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All Journal 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 Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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Pembangunan Model Ontologi pada Sistem Informasi Manajemen Program Kreativitas Mahasiswa (PKM) Diani, I Dewa Ayu; Suhartana, I Ketut Gede
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

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. Keywords: PKM, Ontology, Methontology, Protégé
Peningkatan Layanan Aplikasi Spotify Menggunakan Naïve Bayes Wahyuni, Era; Suhartana, I Ketut Gede
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

Spotify is a platform that provides services for listening to music digitally that can be downloaded through the Google Play Store and The App Store can also be downloaded for PC. Currently, Spotify has 433 million users. With so many users, it is certainly inseparable from the reviews or ratings given to the Spotify application which will also have an impact on the platform. In order to improve the service to be better, these reviews need to be reviewed so that users are more comfortable in using the application. Through this research, it will be known the performance of the Naïve Bayes algorithm in conducting sentiment analysis on user reviews of the Spotify application on the Google Play Store. The results of this study show that the accuracy value of using Naïve Bayes is 85% from a ratio of 70:30 training data and testing data using a music dataset from 1 January 2022 to 9 July 2022, there were 61586 reviews from users taken from the Kaggle website.
Identifikasi Tingkat Kematangan Buah Tomat Menggunakan Convolution Neural Network (CNN) Nurbidin, krisphino Saputra; Suhartana, I Ketut Gede
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

Tomatoes are one of the agricultural commodities, where distribution from farmers to sellers requires a series of processes and a long time. The problem is that tomatoes are easily damaged and rotted, so they are easily exposed to fungal infections, are watery and have a bad smell, which can harm farmers or traders. To prevent spoilage of tomatoes at the time of distribution, a system is needed that can help the process of checking tomato maturity. The solution uses the (CNN) method which has the most significant results in digital image recognition. This is because CNN is implemented based on an image recognition system in the human visual cortex. CNN is a type of neural network that is commonly used in image data. CNN can be used to detect and recognize objects in an image.
Klasifikasi Penyakit Kardiovaskular Menggunakan Metode Fuzzy K-Nearest Neighbor (FKNN) Cahyani, Ni Komang Santi; Suhartana, I Ketut Gede
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

Cardiovascular disease is a non-communicable disease that can cause death due to interfering with heart function. In this case, the classification of cardiovascular disease does make it easier for other medical personnel to help diagnose cardiovascular disease. Cardiovascular disease classification uses the Fuzzy K-Nearest Neighbor (FKNN) method to determine the optimum value of the nearest neighbor. The classification of cardiovascular disease is divided into two, having cardiovascular disease and not having cardiovascular disease. The results of classification using the Fuzzy K-Nearest Neighbor (FKNN) method with testing using a confusion matrix resulted in an accuracy of 80.21% at values of K = 13 and K = 15.
Analisis Performa Write/Read Kompresi Delta Encoding pada Data Logging Menggunakan Go Benchmark Mahardika Adi Putra, I Putu Gede; Suhartana, I Ketut Gede
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

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. Keywords: Data Compression, Go Benchmark, Delta Encoding, Data Logging, Performance.
Perbandingan Akurasi Algoritma Regresi Linier Putra, Indra Permana; Suhartana, I Ketut Gede
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

Along with technological advances, there is an approach to giving consideration when buying a house by analyzing prediction system. Research related to the accuracy comparison of the algorithm on the house price prediction gets conducted for precise prediction results. The algorithms used are Linear Regression, Polynomial Regression, and Support Vector Regression. The goal is as a reference for developers to be able to use the suitable algorithm and can provide accurate house price predictions. Linear Regression algorithm modeling produces a prediction score of 69% with a coefficient of determination (R2) of 0.69 and an RMSE value of 4395785322.216207. Support Vector Regression algorithm makes a prediction score of 97% with a coefficient of determination (R2) of 0.97 and an RMSE value of 31.19812999869066. Polynomial Regression algorithm modeling has a prediction score of 99% with a coefficient of determination (R2) of 0.99 and an RMSE value of 0.000403824405323. Based on these results, it can consider that the modeling of the house price prediction system with Polynomial Regression has the best level of accuracy.
Klasifikasi Musik Berdasarkan Genre Menggunakan Metode K-Nearest Neighbor Soeparman, William; Suhartana, I Ketut Gede
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

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%. Keywords: Music clasification, music genre, k-nearest neighbor
Klasifikasi Kematangan Buah Apel dengan Ekstraksi Fitur Haralick dan KNN Putra, I Kadek Bagus Deva Diga Dana; Suhartana, I Ketut Gede
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 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. Keywords: apple fruit, ripeness classification, texture features, color features.
Low-level Images Extraction Features pada Algoritma SVM untuk Mengidentifikasi Kematangan Jeruk Wijaya, I Made Agus Rama; Suhartana, I Ketut Gede
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

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%. Keywords: SVM, Klasifikasi, Low-level Extraction Features
Klasterisasi Frequently Asked Question menggunakan K-means Clustering Anwar, Khaerul; Suhartana, I Ketut Gede
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

Frequently asked questions are an important part of providing good service to customers. information provided in the form of questions and answers related to products, applications, companies that are available in detail, concise and easily accessible .The determination of the format of the frequently asked question list should be based on the questions asked by the customer so that they are relevant to the customer's needs. clustering the list of questions using K-means and TF-IDF as the feature extraction method provides an optimal solution of 50000 list questions divided into 18 clusters with a silhoutte coefficients = 00. each cluster is taken 1 document which will be a question in that category provided that the document has at most the term frequncy of the features on the cluster.
Co-Authors -, Daniel Surya Wijaya Adhana, Finandito Adi Guna, I Made Dirga Anak Agung Istri Ngurah Eka Karyawati Anggrek, Denise Valeria Ari Mogi, I Komang Artawan, Komang Nova Astrawan, Anak Agung Made Krisna Bagaskara, Aditya Caesar Brahmantha, Gede Putra Aditya Cahyani, Ni Komang Santi Canistya Chandra, Putu Isthu Chelsy Elisabet Gultom Cokorda Pramartha Dewi, Ni Kadek Yulia Diani, I Dewa Ayu 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 Ngurah Anom Cahyadi Putra I Kadek Bagus Deva Diga Dana 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 Gede Dwidasmara Ida Bagus Made Mahendra Ida Putu Ari Jayadinanta Indra Permana Putra Jaya, I Gede Wilantara Kartika Maharani, Ida Ayu Bintang 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 Maha, Ni Made Krisna Maharani Putri Suari Mahardika Adi Putra, I Putu Gede Masduki, Aan Ngurah Agus Sanjaya ER Ni Luh Eka Suryaningsih 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 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 Winata, Mas Adi Wirasih, Ni Made Ayu Wulandari, Desak Putu Sri Yasinta Anita Kewa Nilan Yoel Samosir Yudha, I Wayan Pande Putra