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INDONESIA
Jurnal Ilmu Komputer
Published by Universitas Udayana
ISSN : 19795661     EISSN : 2622321X     DOI : -
Core Subject : Science, Education,
Computer Science & IT Languange, Linguistic, Communication & Media Library & Information Science
JIK is a peer-reviewed scientific journal published by Informatics Department, Faculty of Mathematics and Natural Science, Udayana University which has been published since 2008. The aim of this journal is to publish high-quality articles dedicated to all aspects of the latest outstanding developments in the field of computer science. JIK is consistently published two times a year in April and September. This journal covers original article in computer science that has not been published. The article can be research papers, research findings, review articles, analysis and recent applications in computer science.
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Articles 189 Documents
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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.
Sentimen Analisis Pengguna Media Sosial Berdasarkan Metode Ekstraksi Fitur dan Klasifikasi Putra, Fathiyarizq Mahendra; Hardjita, Pahlevi Wahyu; Tyas, Dyah Aruming
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.p02

Abstract

Analisis sentimen adalah gabungan dari berbagai bidang penelitian seperti NLP (natural language processing), data mining, dan text mining dengan tujuan untuk menemukan pendapat orang yang diungkapkan dalam bentuk teks. Terdapat beberapa tugas dalam analisis sentimen seperti ekstraksi sentimen, klasifikasi sentimen, peringkasan, Terdapat beberapa tantangan dalam melakukan analisis sentimen antara lain sinonim dan polisemi, sarkasme, kalimat majemuk, data tidak terstruktur, Tujuan penulisan ini adalah mereview penelitian lain mengenai Sentimen Analisis berdasarkan dataset, seleksi fitur, dan algoritma klasifikasi dan juga penggunaan multilabel pada sentimen analisis, serta evaluasi hasil akurasi, untuk mendapatkan pendekatan terbaik terhadap pemilihan metode yang digunakan dalam melakukan pemrosesan penambangan
Model Prediksi Umur Kepiting Berdasarkan Data Morfometrik dan Gender: Pendekatan Model Support Vector Regression Ramadhani, Tirta Samudera; Mudaim, Syarifah; Sabitta, Valin Rizkia; Maulidia, Raisa
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.p07

Abstract

Crab is one of the most important marine commodities and resources in Indonesian waters, both economically and ecologically. Crab age determination can provide a better understanding of crab growth and development, so that crab farming can be carried out effectively, efficiently, and profitably for its supporters. In addition, determining the age of the crab can also help the sustainability of the crab population. This study was conducted using support vector regression (SVR) modeling to predict crab age by establishing a predictive relationship between the dependent variable (x) and the independent variable (y). The attributes of the dependent variable (x) include length, diameter, height, and weight. While the independent variable (y) only includes crab age. SVR modeling is carried out to show predicted data with actual data, where the results of the SVR modeling will be evaluated based on the results of the RMSE value test. This study resulted in an RMSE value of 0.019814 so it can be said that the model to predict crab age is very accurate. The purpose of this research is to develop a statistical model that can predict crab age based on morphometric data and crab gender using the Support Vector Regression (SVR) model approach.
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.
Aplikasi Penilaian Tes Psikologi Menggunakan Metode FAST Framework Wardhanie, Ayouvi Poerna
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.p01

Abstract

Psikologi merupakan ilmu pengetahuan yang mempelajari tentang kondisi mental dan psikis seseorang, dalam memeriksa psikologi seseorang terdapat beberapa macam tes psikologi yang dapat dilakukan salah satunya adalah tes psikologi kepribadian. Pada dunia kerja, tes psikologi dibutuhkan oleh perusaahan untuk menempatkan karyawan pada posisi yang sesuai dengan keahlian dan kepribadian mereka. Dalam menilai karyawan dibutuhkan tes psikologi untuk mendeskripsikan kepribadian mereka. Penelitian ini bertujuan untuk meningkatkan proses layanan dan menjaga kualitas pelayanan pada Poli Psikologi dan mengatasi masalah yang ada pada Poli Psikologi. Permasalahan terjadi pada Poli Psikologi di RSUD Jombang adalah lambatnya proses layanan seperti pendataan, tes psikologi dan rekap data dikarenakan masih dilakukan secara manual. Oleh sebab itu, solusi untuk mengatasi masalah tersebut adalah dengan membuat aplikasi penilaian tes psikologi yang lebih cepat dengan metode yang digunakan adalah FAST Framework yaitu metode pengembangan sistem dalam pembuatan fitur dan fungsional dari aplikasi yang mencakup proses pendataan pemeriksaan, perhitungan tes psikologi dan pembuatan rekam medis pasien. Aplikasi yang dibuat diuji dengan metode pengujian Blackbox dan User Acceptance Testing. Hasil dari penelitian ini adalah proses layanan yang semula memakan waktu 35 – 45 menit menjadi 10 – 20 menit untuk menangani setiap pasien dengan hasil pengujian yang menyatakan bahwa fitur dan fungsional aplikasi telah berjalan dengan baik pada pengujian Blackbox, dan aplikasi telah diterima oleh pengguna dengan presentase penerimaan sebesar 84.5%.
Perbandingan Klasifikasi antara Naives Bayes dan Decision Tree dalam Prediksi Penyakit Diabetes Tahap Awal Putra, Akbar Wibowo; Kusumo, Kevin; Ratu, Ayu Sitho Resmy; Mujayanto, Radik Rosyadi; Rafly, Muhammad; Mintarum, Melati Mahandani; Nurcahyawati, Vivine
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.p06

Abstract

Diabetes is a health condition characterized by an elevated blood glucose level. There are two types, namely diabetes type 1 and diabetes type 2. Diabetes type 1 is caused by a lack of insulin production by the pancreas. Symptoms of diabetes include excessive thirst, frequent urination, and constant hunger. Classification is a process that helps us group data or information into categories based on similar characteristics. In the context of diabetes, classification methods can be used to group individuals based on their risk levels of developing diabetes. By using classification methods, doctors can determine an individual's risk of diabetes and design an appropriate treatment plan. This study involves a comparison between the Naïve Bayes and Decision Tree methods. The results of this research indicate that the algorithm generated is the best among the two algorithms in identifying diabetes patients. An accuracy of 66.67% was obtained for Naïve Bayes, while an accuracy of 91.67% was obtained for Decision Tree. In this study, it was found that the Decision Tree method has a higher accuracy rate than the Naïve Bayes method in the case study and data testing.
Balancing Dataset Untuk Klasifikasi Komentar Program Kampus Merdeka Menggunakan Synonym Replacement Nifanto, Soleh
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.p02

Abstract

The classification of comments in the Merdeka Campus program is an essential step in analyzing user sentiment towards the various features and services offered by the program. However, in the dataset processed in this study, problems are encountered, namely the imbalance of the amount of data in each class. The Imbalanced Ratio in this dataset is relatively high by 5:1. This generally leads to a classification model that prioritizes the majority class and results in low performance in the minority class. Therefore, a data augmentation approach is used in this study with the Synonym Replacement method to produce data variations in minority classes, thereby reducing the imbalance and improving classification performance. This method utilizes the technique of replacing synonyms in sentences in comments to enrich the dataset and increase the representational features. The study's results showed an increase in the F-Measure value from 0.6672 to 0.7875. Evaluation using ROC shows a maximum value of 0.96. In contrast, the class that did not get augmentation tended to have low ROC values between 0.81 to 0.88.
Sistem Penentuan Indeks Massa Tubuh Menggunakan Pengolahan Citra Digital Safitri, Evyra Rizki
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.p07

Abstract

Obesity stands as a significant and perilous health concern, holding utmost importance for the well-being of the body. To mitigate the associated health risks, its identification can be achieved through the utilization of a standardized technique known as the Body Mass Index (BMI) for calculating the optimal body weight. To get information about person’s BMI value and category, data on weight and height are needed, which are then calculated to produce the appropriate BMI value and category. To implement a more pragmatic strategy, this study will be executed by developing an application that captures images of the human body using a mobile phone camera. Image processing with digital image processing stages such as preprocessing, morphology, BS and then calculating the weight and height and BMI category. Based on the system test that was carried out, the best approximate value was obtained at a distance 200cm with a body height value is 96% while the body weight is 90,8% and accuracy value of BMI category is 80%.
Evaluasi User Experience SIPENA Menggunakan Metode User Experience Questionnaire Budi Mas Aryawan, I Komang
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.p03

Abstract

SIPENA is an employee attendance application regularly used by employees at the University of Udayana since the beginning of 2022. As a newly operational application, the quality of user experience becomes crucial in ensuring maximum adoption and acceptance from users. The use of the User Experience Questionnaire (UEQ) method is a relevant and effective approach, as UEQ can provide comprehensive measurement results of user experience with six scales, including: (1) attractiveness, (2) perspicuity, (3) efficiency, (4) dependability, (5) stimulation, and (6) novelty. Therefore, the research results can provide recommendations and guidance for the development and improvement of the application, which can offer greater added value for the institution in terms of efficiency and accuracy in attendance data management.
Grouping of Electricity Regions with K-Medoid Algorithm Cahya Dewi, Dewa Ayu Indah
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.p04

Abstract

All human activities are inseparable from the current use of electricity. Electricity sourced from renewable energy is an alternative for areas that have difficulty accessing electricity from PLN. This research gathers data on electricity consumers in Indonesia by utilizing the K-Medoid algorithm and the Davies-Bouldin Index (DBI). The DBI is used to test the quality of clustering so that the optimal cluster in this study is obtained with the lowest DBI value of 0.386 with 3 clusters. The cluster analysis results reveal that fewer areas utilize electricity sourced from PLN compared to non-PLN or renewable energy sources. Based on this, recommendations can be proposed to enhance development, making it more accessible for villages to be electrified from PLN or to increase the electrification of villages currently not using electricity. source of electricity from non-PLN to realize the Bright Indonesia Program.

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