bluuebunny/crossref_metadata_2025 huggingface dataset:
Handling Imbalance Dataset on Hoax Indonesian Political News Classification using IndoBERT and Random Sampling
Authors: Fathin Muhammad Ammar , Sibaroni Yuliant , Prasetyowati Sri Suryani
Published: July 2024

The rapid adoption of the internet in Indonesia, with over 200 million active users as of January 2022, has dramatically transformed information dissemination, particularly through social media and online platforms. These platforms, while democratizing information sharing, have also become hotbeds for the spread of misinformation and hoaxes, significantly impacting the political landscape, as seen in the Jakarta gubernatorial election from late 2016 to April 2017. Research by the Indonesian Telematics Society (MASTEL) revealed a high prevalence of hoax content, predominantly socio-political, underscoring the critical need to address this misinformation and hoaxes challenge. This research delves into the challenge of detecting hoaxes in Indonesian political news, particularly focusing on the classification of news as factual or hoax in the presence of class imbalances within datasets. The dataset exhibits a significant class imbalance with 6,947 articles identified as hoaxes and 20,945 as non-hoaxes, Utilizing the IndoBERT model, a specialized variant of the BERT framework pre-trained on the Indonesian language, the study aims to assess its effectiveness in discerning between factual and hoax news. This involves fine-tuning IndoBERT for specific text classification tasks and exploring the impact of various resampling techniques, such as Random Over Sampling and Random Under Sampling, to address class imbalances since the dataset, significantly imbalanced with 6,947 articles labeled as hoaxes and 20,945 as non-hoaxes, necessitated these approaches. The study's findings demonstrate the IndoBERT model's consistent accuracy across different resampling methods like Random Over Sampling (ROS) and Random Under Sampling (RUS), highlighting its effectiveness in handling imbalanced datasets produce the accuracy of hoax detection with the 98.2% accuracy, 97.5% Recall, 97.8% F1-score, and 97.2% Precision. This is particularly relevant for tasks like misinformation detection, where data imbalance is common. The success of IndoBERT, a language-specific BERT model, in text classification for the Indonesian language contributes to the understanding of BERT-based models in diverse linguistic contexts.
Seleksi Fitur Information Gain untuk Optimasi Klasifikasi Penyakit Tuberkulosis
Authors: Kurniawan Ardi Caesar , Salam Abu
Published: July 2024

Tuberculosis (TB), caused by Mycobacterium tuberculosis, is a global health threat that spreads through the air. Factors such as gender, age, and geographical location influence its spread. Indonesia, the country with the second-highest number of TB cases globally, recorded a significant increase in TB cases from 2020 to 2022, especially in Semarang City. To minimize TBs impact, its crucial to identify the factors influencing its progression. Machine Learning techniques like feature selection (Information Gain) and classification algorithms (Random Forest) can be utilized. Feature selection helps determine which factors most influence TB by ranking attribute weights, while Random Forest is used for classification. Oversampling techniques like Synthetic Minority Oversampling Technique (SMOTE) are used to handle data imbalance and improve classification performance. The study concluded that the Random Forest classification model showed the best performance using all features or attributes from the highest to the lowest weight namely; tipe_diagnosis, jenis_fasyankes, usia, kelurahan_kecamatan, riwayat_dm, riwayat_HIV, tahun, paduan_OAT, status_pekerjaan, jenis_kelamin, tipe_TBC, riwayat_TBC, bulan and sumber_obat on the original TB disease dataset in Semarang City. The recall and accuracy rate reached 75%. This result is better than the TB classification model in Semarang City that uses the oversampling dataset with SMOTE and only uses the top 10-12 attributes, with a recall and accuracy rate of 74%. This research shows that certain techniques in Machine Learning can help understand the factors influencing TB treatment outcomes.
Comparative Analysis of Transformer Models in Object Detection and Relationship Determination on COCO Dataset
Authors: Hafizh Raihan Atsal , Wiharja Kemas Rahmat Saleh , Fikriansyah Muhammad Arya
Published: July 2024

This research investigates the integration of object detection and relationship prediction models to enhance image interpretability, addressing the core question: What challenges necessitate a Comparative Analysis of Object Detection and Transformer Models in Relationship Determination? A robust object detection model exhibits commendable performance, especially at lower Intersection over Union (IoU) thresholds and for larger objects, laying a solid foundation for subsequent analyses. The transformer models, including GIT, GPT-2, and PromptCap, are evaluated for their language generation capabilities, showcasing noteworthy performance metrics, including novel keyword-based metrics. The study transparently addresses limitations related to dataset constraints and potential challenges in model generalization, offering a clear rationale for the research. The evaluation of both object detection and transformer models provides valuable insights into the dynamic interplay between visual and linguistic understanding in image comprehension. By candidly acknowledging limitations, including data constraints and model generalization, this research paves the way for future refinements, addressing identified limitations and exploring broader application domains. The comprehensive approach to understanding the interplay between visual and textual elements contributes to the evolving landscape of computer vision and natural language processing research.
Win Probability of Heroes in Mobile Legends MPL ID S12 Competitions Using Na�ve Bayes Algorithm
Authors: Putra Angga Permana , Andono Pulung Nurtantio
Published: July 2024

The development of the gaming industry into digital formats has become a rapidly growing trend. E-Sports, particularly in Indonesia, has shown significant growth alongside technological advancements. The increased interest in E-Sports is evidenced by the higher quality tournaments organized by local game developers, such as Moonton, a subsidiary of ByteDance, hosting tournaments for Mobile Legends: Bang Bang. This article aims to analyze the probability of winning heroes in the Mobile Legends Professional League Season 12 using the Naive Bayes algorithm. The results of calculating the probabilities for various hero roles show varying levels of winning potential. By utilizing this method, it becomes possible to predict hero victories or losses more systematically, aiding players in developing more effective strategies during matches. The results obtained from predicting hero victories and losses indicate that for the jungler role, the win rate is 0.145 and the loss rate is 0.088. For midlaners, the victory rate reaches 0.492 and 0.661 for losses. As for roamers, the win rate is 0.120 and the loss rate is 0.102. For goldlaners and explaners, they achieve win rates of 0.528 and 0.177, respectively, while their loss rates are 0.339 and 0.132. Furthermore, after testing the data, the accuracy obtained for the roles is as follows: jungler role 67.61%, midlaner role 67.5%, roamer 67.65%, goldlaner 67.29%, and explaner 67.71%.
Word2Vec Optimization on Bi-LSTM in Electric Car Sentiment Classification
Authors: Hasanah Siti Uswah , Sibaroni Yuliant , Prasetyowati Sri Suryani
Published: July 2024

The Indonesian government is actively promoting electric vehicles. This policy has generated many sentiments from the public, both positive and negative. Public sentiment can have a significant impact on the success of government policies. Therefore, it is important to understand public sentiment towards these policies. This research develops a sentiment classification model to understand public sentiment towards electric vehicles in Indonesia. Sentiment classification is the process of identifying and measuring the positive or negative sentiment in a text. This research uses a Bi-LSTM model to perform classification on a dataset of tweets related to electric vehicles. To evaluate the performance, testing was conducted through two main scenarios. In Scenario I, the focus was on finding the optimal embedding size for two Word2Vec architectures, namely CBOW and Skip-gram. Model evaluation was performed using cross-validation to gain a deeper understanding of model performance. Scenario II focused on searching for the best dropout parameters for the Bi-LSTM model. This step aimed to find the optimal configuration for the model to generate more accurate and consistent predictions in classifying tweets related to electric vehicles. The results showed that in the context of sentiment classification on tweets about electric vehicles, the combination of CBOW with an embedding size of 200 and the Bi-LSTM model with a Dropout value of 0.2 is the best choice and achieves an accuracy of 96.31%, precision of 92.57%, Recall of 98.61%, and F1-Score of 95.49%.
Classification of Company Level Based on Student Competencies in Tracer Study 2022 using SVM and XGBoost Method
Authors: Revandi Tyo , Gunawan Putu Harry
Published: July 2024

Assessing the quality level of companies where graduates are employed is crucial for understanding the impact of academic programs on career placements. The use of methodologies that do not match the research objectives may lead to inaccurate or irrelevant analysis. When company classification methods are not aligned with the nature of the data collected in a tracking study, the risk of misinterpretation and the formulation of invalid generalizations becomes apparent. This study utilizes the 2022 Tracer Study Data from Telkom University, encompassing responses from 4306 graduates working across Local, National, and Multinational companies. The research employs support vector machine (SVM) and XGBoost algorithms to analyze and classify the company levels of the surveyed graduates. The primary objective is to enhance the accuracy of company level classification, thereby facilitating a more precise analysis of the Tracer Study dataset. The SVM and XGBoost algorithms are rigorously tested, and the results indicate an accuracy improvement with the XGBoost method, yielding a 2% increase over the SVM method. The evaluation is conducted with a data separation of 20% test data and 80% training data. This research not only contributes to the refinement of company level classification in the context of Tracer Studies but also underscores the potential of machine learning algorithms, specifically SVM and XGBoost, in providing valuable insights into graduates' professional trajectories. The findings of this study pave the way for more informed decision-making processes in academic and career development initiatives.
YouTube Viewership Increation Analysis and Prediction using Facebook Prophet Model
Authors: Pratama Rezqie Hardi , Gunawan Putu Harry
Published: July 2024

YouTube, a widely accessed video-sharing platform available through both mobile applications and web interfaces, serves as a medium for content creators, commonly referred to as YouTubers, to engage with their audience. The success of a YouTuber is intricately tied to their audience engagement, encompassing metrics such as total views, comments, and likes garnered by their videos. This study involves the analysis of 7,600 English-language videos uploaded on YouTube between August and September 2020. To assess the predictive success value of a video, the study employs the Facebook Prophet method. Focusing on the upload time as a primary parameter, this method forecasts the growth in the number of YouTube viewers using datasets obtained from the YouTube API. Leveraging Time Series modeling, Facebook Prophet processes data by considering audience interactions throughout a video broadcast. The results derived from the Facebook Prophet model indicate a predictive trend of increasing viewership on YouTube in the coming months. The evaluation of model linearity, measured using the R² score to gauge data reliability, reveals a score of 0.39 or 39% which indicates a positive linearity score. And using Pearson correlation it gives 75 accuracy score. This signifies the model's capability to reasonably predict the growth in the number of viewers, contributing valuable insights into the dynamics of YouTube audience engagement over time.
Deteksi Jamur Beracun dengan Algoritma Convolutional Neural Network dan Arsitektur EfficientNet-B0
Authors: Mauludy Muhammad Wildan , Rulyana Devita , Hardjianto Mardi
Published: July 2024

Indonesia is a tropical country that has abundant natural resources and biodiversity, one of which is mushrooms. Mushrooms have various shapes and types. Some of them contain mushrooms that cannot be consumed because they contain toxins that will have an impact on human health. Mushroom species that can be consumed sometimes have a similar shape to mushrooms that cannot be consumed, causing cases of poisoning due to consuming the wrong mushrooms. This research focuses on detecting poisonous mushrooms using a Convolutional Neural Network (CNN) with the EfficientNet-B0 architecture. Mushroom data was obtained from Kaggle, and after praprocessing, the model was trained by varying the number of epochs and batch size. Based on the results of research and discussion on the detection of poisonous and non-toxic mushrooms, it is concluded that the CNN algorithm with the EfficientNet-B0 architecture can differentiate between poisonous and non-toxic mushrooms with a high level of accuracy. In scenario testing, the model trained using batch size 32 had an accuracy of 84.2% and loss of 0.39, precision of 0.855, recall of 0.805, and f1 score of 0.815. This shows that the CNN architecture EfficientNet-B0 is an efficient and accurate approach in classifying poisonous and non-poisonous mushrooms. Apart from that, this research also found that parameters such as the number of epochs and the number of batch sizes influence the model training process.
Prediksi Jumlah Sampah di TPSA Menggunakan Pendekatan Machine Learning
Authors: Almira Venia , Maimunah Maimunah , Sukmasetya Pristi
Published: July 2024

The amount of waste at landfills is increasing along with the growing population and human activities. Predicting the amount of waste has become one of the ways to address waste management issues. The quantity of upcoming waste can be determined through waste prediction, providing essential information for solving waste-related problems. This research involves modeling daily waste predictions using three machine learning algorithms: Linear Regression, Support Vector Regression, and Random Forest Regressor. The data used in this study is the waste data at Banyuurip landfill, Magelang City, covering the period from 2019 to 2022. In the data processing stage, attributes for data usage are selected, daily waste summation is performed, missing values are handled, and normalization is carried out using min-max normalization. The three machine learning algorithms are employed in the prediction modeling stage to obtain optimal parameters. The prediction model is evaluated by calculating the MSE. The results of the three waste prediction models using Linear Regression show a model with an MSE-train of 0.0086 and an MSE-test of 0.0083, while the RMSE-train is 0.0930 and the RMSE-test is 0.0915. The optimal SVR prediction model is obtained with hyperparameter combination C = 1, gamma = 1, and epsilon = 0.05, yielding MSE-train of 0.0030 and MSE-test of 0.0089, with RMSE-train at 0.0556 and RMSE-test at 0.0943. The Random Forest Regressor model results in a model with n_estimators of 500, random_state of 1, without the hyperparameter max_depth, and has MSE-train of 0.0012 and MSE-test of 0.0081, along with RMSE-train at 0.0353 and RMSE-test at 0.0901. Based on these three models, it is concluded that the best model is the Random Forest Regressor with the smallest MSE and RMSE values.
Comparative Prediction of Physical Fatigue Patterns in Bandung, Indonesia Workers using CNN and ANN
Authors: Ardiansyah Muhammad Fikri Raihan , Wijaya Rifki , Wulandari Gia Septiana
Published: July 2024

This research explores the impact of physical fatigue on task performance and evaluates the effectiveness of Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) in predicting fatigue levels. Physical fatigue, as a critical factor influencing performance and safety, serves as a signal for the body's need for rest. Utilizing a smartwatch with heart rate sensors, this study applies ANN for subjective fatigue assessments and CNN for time series analysis. With a structured approach encompassing data collection, preprocessing, and model training, a confusion matrix evaluates the model's performance. Results indicate an accuracy of 92.4% for the ANN model with an RMSE of 0.275, while the CNN model achieves an accuracy of 85.46% with an RMSE of 0.381. These findings affirm the effectiveness of both models in predicting fatigue, providing valuable insights for future research and emphasizing the importance of comprehensive data analysis for a nuanced understanding of individual performance (Number of data: 149,796 from 6 subjects).
One-Star Hotel Selection for Staycation using Simple Additive Weighting and Rank Order Centroid
Authors: Singgalen Yerik Afrianto
Published: July 2024

The development of the hospitality industry provides many choices to staycation enthusiasts in choosing the right accommodation service. Accommodation service providers also increase competitiveness through excellent service to the addition of facilities to adjust guest preferences and increase sales. The primary objective of this study is to analyze the decision-making behavior associated with choosing one-star hotels for a staycation. The research investigates the decision-making patterns in the context of one-star hotel stays for recreational purposes. The study utilizes the SAW method to evaluate the criteria of facilities, services, rating, cleanliness, value for money, and location, employing Rank Order Centroid (ROC) weighting. In light of the computations using Rank Order Centroid, it is discerned that the weights assigned to the criteria are as follows: W1 with a weight of 0.4083, w2 with a weight of 0.2416, w3 with a weight of 0.1583, w4 with a weight of 0.1027, w5 with a weight of 0.0611, and w6 with a weight of 0.0277. The research findings reveal the aggregation of weighted scores and ranking of alternatives, delineating that A4 attains the first rank with a value of 0.97395, followed by A3, securing the second rank with a value of 0.96511, and A1 acquires the third rank with a value of 0.95191. Thus, the significance of the identified alternative is that it sheds light on its superior performance according to the specified criteria. In conclusion, this research contributes valuable insights into the decision-making dynamics of staycation enthusiasts, pinpointing alternative A4 as the most favorable choice within one-star hotel accommodations.
Prediksi Kepribadian Big Five Pengguna Twitter Menggunakan Metode Decision Tree dengan Pendekatan Semantik BERT
Authors: Widyanto Jammie Reyhan , Setiawan Erwin Budi
Published: July 2024

Individual personality can be seen easily in this day. There are several approaches in classifying personality, one of which is the big five personality. The big five personality consists of 5 dimensions, namely Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. One way of knowing an individual's personality can be seen from their social media, because today almost all individuals have social media. One of the social media that is still widely used is Twitter. Twitter is a social media that contains tweets from each individual with a maximum of 280 characters per tweet. There have been several studies related to the big five personalities of Twitter users. Based on previous big five personality research problems, this study carried out predictions of the big five personalities of Twitter users using the Decision Tree Classification And Regression Tree (CART), Term Frequency Inverse Document Frequency (TF-IDF), Synthetic Minority Oversampling Technique (SMOTE), Linguistic Inquiry Word Count (LIWC), and Bidirectional Encoder Representations from Transformers (BERT) methods. The study aims to determine the application of the methods used in this study to the prediction of big five personalities and to get better accuracy results than previous studies. Data obtained from 315 twitter users and 672,866 tweets obtained from surveys and have been labeled with big five personalities, resulting in an accuracy of 97.62% from the baseline with an increase of 23.1%, by applying the CART+TF-IDF+SMOTE+LIWC+BERT method.
Implementation of K-Means Algorithm to Determine Marketing Strategy
Authors: Narestami Afah Uly , Suhartono Didit , Tarwoto Tarwoto
Published: July 2024

In the ever-evolving digital era, the use of artificial intelligence (AI) technology is becoming increasingly important for companies in optimizing their marketing strategies. Through this study, applying K-Means analysis to product sales data at ABC Soap Stores, especially for several soap brands, results were obtained from the last three months of sales data, namely October to December 2023. The K-Means algorithm was used to uncover hidden patterns in the data, allowing customization of marketing approaches by ABC Soap Shop. The analysis process showed consistency in data grouping in Iteration I and Iteration II, with random cluster center selection in Iteration I involving data 1 and data 3. The results of Iteration I are used in Iteration II, where the same cluster center is found. Analysis of soap product sales data, it was identified that Cluster 1 consisted of nuvo, dove, Dettol, Lifebouy and shinzui soap products. Products in Cluster 1 show a consistent buying pattern, so it is advisable to add more stock because these products have a high level of interest from consumers, which is reflected in the increase in purchases every month. Meanwhile, Cluster 2 consisting of Lux Soap showed lower interest, and therefore, it was not recommended to increase the stock. Products in Cluster 2 still have sufficient stock, and there is no significant increase in purchases from consumers In cluster I the product can be concluded to have increased by 70% while cluster II nuvo soap decreased by 30%. The results of the implementation of the K-Means Algorithm contribute to the understanding of consumer purchasing patterns towards certain products in addition to optimizing marketing strategies and inventory management.
Multilabel Classification in Indonesian Translation of Religious Text using Word Centrality Term Weighting
Authors: Dewantara Muhammad Pascal , Lhaksmana Kemas Muslim
Published: July 2024

This research focuses on enhancing the understanding of the Quran in the Indonesian translation dataset by employing a word centrality that feeds into a classifier model. The primary goal is to compare the hamming loss score from the TF-IDF and TW-IDF feature extraction methods in the Indonesia translation case study. The TF-IDF is commonly used in prior research. It has a higher hamming loss (which is worse in accuracy) than the TW-IDF incorporating centrality measurement more specifically in degree and closeness centrality. This research adds eigenvector centrality for a new compartment from the other methods. We used SVM, Random Forest (Bagging), and AdaBoost (Boosting) for the classifier model, with Mutual Information as the feature selection method. In evaluating the classifier, Hamming Loss is used given that the method is suitable for multilabel classification. Results indicate that the centrality measurement value for the term weighting method offers a significant improvement over regular TF-IDF. Each centrality method gives the best Hamming Loss score in each classifier model. Degree centrality gets 0.1275 in SVM, closeness centrality gets 0.1367 in AdaBoost, and eigenvector centrality gets 0.1204 in Random Forest. However, eigenvector centrality still can be a strong measurement method to lower the Hamming Loss score. Random Forest and AdaBoost give a significance better over SVM.
Analisis Sentimen Ulasan Pengguna Pada Aplikasi BRImo BRI Menggunakan Metode Klasifikasi Algoritma Naive Bayes
Authors: Umair Muhammad , Susanto Erliyan Redy
Published: July 2024

Sentiment analysis is the process of collecting, measuring, understanding and interpreting opinions or sentiments expressed in text to understand or evaluate the opinions contained in a topic. BRImo BRI application analysis research aims to analyze the sentiment of user reviews of the BRImo BRI application via the Google Play Store platform using the Naive Bayes Algorithm Classification method. This algorithm is used to identify Positive, Negative and Neutral sentiment patterns contained in user reviews of the BRImo BRI application via the Google Play Store platform. The data used in this research was 199 data. The sentiment results obtained from these data were Positive sentiment as many as 47, Negative as many as 125 and Neutral as many as 27. After analyzing the data using the Naive Bayes algorithm classification, the data results obtained were accuracy results of 65%, precision results of  67%, recall results of  92%. and f1_score results 77%. This research aims to analyze the sentiment of user reviews of the BRImo BRI application regarding payment features via the Google Play Store platform using the Naive Bayes Algorithm Classification method. The Naive Bayes algorithm is used to analyze Positive, Negative and Neutral sentiments that appear in user reviews of the BRImo BRI Application regarding payment features. Analysis of user sentiment towards the application is very important for service providers to understand user perceptions and needs in the BRImo BRI application.
Analisis Sentimen Pengguna Aplikasi Instagram Pada Situs Google Play Menggunakan Metode Naïve Bayes
Authors: Aida Amrina Nur , Arsi Primandani , Aji Ranggi Praharaningtyas , Tarwoto Tarwoto
Published: July 2024

Instagram is an application that is widely used by students because it is easy to use and information is easy to get. Instagram has features that allow you to send photos and videos with unique designs, making it an interesting learning medium. In 2023 the Instagram app has managed to achieve more than 5M downloads on the Google Play Store platform and obtained a rating of 4.2. In some situations, there may be a discrepancy between the rating given and the actual review content. In the app development process, it is important for developers to not only pay attention to the number of ratings, but also understand the views and opinions of users. Therefore, developers need to have the ability to analyze each opinion given by users. One approach that can be used is through sentiment analysis using the Naïve Bayes algorithm. The data used is Instagram app reviews, with positive reviews and negative reviews. This analysis produces the best performance with accuracy 78,85%, precision 78,85%, recall 100% and f1-score 88,17% values.
Penerapan Data Mining Untuk Pengelompokan Terhadap Kualitas Kinerja Karyawan Dengan Menggunakan Algoritma K-Medoids Clustering
Authors: Karim Abdul , Esabella Shinta , Kusmanto Kusmanto , Hidayatullah Muhammad , Suryadi Sudi
Published: July 2024

HR management is recognized as a global issue and an integral part of competitiveness in the arena of globalization. The organizational structure is the placement of tasks from the very top to the placement of very basic tasks. Assessing the quality of employee performance is one of the work evaluation sessions that can provide the best for industry and citizens. In position placement, if someone does not suit the position they have, it will cause problems such as the company's operational processes not running well. Performance appraisal of employees aims to see the performance results that have been carried out or given by employees when occupying a position. Problems related to performance appraisal are important problems that must be resolved immediately. Data mining is a data processing process in the past, where data in data mining is a collection of data that has been collected over a certain period of time. Information data mining is a series of processes for exploring added value in the form of data produced by extracting and identifying patterns in an information base. Clustering is a part of data mining that aims to group based on the formation of new clusters. The K-Medoids algorithm is a partitional clustering procedure that minimizes the distance between labeled points. The K-Medoids algorithm is a classic Clustering partition technique that groups data sets of ni objects into k groups known a priori. From the results of research conducted using the K-Medoids method, 3 clusters were obtained. Where in cluster 1 there are 4 employees, in cluster 2 there are 3 employees and in cluster 3 there are 3 employees.
Sentimen Analisis Masyarakat Terhadap Pembangunan IKN Menggunakan Algoritma Lexicon Based Approach dan Naïve Bayes
Authors: Setiawan Samuel Budi , Isnain Auliya Rahman
Published: July 2024

The relocation and construction of IKN (Capital City of the Archipelago) as a center for state administration activities has many benefits and shortcomings, starting from the selection of locations, the ratification of laws that are considered too hasty then raises pros and cons by the Indonesian people. President Joko Widodo decided to move the country's capital outside Java in a meeting on April 29, 2019. The location of the IKN development was determined in East Kalimantan. This research was conducted by retrieving data via Twitter with the keyword "IKN Development". The data that has been collected totals 3,680 tweets. Data analysis was carried out with two methods, namely Naïve Bayes Classifier and Lexicon Based, and the best accuracy value was found between the two methods in analyzing data on public responses to IKN Development. The initial step of the data analysis process is the preprocessing process which contains stages such as labelling, case folding, cleaning, tokenizing, stopword removal, stemming. It is known that the results obtained from the analysis of the Naïve Bayes Classifier method have an accuracy value of 79%, and Lexicon Based has an accuracy value of 76%. Sentiment analysis of the two methods has Positive, Negative, and Neutral sentiments. With the stages of the analysis process using the Naïve Bayes Classifier and lexicon based methods, it can be seen that the Naïve Bayes Classifier method shows a Positive sentiment of 47.18%, Negative of 6.33%, and Neutral of 46.49%, while for Lexicon Based, Positive sentiment reaches 54.15%, Negative 29.36%, and Neutral 16.49%. It should be noted that the highest positive polarity result is found in the Lexicon Based algorithm at 54.15%, while in the Naïve Bayes Classifier 47.18%. It can be concluded from the results of both methods that Naïve Bayes Classifier has a better analysis compared to Lexicon-Based analysis.
Deteksi Dini Hipertensi Pada Perusahaan Jasa Keamanan Menggunakan Metode Decision Tree
Authors: Hamid Abdul , Harmaji Lilik , Mahdiana Deni
Published: July 2024

Hypertension poses a serious threat to employees in the security services sector, especially those requiring physical and mental readiness. Early detection of hypertension is crucial to prevent potentially fatal complications. This research aims to develop a hypertension prediction model using machine learning algorithms to enhance accuracy compared to conventional methods. Data from Medical Check-ups (MCUs) of 800 employees from PT Demitra Karsa Perdana Site Adaro Tanjung Kalimantan Selatan in 2023 were utilized. The data encompassed health factors and lifestyle choices influencing hypertension, such as age, stress history, cholesterol levels, weight gain, family medical history, smoking habits, alcohol consumption, and diagnoses. The Decision Tree algorithm was employed to classify the data. Classification results were evaluated by calculating accuracy, precision, and recall for hypertension classes (high or normal). Findings indicated that Decision Tree was the best algorithm for predicting hypertension, achieving an accuracy of 92.01%, precision of 99.41%, and recall of 84.42% for high hypertension class. However, the recall value for high hypertension class still needs improvement to prevent high-risk employees from going undetected. This research benefits PT Demitra Karsa Perdana in predicting hypertension among their employees and contributes to the advancement of science and technology. Thus, this study not only enhances our understanding of hypertension but also provides insights into the use of machine learning for early detection of serious diseases in the workplace.
Analisis Sentimen Masyarakat Indonesia Terhadap Pengalaman Belanja Thrifting Pada Media Sosial Twitter Menggunakan Algoritma Naïve Bayes
Authors: Wulandari Sania , Hasan Firman Noor
Published: July 2024

Thrifting is an increasingly popular second-hand shopping activity in Indonesia, especially among millennials and generation Z as a cost-saving shopping alternative. Thrifting activities have a clear positive impact on the Indonesian people in protecting the environment by reducing the purchase of new goods. However, thrifting is considered illegal and can harm the domestic textile industry. So sentiment analysis needs to be done to find out how people respond to thrifting activities. This study aims to calculate the number of positive and negative comments from Twitter users, and find out how accurately the Naïve Bayes algorithm is used in the classification. The data used is taken from Twitter social media as many as 900 tweets, then processed through several advanced stages such as pre-processing which consists of cleansing, tokenize, and filter stopwords. Then at the labeling stage the data is divided into training data and test data with a ratio of 60:40. After being classified using the Naïve Bayes algorithm, the results obtained tend to be positive with a total of 368 positive comments and 181 negative sentiments. After going through the evaluation stage, the accuracy value is 95.92%, the precision value is 95.76%, and the recall value is 97.41%. The evaluation results show that the Naïve Bayes algorithm is proven to have a high level of accuracy used in classification.
Analisis Sentimen Produk Skincare Somethinc Niacinamide di Female Daily dengan Naïve Bayes Classifier
Authors: WP Dwi Atmodjo , Firizqi Januponsa Dio , Amalia Zalfa Azzah
Published: July 2024

One of the skincare products from Somethinc that is currently hotly discussed is Niacinamide Moisture Beet Serum. On the Female Daily site, people who have used the beauty products they use express their opinions in a review on Female Daily. However, the many thousands of reviews that exist have not been structured, so it is still difficult for the public and potential consumers to understand the essence of opinions on a product and classify them into the appropriate polarity of opinion. This research aims to analyze public sentiment regarding reviews of the Somethinc Niacinamide skincare product in Female Daily using the Naïve Bayes Classifier algorithm. This method was chosen because of its capabilities in probability-based text classification and popularity in sentiment analysis. The study began with collecting review data from the Female Daily site, followed by a text pre-processing process which included case folding, tokenizing, stopwords removal, and stemming. The dataset is then divided into training data (80%) and test data (20%). The Naïve Bayes Classifier algorithm is applied to classify reviews into positive, negative, or neutral categories. The research results show that this model is successful in classifying sentiment with 61% accuracy. The conclusions of this study indicate that although the Naïve Bayes Classifier algorithm is quite effective in classifying the sentiment of skincare product reviews, there is room for improvement, especially in handling meaningful and contextual words. This research provides new insight for brand owners and potential consumers in understanding public opinion regarding the Somethinc Niacinamide skincare product.
Analisis Sentimen Terhadap Media Sosial Twitter dengan Kasus Kampanye Anti-Korupsi di Indonesia Menggunakan Naive Bayes
Authors: Aprilia Ni Wayan Ayu Sekar , Isnain Auliya Rahman
Published: July 2024

In Indonesia, the issue of the Anti-Corruption Campaign is often discussed on Twitter, which has become an important platform for voicing public opinions and sentiments. Sentiment analysis of the Anti-Corruption Campaign can provide valuable insights for the government and anti-corruption institutions such as the Corruption Eradication Commission. The aim of this research is to analyze the sentiment felt by the public towards the Anti-Corruption Campaign in Indonesia using the Naïve Bayes method based on data from Twitter. Tweet data related to the Anti-Corruption Campaign in Indonesia is collected via the Twitter API, then preprocessed to clean, tokenize and remove stopwords. A dictionary of positive and negative sentiments is created based on tweet analysis, and the Naïve Bayes method is used to classify tweet sentiments into positive or negative. Method performance is evaluated using a confusion matrix. The research results show that most of the tweets related to the Anti-Corruption Campaign have positive sentiments, but there are also negative ones. This research provides an understanding of public perceptions of the Anti-Corruption Campaign in Indonesia through Twitter sentiment analysis, which can help in formulating anti-corruption policies and strategies in the future. Analysis shows that 58.64% of Twitter accounts have positive sentiment and 41.36% have negative sentiment. Evaluation of test data shows an accuracy level of 74%, a precision level of 79%, and a recall of 76%.
Penerapan Algoritma K-Means Menggunakan Model LRFM Dalam Klasterisasi Nilai Hidup Pelanggan
Authors: Afifah Tiara Afrah , Novita Rice , Ahsyar Tengku Khairil , Zarnelly Zarnelly
Published: July 2024

In implementing customer relationship management, there are still many companies that have not utilized CRM optimally as part of their business strategy. As is the case with UD Sandeni. UD Sandeni still has problems in managing its relationships with customers because UD Sandeni does not fully understand the difference between customer information that is profitable and unprofitable for the company's sustainability. UD Sandeni has used a system to manage customer transaction data. However, this system is only used to calculate profits and create bookkeeping for registered agents so that UD Sandeni does not have an in-depth understanding of the characteristics of its customers. To overcome this problem, the solution that can be applied is to use customer grouping techniques, such as clustering. Customer transaction data is processed using a clustering process with K-Means and LRFM. Test the validity of cluster results using DBI and calculate CLV values using AHP weights to produce cluster rankings. The results of this research obtained customer clustering which consists of 2 segments, namely cluster 1 which has the highest CLV value of 0.3171156 with a total of 298 customers and includes the High Value Loyal Customers segmentation, and cluster 2 with a CLV value of 0.1434054 with a total of 72 customers. which is included in the segmentation of uncertain new customers (uncertain lost customers).
Analisis Peramalan Harga Telur Ayam Ras Dengan Menggunakan Metode SARIMA
Authors: Hakim Ilham Luqman , Sanglise Marlinda , Suhendra Christian Dwi
Published: July 2024

Eggs are a widely favored source of animal protein. One commonly consumed type is chicken eggs. Besides being easy to prepare, a notable advantage of chicken eggs is their affordability compared to other sources of animal protein. However, chicken eggs are subject to price fluctuations at specific times, such as religious holidays. These fluctuations can lead to losses for both producers and consumers. One way to address this issue is by forecasting. Forecasting is crucial as it provides estimated price information for the future. With this information, producers and consumers can prepare appropriate strategies to cope with price changes. The price of chicken eggs fluctuates periodically, thus the method employed in this research is Seasonal Autoregressive Integrated Moving Average (SARIMA). SARIMA is a forecasting method specifically used for data exhibiting seasonal patterns. Based on the results of this study, the best SARIMA model obtained is , demonstrating excellent performance with an RMSE value of 1491.30 and a MAPE of 3.40%. From this model, forecasted prices of chicken eggs in the traditional market of Manokwari city for the next 12 months are obtained, spanning from March 2024 to February 2025. According to the forecast results, the price of chicken eggs is expected to rise in March to June 2024 and December 2024 to January 2025.
Penerapan Algoritma Fuzzy C-Means untuk Klasterisasi Customer Lifetime Value menggunakan Model LRFMD
Authors: Ramadhani Indah , Afdal M , Mustakim Mustakim , Zarnelly Zarnelly
Published: July 2024

PT X is a retail company engaged in printing. The company has not differentiated between information about profitable and unprofitable customers for the company. Transaction data is only used as profit and loss information so they do not know the characteristics of the customers they have. In addition, the lack of extensive services in the merchandise category is one of the reasons the company's revenue has not reached the predetermined target. Currently, the company has opened additional services in the merchandise field. This research aims to identify customer segmentation as well as analyze the characteristics and provide a strategy proposal that will be submitted to PT. X. Customer loyalty and characteristics have a significant impact on a company. To identify customers who show loyalty to the company, the Fuzzy C-Means algorithm is used to perform clustering, using the Davies Bouldin Index (DBI) to evaluate the clustering results. The model used is in accordance with the principles of Length, Recency, Frequency, Monetary and Diversity (LRFMD) to categorize purchasing patterns. By analyzing LRFMD variables, it is possible to identify customers who are loyal to the company and those who are not. This research produces 6 clusters with the best cluster or supestar customer in cluster 6, the second best value customer or golden customer is cluster 2, the average value customer or typical customer is cluster 4 and 5 and the lowest cluster or dormant customer is in cluster 3.
Implementasi Data Mining Metode K-Means Menggunakan Framework Python Dalam Mengelompokkan Pegawai Berdasarkan Data Presensi
Authors: Mira Mira , Nurcahyo Azriel Christian , Gudiato Candra , Kusnanto Kusnanto
Published: July 2024

Computerized employee attendance data collection provides convenience in terms of real-time monitoring. The computerized attendance data collection process has been widely implemented by government and private agencies. With a computerized system, the data collection process is easier to carry out. The data that has been collected increases as time passes. To improve discipline in employee attendance patterns. Perumdam Tirta Bengkayang implements an attendance application with features consisting of working hours, shifts, attendance location and is equipped with documentation of incoming and outgoing absences in the form of selfies attached to the application. The data that has been collected is then analyzed to assess employee discipline levels using data mining techniques, namely the K-Means method. Data mining methods are used to group employee attendance data patterns. Data mining is the process of collecting data, and finding patterns or relationships between data. The K-Means method works by dividing data into k closest clusters. The calculation begins by determining the value of k, centroid, and closest point. Meanwhile, the analysis uses the Python library by importing the necessary libraries such as numpy, pandas, matplotlib, sklearn. Based on the results of the analysis and grouping of employees, 26.76% of employees fall into cluster 0, namely the low level of discipline, 71.83% of employees fall into cluster 1, namely the medium level of discipline, and 1.41% of employees fall into cluster 2, namely the high level of discipline.
Analisis Klasifikasi Komoditas Harga Pangan Menggunakan Artificial Neural Network pada Kualitas Beras Pulau Jawa
Authors: Lifera Fruita Cancer , Ahdika Atina
Published: July 2024

Rice is a highly important food commodity that significantly influences the welfare of the Indonesian population, with approximately 98% of the Indonesian population relying on it as their main food source. There are several qualities of rice such as low-quality rice, medium-quality rice, and super-quality rice. The differences in rice quality types are usually influenced by several factors, including the level of processing, nutrient content, and quality level. Thus, in general, many consumers are unable to differentiate rice quality based on its physical appearance when purchasing rice. Instead, they tend to assess rice quality solely based on its price aspect. Therefore, this study classifies rice quality by focusing on the price variable as the main reference. Artificial Neural Network (ANN) method is used to classify rice quality based on price. ANN is a non-linear technique commonly used for model classification. ANN method is one of the famous data mining prediction models known for its accuracy in classification. By using the ANN method, it is found that the best proportion is using an 85% training and 15% testing proportion, and obtaining an accuracy value of 70% which can be considered quite good. The ANN method shows that the ANN model can learn significant patterns from the given data.
Rancangan Alat Absensi Berbasis Internet of Things dan Notifikasi Smartphone Menggunakan App Inventor
Authors: Warman Aziz , Jufrizel Jufrizel , Ullah Aulia , Zarory Hilman
Published: July 2024

Technological developments have become a major focus when it comes to improving everyday productivity and innovation. Intelligent tools are created to facilitate and assist human work in life. Smart attendance machines are designed to replace the role of manual paper attendance and switch to an automated system. The problem with manual attendance is that there is potential for fraud in attendance and attendance data is vulnerable to damage and loss of data because it only uses paper media for storage and the use of manual attendance also has disadvantages related to attendance data that can be known by student guardians every day. This paper discusses the design of an attendance system using a Radio Frequency Identification (RFID) card as an identity used for the attendance process. The use of Liquid Crystal Display (LCD) to display information from card readings. ESP8266 microcontroller is used to run commands according to the program and access to Wifi that can connect to the internet. To store attendance data using application software that can be accessed by the admin. To monitor attendance, App Inventor is used which can be connected to a smartphone. The results of testing the tool show that the attendance machine can operate in accordance with the design and provide satisfactory results, the tool works by attaching the identity card to the card reader and the data will appear on the LCD in the form of card ID and attendance information, the time it takes to read the card until it appears on the LCD is 2-3 seconds.   Attendance data in the form of names, hours in, hours out and attendance information is successfully sent to the website application via an internet connection with the average time required of 4.43 seconds. Data in the form of sample photos was successfully sent by ESPcam to the website application via an internet connection with an average delivery time of 9.36 seconds. The attendance message was successfully sent to the Smartphone using App Inventor.