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User reviews on the Shopee e-commerce platform represent an important source of information for understanding consumer perceptions of products and services. Sentiment analysis is commonly applied to classify user opinions into positive, neutral, and negative sentiment categories based on textual data. This study aims to analyze the performance of the Long Short-Term Memory (LSTM) method in sentiment classification of Shopee user reviews. The dataset used in this study consists of Indonesian-language user reviews that have undergone preprocessing stages, including case folding, text cleaning, tokenization, and stopword removal. The LSTM model was trained using preprocessed text represented as word sequences. Model performance was evaluated using overall accuracy and class-wise classification results. The experimental results indicate that the LSTM method achieved an overall accuracy of 87.62%. In addition, the classification performance for the positive sentiment class reached 95.27%, the neutral class achieved 4.96%, and the negative class reached 74.26%. These results demonstrate that the LSTM method performs well in classifying sentiment in Shopee user reviews, particularly for positive sentiment. This study is expected to provide insights and references for the application of deep learning methods in sentiment analysis of Indonesian e-commerce review data.

This research aims to develop a multiclass flower image classification system using the Convolutional Neural Network (CNN) algorithm with the EfficientNet architecture. The main problem addressed is the difficulty of manual identification of flower species that share high visual similarity. The research stages include collecting 17,299 flower images across 19 classes, performing data preprocessing such as image resizing, pixel normalization, and augmentation, followed by model training using the EfficientNet transfer learning approach. The model was trained for 10 epochs with an 80:20 training-validation data split. The evaluation results show that the model achieved a validation accuracy of 98.05% with a loss value of 0.0968, and an average precision, recall, and F1-score of 0.98. The trained model was then implemented into a web-based application built using the Next.js framework, enabling users to upload flower images and obtain real-time classification results via the Hugging Face API. The system successfully identified flower species with a confidence level of 99.87%. These findings demonstrate that combining a modern CNN architecture with transfer learning provides efficient and highly accurate flower classification performance, which can be effectively implemented for educational and digital conservation purposes.

Detection and identification of plant diseases is critical to the success and efficiency of agricultural production. Plant disease outbreaks are becoming more frequent throughout the world, and the presence of these diseases in cultivated plants has a significant impact on productivity. Therefore, researchers are focusing on developing effective and reliable plant disease detection methods. Thus, farmers can take advantage of early detection of this disease to minimize future losses. This article discusses machine learning approaches as well as decision trees, K-nearest neighbors, naive Bayes, support vector machines (SVM), and random forests for detecting coffee leaf diseases using leaf images. The above-mentioned classifications were researched and compared to determine the most suitable plant disease prediction model with the highest accuracy. Compared with other classification algorithms, the SVM algorithm achieves the highest accuracy of 99.75%. All the models trained above will be used by farmers to quickly identify and classify new diseases in images as a prevention strategy. As a preventive measure, farmers can detect and classify new diseases in images early.

The performance of the Indonesian National Team (Timnas) in the 2026 World Cup qualifications has triggered massive and diverse responses on social media, particularly on platform X. This study aims to identify and classify public sentiment regarding Timnas Indonesia's performance into positive, negative, and neutral categories using a data mining approach. Text data was processed through pre-processing stages, term weighting using TF-IDF, and the application of the Synthetic Minority Over-sampling Technique (SMOTE) to address significant class distribution imbalance. The classification algorithm employed was Multinomial Naïve Bayes. Model performance evaluation was conducted by comparing two training-testing data split scenarios: 90:10 and 80:20 ratios. The results indicate that public opinion is dominated by negative sentiment at 73.2%, reflecting public disappointment. In terms of model performance, the 90:10 ratio scenario yielded the best accuracy of 80%, outperforming the 80:20 ratio which recorded an accuracy of 75%. These findings demonstrate that combining Multinomial Naïve Bayes with the SMOTE technique is effective in handling imbalanced text data and is capable of accurately mapping public perception.

The development of social media platforms like TikTok has created new spaces for digital economic activities, including the practive of thrifting, which has now become a trend among the public. However, government policies that block these activities have sparked various public reactions. This study aims to analyze public sentiment regarding the issue of thrifting bans on the TikTok platform using the Bidirectional Long Short-Term Memory (Bi-LSTM) method. This method was chosen because it can understand text context from both directions, allowing it to capture deeper semantic meaning. The dataset consist of 4,000 TikTok user comments collected through a crawling process. The research stages include data preprocessing, sentiment labeling, splitting training and test data, training the Bi-LSTM model, and evaluating performance using accuracy, precision, recall, and F1-score metrics. The research results show that the Bi-LSTM model achieved an accuracy of 86.15%, with stable classification performance and minimal error rate. These findings indicate that Bi-LSTM is effective for sentiment analysis of public opinions on Indonesian language social media, particularly on context specific policy issues. Further development can be carried out by adding pre-trained embeddings or attention mechanisms to improve the model’s performance.

Manual quality assessment of Platelet Concentrate (TC) is highly subjective and inconsistent, necessitating an objective, automated classification system. This study aims to develop a computationally efficient, low-cost model for TC quality classification using Histogram Features extracted from grayscale images combined with the K-Nearest Neighbor (KNN) algorithm. The methodology employed critical preprocessing steps, including StandardScaler for normalization and SMOTE for balancing the training data, followed by optimization across K=1 to K=30. The optimal model achieved a maximum accuracy of 69.23% at K=6, with an F1-Score of 71.43%, confirming robust performance on the imbalanced testing set. The results validate the effectiveness of the Histogram-KNN approach as a consistent and reliable decision support system for rapid TC quality screening in resource-limited settings.

Tourism is one of the sectors that plays an important role in boosting economic growth through travel activities and destination exploration. Tourists' preferences for nature-based tourism options, such as mountain hiking or beach tourism, are influenced by various factors, ranging from personal experiences and recreational interests to social characteristics. Therefore, a technology-based approach is needed to predict destination choice tendencies more accurately. As artificial intelligence technology develops, deep learning methods have been widely used in classification processes due to their ability to process large amounts of data and recognize complex patterns. In this study, a Multilayer Perceptron (MLP) model is used to classify tourists' preferences between mountain or beach destinations based on a survey dataset. The research stages include data processing, data splitting using a train-test split, model training, and performance evaluation using accuracy, precision, recall, and F1-score. The test results show that the MLP model is capable of achieving an accuracy rate of 99%, confirming that deep learning methods are effective in automatically mapping tourism preference trends. This research is expected to serve as a basis for the development of more personalized travel destination recommendation systems, as well as to support tourism management in formulating targeted promotional strategies.

Waste has become a serious environmental problem in Indonesia, which continues to increase along with population growth. The issue of waste management poses serious challenges for the environment, especially in the process of separating organic and inorganic waste. In the field of computer vision, recognising the type and shape of waste through camera images remains a challenge due to variations in shape, colour, and complex lighting conditions. Therefore, this problem utilises Deep Learning technology, which is expected to be widely applied in Indonesia, especially in large cities with high waste volumes. This study aims to distinguish between organic and inorganic waste using the Convolutional Neural Network (CNN) method based on digital images. The developed CNN model was trained to recognise the visual patterns of each type of waste and tested to measure its accuracy. The test results show that the CNN-based classification system is capable of achieving an accuracy rate of 95%, thus proving the effectiveness of this method in supporting artificial intelligence-based automatic waste sorting systems.

Hijaiyah letters have varying shapes, and some of them are very similar, often causing errors in the manual character recognition process. This study aims to classify Hijaiyah letters based on digital images using the Convolutional Neural Network (CNN) method. This method was used in this study with a dataset consisting of 28 letter classes and a total of 4,480 images obtained from various public sources and private data. All images underwent a preprocessing stage that included labeling, resizing, normalization, and augmentation, then were divided into three parts, namely training data, validation data, and test data with a ratio of 70:20:10. The training process was carried out using the Python programming language with the help of the TensorFlow and Keras libraries on the Google Colab platform. The test results showed that the CNN model achieved an accuracy of 97.10%, with an average precision, recall, and F1-score of 0.97, respectively. Classification errors only occurred in letters that had similar shapes, such as Syin and Sin. Based on these results, the CNN method proved to be effective, efficient, and accurate in recognizing Hijaiyah letter image patterns, so it can be used as a basis for developing classification models with higher accuracy in the future.

The rapid expansion of e-commerce in Indonesia has resulted in a significant rise in the number of customer reviews, which serve as a valuable source of insight for understanding consumer satisfaction. This study aims to classify or identify sentiments from product reviews on the Tokopedia platform into three categories, using the Support Vector Machine algorithm. The classification method data were ethically collected through web scraping and include review text, ratings, and the number of “likes.”  The preprocessing stage involved several NLP techniques such as pre-procesesing data representation was generated using the Term Frequency–Inverse Document Frequency method, while the issue of class imbalance was addressed using the Synthetic Minority Over-sampling Technique.  Based on the test results, the SVM model achieved an accuracy of 79.48% on the test data using a linear kernel, showing the best performance in classifying positive sentiments. However, the classification of neutral and negative sentiments still requires improvement. This study demonstrates that the combination of the TF-IDF method, additional numerical features, and data balancing techniques can produce an an efficient sentiment analysis model within the e-commerce domain.

Penelitian ini bertujuan untuk mengembangkan sistem pendukung keputusan yang dapat membantu tim Marketing Officer (MO) PT. Alvarel Technology Innovation dalam menentukan status pelanggan secara objektif dan terstruktur. Sistem ini dirancang menggunakan kombinasi metode Analytical Hierarchy Process (AHP) dan Weighted Sum Model (WSM). Metode AHP digunakan untuk menentukan bobot kriteria yang meliputi Potensial Pasar, Urgensi, Finansial, serta Hubungan dan Reputasi, dengan memastikan konsistensi matriks perbandingan berpasangan. Hasil pembobotan kemudian digunakan dalam metode WSM untuk melakukan perhitungan skor total pelanggan dan menyusun pemeringkatan status berdasarkan nilai tertinggi hingga terendah. Data penelitian diperoleh dari catatan internal perusahaan dan wawancara dengan Marketing Officer, dengan jumlah sampel 30 pelanggan. Hasil pengujian menunjukkan bahwa sistem dapat menghasilkan peringkat status pelanggan dalam lima kategori, yaitu potensial, prospek, pending, pasif, dan skip. Temuan utama memperlihatkan bahwa kategori prospek memperoleh skor tertinggi dan menjadi prioritas tindak lanjut. Dengan demikian, sistem pendukung keputusan berbasis AHP–WSM ini mampu mengurangi subjektivitas, meningkatkan efisiensi, serta memberikan rekomendasi yang lebih akurat dan terukur untuk mendukung pengambilan keputusan strategis perusahaan dalam pengelolaan pelanggan.

Peningkatan transaksi dalam platform e-commerce seperti Shopee menuntut adanya sistem prediksi status pesanan yang akurat, guna mengoptimalkan pelayanan dan mengurangi pembatalan maupun keterlambatan pengiriman. Penelitian ini bertujuan membangun model klasifikasi status pesanan (selesai atau batal) pada toko Stuftech.Id menggunakan algoritma C4.5. Data yang digunakan merupakan transaksi pesanan mencakup metode pembayaran, kategori wilayah pengiriman, dan ongkos kirim. Proses klasifikasi dilakukan menggunakan RapidMiner dengan tahapan preprocessing, pembangunan decision tree, dan evaluasi model. Hasil analisis menunjukkan bahwa atribut “Kategori Pulau” memiliki nilai gain tertinggi sehingga dipilih sebagai node akar. Model yang dibentuk menghasilkan akurasi sebesar 86%, dengan recall 100% untuk pesanan selesai namun hanya 6,67% untuk pesanan batal. Temuan ini mengindikasikan bahwa algoritma C4.5 efektif dalam memprediksi pesanan yang berhasil, namun perlu peningkatan dalam mendeteksi potensi pembatalan. Implementasi model ini dapat membantu pelaku usaha dalam mengambil keputusan operasional secara proaktif.

State Senior High School 7 Surakarta is one of the public schools located in Surakarta City. The library activities, including member data management, book processing, and book borrowing and returning, are still conducted manually using physical logbooks. This manual process is considered inefficient and prone to errors. The purpose of this study is to develop a book borrowing system at State Senior High School 7 Surakarta that serves as a tool to assist officers in recording, loans, and returning books. This system is designed using the CodeIgniter framework to support WEB displays, programming in PHP, and using MySQL for database management. This system is created using the System Development Life Cycle (SDLC) method with a waterfall model that includes the stages of analysis, design, implementation, testing, and maintenance. System testing was conducted using Black-Box Testing and the System Usability Scale (SUS). The Black-Box Testing results showed that all features and functions operated correctly. The SUS evaluation produced a score of 75.68%, indicating that users generally agreed with the implementation of the system, which falls under the "acceptable" classification.

Kecelakaan lalu lintas di Kabupaten Brebes merupakan masalah kritis karena tingginya frekuensi insiden yang terjadi di wilayah tersebut. Penelitian ini bertujuan untuk menentukan area yang rentan terhadap kecelakaan dengan menggunakan algoritma K-Means Clustering , yang mendukung proses pengambilan keputusan berbasis data. Isu utama yang dieksplorasi dalam penelitian ini adalah bagaimana algoritma K-Means dapat diimplementasikan untuk mengelompokkan zona rawan kecelakaan dan meningkatkan kesadaran masyarakat terhadap keselamatan jalan. Metodologi yang digunakan meliputi pengumpulan data melalui tinjauan pustaka, observasi langsung, dan wawancara, yang dilanjutkan dengan penggunaan algoritma K-Means untuk mengklasifikasikan data kecelakaan berdasarkan jumlah kejadian, korban jiwa, dan cedera. Temuan menunjukkan bahwa algoritma K-Means secara efektif mengelompokkan lokasi rawan kecelakaan ke dalam tiga tingkat risiko yang berbeda: tinggi, sedang, dan rendah. Dengan demikian, informasi yang terklasifikasi ini dapat membantu otoritas terkait dalam meningkatkan langkah-langkah keselamatan lalu lintas dan mengedukasi masyarakat tentang area berisiko tinggi. Hasil penelitian ini diharapkan dapat berkontribusi pada pengembangan kebijakan keselamatan lalu lintas yang lebih terinformasi dan strategis di Kabupaten Brebes.

The growing demand for affordable transportation has significantly expanded the used car market. This study aims to develop optimal sales strategies by analyzing seller types and vehicle categories through exploratory data analysis and linear regression techniques. Using a cleaned public dataset of over 150,000 used car listings in Germany, key variables such as registration year, mileage, vehicle type, and seller type were examined. Results indicate that individual sellers dominate the market, although dealers set higher and more stable prices. SUVs and limousines typically hold higher market value. The linear regression model achieved an R-squared value of 0.34, suggesting that registration year and mileage account for 34% of the price variance. These findings offer practical insights for stakeholders in the used car business to tailor pricing strategies based on vehicle attributes and seller classification

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%.

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 insurance business within an insurance company offers insurance products owned by the insurance company. In every insurance product there is a premium payment and the premium is the income of an insurance company at the rate of the amount insured. The problem that PT BNI Life Insurance has is that there are many stops in premium payments such as policy redemptions due to errors in the benefits received or incorrect selection of the insurance product, this can reduce the achievement of targets for an insurance company. The aim of this research is to find out the best classification algorithm compared between K-Nearest Neighbor and Naive Bayes to predict the type of insurance product that customers will choose. In this research, data mining methods are applied to compare two different methods, namely the K-Nearest Neighbor method and the Naïve Bayes method. The level of accuracy results for the K-Nearest Neighbor method is 80% and the Naïve Bayes method is 70.53%, which means that the K-Nearest Neighbor method is the best method to apply to an insurance product classification system based on the demographics of prospective customers.

The global economic recession is a global economic downturn that affects the domestic economies of countries in the world. The stronger the economic dependence of one country on the global economy, the faster a recession will occur in that country. In 2020 the country of Indonesia and even the world are exposed to the COVID-19 virus which has an impact on the country's economic growth, even the world economy. This is the trigger for an economic recession. This has led to many different public perspectives on the occurrence of a global economic recession whose opinions or reactions are expressed on social media Youtube. The data was obtained by crawling techniques from social media Youtube with a total of 500 comments used. The data is then labeled (class) with a lexicon-based method with an Indonesian language dictionary. From the labeling results, it was obtained 185 positive labeled data (37%) and 315 negative opinions (63%). The data preprocessing stage is carried out in preparation for the data to be processed for sentiment analysis. Of the many opinions obtained, an analysis of public sentiment regarding the 2023 global economic recession will be carried out using the Naïve Bayes classification algorithm. This study also applied the TF-IDF word weighting method with the n-gram feature used, namely bigram (n=1). The system will be evaluated using a confusion matrix. The implementation results show a prediction model with a total of 500 opinion data with a comparison of training data and test data of 9:1, producing an accuracy value of 84.00%, a precision value of 75.00%, a recall of 30.00%, and an f1-score of 42.86%. The performance of the system model built in this study can be said to be good.

Mushrooms are very diverse with characteristics of each type, there are 1,433,800 types of mushrooms that have not been recognized. In this study, researchers used the Neural Network and Deep Learning Inception V3 methods as a feature extraction process in images to classify mushroom images based on genus with the Orange Data Mining application. There are 9 genera of mushrooms used in this study, namely Agaricus, Amanita, Boletus, Cortinarius, Entoloma, Hygrocybe, Lactarius, Russula, and Suillus. The total dataset used is 2,700, with 300 images for each genus. The test uses the cross-validation method which is applied to the confusion matrix to get precision, recall, F1-score, and accuracy values. In this study, the final classification results were obtained with an accuracy of 82.5% and the genus Boletus mushroom obtained the best results with an accuracy of 98.9%.