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Customer satisfaction is a crucial indicator in assessing the quality of a company's products, services and overall experience. This research aims to identify the level of customer satisfaction and optimize the available data for effective use in sentiment analysis. In this study, we analyzed 4,353 customer reviews collected over the past year, with 3,481 reviews used as training data and 871 reviews as testing data. The analysis process was conducted using the Cross-Industry Standard Process for Data Mining (CRISP-DM) approach and leveraged the Logistic Regression algorithm to build a predictive model. Model evaluation using the confusion matrix yielded an accuracy of 94.60%, a precision of 94.26%, and a recall of 94.60%. The analysis was conducted using Jupyter Notebook and the Python programming language. The results indicate that sentiment analysis is effective in identifying and predicting customer satisfaction levels, which in turn can help a company’s products improve its service strategies. The optimization of previously underutilized data now provides deeper insights into customer perceptions and expectations, enabling the company to make more targeted decisions and enhance overall customer satisfaction.

This research explores the factors influencing the adoption of Islamic digital banking among millennials in Indonesia. Employing a qualitative approach through a comprehensive literature review, the study analyzes existing research on Islamic digital banking adoption, focusing on academic journals, conference proceedings, and industry reports. The findings reveal that perceived ease of use and usefulness of digital banking services are crucial. Additionally, social influences, such as peer and family recommendations, and personal factors, including demographics and cultural background, significantly impact adoption rates. This research provides valuable insights for Islamic banks in Indonesia to develop targeted strategies for millennial engagement. By understanding these influencing factors, Islamic banks can tailor digital banking services to meet the specific needs and preferences of this demographic, thereby enhancing market penetration and fostering growth within the evolving digital banking landscape.

This study examines the effect of market risk, investor sentiment, and interest rates on the performance of energy sector stocks in Indonesia for the period 2019–2023, with market liquidity as a mediating variable. The study was conducted through a literature review by identifying and analyzing various related studies. Secondary data was obtained from scientific journals and related reports using relevant literature selection techniques. The results of the study indicate that oil price volatility, investor sentiment, and interest rate changes significantly affect the performance of energy sector stocks. Market liquidity was found to be able to reduce the negative impact of market risk and interest rates, and strengthen the positive influence of investor sentiment on stock prices. This study concludes that market liquidity plays an important buffer in maintaining the stability of stocks in the energy sector, especially in dealing with market fluctuations. The results of this study contribute to investment managers in developing more adaptive portfolio strategies and to regulators in creating policies that support market liquidity.

This study aims to analyze the sentiment of iPhone product reviews fromAmazon using the BERT (Bidirectional Encoder Representations from Transformers) model to classify reviews as either positive or negative. The dataset, sourced from Kaggle, includes text reviews and star ratings, where high ratings indicate positive sentiment and low ratings indicate negative sentiment. After text preprocessing steps, including data cleaning, tokenization, and sentiment labeling, the BERT model was fine-tuned for sentiment classification, with the data split into training, validation, and test sets. Evaluation results demonstrate that the BERT model achieves a high classification accuracy, with an accuracy rate of 93.9% and a balanced F1 score between precision and recall. Confusion matrix evaluation also indicates that the model consistently identifies both positive and negative sentiments. This study shows that Transformer-based models like BERT are highly effective in understanding customer opinions in e-commerce, with broad application potential for data-driven decision-making in marketing strategies and product development.

The news about the proposal of the government of the Republic of Indonesia regarding the postponement of the 2024 elections is certainly an interesting discussion. In this research, sentiment analysis will be carried out on the issue of postponing the election. In this study, a dataset obtained using the crawling technique was obtained in the amount of 1280 tweet data about the postponement of the 2024 election. Data labeling in this study uses lexicon-based techniques with Indonesian dictionaries. By applying this technique, the details of the data in the positive class are 67.7%, namely 157 opinion data, and 32.3% negative, namely 75 opinion data. The sentiment classification system's training and test data yield a 9:1 ratio when the Naïve Bayes Classifier method is applied, and word weighting using TF-IDF yields an accuracy value of 91.67%, precision of 90.91%, recall of 100%, and f1-score of 95.24%.

The emergence of the resistance movement, of course, because there are factors that influence it. The birth of DI/TII in Aceh was caused by the central government's policy to merge Aceh province into North Sumatra province on August 8, 1950. This paper will explain the application of emergency military law. Specifically during the conflict that occurred in the Aceh region which led to the formation of the Free Aceh Movement from 1976 to 2005. By using normative legal research methods. The trigger for the proclamation of Aceh's independence was the exploitation of Aceh's natural resources during the Soeharto era, where petroleum and natural gas were managed by Exxon Mobil Oil Indonesia which caused regional sentiment, that all Aceh's wealth was sent to Jakarta, even Hasan Tiro once said Aceh should have the advantage of its natural resources like Brunei Darussalam. The Presidential Decree (Keppres) to launch a massive military operation in Aceh had been drafted long before, after cabinet meetings held at the Palace, as well as through the deliberations of the people's representatives in Senayan, Jakarta in early 2003. The aim was to crush the Free Aceh Movement (GAM). The Helsinki MOU is one of the negotiations carried out between the Government of the Republic of Indonesia and the Free Aceh Movement held in Helsinki, Finland, August 15, 2005 creating a new system and method of relations between the central government and the Aceh government, which is fully supported by the international community.  

The growth of e-commerce in Indonesia has led to the emergence of various online shopping platforms, with Shopee being one of the most popular in Semarang City. User reviews on the Shopee application serve as a valuable data source for analyzing customer satisfaction levels; however, the large volume of data requires a systematic and accurate analytical approach. This study aims to analyze user review sentiments of the Shopee application using three machine learning algorithms: Random Forest, Naïve Bayes, and Support Vector Machine (SVM), as well as comparing the accuracy of these three algorithms. This research utilized 1000 reviews collected through web scraping from the Play Store, which were categorized into three classifications: positive, neutral, and negative sentiments. The analysis process encompassed pre-processing stages, feature extraction using TF-IDF, and classification using Random Forest, Naïve Bayes, and Support Vector Machine algorithms. The results demonstrated that the Random Forest algorithm achieved the highest accuracy at 96.19%, followed by Support Vector Machine with 95.71% accuracy, and Naïve Bayes with 84.76% accuracy. This research highlights the effectiveness of Random Forest and SVM in classifying user review sentiments towards the Shopee application.

This study explores the perspectives of literature students on utilizing ChatGPT, an AI-powered tool, in creative writing. Focusing on 30 English literature students from Universitas Negeri Medan, the research examines the effectiveness of ChatGPT in fostering creativity, its perceived benefits and challenges, and its role in the writing process. Data were collected via questionnaires and analyzed thematically. The findings reveal that ChatGPT significantly enhances creativity, with students rating it highly for its ability to inspire ideas and improve the writing process. The tool was praised for accelerating the writing process and improving writing confidence. However, challenges such as the need for revisions and limitations in contextual understanding were also noted. Despite these drawbacks, the overall sentiment remained positive, with many participants feeling that the benefits of using ChatGPT outweighed its limitations. This study underscores ChatGPT’s potential as a valuable tool in literary education, supporting students' creative autonomy and enriching their writing experiences. This is evidenced by data highlighting the effectiveness of ChatGPT in speeding up the writing process (60%), increasing creativity (46.67%), and increasing confidence in writing (40%), despite challenges such as unmet expectations (46.67%) and requires a lot for revision (33.33%).    

Sentiment Analysis is a technique for extracting text data to obtain information about positive, neutral or negative sentiments. The purpose of sentiment analysis is given by internet users on social media to provide a personal assessment or opinion. Paga Lewu Shop that often gets user sentiment through social media is Paga Lewu Shop. The existence of consumer opinion sentiments about Paga Lewu Shop can be analyzed and utilized to obtain useful information for other customers and the Paga Lewu Shop. By using the Text Mining technique classification method, a sentiment will be known as positive, neutral or negative. One of the algorithms widely used in sentiment analysis is the Naïve Bayes classification method. This study uses the Naïve Bayes Classifier (NBC) method with tf-idf weighting accompanied by the addition of an emotion icon conversion feature (emoticon) to determine the existing sentiment class from tweets about the Paga Lewu Shop. The results of the study show that the Naïve Bayes method without additional features is able to classify sentiment with an accuracy value of 96.44%, while if the tf-idf weighting feature is added along with the conversion of emotion icons, the accuracy value can be increased to 98%.

The rapid development of information and communication technology allows society to access various information needed in daily life. The Law of the Republic of Indonesia Number 23 of 2006 concerning population administration serves as an important element in population management. Population documents are issued by official institutions and have legal legitimacy as valid evidence. The method used in this research regarding public sentiment towards e-ID card services is the survey method, which aims to collect data from a large population using a smaller sample. The steps or processes in this research using the SVM method consist of case folding, cleaning, tokenizing, normalization, stopword removal, and stemming. Based on the classification of 150 test data using SVM, the number of positive sentiments recorded is 110 opinions, while negative sentiments recorded are 40 opinions.

Amid rising challenges of intolerance, social polarization, and the strengthening of exclusive religious sentiments, interreligious dialogue has become a strategic instrument for nurturing diversity and reinforcing national harmony. This study examines how interreligious dialogue contributes to strengthening the pillars of harmony as outlined in the concept of the Tri Kerukunan Umat Beragama (Three Pillars of Religious Harmony). It also explores the extent to which this concept remains relevant and effective in addressing the current dynamics of religious diversity in Indonesia. The purpose of this research is to analyze the role of dialogue as a mechanism of cross-faith communication in fostering mutual understanding, cultivating respect for differences, and promoting harmonious social relations within a pluralistic society. Using a qualitative approach through a comprehensive literature review, this study investigates various scholarly works related to interfaith dialogue, religious harmony, and Indonesia’s socio-religious landscape.The findings indicate that interreligious dialogue functions not only as a means of resolving differences and preventing conflict but also as a transformative space that strengthens trust, cooperation, and solidarity across religious communities. Thus, dialogue remains a crucial pillar in achieving sustainable and inclusive religious harmony in Indonesia, although it requires

It has been demonstrated that using computers greatly improves our ability to perform our duties. Information services are vital because, while employee performance may still be predicted manually, the process takes a long time. Data mining technologies, on the other hand, make it easier to anticipate employee success for loyal employees. Employee performance evaluation criteria are necessary in order to increase the accuracy of the assessment results, as Toko Merpati Simpang's employee performance assessments cannot be conducted carelessly. Employee performance has to be analyzed and categorized because up until now, manual employee performance evaluations have only used subjective criteria. The C4.5 Algorithm data mining approach is used in this evaluation of employee performance. The degree of accuracy will be ascertained by comparing these two approaches. Positive and negative emotions are the two categories of sentiment. The aim of this study is to ascertain the degree of accuracy of the comparison between the two tested techniques and to offer information on the quality of one of Toko Merpati Simpang's employee performance assessments using visitor sentiment. The test results will be evaluated using the Rapidminer tool to demonstrate the degree of accuracy for both testing approaches.   Keywords: , 

State regulations state that village funds come from the State Budget (APBN) and are used to support governance, development, development, and social activities as well as community empowerment. It is hoped that the existence of village funds will increase the sources of income for each village, and the addition of village income by the government will improve public service facilities.1. As a result, a sentiment analysis of village officials will be carried out in this study. The Naive Bayes approach will be used to classify public sentiment as part of this investigation. We will evaluate two methods to see which produces more accurate results. In addition, the village government's function as the most important social institution in the community is essential for setting standards, facilitating socialization, and allocating resources. Furthermore, some Eweta community members have not received assistance, which could cause social rivalry among neighbors. Through sentiment categorization, responses will be categorized as either positive or negative. Based on feedback from visitors, this study attempts to assess the validity of the two approaches put to the test and offer insights into the caliber of services rendered by the village administration.

The North Sumatra Aceh National Sports Week (PON) which will be held in September 2024 in Papu, North Sumatra has drawn many pros and cons from the public. This topic allows the public to provide criticism, suggestions and opinions regarding the 2024 North Sumatra Aceh PON. Instagram is a popular social media for conveying public opinion. The sentiment analysis process can find and resolve problems based on public opinion on social media such as Instagram. The classification method used in this research is the Naïve Bayes Classifier. Datasets can be obtained from the data crawling process using the Google Chrome extension: IGCommentExport. The data is labeled positive, neutral, or negative. The results of the labeling process showed 770 negative data, 256 neutral data and 920 positive data. Then pre-processing is carried out on the data that has been previously labeled, and a word weighting process is also carried out using TF-IDF. After that, modeling was carried out using the Naïve Bayes Classifier and the final process was evaluation-testing. The high accuracy results from the fourth experiment which compared 90% of the training data with 10% of the testing data resulted in an accuracy of 75%. Meanwhile, the sentiment test results show that positive sentiment is more numerous than negative sentiment and neutral sentiment.

The policy for using the MyPertamina application issued does not rule out the possibility of differences of opinion due to changes in the policy. There are many positive, neutral, and negative responses to the MyPertamina application implementation policy. To see the public's reaction to the MyPertamina application implementation policy, it can be seen through various media, including social media. Twitter is a social network that is widely used by people in Indonesia. The number of Twitter users in Indonesia reached 18.45 million in 2022, making Indonesia the fifth largest Twitter user country in the world. Researchers conducted a sentiment analysis of the search results for tweets containing the keyword "MyPertamina" using the support vector machine algorithm. 382 tweet data were obtained and classified using the support vector machine algorithm. Support vector machine is a supervised learning algorithm for data classification. SVM is very fast and effective in solving text data problems. Text data is suitable for classification with the SVM algorithm because the basic nature of text tends to be high-dimensional. Of the 382 data analyzed, the support vector machine classification using the RBF kernel with parameter C=2 gave the highest accuracy value of 80.51%, precision value of 81%, recall value of 81%, and F1 score value of 80%.

  Abstract. This study aims to analyze the effect of Market Volatility, Fundamental Factors, and Investor Sentiment on Stock Investment Decisions of Management Study Program Students, Faculty of Economics, Slamet Riyadi University, Surakarta. The research sample was taken using the Slovin technique, which resulted in a sample size of 78 students. The data collection technique was carried out through a questionnaire distributed to students who met the criteria. The research instrument test used validity and reliability tests. The classical assumption test used multicollinearity test, autocorrelation test, heteroscedasticity test, and normality test. The data analysis technique used descriptive analysis, multiple linear regression analysis, t-test, F-test and coefficient of determination (R²). The conclusion obtained from this study is that Market Volatility, Fundamental Factors, and Investor Sentiment have a significant influence on students' Stock Investment Decisions. These findings indicate that a deep understanding of the capital market and the factors that influence it is very important in making the right investment decisions. The results show that the coefficient of determination (adjusted r2) for this model is 0.700, meaning that the contribution of the independent influence of market volatility, fundamental factors and investor sentiment to stock investment decisions is 70%. The rest (100% - 70%) = 30% is explained by other variables outside the model such as gender, age, overconfidence, market awareness, income, etc.

The business of selling coffee in the world is growing more easily and quickly with the provision of a place which is usually called a cafe or shop. One of them is the Titik Hitam Nalar cafe in Jakarta, but the ordering process with customers takes longer to wait for orders and customer payments that are not neatly arranged create conditions that’s are not conducive, resulting in continuous negative criticism. Therefore, an application is needed that helps employees work in every part, both in terms of ordering, manufacturing, and payments, using the FIFO algorithm which focuses on which services are prioritized first in order to reduce negative sentiment from customers. The Android-based application was created by involving every design phase, making it easier for customers and employees to provide a service facility that works well and is suitable for use.  

n today's digital era, customer reviews play a crucial role in purchasing decisions, but the large volume of reviews makes manual analysis difficult. Thus, a fast and accurate sentiment analysis method using Natural Language Processing (NLP) is needed. This research aims to analyze product reviews for the ZALIKA STORE 88 on Shopee using NLP. It involves preprocessing reviews, applying NLP techniques like tokenization, stemming, and lexical analysis, and automatically classifying sentiments. The analysis of ZALIKA STORE 88's reviews reveals mostly positive sentiments, with some negative and neutral reviews. The sentiment analysis achieved an 87% accuracy rate. This research is intended to help ZALIKA STORE 88 make informed decisions based on customer reviews.

Pada tahun 2024, Indonesia akan menyelenggarakan pemilihan umum serentak yang meliputi pemilihan presiden dan pemilihan wakil rakyat di seluruh Indonesia. Masyarakat menanggapi kejadian ini dengan perasaan campur aduk, membagikan pemikirannya di situs media sosial seperti Twitter. Penelitian analisis sentimen calon presiden Indonesia tahun 2024 dilakukan terkait peristiwa ini. Sebanyak 1458 tweet digunakan dalam penelitian ini. Dengan 40,31% responden menyatakan sikap positif dan 43,46% menyatakan sentimen negatif, temuan analisis menunjukkan keseimbangan antara kedua sentimen tersebut. Menggunakan frasa "calon presiden," program Python di situs web Google Colab mengambil data twitter. Pendekatan K-Nearest Neighbor digunakan dalam proses klasifikasi. Selain itu data latih dibagi 6 : 4. 40% data uji dan 60% data latih. Nilai evaluasi yang diperoleh dari pengujian model dengan teknik K-Nearest Neighbor adalah akurasi sebesar 90,95%, presisi sebesar 62,17%, recall sebesar 62,33%, dan F-Measure sebesar 61,87%.

According to the Jakarta Air Quality Index (AQI US) 12 July 2023, 200 indicates unhealthy air quality with an index value between 151 and 200. This figure even shows that Jakarta is currently the second most polluted city in Southeast Asia. (CNN Indonesia., 2023). This incident gave rise to responses from the public which were expressed via social media Twitter. From this incident, sentiment analysis was carried out regarding Jakarta's air quality. The amount of data used for this research was 500 tweet data. The results of the positive and negative sentiment analysis show that negative sentiment appears more frequently than positive sentiment with a percentage of 7% positive sentiment and 14% negative sentiment, by using the Rstudio application. This method uses the naïve Bayes classifier. Data division in the dataset with training data 1:499 and test data 1:476. It was found that the results of the Accuracy, Precision, Recall, and F1-Score values were Accuracy 87.50%, Precision 87.50 Recall 93.33%, and F1-Score 82.35%.