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PeduliLindungi is an application from the Government of Indonesia that was made in response to the COVID-19 pandemic. Since its initial release in 2020, this application has received many updates with the goal of improving its overall performance. One of the basics of updating applications is to process the reviews given by users at the Google Play Store using sentiment analysis. The methods used this time are Naive Bayes Classifier (NBC) and Support Vector Machine (SVM). The sample data used were 300 reviews with positive feedback and 300 reviews with negative feedback, for a total of 600 user reviews. The results of the NBC algorithm calculations produce an accuracy of 76%, a precision of 76%, a recall of 82%, and an f1-score of 79%. As for the SVM algorithm, it produces an accuracy rate of 80%, a precision of 83%, a recall of 80%, and an f1-score of 81%.

The Mediterranean region, in particular Algeria, is experiencing serious challenges due to the increased opportunities for forest fires. Since the mid-1970s, there has been a 50% reduction in rainfall over northwestern Algeria, making northern Algeria particularly vulnerable to the problem for many years. More than 37,000 hectares of sensitive forest are lost every year due to this extreme drought. The findings of this study, which assessed the hazard of forest fires from 2006 to 2019, agree with those of Bentchakal,Chibane (2022), who examined the problems caused by forest fires in the region. The aim of this investigation is to gain a better understanding of the problems caused by local forest fires and to use that expertise to provide insight for the authors and readers of this report. The report was written by presenting the findings of observations made using the Rapid Miner classification approach, which includes the categorization of areas affected by forest fires. Data is collected using a variety of algorithmic techniques, including Naive Bayes, KNN, and decision trees, which are used as tests of data to identify the most accurate results. The findings show that the Decision Tree technique has the best accuracy of 86.49% and provides a thorough explanation of the data.

Technological developments today make it easy for people to use social media as a means of expressing opinions, including Twitter. The case study taken by the researcher is the sentiment towards the performance of the mayor of Medan. The case was taken because it was widely discussed by Indonesian people, especially the city of Medan on Twitter social media. One of the uses of this research is to find out the trend of Twitter user comments on the performance of the mayor of Medan by conducting a sentiment analysis. Sentiment will be classified as positive, negative and neutral. The algorithm used in sentiment analysis is Naïve Bayes. The stages in conducting sentiment analysis in this study are data preprocessing, data processing, classification, and evaluation. The results of this study are using the SMOTE method, the training and testing ratio is 80:20 because it has the highest accuracy, which is 78% compared to other ratios. The prediction results resulting from the classification turned out to be more dominant towards neutral labels. In addition to classifying for sentiment analysis, this study also measures the performance of the model created. The results showed that the Naïve Bayes algorithm has a precision value of 78%, a recall of 78%, and an f1-score of 77%.

The rapid growth of e-commerce in Indonesia has generated a massive and continuous volume of product reviews. This user-generated content is vital for business intelligence, yet its sheer scale makes manual analysis inefficient, subjective, and practically impossible. Automated sentiment analysis is therefore crucial for businesses to efficiently understand customer feedback and market perception. This research addresses this gap by implementing the Naïve Bayes Classifier (NBC) algorithm to automatically classify the sentiment of Indonesian-language e-commerce product reviews. This study utilized a dataset of 2,000 reviews collected from a major e-commerce platform's "Electronics" category. The data underwent critical text preprocessing stages (case folding, tokenizing, stopword removal, and stemming using the Sastrawi library) to handle the complexities of informal Indonesian text. The dataset was split using an 80/20 ratio, resulting in 1,600 training reviews and 400 testing reviews. Model performance was then evaluated using a Confusion Matrix, focusing on the key metrics of Accuracy, Precision, and Recall. The test results showed excellent performance, achieving an Accuracy of 90.00%, Precision of 91.93%, and Recall of 95.00%. These results demonstrate that the Naïve Bayes algorithm, when supported by robust preprocessing, is a highly effective, reliable, and computationally efficient method for this task, providing a valuable tool for e-commerce stakeholders.

Organisasi Kesehatan Dunia (WHO) secara resmi menyebut virus Covid-19 sebagai pandemi global, oleh karena itu semua negara di dunia berusaha meminimalkan dampak yang ditimbulkan oleh virus tersebut, yaitu dengan mengembangkan vaksin. Dalam konteks pandemi Covid-19, pemerintah Indonesia juga meminta dan mendorong masyarakat untuk turut serta mendukung vaksinasi, namun upaya tersebut sebenarnya memiliki kelebihan dan kekurangan, sehingga banyak masyarakat yang mengutarakan pendapatnya di jejaring sosial salah satunya Twitter. Penelitian ini bertujuan untuk mengetahui hasil penerapan analisis sentimen dan mengukur performansi algoritma Naïve Bayes Classifier (NBC) dan Support Vector Machine (SVM) terhadap data vaksin Covid-19 dengan cara mengklasifikasikan data tersebut ke dalam kelas positif dan negatif. Data tweet yang didapat kemudian dilakukan text preprocessing untuk mengoptimalkan pengolahan data. Terdapat 4 tahapan text preprocessing antara lain Case Folding, Tokenizing, Filtering, dan Stemming. Penelitian ini mengkaji kinerja Naïve Bayes Classifier (NBC) dan Support Vector Machine (SVM) dengan menambahkan teknik TF-IDF (Term Frequency-Inverse Document Frequency) yang bertujuan untuk memberikan bobot pada hubungan kata (term) sebuah dokumen. Kemudian melakukan splitting data yaitu membagi data training 80% dan data testing 20% dengan harapan mendapatkan model dengan performansi terbaik dan yang terakhir melakukan visualisasi data tweet dengan menggunakan Word Cloud agar bisa menarik sebuah kesimpulan. Hasil klasifikasi data tweet vaksin Covid-19 menggunakan algoritma Naïve Bayes Classifier mendapatkan nilai accuracy sebesar 81%, precision sebesar 80%, recall sebesar 99%, dan f1-score sebesar 89%, Sedangkan untuk algoritma Support Vector Machine mendapatkan nilai accuracy sebesar 87%, precision sebesar 88%, recall sebesar 96%, dan f1-score sebesar 92%.

Sentiment analysis of comments from visitors to tourist attractions and the public on tourist attractions in Sukabumi Regency which is one of the areas with various categories of tourist objects and is a sector of economic income for the surrounding community or for related parties such as the government and managers, in sentiment analysis research This includes using the Nave Bayes classification algorithm to examine the sentiment of tourist visitors and the performance of the classification model used. The data used in this research was taken from the website from Tripadvisor and Google Maps using a crawling technique, which then processed the data by a pre-processing process and then applied a classification to the data and got a sentiment visualization by processing word frequency on tourist visitor sentiment data. The results of the accuracy of the model used were re-tested with the k-fold cross validation method and the results of sentiment visualization got the frequency of words that most often appear on negative sentiment labels are garbage, beaches, lacking, places, roads, parking, dirty, entering, caring, clean , expensive, pay, manage, good and water.

In this study a web-based expert system was designed using the Naïve bayes method which was intended to help farmers diagnose chili plants. The system development method used is extreme programming which consists of planning, design, coding, testing. A web-based chilli plant disease diagnostic expert system was developed using the PHP and MySQL programming languages. This expert system is capable of diagnosing chili plants by submitting disease symptoms at the time of inspection. Based on the selected symptoms, this system will provide diagnostic results and then display the disease and solutions for the chili plant disease. The results in this study concluded that there was compatibility between the results of the expert system diagnosis using the Naïve bayes method with experts.

The development of Information Technology (IT) is now very rapid and has been used in various aspects of life both in the field of government, banking, socio- cultural, industrial, education, and even health. One type of disease that gets attention for the application of IT is corona virus or better known as Covid 19 because the spread is quite widespread throughout the country, especially in the territory of Indonesia. Corona virus disease development in Indonesia is growing, based on WHO data as of today on August 30, 2020 positive cases have reached 172,053 people, cases died 7,343 people and recovered 124,185 people and the number of cases is increasing every day. Based on these conditions, Central Aceh is in a state of alert against the threat of corona virus. then a form of prevention  of the widespread spread of the virus can be done by breaking the chain of transmission by doing social distancing. In this study, a system will be designed to anticipate the Covid-19 pandemic by predicting the rate of spread of covid-19, especially in central Aceh districts using the Naive Bayes Classifier method. The accuracy level of this system is a positive case of 60%.

Management of medical actions carried out in handling patients who are ODP (people under monitoring), OTG (asymptomatic people), PDP (patient under monitoring) and positive Covid-19 patients is carried out based on assumptions, such as self-isolation, hospitalization, or special treatments in the ICU (Intensive Care Unit) room. The condition of the body in each patient is different, a patient may have same symptoms but the treatment is different, especially in elderly patients. Many problems occur in determining medical action because the patient's body condition is different. Therefore, it needs to be appointed as a research. The research method used in this study was Nive Bayes algorithm with supporting application Rapid Miner. It was applied to carry out the process of testing on patient data as much as 500 data, 25 variables or patient symptoms and 3 outputs as a form of medical action. Based on the results of the analysis carried out in this study, prediction of medical actions for ODP, PDP, OTG and positive Covid-19 patients were obtained by comparing training data with testing data using Rapid Miner application. It resulted that an accuracy rate of 76.00% was obtained

Keseimbangan antara pasokan dan permintaan listrik sangat diperlukan untuk mendapatkan jaringan listrik yang stabil, agar dapat diketahui pola data kestabilan jaringan listrik ini maka diperlukan pengelompokkan atau pengklasifikasian terhadap data dengan memanfaatkan teknik data mining guna mengolah informasi. Untuk mencari metode data mining yang bisa menghasilkan akurasi terbaik dalam mengklasifikasikan data Kestabilan jaringan listrik, maka pada penelitian ini dilakukan perbandingan penerapan algoritma klasifikasi SVM dan Naïve Bayes terhadap dataset Electrical Grid Stability Simulated yang yang diambil dari UCI Machine Learning. Dari hasil pengujian klasifikasi kestabilan jaringan listrik yang telah dilakukan menggunakan aplikasi WEKA 3.8.2. Metode Support Vector Machine (SVM) menunjukan tingkat accuracy yang lebih baik yaitu sebesar 98.9%  jika  dibandingkan dengan metode Naive Bayes yang meghasilkan nilai akurasi sebesar 97.64% Hasil akurasi ini akan menunjukan hasil yang berbeda tergantung dengan jenis data, jumlah instance, label class dan Percentage split data yang digunakan.  

Diabetes is a serious chronic disease that occurs because the pancreas does not produce enough insulin (a hormone that regulates blood sugar or glucose), or when the body cannot effectively use the insulin it produces. WHO data shows that the incidence of non-communicable diseases in 2004 reached 48 , 30% is slightly higher than the incidence rate of infectious diseases, namely 47.50% [1]. According to the Ministry of Health in 2012 diabetes caused 1.5 million deaths. Some Indonesian people, this disease is better known as diabetes or blood sugar. This research was developed through secondary data processing from the Pima Indians Diabetes Dataset health database which was taken from the Kaggle dataset and can be accessed through https://www.kaggle.com/uciml/pima-indians-diabetes-database. Where the data itself consists of 768 records with several medical predictor variables (Pregnancies, Glucose, Blood Pressure, Skin Thickness, Insulin, BMI, Diabetes Pedigree Function, Age and Outcome). Then the data will be processed using the Particle Swarm Optimization (PSO) feature selection to increase the accuracy value and the Naive Bayes algorithm to determine the accuracy results of the diagnosis of diabetes. From the results of research that has been done for the accuracy of the classification algorithm Naive Bayes is 74.61%, while the accuracy of the classification algorithm with Particle Swarm Optimization is 77.34% with an accuracy difference of 2.73%. So it can be concluded that the application of the Particle Swarm Optimization technique is able to select attributes in the Naive Bayes Algorithm, and can produce a better level of diabetes diagnosis accuracy than using only the individual method, namely the Naive Bayes algorithm. Keywords: Diabetes, Particle Swarm Optimization, Naive Bayes Algorithm

A house for every human being is the main and most important need compared to others needs in general. A financial institution is an institution engaged in the financial sector where its customers are people from various walks of life with various behaviors. Lending is a business activity that carries a high risk and affects the business continuity of a banking company. The problem that is often faced in providing home loans is determining the decision to extend credit to prospective customers, while another problem is that not all home loan payments by customers can run well or commonly known as bad credit. One of the causes of bad credit is an assessment error in making credit decisions. Data mining is a process used to analyze cases in order to find the best performance of an algorithm being tested. One way to get information or patterns from a large data set is to use techniques in data mining. There are many classification methods that can be used to produce precise accuracy values. In this study, two classification algotihm methods are used in classifying the home crediting dataset, namely the C4.5 decision tree algorithm and the Naïve Bayes algorithm. The comparison of the two algorithms produces an accuracy value fo the Naïve Bayes algorithm of 36.36% and the Decision Tree C4.5 algorithm has an accuracy rate of 59.54%.

With the increasing development of technology the more variety of books circulating on the internet. As is the recommendation system on online book sites that provide books relevantly and as needed with one's preferences. One alternative is GoodReads, a social networking site that specializes in cataloging books and users can share reading book recommendations with each other by rating, reviewing, and commenting. As a large book recommendation site, it has a lot of data that can be processed by applying machine learning methods, but still not known as the most accurate model. By using the right model, we can provide more accurate recommendations. Therefore, this study will analyze the data obtained from the www.kaggle.com namely the goodreads-books dataset. This study proposed a data mining classification model to get the best model in recommending books on GoodReads. The algorithms used are Decision Tree, K-Nearest Neighbor, Naïve Bayes, Random Forest, and Support Vector Classifier, then for model evaluation using accuracy, precision, recall, f1-score, confusion matrix, AUC, and Mean Error Absolute. The test results of several classification algorithms found that Decision Tree has the highest accuracy among the methods presented by 99.95%, precision by 100%, recall by 96%, f1-score of 98% with MAE of 0.05 and AUC of 99.96%. This is proof that decision tree algorithms can be used as book recommendations based on book categories on GoodReads.

Classification is one of the most widely used techniques in machine learning. Text classification is the process of classifying data according to pre-determined groups or classes. Where in most cases, text classification uses labeled training data to obtain the rules used to classify test data into predefined groups. In this study, it is proposed to use CountVectorizer for Indonesian text classification which will be compared with TF-IDF Term Weighting and its three feature levels, namely Character Level, Word Level and N-gram Level as feature extraction which is implemented together with Naive Bayes classification and the BPPPTIndToEngCorpusHalfM dataset. To compare the classification performance, this study uses 10-Fold Cross Validation and Split Data using a ratio of 90:10, while to evaluate the accuracy of the authors using the F1-Score and AUC with the hope that this study will get good accuracy results so that it can be used as a reference to be developed using another method. The F1-Score accuracy obtained in this study was 0.93 and the AUC score was 0.95.

Hepatitis merupakan penyakit yang diderita oleh banyak orang, bahkan bisa menyebabkan kematian. Prediksi awal dapat mencegah kematian tersebut yaitu denganmengumpulkan data pasien hepatitis yang dilihat dari faktor - faktornya. Faktor-faktor tersebut antara lain Protime, Alk Phosphat, Albumin, Bilirubin dan Usia. Untuk mengolah datatersebut, dibutuhkan Data Mining. Salah satu metode data mining yang digunakan pada penelitian ini adalah klasifikasi.Tujuan penelitian ini yaitu bagaimana memprediksi hidup atau meninggalnya pasien penyakit hepatitis dengan tingkat akurasi dan mencari atribut paling berpengaruh terhadapprediksi hidup atau meninggalnya pasien penyakit hepatitis dengan menggunakan algoritma Algoritma K-Nearest Neighbor, Naïve Bayes Dan Neural Network dan kemudianmembandingkan ketiga hasil analisis dari ketiga algoritma tersebut.Dari hasil analisis 20 atribut dilakukan 3 kali percobaan dengan algoritma Naïve Bayes didapat model klasifikasi dengan tingkat akurasi yang terbaik yaitu 76.92 %, tingkat error23.01% dan atribut Acites dan Spider merupakan atribut yang berpengaruh terhadap keputusan hidup atau meninggalnya pasien yang terkena penyakit hepatitis.Dengan menggunakanAlgoritma Neural Network didapat model klasifikasi dengan tingkat akurasi yang terbaik yaitu 82,97%, tingkat error 17.03% dan atribut yang paling berpengaruh yaitu anorexia, spiders dan protime. Dengan menggunakan algoritma K-Nearest Neighbor didapat model klasifikasi dengan tingkat akurasi terbaik yaitu 93%, tingkat error 7% dan atribut yang paling berpengaruh terhadap penderita penyakit hepatitis yaitu Albumin.

Kesan masyarakat terhadap suatu penyedia jasa layanan komunikasi dapat dianalisa melalui respon mereka. Salah satu media yang dapat digunakan untuk mendapatkan kesan tersebut adalah media sosial twitter. Seiring dengan perkembangan Internet, saat ini WoM telah berkembang menjadi electronic word-of-mouth. Electronic  Word  of  Mouth  (e-WoM)  communication  merujuk  pada  pernyataan  positif  atau  negatif  dari pelanggan potensial, pelanggan aktual atau mantan pelanggan mengenai suatu produk atau perusahaan via internet. Salah satu motif dalam e-Wom adalah Venting Negative Feelings, yaitu keinginan mengungkapkan ketidakpuasan konsumen terhadap produk atau perusahaan. Penelitian ini akan menganalisa percakapan tentang venting negative feelings pada media sosial twitter. Untuk mendapatkan data  dari  twitter akan  mengunakan  bahasa  R  yang  telah  menyediakan library untuk mengaksesnya. Hasil penelitian ini berupa data motif e-wom Venting Negative Feelings yang dapat digunakan sebagai pendukung keputusan pengguna internet dalam memilih penyedia jasa layanan internet yang baik.

Lama studi dari mahasiswa ini sangatlah penting bagi mahasiswa, program studi serta perguruan tinggi. Permasalahan lama studi setiap mahasiswa bisa disebabkan atau dipengaruhi oleh banyak faktor. Hal tersebut telah dibuktikan dengan beberapa penelitian pada permasalahan tersebut yang mendapati sejumlah faktor yang berpengatuh terhadap lama studi mahasiswa. Dengan menggunakan teknik data mining khususnya klasifikasi untuk prediksi dengan algoritma naive bayes dapat dilakukan prediksi terhadap ketepatan waktu studi dari mahasiswa berdasarkan data training yang ada. Data training dan testing yang digunakan diambil secara random pada tabel data master yang digunakan. Algoritma naive bayes, menghitung perbandingan peluang antara jumlah dari masing-masng kriteria nilai fields terhadap nilai hasil prediksi sesunggunya. Fungsi untuk prediksi dibuat menggunakan Query pada MySql dalam bentuk function(fbayesian). Dari hasil uji coba diperoleh tingkat kesalahan prediksi berkisar 20% sampai dengan 50% dengan data training dan testing yang diambil secara random. Namun rata-rata tingkat kesalahan berkisar 20 % hingga 34%. Tinggi rendahnya tingkat kesalahan dapat disebabkan oleh jumlah record data dan tingkat konsistensi dari data training yang dgunakan. Sedangkan hasil prediksi dari ketepatan lama studi dari mahasiswa angkatan 2008 adalah sebesar 254 mahasiswa diprediksi ”Tepat Waktu” dan sisanya yaitu 4 orang diprediksi ”Tidak Tepat Waktu”.   Kata Kunci : Prediksi, Lama Studi, Data Mining, Naive bayes, MySql