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INDONESIA
KLIK: Kajian Ilmiah Informatika dan Komputer
Published by Sekolah Tinggi Manajemen Informatika dan Komputer Budi Darma
ISSN : -     EISSN : 27233898     DOI : -
Core Subject : Science,
Computer Science & IT
Topik utama yang diterbitkan mencakup: 1. Teknik Informatika 2. Sistem Informasi 3. Sistem Pendukung Keputusan 4. Sistem Pakar 5. Kecerdasan Buatan 6. Manajemen Informasi 7. Data Mining 8. Big Data 9. Jaringan Komputer 10. Dan lain-lain (topik lainnya yang berhubungan dengan Teknologi Informati dan komputer)
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 41 Documents
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Search results for , issue "Vol. 4 No. 4 (2024): Februari 2024" : 41 Documents clear
Aplikasi Pengenalan Jenis Penyakit Ayam dan Cara Pengobatan Berbasis Augmented Reality Ragil Aris Nurmanto; Zakariyah, Muhammad
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1696

Abstract

Chicken disease is a problem that is often faced by breeders. The impact of the disease in chickens if handled late will result in fatal things for chickens and become one of the factors of death in chickens. Augmented reality is a technology that is developing now, this technology is currently being widely used as a learning medium that uses interesting and interactive 3d objects. This application is made through several steps, namely, analysis and design, system design, interface design. The application we created aims to be able to overcome problems among farmers and the general public related to recognizing the types of diseases in chickens and how to treat them. The method used in this test is blackbox testing which focuses on testing every function of the button or button in the application, so that it is known whether the buttons are appropriate or not with the expected output results. So the results of testing the black box unit in the application designed in this study all buttons can function properly. So that we get final result in the form of an application for the introduction of chicken disease types and treatment methods based augmented reality, which can be used easily and interactively.
Pemanfaatan Teknologi Augmented Reality Untuk Pengenalan Jaringan Tumbuhan Berbasis Android Dwi Bintang Rabani; Zakariyah, Muhammad
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1697

Abstract

In the world of learning, students' interest in learning is decreasing because they are still using a book media learning system that only contains images and text. Learning systems using text and images seem boring. In the world of education, the use of augmented reality technology as an alternative learning media for students. AR technology makes it easier for users to understand because the 3D objects displayed are more interesting and easy to understand. With marker-based method in augmented reality marker or two-dimensional object marker as a marker that will be read by the camera. The research method applied involves a series of stages that include system analysis, system design, implementation, and testing. From the problems that have been mentioned and the solutions provided by providing the creation of augmented reality applications can help teachers and students regarding learning with new learning methods. the development of applications that I make is expected that students can better understand the learning process. With the development of augmented reality plant tissue recognition applications, Augmented Reality (AR) in strengthening interactive learning experiences for its users. This shows that AR has the ability to change the learning paradigm by providing a more dynamic and interesting experience for users.
Evaluating Machine Learning Models for Mental Health Diagnostics: A Comparative Analysis and Visual Insights Airlangga, Gregorius
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1702

Abstract

This study addresses the critical challenge of enhancing mental health diagnostics amidst a surge in global mental disorder prevalence. With mental health conditions predicted to become the leading cause of disability by 2030, there is an urgent need for more effective diagnostic methods that transcend the limitations of traditional frameworks, such as subjectivity and clinician bias. Leveraging the capabilities of machine learning (ML) to analyze complex datasets, this research aims to fill the gap in the comparative effectiveness of various ML models, particularly within the context of imbalanced mental health datasets. We systematically evaluated the performance of diverse ML models—including Random Forest, Gradient Boosting, Support Vector Machines, and others—on a rich dataset embodying a wide spectrum of symptoms and diagnoses. Through advanced data preprocessing techniques, such as innovative handling of missing values and categorical encoding, coupled with RandomizedSearchCV for model optimization, we provided a comprehensive analysis of the models' effectiveness. The application of oversampling strategies addressed the challenge of dataset imbalance, ensuring realistic clinical scenario evaluations. The study's findings are presented through detailed model performance metrics and visual analytics, such as symptom distribution visualizations and correlation cluster maps, enhancing interpretability and clinical relevance. The discussion section explores the practical applicability of these findings in clinical settings, acknowledging limitations and outlining future research directions. In conclusion, the study presents a nuanced narrative of ML model selection and performance evaluation complexities. The superior performance of ensemble methods like Random Forest and Gradient Boosting classifiers for certain diagnoses demonstrates the potential of ML in mental health diagnostics. However, the varied performance across models underscores the importance of context-specific model selection, considering the trade-offs between accuracy, interpretability, and computational efficiency. This research contributes significantly to the field of mental health diagnostics by highlighting models with the greatest promise for clinical application and by providing a framework for future advancements integrating ML into mental health diagnostics.
Comparative Analysis of Neural Network Architectures for Mental Health Diagnosis: A Deep Learning Approach Airlangga, Gregorius
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1703

Abstract

Mental health conditions present a complex diagnostic challenge due to the subtlety and diversity of symptoms. This study provides a comprehensive analysis of various neural network architectures, including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory networks (LSTM), and Dense Neural Network (DNN), in their ability to classify mental health conditions. Utilizing a rich dataset of symptoms and expert diagnoses, we preprocessed the data to address class imbalances and trained each model to evaluate its diagnostic performance. Our results are presented through confusion matrices that reveal the accuracy, precision, recall, and F1-scores for each model. The MLP and DNN models demonstrated high accuracy in identifying distinct conditions but struggled with overlapping symptoms. LSTM and RNN models captured temporal patterns to some extent yet required further optimization for improved accuracy. CNN models showed robust feature detection capabilities, with the CNN 1D model excelling in specificity for certain conditions. However, a common challenge across all models was the differentiation between conditions with similar symptom presentations. Our findings suggest that while individual models have their strengths, an ensemble approach may be necessary for enhanced diagnostic precision. Future work will focus on integrating models, refining feature extraction, and employing explainable AI to increase transparency and trust in model predictions. Additionally, expanding the dataset and conducting clinical trials will ensure the models' effectiveness in real-world settings. This research moves us closer to achieving nuanced, AI-driven diagnostics that can support clinicians and benefit patient outcomes in mental healthcare.
Penilaian Kinerja Tenaga Pemasaran Untuk Menentukan Reward dan Benefit dengan Menggunakan Metode Weighted Product Nabilah, Jihan; Ade Syahputra; Budi Arifitama
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1704

Abstract

Employees are one of the most important assets a company has in its efforts to sustain its business. In this regard, employee management and employee performance assessment are very important to measure human resource management governance in order to achieve the company's goals, vision and mission. Many employees are affected by one individual whose performance declines. As a result, it will also affect the performance of a company. To maintain the performance of the company and employees, namely by providing the best rewards and benefits. Determining rewards and benefits must go through a work assessment or evaluation process. Therefore, it is necessary to analyze employee performance using the Weighted Product method. This method is able to complete employee performance analysis supported by historical data such as achievement, absenteeism, honesty, communication and cooperation. In this research, we try to find criteria that can be used to provide value to employee performance. In addition, a system will be designed that can speed up the provision of value to employees in the form or design model in the form of a Decision Support System (DSS), with the hope that the process or mechanism for providing value to employee performance will run more quickly and precisely. This increasing PT XYZ  productivity and supporting the company's future development. It is hoped that the results of this research can help provide solutions regarding rewards and benefits for PT XYZ employees according to their performance
Analisis Sentimen Aplikasi Tokocrypto Berdasarkan Ulasan Pada Google Play Store Menggunakan Metode Naïve Bayes Rizki Adi Saputra; Dion Parisda Ray; Faldy Irwiensyah
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1707

Abstract

The advancement of increasingly sophisticated technology has brought numerous changes and conveniences for humans in all aspects, including the financial sector. Cryptocurrency has emerged as an innovation in the financial world. A cryptocurrency exchange is an electronic platform that enables sellers and buyers to conduct cryptocurrency trading transactions through a website or mobile application. Currently, many cryptocurrency exchange applications suffer from poor service, unreliable security, lengthy withdrawal processes, high administrative fees, and other issues. As a result, many people in Indonesia rely on reviews on the Google Play Store to check user feedback before deciding to use these cryptocurrency exchange applications. Many Indonesians seek information on cryptocurrency exchange applications that provide the best services for buying and selling cryptocurrency. One such application, according to reviews on the Google Play Store, is Tokocrypto. This study aims to understand the sentiment towards user reviews of the Tokocrypto application using the Naïve Bayes algorithm for data classification. The data obtained consists of 2,000 reviews from the Google Play Store in February 2024, collected using Google Colaboratory. The research stages include data scraping using web scraping techniques, data labeling, preprocessing, TF-IDF weighting, implementing the Naïve Bayes algorithm, and evaluation. The cleaned data resulted in 1,000 reviews, with 396 positive sentiments and 604 negative sentiments. The results of sentiment analysis research using the Naïve Bayes algorithm method show 74.22% for accuracy, 63.25% for precision, and 81.40% for recall.
Performance Evaluation of SVM Algorithm in Sentiment Classification: A Visual Journey of Wonderful Indonesia Content Singgalen, Yerik Afrianto
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1709

Abstract

This study addresses the research problem of understanding public sentiment towards tourism-themed content on YouTube, with a specific focus on "A Visual Journey of Wonderful Indonesia." The primary aim is to explore how viewers perceive and depict Indonesia as a tourism destination through their comments on YouTube videos. Employing the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, sentence analysis is conducted using the Support Vector Machine (SVM) algorithm with the Synthetic Minority Over-sampling Technique (SMOTE) to classify sentiments within a dataset of YouTube comments as positive, negative, or neutral. The analysis of frequently used words in the comments provides valuable insights into Indonesia's perception, revealing positive sentiments reflected in terms such as "beautiful," "wonderful," and "amazing," emphasizing the country's aesthetic appeal. Notably, terms like "orang" and "Indonesian" indicate appreciation for Indonesia's rich cultural heritage and its people. These findings highlight the pivotal role of destination branding efforts in shaping positive perceptions and emotions toward Indonesia. The results indicate the efficacy of the SVM-SMOTE model, achieving high accuracy (84.26%), precision (100.00%), recall (68.51%), f-measure (81.25%), and AUC (0.996) in accurately classifying sentiment patterns within analyzed YouTube content. This offers practical implications for destination managers and marketers. Conversely, the SVM algorithm without SMOTE demonstrates impressive accuracy, precision, and recall scores of 97.08%, but its AUC value of 0.607 suggests potential challenges in discriminating between positive and negative sentiment instances. These findings provide valuable insights into the role of digital media platforms in shaping destination perceptions and offer practical implications for destination marketers and managers
Social Network Analysis and Sentiment Classification of Robotic Restaurant Content using Naïve Bayes Classifier Singgalen, Yerik Afrianto
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1710

Abstract

Sentiment analysis is crucial in understanding public opinion, particularly in emerging technologies such as automation AI and robotic restaurant services. However, achieving accurate sentiment classification in sentiment analysis tasks poses challenges, especially when dealing with imbalanced data. This study employs the Cross-Industry Standard Process for Data Mining (CRISP-DM) through the Naive Bayes Classifier (NBC) algorithm and Synthetic Minority Over-sampling Technique (SMOTE) to address imbalanced data challenges in sentiment analysis. Social network analysis (SNA) collects and analyzes user-generated content related to automation AI and robotic restaurant services, providing insights into public sentiment. Additionally, the occurrence of frequently used words such as "people" (182), "food" (158), "jobs" (135), "robots" (137), "wage" (102), "work" (78), "robot" (79), "minimum" (78), "fast" (70), and "workers" (65) is examined. The performance of the NBC algorithm with and without SMOTE integration is compared. With SMOTE, the algorithm exhibits an accuracy of 70.11% +/- 3.52%, precision of 88.82% +/- 5.06%, recall of 46.06% +/- 6.13%, AUC of 0.967 +/- 0.016, and F-measure of 60.46% +/- 6.02%. Without SMOTE, the algorithm yields an accuracy of 48.90% +/- 4.36%, precision of 72.15% +/- 5.25%, recall of 44.32% +/- 7.15%, AUC of 0.777 +/- 0.051, and F-measure of 54.57% +/- 5.78%.  Recommendations to further enhance the algorithm's performance include exploring additional optimization techniques, such as feature engineering and ensemble methods, and continuing data collection and augmentation efforts to improve dataset representativeness. Regular monitoring and evaluation and iterative refinement based on evolving data patterns are also recommended to ensure sustained effectiveness in sentiment analysis tasks.
Deteksi Nominal Mata Uang Rupiah Menggunakan Metode Convolutional Neural Network dan Feedforward Neural Network Dede Aprillia; Tatang Rohana; Tohirin Al Mudzakir; Deden Wahiddin
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1711

Abstract

This research aims to develop a nominal detection system for the Rupiah currency for the 2022 emission year using the Convolutional Neural Network (CNN) and Feedforward Neural Network (FNN) methods, especially in the context of applications for vending machines. This research explores the potential of computer vision technology to facilitate the introduction of Rupiah banknotes and contribute to the development of vending machines. The dataset used includes variations in lighting conditions, orientation, and position of banknotes, thus involving various augmentation and preprocessing processes. The model evaluation results include nominal detection accuracy in various conditions, considering the success of the system to support the performance of the vending machine. This research is expected to contribute to the development of more comprehensive technology and expand the application of CNN and FNN in the context of currency detection. In this research, the CNN method produced the best accuracy of 100% for testing in bright conditions, then in sufficient light conditions it produced an accuracy of 96.43%. Meanwhile, testing in dark conditions got quite low results, only 78.56%. Then the FNN method produces the same accuracy of 53.57% in bright light, sufficient light and low light conditions.
Social Network Analysis and Sentiment Classification of Extended Reality Product Content Singgalen, Yerik Afrianto
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1712

Abstract

This study explores Extended Reality (XR) products, specifically focusing on the Apple Vision Pro, to elucidate consumer perceptions and the underlying social dynamics of these innovative technologies. This research delves into Extended Reality (XR) products, specifically focusing on the Apple Vision Pro, aiming to understand consumer perceptions and social dynamics surrounding these innovative technologies. By leveraging sentiment analysis and Social Network Analysis (SNA) alongside CRISP-DM and SVM algorithms, this study provides a comprehensive insight into sentiment patterns, network structures, and influential factors within the XR community. A multi-faceted approach is adopted to achieve the research objectives. Sentiment analysis and SNA dissect sentiment patterns and uncover network structures within the XR community. The CRISP-DM framework guides the research process, ensuring systematic data analysis and interpretation. SVM algorithms classify sentiments, providing a robust analytical framework for understanding consumer sentiments towards XR products. The analysis yields significant insights into XR consumer perceptions and social dynamics. The calculated network metrics, including a density of 0.000124, absence of reciprocity, centralization value of 0.001331, and modularity value of 0.999000, shed light on crucial network dynamics within the XR community. Examining frequently used words reveals prevalent topics within the XR discourse, providing valuable context for understanding consumer sentiments. Furthermore, the evaluation of SVM algorithms demonstrates commendable performance metrics, with the SVM without SMOTE achieving an accuracy rate of 84.33%, precision of 84.67%, recall of 99.28%, and f_measure of 91.39%. In comparison, the SVM with SMOTE exhibits an accuracy of 81.82% and a precision of 97.58%. This research contributes valuable insights into the consumer landscape of XR products, mainly focusing on the Apple Vision Pro. By combining sentiment analysis, SNA, and established methodologies, the study offers a nuanced understanding of consumer perceptions and social dynamics within the XR community. These findings inform strategic decisions and contribute to advancements in XR technologies, offering valuable insights into the efficacy of sentiment analysis techniques in understanding consumer sentiments

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