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INDONESIA
(JELIKU) Jurnal Elektronik Ilmu Komputer Udayana
Published by Universitas Udayana
ISSN : 23015373     EISSN : 26545101     DOI : https://doi.org/10.24843/JLK
Core Subject : Science,
Computer Science & IT
Aim and Scope: JELIKU publishes original papers in the field of computer science, but not limited to, the following scope: Computer Science, Computer Engineering, and Informatics Computer Architecture Parallel and Distributed Computer Computer Network Embedded System Human—Computer Interaction Virtual/Augmented Reality Computer Security Software Engineering (Software: Lifecycle, Management, Engineering Process, Engineering Tools and Methods) Programming (Programming Methodology and Paradigm) Data Engineering (Data and Knowledge level Modeling, Information Management (DB) practices, Knowledge Based Management System, Knowledge Discovery in Data) Network Traffic Modeling Performance Modeling Computer Security IT Governance Networking Technology Robotic Instrumentation Information Search Engine Multimedia Security Information Retrieval Mobile Processing Natural Language Processing Artificial intelligence & soft computing and their applications Neural networks Machine Learning Reasoning and evolution Intelligence applications Computer vision and speech understanding Multimedia and cognitive informatics Data mining and machine learning tools, heuristic and AI planning strategies and tools, computational theories of learning
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 488 Documents
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Implementasi BERT pada Analisis Sentimen Ulasan Destinasi Wisata Bali Tristan Bey Kusuma; I Komang Ari Mogi, S.Kom., M.Kom.
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 12 No 2 (2023): JELIKU Volume 12 No 2, November 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/JLK.2023.v12.i02.p19

Abstract

In recent years, the contribution of the tourism sector in Bali has increased significantly. The tourism sector has an important role as a source of foreign exchange earnings, and can encourage national economic growth. With the digital age, online opinion are increasingly vital to the growth of Indonesian tourism internationally. Public opinion and reviews on these tourist destinations can be used to identify new tourist destinations which are gaining traction and are in demand. Which is why it will be important to leverage these positive or negative opinions to acquire interesting and vital information on these tourist destinations for further use. One such method to acquire such information are through sentiment analysis to determine whether the a review’s attitude towards a particular tourist destination or experience is positive, negative, or neutral. This study aims to use IndoBERT, a pre-trained BERT (Bidirectional Encoder Representations from Transformers) model for the Indonesian language. This model is trained using a masked language modeling (MLM) objective and next sentence prediction (NSP) objective. This study also compares two different optimizers with a weight decay fix, AdamW and AdaFactor. The results show that sentiment analysis using the IndoBERT model with the AdamW optimizer reaches 97% accuracy and AdaFactor reaches 98,2% accuracy.
Klasifikasi Jenis Sampah Menggunakan Metode Transfer Learning Pada Convolutional Neural Network (CNN) Wahyu Vidiadivani; I Ketut Gede Suhartana
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 12 No 3 (2024): JELIKU Volume 12 No 3, February 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/JLK.2023.v12.i03.p11

Abstract

Indonesia merupakan salah satu negara sebagai penghasil sampah terbanyak di dunia. Produksi sampah mengalami penambahan seiring pertumbuhan penduduk yang signifikan dan meningkatnya kebutuhan masyarakat. Jumlah sampah yang sangat besar dan beragamnya jenis sampah yang tersebar di masyakarat, perlu adanya klasifikasi yang dapat mengidentifikasi jenis-jenis sampah ke beberapa kategori sehingga mudah untuk didaur ulang kembali. Klasifikasi jenis sampah pada penelitian ini dibagi menjadi 12 jenis, yaitu battery, biological, brown-glass, cardboard, clothes, green-glass, meal, paper, plastic, shoes, trash, dan white-glass menggunakan metode Transfer Learning pada Convolutional Neural Network (CNN). CNN (Convolutional Neural Network) merupakan salah satu algoritma deep learning yang populer digunakan untuk klasifikasi citra dan dinilai memiliki performa yang bagus. Pada penelitian ini, arsitektur yang digunakan adalah EffecienNetB0. Dataset yang digunakan dengan total data sebanyak 12412 data, data yang tervalidasi sebanyak 1552 data, dan data yang digunakan pada proses testing sebanyak 1552 data yang terbagi ke 12 kelas.
Deteksi Rasa Buah Jeruk Siam Kintamani Menggunakan SVM dengan Optimasi Algoritma Genetika Ni Wayan Yulia Damayanti; I Gede Arta Wibawa; I Gede Santi Astawa; Anak Agung Istri Ngurah Eka Karyawati
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 13 No 1 (2024): JELIKU Volume 13 No 1, August 2024
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Kintamani Siam oranges are one of the important commodities in Indonesian agriculture, especially in Bangli Regency, Bali. However, assessing the quality of orange taste still often relies on subjective manual identification. In an effort to enhance objectivity and consistency in assessing orange quality, this study proposes the use of Support Vector Machine (SVM) algorithm optimized with genetic algorithm. The aim of this research is to detect the quality of Kintamani Siam orange taste based on texture characteristics in orange images. Test results show that SVM optimized with genetic algorithm has better accuracy than SVM without optimization. For instance, SVM without optimization yields an accuracy of 0.78, while after optimization with genetic algorithm, the accuracy increases to 0.80. These results indicate the significant potential of genetic algorithm in improving the performance of SVM in detecting the quality of Kintamani Siam orange taste, which can help enhance efficiency and consistency in the orange industry.
Desain dan Implementasi Data Warehouse Penjualan pada Chinook Sample Database Gusti Ngurah Deva Wirandana Putra; Cokorda Rai Adi Pramartha
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 12 No 2 (2023): JELIKU Volume 12 No 2, November 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/JLK.2023.v12.i02.p10

Abstract

The company's decision making is very important for analysis. With the data warehouse can support the process of analysis, design, and business decision making of the company. The company stores operational data that is useful in the business analysis process into a data warehouse. This study will develop the design and implementation of a data warehouse using the Chinook Sample Database as the source. Used Nine-Step Design Methodology to design the data warehouse and through the ETL (Extract, Transformation, Loading) process. The results form a Dashboard that is visualized with Tableau according to a sales fact chart that contains information used to assist the company's business analysis and decisions.
Analisis Dan Visualisasi Data Untuk Meningkatkan Penjualan Menggunakan Exploratory Data Analysis Dan Looker Studio (Studi Kasus : Nies Collection) Melanie Putri; Apriade Vaoutama
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 12 No 4 (2024): JELIKU Volume 12 No 4, May 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/JLK.2024.v12.i04.p15

Abstract

Dengan kemudahan akses dan penggunaan teknologi yang semakin luas, berbagai sektor dihadapkan pada tuntutan untuk terus berinovasi menuju perubahan yang lebih baik. Fenomena maraknya perdagangan online menimbulkan tantangan baru bagi semua pelaku ekonomi, yang harus berlomba-lomba dalam memikat konsumen untuk bertransaksi. Nies Collection, sebuah toko fashion wanita yang beroperasi dalam salah satu platform e-commerce terkemuka yaitu Shopee, turut serta dalam dinamika ini. Penelitian ini bertujuan untuk menggambarkan dan menganalisis pola penjualan Nies Collection selama beberapa bulan terakhir. Data yang digunakan merupakan data penjualan yang dikumpulkan dari rentang waktu 16 Agustus hingga 16 November 2023 yang kemudian akan diproses dan dianalisis secara menyeluruh untuk menghasilkan informasi yang bermanfaat guna meningkatkan kinerja penjualan, serta mengembangkan Toko Nies Collection ke depannya. Metode yang digunakan dalam analisis data ini adalah Exploratory Data Analysis (EDA), didukung oleh platform Google Colaboratory. Selain itu, visualisasi data akan dilakukan secara real-time melalui platform Looker Studio, memungkinkan pemahaman yang lebih mendalam terhadap tren dan pola penjualan. Hasil analisis dan visualisasi data akan menjadi dasar untuk menyusun rekomendasi strategis bagi Nies Collection. Tujuan utamanya bukan hanya untuk meningkatkan volume penjualan, tetapi juga untuk memperkuat posisi toko dalam pasar dan menciptakan pertumbuhan yang berkelanjutan di masa mendatang.
Development of Ontology Models on Vitamin Suplements Domain I Made Dirga Adi Guna; I Ketut Gede Suhartana
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 12 No 2 (2023): JELIKU Volume 12 No 2, November 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/JLK.2023.v12.i02.p01

Abstract

The amount of vitamin suplements that sold in the world today causes people have more option to consider when choosing the right vitamin suplements to maintain their health and fulfill their nutritional adequacy rate. The number of vitamin suplements product available with various types and criteria requires consuments to be more careful when choosing the right vitamin suplements for their body. The right solution to overcome this problem is to use ontology models. The ontology method used in this research is Methontology. This method is one of the most popular method of building an ontology model that can be used as a reference for further research. The development of ontology models in this research use an app called Protégé 5.5.0. and in the evaluation process, ontology gives great result on answering the questions that given by users.
Implementasi Sistem Informasi E-Learning Berbasis Odoo Studi Kasus MI YPPI 1945 Babat Ahmad Assrorul Abidin; Nufan Balafif; Eddy Kurniawan
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 12 No 4 (2024): JELIKU Volume 12 No 4, May 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/JLK.2024.v12.i04.p06

Abstract

The Odoo e-learning information system is a digital learning media to support the process of delivering material and assessing work. Adequate technological facilities at the MI YPPI 1945 Babat school are the school's main capital. Selection of website modules and e-learning modules in Odoo which are used for the display design process and filling in the database according to needs is the initial stage of implementing the Odoo e-learning system. The system can carry out the process of delivering material, assigning assignments, performance assessments, and learning target data to provide solutions to learning problems that are less interesting and less effective and efficient in schools. UAT (User Acceptance Testing) testing method with indicators of Completeness, Consistency, Tracking, Operability, Training, Security, Accuracy, Simplicity and Ease of Execution for students and teachers by taking samples of the student population using the Slovin formula and teachers using saturated samples to get sample data used to test the quality of the running system produced a percentage of 88%, which means the system runs very well at the MI YPPI 1945 Babat school.
Perbandingan Kriptografi Klasik Hill Cipher dengan Affine Cipher dalam Pengamanan Data Citra I Nyoman Dwi Pradnyana Putra; I Gede Santi Astawa
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 12 No 3 (2024): JELIKU Volume 12 No 3, February 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/JLK.2023.v12.i03.p06

Abstract

The development of technology affects several aspects of life, especially in securing confidential data and information. Because of this, there is a way to secure data, namely cryptography, many cryptography techniques have been implemented to hide data information, one of which image data, the purpose of securing image data is to prevent unwanted things such as fraud by using other people’s identities supported by personal photos. In this research, two classical cryptography algorithms will be tested, namely hill cipher and affine cipher by comparing the MSE and PSNR Image values when encryption is carried out. The encryption process is done 6 times with different image in each algorithm, the results of the encryption are compared between the results of the hill cipher encryption image with the affine cipher
Implementasi Docker Container untuk Sistem Monitoring dan Pengontrolan Peralatan Listrik di Laboratorium Cerdas Sahirul Alam; Sri Lestari; Anni Karimatul Fauziyyah; Dzulfikar Dzulfikar
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 12 No 3 (2024): JELIKU Volume 12 No 3, February 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/JLK.2023.v12.i03.p25

Abstract

Smart laboratory atau laboratorium cerdas adalah laboratorium yang dilengkapi dengan teknologi canggih seperti Internet of Things (IoT), robotika, dan kecerdasan buatan (Artificial Intelligence/AI) untuk meningkatkan efisiensi, akurasi, dan keamanan dalam melakukan penelitian dan pengujian. Dalam sebuah smart laboratory, perangkat IoT dapat digunakan untuk memantau suhu, kelembaban, dan kualitas udara di dalam ruangan, sehingga dapat memastikan kondisi lingkungan yang ideal untuk menjaga kualitas sampel yang diuji. IoT sendiri adalah sebuah konsep yang mengacu pada konektivitas antara berbagai perangkat atau objek yang terhubung ke internet, sehingga memungkinkan perangkat tersebut saling berkomunikasi dan bertukar data. Penelitian ini bertujuan untuk membangun sebuah sistem pemantauan dan pengontrolan peralatan listrik di dalam laboratorium. Sistem yang dibangun menerapkan teknologi IoT sehingga pemantauan dan pengontrolan peralatan listrik akan menjadi lebih mudah dan dapat dilakukan dari mana saja. Selain itu, sistem memanfaatkan teknologi docker container sehingga instalasi dan pengelolaan perangkat lunak dapat dilakukan dengan lebih mudah dan efisien.
Klasifikasi Biji Jagung Berdasarkan Tekstur Dan Warna Menggunakan Metode Backpropagation Berbasis Web M . Syafiih; Nadiyah Nadiyah
JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 12 No 4 (2024): JELIKU Volume 12 No 4, May 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/JLK.2024.v12.i04.p03

Abstract

Indonesia is an agrarian country because most people rely on the agricultural sector for their livelihood. Paiton sub-district of Probolinggo district in East Java is one of the majority of people who are farmers until now still cultivating corn crops. Corn is widely consumed by the surrounding community because it is rich in nutrients, corn can also be used as food and animal livestock. Therefore, the quality of corn quality must be maintained in such a way because corn production is decreasing in productivity every year due to the reduction of planting land. Gray Level Co-occurrence Matric (GLCM), RGB (Red, Green, Blue) and Backpropagation methods. So the researcher will use this method to classify the quality of corn kernels. It is hoped that this utilization can solve the problem of middlemen so as not to lose money when buying corn from farmers. The result of this research is the process of determining the quality of corn kernels based on color and texture features using the GLCM, RGB, and Backpropagation methods with a total data of 150 images consisting of 120 training data and 30 testing data. The results of testing the classification system obtained an accuracy value of 75%. So that the backpropagation method can determine the quality of corn kernels based on images using a computer system so that it can be implemented into Web Flask.

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