bluuebunny/crossref_metadata_2025 huggingface dataset:
Decision Support System for Manufacturing Division Priority using AHP Method
Authors: Kencana Christian
Published: July 2024

In the competitive manufacturing landscape, especially in stamping and automobile parts production, optimizing production processes is a paramount challenge. This study addresses a manufacturing company's vulnerability to production reporting manipulation and inefficiencies in manual report generation. To tackle these challenges, we propose a vital system to combat the complexities and inaccuracies in existing reporting methods that significantly impede decision-making and operational efficiency. We introduce a novel, integrated system combining a production information system with an Analytical Hierarchy Process (AHP)-based decision support system. This hybrid solution aims to streamline report generation and boost decision-making within production divisions. The approach involves deploying a web-based system to simplify report creation and ensure accuracy and timeliness. The decision support system utilizes AHP to facilitate division prioritization, addressing critical issues promptly with data-driven insights. AHP's practicality and reliability assist in evaluating criteria for effective division prioritization. The implementation of this system marked significant improvements in production data management, evident from a substantial 68.17% user satisfaction rating. These results demonstrate the system's efficacy in enhancing decision-making, refining production processes, and boosting overall productivity. Furthermore, the system provides valuable insights into operator performance, fostering management recognition and appreciation, thus promoting transparency and accountability. The integration of an information system with AHP-based decision support emerges as a potent solution for manufacturing companies confronting similar challenges.
Prediksi Harga Cryptocurrency Binance Berdasarkan Informasi Blokchain dengan Menggunakan Algoritma Random Forest
Authors: Asbullah Jumjumi , Samsudin Samsudin
Published: July 2024

This study proposes the use of the Random Forest algorithm to forecast the cryptocurrency Binance's daily prices. With a dataset covering 1992 observations from January 1, 2018, to June 15, 2023, the research focuses on PT. Tennet Depository Indonesia. Through Python implementation, the experimental results indicate that Random Forest is effective in providing accurate price predictions, with an average Mean Absolute Percentage Error (MAPE) of approximately 1.38% and an average Root Mean Squared Error (RMSE) of about 4.38. The uniqueness of the system lies in the algorithm's capability to handle market complexities and volatility, offering adaptive solutions to the unpredictable dynamics of the market. Nevertheless, limitations in historical data and market volatility persist as inhibiting factors, emphasizing the need for a holistic approach. The average MAPE and RMSE results provide an indication of the overall reliability of the model in facing cryptocurrency market volatility. These conclusions can contribute to the development of more robust and adaptive models to respond to the evolving market conditions.
Algoritma Jaccard Similarity untuk Deteksi Kemiripan Judul Disertasi dengan Pendekatan Variasi Stop Word Removal
Authors: Mayola Liga , Hafizh M. , Putra Deri Marse
Published: July 2024

Choosing an unique dissertation title is a challenge. The number of dissertation titles rises as the number of students increases. The title of the dissertation must differ between students. Anticipation that can be done is to adopt a similarity algorithm to detect similarities in dissertation titles. The similarity algorithm chosen is the Jaccard Similarity Algorithm. Jaccard algorithm can be used to detect document similarities. Analysis process begins with preprocessing text. The stages of preprocessing text are case folding, tokenizing, stop word removal and stemming. In this study, variations of stop word removal were tested and the accuracy results obtained were tested after being analyzed using Jaccard Similarity. Researchers call it Stop Word Removal Version One (SWR1) and Stop Word Removal Version Two (SWR2). In SWR1 only prepositions and conjunctions are deleted. Meanwhile SWR2; what was done was the deletion of words in SWR1 plus the deletion of words that were often used in the title but did not make a significant contribution to the meaning of the title. The aim of this approach is to test the accuracy produced by Jaccard against these two stop word removal approaches. The research results show that Jaccard accuracy with SWR2 has an accuracy of 97.8% and SWR1 accuracy is 57.7%. stop word removal , is a critical stage in determining similarity and has a significant influence on the results of the Jaccard Algorithm.
Comparison of Support Vector Machine and Random Forest Method on Static Analysis Windows Portable Executable (PE) Malware Detection
Authors: Ismail Hazim , Utomo Rio Guntur , Bawono Marastika Wicaksono Aji
Published: July 2024

Malware has emerged as a significant concern for computer system security, as it spreads rapidly and adversely affects system performance. Detecting malware has become crucial, and one of the methods utilized is Machine Learning classification, which learns the characteristics of an application without executing it. In this study, the author evaluates the efficacy of malware detection in the static analysis of Windows Portable Executable (PE) files using the Support Vector Machine (SVM) and Random Forest algorithms. The author employs a dataset containing both malware-related PE files and safe applications to train the SVM and Random Forest models to classify PE files as either malware or safe. The objective is to determine the most effective machine learning algorithm for malware detection in PE files. The research compares the performance of both algorithms to identify the superior one for malware detection. The results indicate that the Random Forest algorithm achieves an impressive accuracy of 98.53%, while the SVM algorithm performs slightly lower with an accuracy of 97.14%.
Deep Learning to Extract Animal Images With the U-Net Model on the Use of Pet Images
Authors: Windarto Agus Perdana , Rahadjeng Indra Riyana , Siregar Muhammad Noor Hasan , Alkhairi Putrama
Published: July 2024

This article explores the innovative application of deep learning techniques, specifically the U-Net model, in the realm of computer vision, focusing on the extraction of animal images from diverse pet datasets. As the digital landscape becomes increasingly saturated with pet imagery, the need for precise and efficient image extraction methods becomes paramount. The study delves into the challenges posed by varying animal poses and backgrounds, presenting a comprehensive analysis of the U-Net model's adaptability in handling these complexities. Through rigorous experimentation, this research refines existing methodologies, enhancing the accuracy of animal image extraction. The findings not only contribute to advancing the field of computer vision but also hold significant implications for wildlife monitoring, veterinary diagnostics, and the broader domain of image processing.
Implementation of Adam Optimizer using Recurrent Neural Network (RNN) Architecture for Diabetes Classification
Authors: Nugroho Nur Cahyo Tio , Hidayat Erwin Yudi
Published: July 2024

Non-communicable diseases (NCDs) present a considerable worldwide health dilemma, resulting in considerable expenses for treatment and heightened rates of mortality. Conditions like diabetes mellitus, cardiovascular diseases, cancer, and chronic respiratory diseases are primary causes of global mortality, making up 71% of total global deaths in 2016, as reported by the World Health Organization (WHO). Diabetes Mellitus (DM), marked by prolonged elevated blood glucose levels, stands out as a significant metabolic disorder. This research delves into the implementation of Recurrent Neural Networks (RNNs) utilizing the Adaptive Moment Estimation (Adam) optimizer for classifying Diabetes Mellitus (DM). RNNs, a subset of artificial neural networks tailored for sequential data processing, are employed to make predictions by incorporating recurrent connections. Situated within the dynamic landscape of Artificial Intelligence and Machine Learning, the research exhibits promising outcomes via k-fold cross-validation, confusion matrix analysis, loss graph examination, and classification report. The RNN-Adam model showcases commendable overall performance, achieving an average accuracy of 80.20% through k-fold cross-validation and 81.60% accuracy as revealed by the confusion matrix. This research offers valuable insights into the effectiveness of the RNN-Adam model for diabetes classification.
Advanced Forecasting of Maize Production using SARIMAX Models: An Analytical Approach
Authors: Airlangga Gregorius
Published: July 2024

Agricultural production forecasting is crucial for food security and economic planning. This study conducts a detailed analysis of maize production forecasting using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, emphasizing the applicability of time-series models in capturing complex agricultural dynamics. Following a comprehensive literature review, the SARIMA model was justified for its ability to integrate seasonal fluctuations inherent in agricultural time series. Optimal model parameters were meticulously determined through an iterative process, optimizing the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The best-performing SARIMA(1, 1, 2)x(2, 2, 2, 12) model achieved an AIC of 339914.85450182937 and a BIC of 339950.64499813004, indicating its strong fit to the historical data. This model was applied to a historical dataset of maize production, providing forecasts that align closely with actual production trends on a short-term basis. Notably, the model's short-term predictions for the subsequent year showed less than a 2% deviation from the actual figures, affirming its precision. However, long-term forecasts revealed greater variability, underscoring the challenge of accounting for unforeseen environmental and economic factors in agricultural production systems. This research substantiates the efficacy of SARIMA models in agricultural forecasting, delivering strategic insights for resource management. It also points towards the integration of SARIMA with other variables and advanced modeling techniques as a future avenue to enhance forecasting robustness, particularly for long-term projections. The findings serve as a valuable resource for policymakers and stakeholders in optimizing decision-making processes for agricultural production.
X Spotify Cares Clustering Analysis using K-Means and K-Medoids
Authors: Pangestu Citra , Shaufiah Shaufiah , Wijaya Rifki
Published: July 2024

The rise of social media platforms, particularly Twitter, has transformed how individuals express opinions and concerns. Companies, like Spotify, leverage platforms such as Twitter for customer support and feedback gathering. This research delves into the world of Spotify Cares tweets using K-Means and K-Medoids clustering methods, aiming to enhance customer support analysis. The study employs the silhouette coefficient and the Davies-Bouldin Index (DBI) to evaluate clustering quality. With an extensive dataset covering more than 3 million Twitter customer service interactions, including 29,479 notes specific to Spotify Cares, this investigation uncovered latent patterns and themes. The versatility of K-Means and K-Medoids, proven effective in a wide range of applications, is highlighted. Therefore, K-means and K-medios were implemented in this research. The results show that K-Means, with 10 clusters (K = 10), with a DBI value of 1.76, shows moderate dispersion, indicating the potential for improvements for better segmentation precision. In contrast, K-Medoids, with 2 clusters (K = 2) and a lower DBI of 1.48, present a clearer and more compact clustering structure. This implies simplified customer categories, which is beneficial for targeted support. In conclusion, although both methods have strengths and weaknesses, K-Medoids with two clusters emerges as a promising method for Spotify Cares, offering cohesive customer groupings for efficient intervention. Future research efforts could focus on refining parameters and exploring the complex relationships between response time, sentiment analysis, and customer satisfaction to achieve a more nuanced analysis.
Data Mining Using Support Vector Machine Model for Baturraden Tourism Visitor Satisfaction Prediction
Authors: Damayanti Suci , Oktaviana Luzi Dwi , Bratakusuma Trias
Published: July 2024

The tourism industry is a significant economic sector and a driver of local and national economic growth. Tourism not only contributes economically, but also plays an important role in introducing and preserving the cultural and natural wealth of an area. One of them is Baturraden tourism, a tourist destination located in Indonesia is experiencing a rapid increase in the number of tourist visits. Baturraden is a tourist destination located in the highlands at the foot of Mount Slamet, Indonesia, precisely in Banyumas Regency, Baturraden District. Tourists who visit every year are increasing but tour managers have not realized whether the tourists are satisfied or not so research is needed to measure the level of satisfaction of tourists so that the Baturraden tourism is better. To measure the level of satisfaction, an algorithm is needed, in this study the algorithm used is a support vector machine (SVM) to collect data that will be used as a dataset by taking reviews on google maps manually then the data is grouped into groups of satisfied and dissatisfied tourists, as many as 100 data are taken and processed. So that the final result obtained an accuracy value of 86.00%, and for reviews tend to be positive or satisfied tourists visiting the baturaden tourist area.
Pengelompokan Siswa Layak Penerima Beasiswa dengan Menerapkan Algoritma K-Means Clustering Data Mining
Authors: Harpad Bartolomius , Fahmi Muhammad , Pahrudin Pajar , Andrea Reza
Published: July 2024

Scholarships are financial assistance given to individuals with the aim of assisting them in financing the education they are pursuing. To overcome the gap between upper middle economic communities and lower middle economic communities in obtaining quality education. The aim of this program is to provide opportunities for financially disadvantaged students to experience quality education. In the process that occurs in awarding scholarships, there should be a strong basic foundation in the process of determining and making decisions that occur. Where the process of providing scholarships carried out so far should not be given to students who truly deserve it. The problem or impact that occurs from this is that the scholarship program does not run in accordance with the program's objectives, namely helping in the economic gap for students. One way that can be used to resolve this problem is to review previous recipient data. Data mining is a process of re-excavating data. Excavation is carried out by reviewing all the information contained in the data. In this research, the cluster analysis method is used, which is a multivariate technique used to group objects based on their characteristics. Clustering is the process of grouping data. Where the grouping process carried out on data is a grouping that does not yet have a class target or is called unsupervised learning. The results obtained in the research show that there are 2 clusters from the application of the K-Means algorithm. In cluster 1 there are 6 students in it and in cluster 2 there are 4 students in it.
Sistem Pakar Deteksi Penyakit Jantung Koroner dengan Perbandingan Metode Dempster Shafer dan Certainty Factor
Authors: Yunita Yunita , Ekawati Hanifah , Harianto Kusno
Published: July 2024

This research aims to develop an expert system for detecting coronary heart disease by comparing the Demster-Shafer and Certainty Factor methods in providing accurate solutions. Coronary heart disease (CHD) is a disease that often threatens human health. To overcome this problem, the development of expert systems has become an important approach in diagnosing CHD accurately and efficiently. The problems faced include the level of complexity in diagnosing CHD and the need for solutions that can provide a high level of confidence. The method used involves collecting data from various sources and analysis using both methods to determine a diagnosis. The research results show that both methods are able to provide satisfactory results, however, a comparison between the two provides additional insight in understanding the reliability and accuracy of the expert system being developed. A thorough analysis shows that the Demster-Shafer method provides a higher degree of accuracy in some cases, while Certainty Factor tends to provide faster results. However, this research also reveals that optimal results can be achieved by combining the two methods. Thus, this research makes an important contribution to the development of an expert system for coronary heart disease detection and provides a foundation for further development in this domain. In conclusion, the integration of the Demster-Shafer and Certainty Factor methods shows the potential to improve the performance and reliability of expert systems in supporting CHD diagnosis effectively. The calculation results of both methods show that the Dempster-Shafer Method produces a certainty level of 99.8%, while the Certainty Factor Method provides a confidence level of 92%.
Komparasi Algoritma Machine Learning dalam Klasifikasi Kanker Payudara
Authors: Afiatuddin Nurfadlan , Wicaksono M Teguh , Akbar Vitto Rezky , Rahmaddeni Rahmaddeni , Wulandari Denok
Published: July 2024

Every year, millions of women are faced with a serious global health issue: breast cancer. This research aims to improve the efficiency of breast cancer classification using machine learning. One of the main challenges encountered is the imbalance between the number of malignant and benign cases in the dataset. Therefore, this study aims to compare the performance of several machine learning algorithms in classifying breast cancer, such as Decision Tree, Naive Bayes, K-Nearest Neighbors, Logistic Regression, and Random Forest. Preprocessing data, dividing data with various ratios, and testing various classification algorithms are the techniques used in this research. The dataset used originates from the Wisconsin Breast Cancer Diagnosis dataset from the Kaggle platform. The Synthetic Minority Over-Sampling Technique (SMOTE) is used to achieve balance in the proportions of imbalanced classes. After hyperparameter tuning, Logistic Regression showed the best performance with accuracy reaching 100% in several situations. This study concludes that the use of machine learning, especially with techniques for handling class imbalance, can improve the ability to detect breast cancer early. Additionally, this research also helps understand the best algorithms to improve accuracy in classifying breast cancer, providing support for healthcare professionals in early diagnosis, and enhancing the quality of patient care.
Klasifikasi Emosi Pada Data Text Bahasa Indonesia Menggunakan Algoritma BERT, RoBERTa, dan Distil-BERT
Authors: Basbeth Faishal , Fudholi Dhomas Hatta
Published: July 2024

Previous studies in the context of sentiment analysis have been conducted in various types of text domains, including product reviews, movies, news, and opinions. Sentiment analysis focuses on recognizing the valence of positive or negative orientations. In sentiment analysis, something that is less explored but is needed in analysis is the recognition of types of emotions / classification of emotions. This research has contributed to knowledge of the applicationof distilBERT to Indonesian Language data which is still relatively new and the success of fine-tuning and hyperparameter-tuning that has been applied to distilBERT with various comparison methods. Emotion classification can help provide the right decisions in a company, government, and marketing strategy. Emotion classification is part of sentiment analysis which has more detailed and in-depth emotion labels. From these various urgencies, this research builds and fine-tuning distilBERT model to help classify emotions from an Indonesian sentence. Emotion classification in Indonesian language data using the distilBERT model is still relatively new. This research consists of 4 steps: data collection, data preprocessing, hyperparameter-tuning and fine-tuning, and comparison of results from various models that have been applied in this research: BERT, RoBERTa, and distilBERT. DistilBERT got the highest training accuracy = 94.76, the highest testing accuracy was obtained by distilBERT-frezee = 86.67, and the highest f1-score was obtained by distilBERT Modif = 87. In distilBERT-frezee the main cause of the model getting significant results is the dropout hyperparameter which reduces the f1-score value by 3 if it is not used, and the second cause is the freeze-layer which reduces the f1-score value by 1 if it is not used.
Penerapan Machine Learning Pada Analisis Sentimen Twitter Sebelum dan Sesudah Debat Calon Presiden dan Wakil Presiden Tahun 2024
Authors: Dwinnie Zairy Cindy , Novita Rice
Published: July 2024

The 2024 Presidential Election has become the hottest topic in the past two years. The KPU has confirmed that there are 3 candidates for President and Vice President. For this reason, as a momentum for voters to assess the 2024 Presidential and Vice Presidential candidates, the KPU is holding the 2024 Presidential Choice Debate which is based on Law Number 7 of 2017 concerning General Elections. Based on the information presented on the kpu.go.id page, the debate will be held 5 times with 3 presidential candidate debates and 2 vice presidential candidate debates. For this reason, it is necessary to carry out an analysis to find out how public sentiment is positive, negative, and neutral on Twitter towards the three candidates for President and Vice President in 2024 before and after the debate was held. The aim is to estimate public support or disapproval of the three candidate pairs. This research uses three algorithms as a comparison of classification accuracy, namely the Support Vector Machine algorithm, Random Forest, and Logistic Regression. Where the data used is tweet data on Twitter related to before and after the debate as many as 30 datasets with a total of 9000 data. From the classification results, the average accuracy obtained for the three algorithms, namely SVM and Random Forest, was 78%, and Logistic Regression was 79%. The highest polarity obtained from the classification of the three algorithms is in the positive class. This indicates that the Logistic Regression algorithm provides better performance in classifying Twitter sentiment regarding the 2024 presidential and vice presidential candidate pairs.
Analisis Sentimen Opini Terhadap Tools Artificial Intelligence (AI) Berdasarkan Twitter Menggunakan Algoritma Naïve Bayes
Authors: Oktavia Ingrid , Isnain Auliya Rahman
Published: July 2024

This research aims to analyze public sentiment towards artificial intelligence (AI) tools via the Twitter platform using the Naive Bayes classifier algorithm. Twitter is a popular social media platform for sharing opinions and thoughts, making it suitable for sentiment analysis. Sentiment analysis is the process of analyzing and understanding opinions, attitudes, or feelings contained in text, such as tweets, product reviews, or other social media posts. The problems discussed in sentiment analysis can vary depending on the context. Tests carried out using the Naïve Bayes Classifier algorithm can conclude that the data collected was 2119. In this research, there are several steps that must be taken to analyzethe data, starting with crawling, labeling, preprocessing, splitting data, dividing test data, and training data, and finally applying the Naïve Bayes Classifier Algorithm. The results of the data analysis were divided into two categories: positive and negative, with 58.41% positive data and 12.43% negative data. In the analysis experiment, the Naïve Bayes accuracy value reached 79.41%, with a precision of 88% and a recall of 88%. The aim of the results of this research is to examine the public's response regarding artificial intelligence tools using the Naïve Bayes Classifier Algorithm to provide better sentiment results. So many see AI as a technology that carries great potential to improve human life. On the other hand, there are concerns about AI's negative impact on employment, privacy, and even its potential to take over human control. Ethical concerns also arise regarding the use of AI in decision-making that can affect human lives without adequate control. So artificial intelligence tools can be accepted by society because they have many benefits. Therefore, sentiment analysis and natural data processing use the Python programming language to categorize user comment data through a breakdown process.
Analisis Sentimen Masyarakat Terhadap Tiktok Shop di Twitter Menggunakan Metode Naive Bayes Classifier
Authors: Andrian Eka , Isnain Auliya Rahman
Published: July 2024

This research aims to analyze public sentiment towards TikTok Shop through the Twitter platform using the Naive Bayes Classifier Algorithm. This algorithm is used to evaluate public views regarding TikTok Shop and identify Positive and Negative sentiments. The data used in this research is 3,816 data. Then, there are Positive sentiment results of 53.45% and Negative of 46.55%. After analyzing the data, the accuracy result is 78.22% using the Split Data operator. After that, for the results of the Naïve Bayes Classifier implementation on the Recall value has a result of 84% and for the class precision result of 86%. The purpose of this research is to evaluate public views on TikTok Shop through the Twitter platform by utilizing the Naive Bayes Classifier Algorithm. This algorithm is used to analyze sentiments that arise regarding TikTok Shop, with a focus on identifying whether the sentiment is Positive or Negative. This analysis is also used to find out different public opinions about TikTok Shop, such as user experience, features used, and impacts experienced. Therefore, sentiment analysis and natural data processing use the Python programming language to categorize user comment data through a splitting process.
Analisis Sentimen Terhadap Penggunaan Chatgpt Berdasarka Twitter Menggunakan Algoritma Naïve Bayes
Authors: Transiska Dwi , Febriawan Dimas , Hasan Firman Noor
Published: July 2024

Chatbots can assist by fostering collaboration between users and companies or organizations. In today's world, artificial intelligence through chatbots has become one of the mainstays in handling various problems. One of the well-known types of chatbots is ChatGPT, which utilizes NLP (Natural Language Processing) technology to understand and respond to user queries and requests. ChatGPT, as a device that is very easy to use for all circles, has a fairly simple interface but does not cause boredom, and the speed in responding to commands given, this is an added value of ChatGPT. Despite the myriad of conveniences offered, ChatGPT also raises concerns on the negative side. The negative side is that there are many concerns that arise, starting from the rampant spread of hoaxes and misunderstandings on social media. The advantages and disadvantages that have been explained above, researchers are encouraged to find out the truth from the public's response regarding ChatGPT more deeply so that this sentiment analysis research is made. Moreover, research related to sentiment analysis can be said to be quite an answer to the confusion of public responses outside related to ChatGPT. This research also starts from the process of Data retrieval on Twitter social media using Rapidminer, in this process the researcher uses the Twitter API token on the Rapidminer application so that it can be obtained. The data that has been obtained is then cleaned through the preprocessing process using the features available in Rapidminer, the result of this process is that the data becomes clean. After being cleaned through preprocessing, it is then labeled as positive or negative which will later be classified by the Naïve Bayes algorithm. This classification aims to divide between positive sentiment and negative sentiment. After performing classification, the data is then evaluated using a confusion matrix and the results are obtained with an accuracy value of 96.55%, a precision value of 89.19%, and a recall value of 95.18%.
Analisis Sentimen Kegiatan Pembersihan Sampah Pada Media Sosial X Menggunakan SVM dan Naïve Bayes
Authors: Nugroho Dendy Aprilianto , Hasan Firman Noor
Published: July 2024

Human daily activities inevitably produce waste, which negatively impacts environmental balance due to the bad habit of indiscriminately disposing of waste. As a result of this issue, there is a youth community named Pandawara Group that wants to help clean up trash on Sukabumi Beach. However, their initiative faced rejection from the local village chief and youth organization, sparking various opinions on social media platform X. Consequently, this research seeks to analyze public sentiment towards Pandawara Group's waste cleanup efforts at Sukabumi Beach using Support Vector Machine and Naïve Bayes methods. The objective is to gauge positive and negative sentiments and compare the accuracy of Support Vector Machine and Naïve Bayes.  In this sentiment analysis using 2,339 datasets, the highest accuracy was achieved using the Support Vector Machine method at 91.67%, whereas the Naïve Bayes method only achieved 63.89%. Thus, it can be concluded that Support Vector Machine is superior in classifying sentiments regarding Pandawara Group's waste cleanup activities at Sukabumi Beach compared to Naïve Bayes.
Analisis Clustering Global Living Cost Berdasarkan Socioeconomic Status Menggunakan Algoritma DBSCAN
Authors: Perdana Putra Siswadi , Aryo Anggoro Dimas
Published: July 2024

The development of the world economy is currently undergoing a very complex phase characterised by very different changes. These changes have a significant impact on the cost of living and quality of life of people in various countries around the world. Factors such as government policies, economy, income, transport, fuel play an important role in various changes in the world economy. Then this research aims to cluster the cost of living on the "Global Living Cost" dataset against socioeconomic status and also determine the performance of the DBSCAN algorithm on several types of "Global Living Cost" datasets that have been modified by perturbing using jitter position, combination of jitter position and scale, and noise perturbation then also using several dimensionality reduction techniques such as PCA, UMAP, t-SNE, and ICA. The data used is taken from "Kaggle. com", then obtained similarity results using the jaccard similarity method that the combination of clustering and dimensionality reduction algorithms for the "Global Living Cost" dataset from the best is DBSCAN and ICA with the highest similarity score of several types of datasets, namely 1.0, DBSCAN and PCA with the highest similarity score of 0.998769735493131, DBSCAN and t-SNE with the highest similarity score of 0.9995897435897436, and the last is DBSCAN and UMAP with the highest similarity score of 0.8065233506300964. The conclusion obtained is that the DBSCAN algorithm is able to work very well for different types of datasets.
Decision Support System Perspective Using Entropy and Multi-Attribute Utility Theory in the Selection of the Best Division Head
Authors: Arshad Muhammad Waqas , Sumanto Sumanto , Setiawansyah Setiawansyah
Published: July 2024

The Division Head is a figure responsible for the management, coordination, and supervision of a division in an organization. Its main tasks include the development of strategy, planning, and implementation of operational activities of the division in accordance with organizational objectives. Problems in choosing the best Division Head often arise due to various complex factors. One of the main problems is the lack of clear and objective criteria in determining the suitability of candidates to the duties and responsibilities of the division's leadership. The combination of entropy weighting with the MAUT (Multi-Attribute Utility Theory) method is an approach that combines two decision-making analysis techniques. First, entropy weighting is used to determine the relative weight of each attribute used in the analysis. This technique helps measure the degree of uncertainty or randomness in the data, so attributes that have higher variation will be given a lower weight. Once attribute weights are determined, the MAUT method is used to evaluate and compare decision alternatives based on the subjective preferences of the decision maker. MAUT allows decision makers to assess and assign the relative utility value of each decision alternative to each attribute taken into account. By combining these two techniques, decision makers can obtain more accurate and structured results in complex decision making, taking into account both data variation and subjective preferences. The results of the alternative ranking of the best division head selection showed results for the Head of Information Technology (IT) Division with a value of 0.6918 ranked 1, Head of Quality Division with a value of 0.5242 got rank 2, and Head of Human Resources (HR) Division with a value of 0.4221 got rank 3.
Toxicity, Sentiment, and Social Network Analysis (SNA) of Borneo Death Blow Video Documentary Reviews
Authors: Singgalen Yerik Afrianto
Published: July 2024

The study aimed to evaluate sentiment classification models using toxicity scores and to conduct Social Network Analysis (SNA) to understand network dynamics. The research used CRISP-DM methodology to comprehensively analyze sentiment classification models and toxicity scores. It utilized various machine learning algorithms, including Decision Tree (DT), Support Vector Machine (SVM), and Naive Bayes Classifier (NBC), with Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. In addition, Social Network Analysis (SNA) was conducted to examine network properties and dynamics. The findings revealed varying toxicity scores, ranging from 0.12409 to 0.98808, across different categories, such as general toxicity, severe toxicity, identity attacks, insults, profanity, and threats. Evaluation of sentiment classification models indicated that the SVM model with SMOTE achieved the highest accuracy of 92.57% +/- 1.17% (micro average: 92.57%), followed by the NBC model with an accuracy of 78.24% +/- 1.30% (micro average: 78.24%), and the DT model with an accuracy of 61.16% +/- 1.20% (micro average: 61.16%). Despite variations in model performance, the SVM model consistently demonstrated robust performance across various evaluation metrics. Furthermore, the SNA findings provided insights into network structural characteristics, including Average Degree, Average Weighted Degree, Diameter, Radius, and Average Path Length, facilitating a comprehensive understanding of network organization and behavior. These findings contribute to advancing the understanding of sentiment analysis models and network dynamics in digital environments.
Peramalan Jumlah Kedatangan Wisatawan Menggunakan Support Vector Regression Berbasis Sliding Window
Authors: Fitriah Ma’idatul , Permana Inggih , Salisah Febi Nur , Munzir Medyantiwi Rahmawita , Megawati Megawati
Published: July 2024

As a developing city, Pekanbaru has the potential for attractive tourist attractions for tourists. The arrival of tourists has had a big positive impact on the economy of Pekanbaru City. The number of tourist arrivals can experience ups and downs every month, for this reason it is necessary to forecast the number of tourists in the future. This research aims to apply the Orange Data Mining application in predicting the number of tourist arrivals by comparing the kernels in the Support Vector Regression (SVR) method and applying Sliding Window size 3 to window size 13 to transform into time series data. As well as sharing data using the K-Fold Validation method with a value of K-10. Then the performance of the kernels used can be seen using the Test and Score widget which presents the results of Root Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), dan R-squared(R2). The results for forecasting the number of tourist arrivals to Pekanbaru City using the SVR method show that the RBF Kernel is the optimal choice compared to the Polinomial and Linear Kernels. The results of the Test and Score widget show that the RBF Kernel with window size 10 has lower MAE, MSE and RMSE values, namely 0.118, 0.022 and 0.147. Apart from that, the comparison of R2 in window size 10 for Kernel RBF shows better performance with a value of 0.519.
Sistem Rekomendasi Pemilihan Komponen Komputer Menggunakan Metode AHP dan Profile Matching
Authors: Salmanarrizqie Ageng , Vitianingsih Anik Vega , Kristyawan Yudi , Maukar Anastasia Lidya , Marisa Fitri
Published: July 2024

Computers have become one of the technological tools that play a crucial role in enhancing society's productivity. Therefore, many desktop computer users assemble their own computers to achieve computer performance according to their preferences or needs. However, some people lack information about the variations, specifications, and capabilities of each computer component to be assembled. This research offers a recommendation system that is part of a decision support system (DSS) to assist users in providing recommendations for computer components that are being sought and needed based on brand, price, and specifications using the Analytic Hierarchy Process (AHP) and Profile Matching methods. Parameters are based on the processor, motherboard, graphics card (VGA), storage, RAM, power supply, and casing with priority categories based on specifications, price, and brand. Data weighting is done using the Analytic Hierarchy Process (AHP) method, while the Profile Matching method is used for ranking the weighting results. The research results show an accuracy rate of 60% using the Profile Matching method, while the AHP method achieves an accuracy rate of 57%.
Analisis Perbandingan Metode Similarity untuk Kemiripan Dokumen Bahasa Indonesia pada Deteksi Kemiripan Teks Bahasa Indonesia
Authors: Pawestri Sheraton , Suyanto Yohanes
Published: July 2024

Ease of accessing information brings diverse benefits, including the ability to develop models that can detect similarities between documents, a plagiarism-checking system, automatic summarization, classification, etc. The various benefits of word similarity detection make research on similarity detection between documents an important area to develop. However, studies regarding similarity detection specifically for Indonesian language documents are still relatively small and the performance can still be developed. Therefore, this research aims to conduct a comparative analysis of the performance of Doc2Vec compared to the Jaccard Coefficient, Cosine Similarity, and Euclidean Distance in detecting the similarity of documents with Indonesian text. Three datasets are used in this analysis, with the first dataset consisting of 200 news from Google News, the second dataset from IndoNLU, and the third dataset from TaPaCo. The findings from this study show that overall Cosine Similarity has better performance than Jaccard Coefficient and Euclidean Distance for average performance. The superior performance was with accuracy of 0.98, precision of 0.84, recall of 0.95, and F-1 score of 0.89, with the model formed in 10.56 seconds using the Cosine Similarity algorithm on the Google News dataset. This is because doc2vec is better suited to datasets with higher dimensions than datasets that only contain a few words.
Analisis Performa Akurasi Klasifikasi Citra Jenis Sayur Salada Menggunakan Arsitektur VGG16, Xception dan NasNetMobile
Authors: Nurafiya Nurafiya , Chandra Albert Yakobus
Published: July 2024

Salad is a type of leafy vegetable belonging to the Compositae family, genus Lactuca. It is rich in nutrients, including fiber, vitamin A, and minerals. Salad greens can cleanse the blood and fat, help people with coughs, and prevent high cholesterol, constipation, and insomnia. With the increasing population and awareness of the benefits of a balanced diet, consumer demand for lettuce has significantly increased. Consequently, farmers have expanded lettuce cultivation to meet consumer demand, which has the potential to cause errors in sorting different types of lettuce. Therefore, research focusing on the classification and detection of lettuce varieties is crucial to help farmers efficiently harvest lettuce based on its type using Convolutional Neural Network (CNN) methods, comparing three models: VGG16, Xception, and NasNetMobile. Data were directly obtained from lettuce farms and Kaggle. After pre-processing steps such as resizing and augmentation, the data were trained with various amounts, 200 epochs, and 64 batches during the architectural modeling stage. Based on the research results, the accuracy analysis with image classification of various types of lettuce concluded that using the Convolutional Neural Network (CNN) method by comparing three models VGG16, Xception, and NasNetMobile can classify each type of lettuce based on its class with high accuracy. In the tests conducted on the trained model, using an input size of 120 x 120, 200 epochs, and a batch size of 64, the NasNetMobile architecture model achieved the highest scores with an accuracy of 98.33%, precision of 97.8%, recall of 97.9%, and an F1-score of 97.8%. With these excellent accuracy values, the researchers hope that this analysis will make a significant contribution to the development of a superior and more efficient image classification system for agriculture, especially in selecting the appropriate CNN architecture.
Sentiment Analysis on TikTok App using Long Short-Term Memory (LSTM) with Stochastic Gradient Descent (SGD) Optimization
Authors: Rizky Muhammad Zacky Faqia , Sibaroni Yuliant , Prasetiyowati Sri Suryani
Published: July 2024

TikTok is currently one of the most popular social media apps. The site contains content that is creative, educational, innovative, as well as content that features lifestyle, cyberbullying, and inappropriate behavior. These diverse contents can trigger both positive and negative sentiments. This research aims to analyze the sentiment of the TikTok application by integrating feature extraction techniques, feature expansion, and optimization algorithms to improve the performance of the Long Short-Term Memory (LSTM) model. This research uses a dataset of 15,049 TikTok app reviews from the Google Play Store. Sentiment analysis is performed through four scenarios: the first scenario uses the LSTM model as the basis for classification, the second scenario combines LSTM with Word2Vec as feature extraction to convert initially unstructured text data into a structured format, the third scenario integrates LSTM and Word2Vec with FastText as feature expansion to improve the quality of representation and the model's ability to understand complex contexts, and the fourth scenario adds the Stochastic Gradient Descent (SGD) optimization algorithm to help improve the performance of the LSTM model. The results obtained showed that through the integration of feature extraction techniques, feature expansion, and optimization algorithms, the performance of LSTM increased by 7.44%. This research successfully developed an effective method that proved positive outcomes and will contribute to the development of a sentiment analysis system designed to help policymakers and application developers solve negative issues.
Analisis Sentimen Calon Presiden 2024 di Media Sosial X Menggunakan Naive Bayes dan SMOTE
Authors: Sunata Muhamad Hafidz Ardian , Irwiensyah Faldy , Hasan Firman Noor
Published: July 2024

In the era of digital advancement, the utilization of social media has surged, enabling individuals to express their viewpoints openly. This research underscores the utilization of social media platform X as the primary avenue for users to express their opinions, particularly on political matters, notably within the framework of the presidential election. Sentiment analysis techniques, specifically employing the Naïve Bayes algorithm and the Synthetic Minority Oversampling (SMOTE) method, have been the central focus of inquiry to infer people's inclinations toward presidential candidates. Despite numerous antecedent studies, deficiencies persist in terms of precision and data imbalance. This study endeavors to enhance the efficacy of sentiment analysis by integrating the Naïve Bayes approach with SMOTE. By scrutinizing tweets on social media X spanning from December 12, 2023, to March 31, 2024, the data is categorized into positive and negative sentiments. The findings reveal that employing SMOTE bolstered accuracy to 88% for the Ganjar-Mahfud dataset, whereas accuracy without SMOTE languished at approximately 69% for the Anies-Imin dataset. Out of 1589 tweets conveying positive sentiments, approximately 27.7% were directed towards Anies-Imin, 28.7% towards Prabowo-Gibran, and 43.5% towards Ganjar-Mahfud. The preponderance of negative sentiments was aimed at Anies-Imin (41.5%) and Prabowo-Gibran (40.8%).
Public Sentiment Dynamics: Analysis of Twitter/X Data on the 2024 Indonesian Election with NB-SVM
Authors: Satyananda Karuna Dewa , Sibaroni Yuliant
Published: July 2024

This research analyzes the dynamics of public sentiment towards three pairs of presidential candidates in the 2024 Indonesian Election. This research was conducted using Twitter data as a source of information to gain a deeper understanding of the pattern of public sentiment during six crucial phases in the context of the election. The data is analyzed periodically during the election period. Sentiment analysis was carried out using the Naïve Bayes-Support Vector Machine classification approach to understand the sentiment patterns that emerged in each phase. NB-SVM utilizes class frequency information from NB to weight features, then trains separate SVMs for each class using these weighted features, improving classification accuracy. Models using NB-SVM classification produce better accuracy than models using NB and SVM classification, with an average accuracy of 76%. In Pair 01, a dynamic pattern was formed, namely a decrease in the level of positive sentiment during the debate and increasing again at a later time. Meanwhile, for Pair 02 and 03, a pattern was not formed for different reasons, namely sentiment that was too stable for Pair 02, and unstable sentiment for Pair 03. While Pair 01 obtained the most positive sentiment, Pair 02 received the most negative, with an average of 65.19% during the election process. This research proves that the results of sentiment analysis on Twitter/X contradict the official results by KPU of the general election in Indonesia.