A COMPARATIVE STUDY OF SENTIMENT CLASSIFICATION: TRADITIONAL NLP VS. NEURAL NETWORK APPROACHES

The current research compares traditional natural language processing methods, such as Naive Bayes and Support Vector Machine, to neural network approaches, particularly Multi-Layer Perceptron, to classify positive and negative sentiments regarding company customer service. This research is motivated by the need to understand the effectiveness of these two approaches in analyzing and classifying sentiment in customer reviews, a crucial aspect of enhancing the quality of customer service. The author evaluated accuracy, speed, and adaptability to complex and diverse review content using a dataset containing various business customer reviews. The findings of this study indicate that neural network approaches, particularly Multi-Layer Perceptron, tend to provide superior performance in classifying customer sentiment with greater precision, albeit at a higher computational cost. Traditional methods such as Naive Bayes and Support Vector Machine still apply in situations with limited resources. The results of this research provide valuable guidance for companies in selecting an appropriate approach to analyzing customer sentiment, with the potential to increase understanding of customer views and improve overall customer service. Nave Bayes achieves 68.75% accuracy, Support Vector Machine achieves 87.5% accuracy, and Multi-Layer Perceptron achieves 100% accuracy

Analisis dan Implementasi Metode Single Layer Perceptron pada Data Mining Penerimaan Mahasiswa Baru Jalur JPPAN

ABSTRAKSI: Pada tugas akhir ini diimplementasikan metode Single Layer Perceptron pada data mining penerimaan mahasiswa baru jalur JPPAN. Metode percepteron adalah algoritma klasifikasi yang membuat prediksi berdasarkan linear predictor function yang menggabungkan satu set bobot dengan feature vector agar mampu menggambarkan masukan yang diberikan. Metode single layer perceptron menggunakan dua node layer yang komponennya terdiri dari nilai sinaptik dan threshold, kedua komponen ini merupakan komponen utama dari metode perceptron yang cukup baik dalam melakaukan penyelesaian solusi suatu kasus. Pada studi kasus penerimaan mahasiswa baru jalur JPPAN terdapat beberapa ruang pengklasifikasian yang cukup besar, sehingga diperlukan metode klasifikasi dengan menggunakan teknik berupa pemprediksian untuk pencarian solusi. Metode single layer perceptron merupakan salah satu dari metode klasifikasi. Metode single layer perceptron bekerja dengan cara menelusuri 2 node yang berisi bobot bobot nilai yang diberikan batasan secara jelas, dan nilai parameter yang sesuai, sehingga metode single layer perceptron dapat menghasilkan keputusan penerimaan mahasiswa baru jalur JPPAN yang sesuai dengan standar perguruan tinggi.Kata Kunci : Single Layer Perceptron, classification data miningABSTRACT: At this final project implement single layer perceptron method in the case of scholar admission JPPAN. Single layer perceptron method is classification methods that make predictions based on linear predictor function for combines a set of weights with a feature vector that is able to describe the given input. Single layer perceptron method using two layer node component consist of synaptic and threshold value, the second component that the component of which is quite good perceptron method in the resolution of a case solutions. In the case study of new admission there are some considerable space classification. So that requiring classification method using a prediction technique to search the solution. Single layer perceptron method works by tracking two nodes that contains of weight value assigned of definited limit. And approtiate parameter values, so that mehod can be produce a single layer perceptron scholar admission JPPAN path to standart college.Keyword: Single Layer Perceptron, classification data mining

Characterisation of hourly temperature of a thin-film module from weather conditions by artificial intelligence techniques

The aim of this paper is the use and validation of artificial intelligence techniques to predict the temperature of a thin-film module based on tandem CdS/CdTe technology. The cell temperature of a module is usually tens of degrees above the air temperature, so that the greater the intensity of the received radiation, the greater the difference between these two temperature values. In practice, directly measuring the cell temperature is very complicated, since cells are encapsulated between insulation materials that do not allow direct access. In the literature there are several equations to obtain the cell temperature from the external conditions. However, these models use some coefficients which do not appear in the specification sheets and must be estimated experimentally. In this work, a support vector machine and a multilayer perceptron are proposed as alternative models to predict the cell temperature of a module. These methods allow us to achieve an automatic way to learn only from the underlying information extracted from the measured data, without proposing any previous equation. These proposed methods were validated through an experimental campaign of measurements. From the obtained results, it can be concluded that the proposed models can predict the cell temperature of a module with an error less than 1.5 °C.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec