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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Komputer (JELIKU) Jurnal Elektronik Ilmu Komputer Udayana Jurnal Buana Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Pengabdian Informatika (JUPITA) Jurnal Nasional Teknologi Informasi dan Aplikasinya
I Ketut Gede Suhartana
Program Studi Teknik Informatika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Udayana
Humanities Computer Science & IT Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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Sistem Monitoring Kamar Tidur Pintar dan Suhu Berbasis IoT dengan Cisco Packet Tracer Ni Made Ayu Wirasih; I Ketut Gede Suhartana
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.p14

Abstract

This research endeavors to develop an Internet of Things (IoT)-based smart bedroom monitoring system, leveraging Cisco Packet Tracer technology as a robust simulation platform. The system facilitates automatic monitoring and management of indoor environment parameters such as temperature, security, and lighting settings, aiming to enhance occupants' comfort and safety. Key components of the system include a temperature sensor, microcontroller, and an LCD information screen, enabling real-time display of bedroom temperature data. System validation was conducted via simulations using Cisco Packet Tracer, consistently demonstrating the system's efficacy in automating room temperature monitoring and management. These findings lay a solid groundwork for advancing IoT technology, emphasizing deeper integration, addressing challenges, proposing solutions, and exploring potential applications across diverse contexts. 
Klasifikasi Musik Berdasarkan Genre Menggunakan Metode K-Nearest Neighbor William Soeparman; I Ketut Gede Suhartana
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.p10

Abstract

Currently the amount of music in digital form continues to increase rapidly. This causes manual genre labeling of music to be inefficient. Genre labeling can be done automatically using artificial intelligence algorithms. The artificial intelligence algorithm used is an algorithm that can classify music based on genre by using the features contained in the music. This study discusses the classification of music based on genre using the K-Nearest Neighbor method or algorithm and 6 musical features, namely beat, energy, danceability, loudness, liveness, and valence. The accuracy value in this study is 54.3%. 
Pembangunan Model Ontologi pada Sistem Informasi Manajemen Program Kreativitas Mahasiswa (PKM) I Dewa Ayu Diani; I Ketut Gede Suhartana
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.p09

Abstract

The Student Creativity Program (PKM) is an initiative used to encourage and develop student creativity and innovation in the academic field. However, in PKM management, there are often challenges in integrating and utilizing the data generated effectively and efficiently. This study aims to develop an ontology model for the Student Creativity Program Management Information System (PKM), with a focus on managing and utilizing PKM data. The PKM ontology models aspects related to PKM, including the type of program, assessment criteria, participant information, and the resulting results. The development method involves need analysis, ontology design, implementation, and evaluation. The ontology model is integrated in the PKM information system, enabling users to access, manage and analyze PKM data efficiently. With the PKM ontology, PKM information can be better integrated, and data processing becomes more structured. This research contributes to the development of an ontology based PKM information system, with the potential to increase student creativity and innovation. 
Deteksi Penyakit Diabetes Menggunakan Gaussian Naive Bayes, Regresi Logistik, dan Random Forest Kenny Belle Lesmana; I Ketut Gede Suhartana
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.p25

Abstract

Diabetes is a very common health problem in the world. The number of people with diabetes is increasing year to year. Therefore, it is necessary to realize the symptoms of diabetes as early as possible. Diabetes is a chronic disease characterized by high sugar levels in the blood. In this study, a system was made about a diabetes detection system based on numerical data using three methods. That three methods are Gaussian Naive Bayes method, Logistic Regression, and Random Forest by taking a dataset in the form of numerical data. The accuracy value on the data tested in this study using Gaussian Naive Bayes, Logistic Regression, Random Forest is 0.74; 0;78; 078. 
Klasifikasi Kematangan Buah Apel dengan Ekstraksi Fitur Haralick dan KNN I Kadek Bagus Deva Diga Dana Putra; I Ketut Gede Suhartana
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.p10

Abstract

This research aims to classify the ripeness level of apple fruits based on texture features using the Haralick method and color features using histograms. A dataset of 76 apple fruit images was collected. In the preprocessing stage, the apple images were converted to grayscale, followed by the application of a median filter to remove salt and pepper noise, and histogram equalization to enhance image contrast. Texture features were extracted using the Haralick method to obtain contrast, correlation, energy, homogeneity, and entropy features. Color features were extracted using histograms to obtain mean, standard deviation, skewness, and kurtosis. A K-Nearest Neighbor (KNN) model with k = 6 was used for classification. The evaluation results showed an accuracy of 89.47%, precision of 93.75%, recall of 93.75%, and F1-score of 93.75%. This research indicates that texture and color features can effectively classify the ripeness level of apple fruits. Future research can explore more diverse datasets and parameter adjustments to further improve model performance. 
Low-level Images Extraction Features pada Algoritma SVM untuk Mengidentifikasi Kematangan Jeruk I Made Agus Rama Wijaya; I Ketut Gede Suhartana
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.p02

Abstract

Terkadang jika melakukan identifikasi secara manual oleh petani buah masih memunculkan peluang terjadinya human error saat panen. Untuk itu, penelitian ini dilakukan bertujuan untuk pelatihan klasifikasi terhadap buah jeruk guna mengurangi error rate dalam pemanenan. Kematangan buah dipisah ke dalam dua kelas yaitu matang dan belum matang. Dimana dataset yang digunakan merupakan data sekunder citra buah jeruk dengan total sebanyak 400 buah, yaitu 200 citra matang dan 200 citra belum matang. Metode yang digunakan adalah Support Vector Machine kernel linear dengan penggabungan Low-level Image Extraction Features, yaitu warna dengan color histogram, tekstur dengan metode Gray Level Co-occurance Matrix (GLCM), dan bentuk dengan kalkulasi kontur. Pembagian klasifikasi dilakukan dengan membagi dataset menjadi 20% data uji dan 80% data latih. Hasil klasifikasi pada penelitian mendapatkan nilai akurasi sebesar 96,34%. 
Analisis Performa WriteRead Kompresi Delta Encoding pada Data Logging Menggunakan Go Benchmark I Putu Gede Mahardika Adi Putra; I Ketut Gede Suhartana
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.p11

Abstract

Compression for data logging in environmental health monitoring is a serious concern. Recording of environmental health history is carried out by monitoring the fluctuations of the physical parameters. The record stored in a warehouse from the logging system will increase over time. So, that’s the reason why compression on time-series data logging is needed. But, the addition of compression algorithm like delta encoding allows for increased latency. Therefore, the performance of write/read of delta encoding must be analyzed. One method to analyze is the Go Benchmark. The test parameter used in this paper is the number of iterations and times per iteration taken from the Go Benchmark’s output. The other test parameter is compression ratio and storage saving taken from the size of data before and after compression. There are 4 tests case used: single data write, single data read, multiple data write, and multiple data read. As the result, single data write/read and multiple data read work optimally in delta compression with the similar test result with baseline. But multiple data write not working optimally with times per iteration 10,254 times higher than baseline. 
Co-Authors -, Daniel Surya Wijaya Adhana, Finandito Adi Guna, I Made Dirga Anak Agung Istri Intan Permata Sari Anak Agung Istri Ngurah Eka Karyawati Anak Agung Istri Ngurah Eka Karyawati Anak Agung Made Krisna Astrawan Anggrek, Denise Valeria Ari Mogi, I Komang Artawan, Komang Nova Astrawan, Anak Agung Made Krisna Bagaskara, Aditya Caesar Bayu Fadjar Dwi Puta Brahmantha, Gede Putra Aditya Cahyani, Ni Komang Santi Canistya Chandra, Putu Isthu Chelsy Elisabet Gultom Cokorda Pramartha Daniel Surya Wijaya Dewi, Ni Kadek Yulia Dian Resvina Diani, I Dewa Ayu Gde Krishna Sankya Yogeswara Giri, Gst Ayu Vida Mastrika Giri, Gst. Ayu Vida Mastrika Gst. Ayu Vida Mastrika Giri Gultom, Chelsy Elisabet Guna Wicaksana, I Gusti Ngurah Gusto Gibeon Ginting Harta, I Gede Bendesa Aria I Dewa Ayu Diani I Dewa Made Bayu Atmaja Darmawan I Dewa Made Bayu Atmaja Darmawan, I Dewa Made Bayu I Gede Arta Wibawa I Gede Bagus Anom Adiputra I Gede Erwin Winata Pratama I Gede Surya Rahayuda I Gede Teguh Satya Dharma I Gede Tendi Ariyanto I Gusti Agung Gede Arya Kadyanan I Gusti Bagus Putrawan I Gusti Ngurah Anom Cahyadi Putra I Kadek Bagus Deva Diga Dana I Kadek Bagus Deva Diga Dana Putra I Komang Arya Ganda Wiguna I Made Agus Rama Wijaya I Made Dirga Adi Guna I Made Widhi Wirawan I Made Widiartha I Nyoman Budhiarta Suputra I Putu Ananta Wijaya I Putu Gede Hendra Suputra I Putu Gede Mahardika Adi Putra I Wayan Pande Putra Yudha I WAYAN SANTIYASA I Wayan Supriana Ida Ayu Gde Suwiprabayanti Putra Ida Bagus A. Swamardika Ida Bagus Gede Dwidasmara Ida Bagus Gede Sarasvananda Ida Bagus Made Mahendra Ida Putu Ari Jayadinanta Indra Permana Putra Jaya, I Gede Wilantara Kartika Maharani, Ida Ayu Bintang Kenny Belle Lesmana Kewa Nilan, Yasinta Anita Khaerul Anwar Khatami, Maula Krishella Naomi D’laila Rumy Lesmana, Kenny Belle Lidya Elisabet Theogracia Silitonga Luh Arida Ayu Rahning Putri Luh Gede Astuti Lusia Elvira Sue Sare Maha, Ni Made Krisna Maharani Putri Suari Mahardika Adi Putra, I Putu Gede Mas, I Made Treshnanda Masduki, Aan Ngurah Agus Sanjaya ER Ni Kadek Arimbi Wirasetia Ni Luh Eka Suryaningsih Ni Luh Gede Cahaya Putri Mahadewi Ni Luh Juli Yetti Ni Made Ayu Wirasih Nuboba, Barneci Henderika Nurbidin, krisphino Saputra Partamayasa, I Wayan Gede Pawitradi, Gede Pramathana, Raindra Pratama, Berlin Putra, I Dewa Agung Cahya Putra, I Kadek Bagus Deva Diga Dana Putra, I Putu Denny Indra Putu Ardi Sudarmika Putu Praba Santika Raharja, Made Agung Rianty, Winda Setiawan, Vinna Soeparman, William Suardana, Komang Yudi Adnyana Suwiprabyanti Putra, Ida Ayu Gde Tri Adi Ningsih Veithzal Rivai Zainal Vidiadivani, Wahyu Wahyu Vidiadivani Wahyuni, Era Wijaya, Daniel Surya Wijaya, I Made Agus Rama William Soeparman Winata, Mas Adi Wirasih, Ni Made Ayu Wulandari, Desak Putu Sri Yasinta Anita Kewa Nilan Yoel Samosir Yudha, I Wayan Pande Putra