Jurnal Nasional Teknologi Informasi dan Aplikasinya
JNATIA (Jurnal Nasional Teknologi Informasi dan Aplikasinya) adalah jurnal yang berfokus pada teori, praktik, dan metodologi semua aspek teknologi di bidang ilmu komputer, informatika dan teknik, serta ide-ide produktif dan inovatif terkait teknologi baru dan teknologi informasi. Jurnal ini memuat makalah penelitian asli yang belum pernah diterbitkan. JNATIA (Jurnal Teknologi Informasi dan Aplikasinya) diterbitkan empat kali setahun (Februari, Mei, Agustus, November).
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316 Documents
<![CDATA[Analisis Ulasan Produk Menggunakan Metode Naive Bayes Classifier ]]>
<![CDATA[Monika Hermiani Yolanda Simamora]]>;
<![CDATA[Ida Bagus Made Mahendra]]>
<![CDATA[ Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 1 (2023): JNATIA Vol. 2, No. 1, November 2023 ]]>
Publisher : <![CDATA[Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University ]]>
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DOI: 10.24843/JNATIA.2023.v02.i01.p20
<![CDATA[Advancements in technology have shifted market activities towards e-commerce, resulting in a substantial increase in user-generated review data. Buyer reviews, which are comments provided after purchasing products online, serve as valuable feedback for sellers to enhance product quality and aid buyers in making informed decisions. However, manually analyzing a large volume of buyer reviews is time-consuming. To address this issue, sentiment analysis methods can be employed to automatically classify product reviews into positive and negative sentiment classes. Sentiment analysis was conducted using Multinomial Naive Bayes in this study. The data used were 400 pieces of data with a division of 80% as training data and 20% as test data. The preprocessing in this study are data cleaning, tokenization, normalization, stopword, and stemming. The feature extraction process is carried out by the Term-Frequency method. Then the classification process is carried out using the Multinomial Naive Bayes method and tested using the Confusion Matrix method. The final results of this study showed that the Multinomial Naive Bayes method could carry out the product review data classification process well and obtained an accuracy value of 85%, a precision value of 77%, a recall value of 72%, and an f1-score value of 74% ]]>
<![CDATA[Perancangan User Interface pada Aplikasi Rekomendasi Tempat Wisata di Daerah Gianyar ]]>
<![CDATA[Made Dhandy Satria Mahagangga]]>;
<![CDATA[Luh Gede Astuti]]>
<![CDATA[ Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 1 (2023): JNATIA Vol. 2, No. 1, November 2023 ]]>
Publisher : <![CDATA[Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University ]]>
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DOI: 10.24843/JNATIA.2023.v02.i01.p21
<![CDATA[Bali, a beautiful island in Indonesia, offers the perfect combination of natural beauty, rich culture, and the friendliness of its people. Known for its stunning beaches, clear sea water, and stunning rice terraces, Bali is a popular tourist destination worldwide. In addition, the rich cultural life with religious ceremonies, traditional dances and sculpture makes Bali a center for artistic and cultural activities. Tourists can enjoy an unforgettable experience while exploring ancient temples, visiting traditional markets or interacting with the friendly locals. Bali is a mesmerizing paradise that promises a life full of adventure and peace. The purpose of this research is to build a recommendation system for tourist attractions which is expected to be able to make it easier for tourists who are on vacation to Bali to find tourist attractions they want to go to, especially in the Gianyar area. ]]>
<![CDATA[Implementasi Algoritma Support Vector Machine dalam Klasifikasi Deteksi Depresi dari Postingan pada Media Sosial ]]>
<![CDATA[Kameliya Putri]]>;
<![CDATA[Made Agung Raharja]]>
<![CDATA[ Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 1 (2023): JNATIA Vol. 2, No. 1, November 2023 ]]>
Publisher : <![CDATA[Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University ]]>
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DOI: 10.24843/JNATIA.2023.v02.i01.p22
<![CDATA[Mental health issues, such as depression, have significant impacts on individuals and society. Early identification and detection of these conditions are crucial steps in providing appropriate interventions and supporting better recovery. With the increasing use of social media, many people have started sharing their thoughts, feelings, and experiences online. Social media provides an abundant platform for users to express themselves and interact with others. Posts on social media often reflect individuals' emotional states. Therefore, analyzing the content of these posts can provide valuable insights for monitoring and early detection of depressive symptoms. Machine learning has been widely used for automated text mining and classification tasks. A classification method that can be used to classify social media posts into depression and normal classes is the support vector machine. Based on the testing results of the Support Vector Machine algorithm in classifying posts on social media, the highest accuracy value obtained was 95.5% using a parameter value of C equal to 0.25. The Precision, recall, and F-1 score values were 96%. ]]>
<![CDATA[Klasifikasi Kateogori Cerita Pendek Menggunakan Support Vector Machine ]]>
<![CDATA[M. Faisal Afandi]]>;
<![CDATA[Ngurah Agus Sanjaya ER]]>
<![CDATA[ Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 1 (2023): JNATIA Vol. 2, No. 1, November 2023 ]]>
Publisher : <![CDATA[Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University ]]>
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DOI: 10.24843/JNATIA.2023.v02.i01.p23
<![CDATA[Short stories are fascinating literary works to read because they present concise narratives that don't require readers to spend a lot of time to complete a story. Although the stories are short, determining the story category still requires careful reading to understand the content. However, it can become challenging when there is a large number of stories to be classified. Therefore, this research aims to develop a system that can automatically classify short story texts. The method used in this research is SVM (Support Vector Machine). The research is conducted to assist in automatically classifying short stories and create a system that bridges people to enjoying written works while enhancing literacy. The data used consists of short stories in the categories of romance, horror, and religion. The best-performing model is obtained through the training and validation process using new data. The results of testing the SVM method with a 70:30 data scenario, and hyperparameter C=10, gamma = 0.1 with kernel rbf or gamma = scale with kernel linear, yield an accuracy of 96% with a precision of 96.72%, recall of 96.36%, and an f1-score of 96.40%. ]]>
<![CDATA[Evaluasi Desain Aplikasi Delivery Menggunakan Metode System Usability Scale ]]>
<![CDATA[Matthew Novan Sidharta]]>;
<![CDATA[Luh Arida Ayu Rahning Putri]]>
<![CDATA[ Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 1 (2023): JNATIA Vol. 2, No. 1, November 2023 ]]>
Publisher : <![CDATA[Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University ]]>
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DOI: 10.24843/JNATIA.2023.v02.i01.p24
<![CDATA[Technology continues to develop from time to time and has been widely used to support various forms of services, such as delivery service. However, not every aspect can be fulfilled by this kind of application. The delivery application which is selected by researcher in this paper is disguised. The application under this research will be evaluated in terms of UI/UX design. The usability testing method that will be used in the evaluation process is the system usability scale. The result shows that the system usability scale’s score on the application is at 54,16. To improve the application performance, especially in terms of UI/UX, the application can be redesigned for the next research. ]]>
<![CDATA[Implementasi Random Forest pada Klasifikasi Penyakit Kardiovaskular dengan Hyperparameter Tuning Grid Search ]]>
<![CDATA[I Ketut Adian Jayaditya]]>;
<![CDATA[I Gusti Agung Gede Arya Kadyanan]]>
<![CDATA[ Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 1 (2023): JNATIA Vol. 2, No. 1, November 2023 ]]>
Publisher : <![CDATA[Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University ]]>
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DOI: 10.24843/JNATIA.2023.v02.i01.p25
<![CDATA[Cardiovascular disease has the potential to cause death if not treated right, because it interferes with the function of the heart. Machine Learning algorithm can be used to do early diagnosis of cardiovascular disease to lower the risk of death. In this study, the classification of cardiovascular disease uses the Random Forest algorithm to determine whether a person has cardiovascular disease or not. Grid Search is also used to do hyperparameter tuning to find the optimal hyperparameter for the Random Forest algorithm. The performance results of the classification model using Random Forest with Grid Search are 73.06% in accuracy, 75.15% in precision, 68.72% in recall, and 71.79% in f1-score. ]]>
<![CDATA[Rancangan Sistem Pendukung Keputusan “TechTrack” Berdasarkan Evaluasi Kualitas UI/UX Aplikasi ]]>
<![CDATA[Made Bayu Maha Krisna Siaka]]>;
<![CDATA[I Gede Arta Wibawa]]>
<![CDATA[ Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 1 (2023): JNATIA Vol. 2, No. 1, November 2023 ]]>
Publisher : <![CDATA[Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University ]]>
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DOI: 10.24843/JNATIA.2023.v02.i01.p26
<![CDATA[Smartphone is a portable electronic communication device that enables individuals to engage in various activities, including work, communication, and entertainment. It serves as a versatile tool that can be easily carried and utilized in different locations, providing convenience and facilitating human interactions across multiple domains."TechTrack," is a decision support system for selecting smartphones based on user interface and user experience feasibility. The ever-growing variety and complexity of smartphone models available in the market make it challenging for consumers to make well-informed purchasing decisions.The TechTrack app provides personalized recommendations by considering user preferences, specifications, and reviews. The system underwent usability testing, with the resulting System Usability Scale (SUS) value. The app aims to simplify the decision-making in selecting smartphones by offering comprehensive information, comparison features, user reviews, and a rating system. TechTrack shows potential in enhancing the user experience and helping users in making informed purchasing decisions. ]]>
<![CDATA[Rancang Model Ontologi untuk Representasi Pengetahuan Busana Tradisional Indonesia ]]>
<![CDATA[Ngurah Kelvin Febryanta Lila Ananda]]>;
<![CDATA[I Komang Ari Mogi]]>
<![CDATA[ Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 1 (2023): JNATIA Vol. 2, No. 1, November 2023 ]]>
Publisher : <![CDATA[Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University ]]>
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DOI: 10.24843/JNATIA.2023.v02.i01.p27
<![CDATA[Indonesia has islands that are inhabited by more than 255 million people, making Indonesia the fourth most populous country in the world. Not far from the population density in Indonesia, there are various kinds of culture, one of which is by showing the identity of each region by wearing their traditional clothes. Traditional clothing in various regions in Indonesia certainly has different uses and meanings and has its own characteristics, so it needs to be studied properly. The appropriate method for documenting Traditional Clothing is with an appropriate onological knowledge base to present the information. In this project, ontology methods are created using the Protege ontology developer tool. We apply the METHONTOLOGY method in the development of the ontology model, which describes in detail each step taken. The designed ontology model has 21 classes, 5 object properties, 2 data properties, and 32 individuals. We focus on explaining which materials, ethnicities and origins are used in Traditional Clothing. Testing is carried out using the ontology model development by performing a SPARQL query. ]]>
<![CDATA[Sistem Rekomendasi Game dengan Metode K-Nearest Neighbor (KNN) ]]>
<![CDATA[I Putu Marcel WIguna]]>;
<![CDATA[Ida Bagus Gede Dwidasmara]]>
<![CDATA[ Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 1 (2023): JNATIA Vol. 2, No. 1, November 2023 ]]>
Publisher : <![CDATA[Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University ]]>
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DOI: 10.24843/JNATIA.2023.v02.i01.p28
<![CDATA[The rapid growth of the gaming industry has resulted in an overwhelming number of game titles available to users. However, the abundance of choices makes it challenging for users to find games that match their preferences and interests. To address this issue, this research paper focuses on the development of a game recommendation system. The goal is to create an effective system that assists users in discovering games that align with their tastes and enhances their gaming experience.In this study, the K-Nearest Neighbor (KNN) method is employed as the underlying algorithm for the game recommendation system. The KNN method is a popular machine learning technique known for its ability to classify data based on similarities.This allows the system to recommend games that are likely to be of interest to users based on their preferences and the characteristics of games they have previously enjoyed. This research contributes to the field by showcasing the potential of the K-Nearest Neighbor (KNN) method in developing an efficient game recommendation system. The system's capability to assist users in discovering engaging games tailored to their interests has implications for improving user experience and driving game sales ]]>
<![CDATA[Chatbot Pelayanan Informasi Kampus ]]>
<![CDATA[Detriasmita Saientisna]]>;
<![CDATA[I Gusti Agung Gede Arya Kadyanan]]>;
<![CDATA[Ida Bagus Made Mahendra]]>;
<![CDATA[V. G. A. Pradika]]>
<![CDATA[ Jurnal Nasional Teknologi Informasi dan Aplikasinya Vol. 2 No. 1 (2023): JNATIA Vol. 2, No. 1, November 2023 ]]>
Publisher : <![CDATA[Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University ]]>
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DOI: 10.24843/JNATIA.2023.v02.i01.p29
<![CDATA[As technology advances in the world of education, information about campuses becomes very important. Searching for information that is fast, precise, and easy is needed for prospective students, university students, other campus residents, and the public. Chatbots are the solution of choice because of their popularity. The use of chatbots is useful in finding information about campuses quickly and easily, where users no longer need to browse every website or go to campus officials to find out information. This chatbot can provide basic information about the services provided by the campus, information on study programs, faculties, and even campus officials. ]]>
