesastra I Made Widiartha I Nyoman Arlan Kusuma Ardika I Putu Bayu Wira Brata I Putu Gede Hendra Suputra I WAYAN SANTIYASA Ida Bagus Made Mahendra Ikegami, Alim Jonathan Federico Tantoro Luh Arida Ayu Rahning Putri Luh Gede Astuti Made Rusdinda Hartani Maha, Ni Made Krisna Mahesastra, I Made Anditya Marselinus Putu Harry Setyawan Mertayasa, I Komang Tryana Muhammad Arief Budiman Ni Luh Putu Dianti Parasmitha Sari Ni Made Wipra Ranum Ratnayu Noak, Devin Reness Novita Dewi, Putu Mira Prianta, I Made Yoga Putra, Angga Pramana Raharja, Made Agung Satya, I Dewa Gede Rama Setyawan, Marselinus Putu Harry Sri Devi, Ni Komang Wiantari, Ni Wayan Yuriko Christian