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JITK (Jurnal Ilmu Pengetahuan dan Komputer)
Published by STMIK Nusa Mandiri
ISSN : -     EISSN : 25274864     DOI : -
Core Subject : Science,
Computer Science & IT
Kegiatan menonton film merupakan salah satu cara sederhana untuk menghibur diri dari rasa gundah gulana ataupun melepas rasa lelah setelah melakukan aktivitas sehari-hari. Akan tetapi, karena berbagai alasan terkadang seseorang tidak ada waktu untuk menonton film di bioskop. Dengan bantuan media internet, berbagai macam aplikasi nonton film android sangat mudah dicari. Hanya bermodalkan smartphone saja para penonton film dapat streaming berbagai macam jenis film di mana saja dan kapan saja mereka inginkan. Akan tetapi, karena banyaknya pilihan aplikasi nonton film android yang bisa digunakan, terkadang seseorang bingung memilihnya. Untuk itu, diperlukan suatu sistem pendukung keputusan yang dapat digunakan para pengguna sebagai alat bantu pengambilan keputusan untuk memilih dengan berbagai macam kriteria yang ada. Salah satu metode yang digunakan adalah metode Analytical Hierarchy Process (AHP). AHP melakukan perankingan dengan melalui penjumlahan antara vector bobot dengan matrik keputusan dengan tujuan agar hasil yang diberikan lebih baik dalam menentukan alternatif yang akan dipilih. Berdasarkan hasil penelitian yang dilakukan oleh 36 sampel responden didapatkan kriteria konten menjadi prioritas pertama pengguna untuk memilih aplikasi nonton film android dengan nilai bobot sebesar 0,224. Sedangkan Netflix menjadi alternatif dengan prioritas pertama keputusan pengguna dalam memilih aplikasi nonton film android dengan nilai bobot sebesar 0,352.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 394 Documents
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OUTSOURCED EMPLOYEE RECRUITMENT DECISION SUPPORT SYSTEM WITH FUZZY TOPSIS INTEGRATED REST API METHOD Asep Denih; Asep Saepulrohman; Febri Febriansyah
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.5521

Abstract

PT Dina Mika Muda Mandiri is a logistics and transportation company that is facing challenges in recruiting outsourced employees to meet the company's standards with complex assessment criteria. In overcoming this problem, the research developed a decision support system that is integrated with Rest API and the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The system aims to improve the efficiency and accuracy of candidate selection by evaluating criteria such as interviews, knowledge, testing, curriculum vitae (CV), processing time, and salary. Two case studies were conducted involving 36 applicants for a website upgrade project and 24 applicants for an outsourced goods transit system. The results demonstrate that the decision support system integrated with Fuzzy TOPSIS significantly enhanced the selection process, improving accuracy by 91% for the website upgrade project and 97% for the goods transit system when compared to traditional human resource development (HRD) decision criteria. This demonstrates the system's effectiveness in aligning with HRD standards, making the recruitment process more effective, accurate and efficient. Future research should explore methods to refine the weighting of criteria and integrate expert opinions or more sophisticated machine learning algorithms to support more objective decision support systems in outsourcing employee recruitment.
MULTIPLAYER ONLINE ROLE-PLAYING GAME VIRTUAL CLASSROOMS USING THE GAME DEVELOPMENT LIFE CYCLE METHOD I Gede Suardika; I Nyoman Suraja Antarajaya; Gusti Ngurah Mega Nata; Putu Pande Yudiastra
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.5528

Abstract

The COVID-19 pandemic has disrupted traditional education, forcing a shift toward online learning, which often lacks engagement and effectiveness. Existing virtual classroom methods struggle to sustain students' attention and motivation, leading to reduced learning outcomes. To address these issues, this study develops an innovative Virtual Classroom application based on Multiplayer Online Role-Playing Game (MORPG) technology. The goal is to provide a more interactive and immersive learning environment, enhancing engagement among students and lecturers. Using the Unity Game Engine, Photon Unity Networking (PUN), and Photon Voice libraries, this application transforms online classes into game-like experiences. The development followed the Game Development Life Cycle (GDLC) methodology, ensuring a structured and effective approach. Blackbox testing confirmed that all functions operated as intended, while usability testing with the System Usability Scale (SUS) among 30 users yielded an average score of 71.92, indicating a satisfactory experience. The results demonstrate the application's potential to make online learning more appealing and effective, contributing a novel solution for remote education challenges by integrating gaming elements into the learning process.
IMPLEMENTATION OF K-MEDOIDS METHOD FOR HEART DISEASE PREDICTION USING QUANTUM COMPUTING AND MANHATTAN DISTANCE Mochamad Wahyudi; Dimas Trianda; Lise Pujiastuti; Solikhun Solikhun
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.5637

Abstract

Heart disease is a severe health condition characterized by dysfunctions in the heart and blood vessels, which can be fatal if not properly managed. Early detection and prediction of heart disease are crucial for understanding the prevalence and determining patients' quality of life. In this study, quantum computing is applied to enhance the performance of the K-Medoids method. A comparative analysis of these methods is conducted, focusing on their performance. The investigation utilizes a dataset of heart disease patient medical records. This dataset includes various attributes used to predict heart disease patterns. The dataset is tested using both the classical and K-Medoids methods with a quantum computing approach, employing Manhattan distance calculations. This study's findings reveal that applying quantum computing to the K-Medoids method results in clustering accuracy stability of 85%, equivalent to the classical method. Although there is no increase in accuracy, the quantum computing approach demonstrates potential improvements in data processing efficiency. These results highlight that the K-Medoids method with a quantum computing approach can contribute significantly to faster and more efficient medical data analysis. However, further research is needed for optimization and testing on more extensive and more diverse datasets.
ASPECT-BASED SENTIMENT ANALYSIS ON TWITTER TWEETS ABOUT THE MERDEKA CURRICULUM USING INDOBERT Andi Wafda; Dhomas Hatta Fudholi; Jaka Nugraha
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.5692

Abstract

The curriculum has changed once again with the introduction of the Merdeka Curriculum to address learning loss in the education sector. Its implementation has elicited various responses, such as support for granting teachers the freedom to innovate, focusing on essential materials, offering diverse learning methods, and fostering student creativity. However, criticism has also arisen, including issues related to teachers’ lack of understanding, parents' concerns, and the increased workload on students due to numerous projects. To improve educational policies, an in-depth analysis of these responses is essential. This study aims to analyze public sentiment toward the Merdeka Curriculum by applying Aspect-Based Sentiment Analysis (ABSA) using data from Twitter. The research focuses on four main aspects: Teaching Modules (MA), Education Reports (RP), the Merdeka Teaching Platform (PMM), and the Strengthening of the Pancasila Student Profile Projects (P5). Data were collected using specific and relevant keywords for each aspect, followed by preprocessing, labeling, and filtering based on sentiment and aspect. The final dataset comprised 2,359 valid tweets. The ABSA model was developed using IndoBERT with fine-tuning, then tested and evaluated. The results showed that the aspect classification model achieved an accuracy of 97%, F1 score of 97%, recall of 97%, and precision of 97%. Meanwhile, the sentiment classification model achieved an accuracy of 85%, F1 score of 85%, recall of 85%, and precision of 85%. This ABSA model is expected to assist in monitoring public responses and provide valuable insights for policy development, particularly within the context of the Merdeka Curriculum.
DESIGNING USER EXPERIENCES IN CASUAL GAMES TO ENHANCE PRODUCT KNOWLEDGE Eva Handriyantini; Stefanus Salem
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.5761

Abstract

The widespread adoption of mobile platforms has transformed the gaming industry, making casual games highly popular due to their accessibility via smartphones and tablets. Beyond entertainment, casual games now serve as effective educational and marketing tools for delivering product knowledge. This study explores how user experience (UX) design can enhance product education in casual games by focusing on game mechanics, UX principles, narrative engagement, and product placement. Using a Design-Based Research (DBR) approach, this study develops, tests, and refines interactive experiences to ensure the effective implementation of design elements. Testing with 50 participants showed a 30% improvement in product recall after playing, along with high satisfaction levels regarding game usability and engagement. Participants also demonstrated improved time management skills and emotional connection to the game content. The game integrates challenges and activities designed to build cognitive and emotional engagement. Artificial intelligence (AI) technology is utilized through Unreal Engine to create a realistic and immersive environment. By incorporating product information into engaging gameplay, the game serves as both an educational and entertainment tool. This research provides practical insights for game developers, marketers, and educators on integrating educational content into casual games. By leveraging AI, user testing, and advanced UX strategies, casual games can become effective tools for game-based marketing and education. This game significantly enhances product knowledge retention, user engagement, and practical skills.
APPLYING K-MEANS CLUSTERING FOR GROUPING PAPUA’S DISTRICTS BASED ON POVERTY INDICATORS ANALYSIS Yusriana Chusna Fadilah; Asrul Sani; Andrianingsih Andrianingsih
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.5865

Abstract

In the context of Indonesia's resource-rich development, poverty remains a major challenge, especially in Papua Province which has the highest poverty rate. Although Papua is rich in resources such as minerals, tropical forests, and biodiversity, challenges such as economic inequality, lack of infrastructure, and social conflict hinder economic and social progress. This research aims to implement the K-Means Clustering algorithm to cluster districts/cities in Papua based on poverty indicators, including the percentage of poor people, poverty line, average years of schooling, human development index, poverty depth index, poverty severity index, unemployment rate, and per capita expenditure. The research methodology includes data collection from the Central Statistical Agency (BPS), data processing through cleaning and transformation stages, and application of K-Means Clustering to determine the optimal cluster using the elbow method and silhouette score. The results show that the districts/cities in Papua can be grouped into two main clusters: C0, which indicates high poverty rates and C1, which indicates low poverty rates. This research is expected to provide a strategic foundation for the government to design more focused and effective development policies in reducing poverty in Papua.
DEVELOPMENT OF VT-UNUJA APPLICATION AS A WEBVR-BASED CAMPUS ENVIRONMENT INTRODUCTION MEDIA Miftahul Huda; Fathorazi Nur Fajri; Maulidiansyah Maulidiansyah
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.5945

Abstract

Conventional campus introductions are often limited in providing an immersive experience to prospective students, especially for those who cannot attend in person. This encourages the need for technology-based solutions that can overcome these limitations. This research develops a WebVR-based VT-UNUJA application as a campus introduction media that offers an interactive experience with 360-degree panoramic image features, hotspot descriptions, navigation, and voice-over. The purpose of this research is to create an application that can increase user understanding of campus locations and facilities more efficiently and easily accessible. The test results show that this application is effective in improving user understanding, with a high level of satisfaction with the ease of use and interactivity of the application. The benefits of this research are to contribute in improving campus professionalism in presenting information digitally, as well as providing innovative alternatives for other educational institutions in supporting the orientation process for prospective students.
COMPARISON OF ENSEMBLE METHODS FOR DECISION TREE MODELS IN CLASSIFYING E. COLI BACTERIA Alvin Rahman Al Musyaffa; Yoga Pristyanto; Nia Mauliza
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.5972

Abstract

Certain strains of Escherichia coli (E. coli) can cause serious illness, so identifying dangerous strains with high accuracy is a priority in supporting public health and food safety. However, traditional machine learning methods, such as Decision Trees, are often not robust enough to handle the complexity of biological data. This research presents a solution by systematically evaluating seven ensemble methods, namely Adaboost, Gradient Boosting, XGBoost, LightGBM, Random Forest, Bagging, and Stacking, using a dataset that includes 336 E. coli samples with eight biological features. These models are evaluated based on accuracy, precision, recall, and F1 score, with parameter optimization to obtain the best results. The results show that XGBoost is superior with accuracy, recall, and F1 score of 88% and precision of 87%, outperforming other methods. This research has the advantage of a comprehensive approach in comparing various ensemble methods simultaneously, accompanied by the application of confusion matrix-based evaluation to ensure the accuracy of the results. Additionally, the ensemble approach proved to be more effective in handling complex data patterns and reducing bias in bacterial strain classification. These findings provide a significant contribution, namely a practical framework for improving laboratory diagnostics and public health surveillance, with machine learning-based solutions that are faster, more reliable, and applicable for both industrial and clinical environments. This research expands understanding of the potential of ensemble methods in microbiological data classification and provides new directions for modern diagnostic technology.
SMART ATTENDANCE TRACKING SYSTEM EMPLOYING DEEP LEARNING FOR FACE ANTI-SPOOFING PROTECTION Bani Nurhakim; Ahmad Rifai; Dian Ade Kurnia; Dadang Sudrajat; Ujang Supriatna
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.5992

Abstract

Conventional attendance systems face challenges in accuracy and efficiency, often vulnerable to spoofing and data manipulation. This study addresses these issues by developing a smart attendance system integrating Deep Learning-based facial recognition with anti-spoofing technology. The system ensures secure and reliable attendance authentication while automating and enhancing management processes. Utilizing a convolutional neural network (CNN) architecture, the system processes raw facial images directly without additional feature extraction, improving accuracy and efficiency. A novel training strategy, termed 50 Random Samples-30 Sub-epochs Count-1 Epoch, is introduced to optimize the training process. This strategy involves random sampling during each forward pass and grouping 30 passes as one epoch, enabling the use of complex CNN architectures and automatic dataset expansion. The system achieves 98.90% accuracy in identifying genuine attendance, maintaining a confidence level above 80%, significantly reducing spoofing risks and errors. This innovative solution has significant implications, particularly for educational institutions. It automates attendance tracking, minimizes manual effort, reduces errors, and supports disciplinary enforcement through accurate data. Moreover, its scalability allows for application across various environments, offering benefits to a wide range of institutions. By enhancing data accuracy and operational efficiency, this system sets a foundation for smarter, more reliable attendance management, strengthening administrative practices in education and beyond.
PREDICTION OF INHIBITOR BINDING AFFINITY AND MOLECULAR INTERACTIONS IN MPRO DENGUE USING MACHINE LEARNING Venia Restreva Danestiara; Marwondo Marwondo; Nayla Nurul Azkiya
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.5994

Abstract

The dengue virus experiences rapid mutation and genetic variability, posing challenges in developing effective antiviral therapies. This study explores the prediction of binding affinities between potential antiviral drug inhibitors and the NS2B-NS3 protease of the dengue virus using machine learning models. Molecular docking simulations were conducted with AutoDock Vina to generate interaction data between viral proteins and ligands. The generated datasets were used to train several machine learning models, including Random Forest Regressor (RF Regressor), Support Vector Regression (SVR), and Extreme Gradient Boosting Regressor (XGBoost Regressor). The RF Regressor model demonstrated the highest accuracy in predicting binding affinities, measured through Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Pearson Correlation Coefficient (R). However, the XGBoost Regressor and SVR models showed better generalization in practical scenarios. This study highlights the potential of machine learning to optimize the drug discovery process and provides significant insights into antiviral drug development for dengue fever.

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