Development Grouping of Synonym Set Thesaurus Vocabulary The Qur’an in English Using Hierarchical Clustering Algorithm

Research in the field of text mining to process entries or words from the Qur'an is very beneficial for Muslims. This study aims to establish a set of synonyms for the thesaurus in the words of the Qur'an. This research is used because the source of knowledge about the science of the Qur'an is still lacking. The dataset in this study uses the Corpus Qur'an and English Translation. This research is a research development of an article that has been published, namely "The Development of Al-Qur'an Vocabulary Set Synonyms with WordNet Approach" by Laras Gupitasari. Input from this research system uses nouns from the translation of English words in the Quran. The output of the system produces several groups that have the same level of closeness of meaning displayed, the first group means the word in the group has a close meaning. To produce output, this study uses word grouping with a hierarchical grouping method and calculates distances using common paths, then groups results according to the closeness of meaning from word entries. The evaluation in this study produced an F-Measure value of 76%, F-Measure Value is an evaluation to measure the accuracy of predictions issued by the system.Research in the field of text mining to process entries or words from the Qur'an is very beneficial for Muslims. This study aims to establish a set of synonyms for the thesaurus in the words of the Qur'an. This research is used because the source of knowledge about the science of the Qur'an is still lacking. The dataset in this study uses the Corpus Qur'an and English Translation. This research is a research development of an article that has been published, namely "The Development of Al-Qur'an Vocabulary Set Synonyms with WordNet Approach" by Laras Gupitasari. Input from this research system uses nouns from the translation of English words in the Quran. The output of the system produces several groups that have the same level of closeness of meaning displayed, the first group means the word in the group has a close meaning. To produce output, this study uses word grouping with a hierarchical grouping method and calculates distances using common paths, then groups results according to the closeness of meaning from word entries. The evaluation in this study produced an F-Measure value of 76%, F-Measure Value is an evaluation to measure the accuracy of predictions issued by the system

Analyzing Public Sentiments on Disaster Relief Efforts Through Social Media Data

Social media has become a source of quick but not necessarily accurate information. Especially in social media X, which is often used to share information. This research aims to conduct sentiment analysis on posts related to natural disasters that aim to maximize assistance to victims of natural disasters. This research takes datasets from tweets on social media X, the data will be labeled into positive and negative. And then the preprocessing process will be carried out, in this study, categorization will be carried out on each tweet related to the category, then the data will be divided into training and testing. Then the Term Frequency-Inverse Document Frequency (TF-IDF) feature is used to assist in reducing the weight of words that often appear in the dataset, The next step involves designing a system with a focus on applying the Support Vector Machine (SVM) Polynomial Kernel algorithm which becomes a classifier which will later be used to find the best hyperline or decision boundary that divides each review into two classes, namely positive tweets and negative tweets. Then obtained with a value of Precision of 86.49%, Recall 99.21%, F1-Score 92.42%, and Accuracy of 87.01%. This research is expected to provide involvement in making a fast and effective decision for victims of natural disasters

Sentimen Analysis Social Media for Disaster using Naïve Bayes and IndoBERT

The rapid advancement of information and communication technology has resulted in a significant surge in data, especially text data from social media platforms. This paper presents a sentiment analysis approach using IndoBERT and Naïve Bayes algorithms to classify sentiment related to natural disasters, specifically from a dataset of tweets derived from social media platform X. The focus of this research is to categorize tweets as positive and negative sentiment to provide useful insights in improving disaster response and management, with a focus on tweets related to earthquakes, floods, and the eruption of Mount Merapi. The goal is to assist the government in allocating aid more efficiently and understanding public sentiment during disasters. The methodology used includes data collection, data preparation, labeling, categorization, word weighting using tf-idf, data separation, and classification using Naïve Bayes and IndoBERT algorithms. The results showed that IndoBERT achieved 91% accuracy, while Naïve Bayes achieved 74% accuracy. The study highlights the potential of sentiment analysis in improving disaster preparedness and more effective response strategies

Development Synonym Set for the English Wordnet Using the Method of Comutative and Agglomerative Clustering

Wordnet is a collection of words that interpret or present a meaning, in its development Wordnet has an important part, the Synonym Set or Synset. In making Synonym sets, synonyms are needed and the commutative nature of words is needed. To get word synonyms, the English language thesaurus becomes the reference data for taking synonym data. Broadly speaking, the difference between Wordnet and the dictionary is that the meaning of the word is related to other words, to determine the equation requires a commutative process. The process is made easy by using commutative methods that will produce a candidate synonym set. Candidates for the synonym set cannot be used for word syntax, the grouping process of words which produces the Synonym set as the final result must be carried out. The process of grouping words can one of them use clustering techniques, in this study will use Agglomerative Clustering techniques. In the process of agglomerative clustering techniques there is a threshold value to determine the number of repetitions or as a condition to stop the iteration process. The clustering process in this study will use a threshold value of 0.1 to 1 to test the best threshold value to produce the best Synonym set and calculate its accuracy value. Accuracy calculation and evaluation will use the F-measure method to find the best results

Typo handling in searching of Quran verse based on phonetic similarities

The Quran search system is a search system that was built to make it easier for Indonesians to find a verse with text by Indonesian pronunciation, this is a solution for users who have difficulty writing or typing Arabic characters. Quran search system with phonetic similarity can make it easier for Indonesian Muslims to find a particular verse.  Lafzi was one of the systems that developed the search, then Lafzi was further developed under the name Lafzi+. The Lafzi+ system can handle searches with typo queries but there are still fewer variations regarding typing error types. In this research Lafzi++, an improvement from previous development to handle typographical error types was carried out by applying typo correction using the autocomplete method to correct incorrect queries and Damerau Levenshtein distance to calculate the edit distance, so that the system can provide query suggestions when a user mistypes a search, either in the form of substitution, insertion, deletion, or transposition. Users can also search easily because they use Latin characters according to pronunciation in Indonesian. Based on the evaluation results it is known that the system can be better developed, this can be seen from the accuracy value in each query that is tested can surpass the accuracy of the previous system, by getting the highest recall of 96.20% and the highest Mean Average Precision (MAP) reaching 90.69%. The Lafzi++ system can improve the previous system

ANALISIS PENGARUH METODE COMBINE SAMPLING DALAM CHURN PREDICTION UNTUK PERUSAHAAN TELEKOMUNIKASI

Churn prediction pada pelanggan telekomunikasi merupakan upaya memprediksi/mengklasifikasi pelanggan jasa telekomunikasi yang berhenti atau berpindah berlangganan dari suatu operator ke operator yang lain. Namun dataset pada kasus churn ini biasanya memiliki kelas yang imbalance dimana jumlah instance suatu kelas (kelas active atau tidak churn atau mayor atau negatif) jauh lebih besar dari jumlah kelas yang lain (kelas churn atau minor atau positif). Akibatnya, kebanyakan classifier cenderung memprediksi kelas mayor dan mengabaikan kelas minor sehingga akurasi kelas minor sangat kecil. Salah satu pendekatan yang dilakukan untuk menangani permasalahan ini adalah dengan memodifikasi distribusi instances dari dataset yang digunakan atau yang lebih dikenal dengan pendekatan sampling-based. Teknik resampling ini meliputi oversampling, under-sampling, dan combine-sampling. Analisis yang dilakukan pada penelitian ini adalah mengetahui bagaimana pengaruh metode combine sampling yang digunakan terhadap akurasi prediksi data churn dengan melakukan penghitungan akurasi model churn prediction yang dinyatakan dalam bentuk lift curve, top decile dan gini coefficient serta f-measure untuk penghitungan akurasi prediksi data sebagai data yang imbalance. Hasil yang didapat dari penelitian menunjukkan bahwa metode combine sampling belum sesuai diterapkan pada data churn, karena cenderung masih menghasilkan nilai top decile yang kecil. Tetapi secara umum metode combine sampling ini mampu meningkatkan akurasi untuk memprediksi data minor. Dengan penerapan metode combine sampling, data churn yang memiliki tingkat imbalance yang besar dapat diklasifikasi tanpa mengorbankan data minor yang menjadi fokus penelitian. Metode combine sampling yang digunakan juga memiliki hasil evaluasi yang berbeda terhadap dataset sebagai data churn dan sebagai dataimbalance

ANALISIS PENGARUH METODE COMBINE SAMPLING DALAM CHURN PREDICTION UNTUK PERUSAHAAN TELEKOMUNIKASI

Churn prediction pada pelanggan telekomunikasi merupakan upaya memprediksi/mengklasifikasi pelanggan jasa telekomunikasi yang berhenti atau berpindah berlangganan dari suatu operator ke operator yang lain. Namun dataset pada kasus churn ini biasanya memiliki kelas yang imbalance dimana jumlah instance suatu kelas (kelas active atau tidak churn atau mayor atau negatif) jauh lebih besar dari jumlah kelas yang lain (kelas churn atau minor atau positif). Akibatnya, kebanyakan classifier cenderung memprediksi kelas mayor dan mengabaikan kelas minor sehingga akurasi kelas minor sangat kecil. Salah satu pendekatan yang dilakukan untuk menangani permasalahan ini adalah dengan memodifikasi distribusi instances dari dataset yang digunakan atau yang lebih dikenal dengan pendekatan sampling-based. Teknik resampling ini meliputi oversampling, under-sampling, dan combine-sampling. Analisis yang dilakukan pada penelitian ini adalah mengetahui bagaimana pengaruh metode combine sampling yang digunakan terhadap akurasi prediksi data churn dengan melakukan penghitungan akurasi model churn prediction yang dinyatakan dalam bentuk lift curve, top decile dan gini coefficient serta f-measure untuk penghitungan akurasi prediksi data sebagai data yang imbalance. Hasil yang didapat dari penelitian menunjukkan bahwa metode combine sampling belum sesuai diterapkan pada data churn, karena cenderung masih menghasilkan nilai top decile yang kecil. Tetapi secara umum metode combine sampling ini mampu meningkatkan akurasi untuk memprediksi data minor. Dengan penerapan metode combine sampling, data churn yang memiliki tingkat imbalance yang besar dapat diklasifikasi tanpa mengorbankan data minor yang menjadi fokus penelitian. Metode combine sampling yang digunakan juga memiliki hasil evaluasi yang berbeda terhadap dataset sebagai data churn dan sebagai dataimbalance

ANALISIS PENGARUH METODE COMBINE SAMPLING DALAM CHURN PREDICTION UNTUK PERUSAHAAN TELEKOMUNIKASI

Churn prediction pada pelanggan telekomunikasi merupakan upaya memprediksi/mengklasifikasi pelanggan jasa telekomunikasi yang berhenti atau berpindah berlangganan dari suatu operator ke operator yang lain. Namun dataset pada kasus churn ini biasanya memiliki kelas yang imbalance dimana jumlah instance suatu kelas (kelas active atau tidak churn atau mayor atau negatif) jauh lebih besar dari jumlah kelas yang lain (kelas churn atau minor atau positif). Akibatnya, kebanyakan classifier cenderung memprediksi kelas mayor dan mengabaikan kelas minor sehingga akurasi kelas minor sangat kecil. Salah satu pendekatan yang dilakukan untuk menangani permasalahan ini adalah dengan memodifikasi distribusi instances dari dataset yang digunakan atau yang lebih dikenal dengan pendekatan sampling-based. Teknik resampling ini meliputi oversampling, under-sampling, dan combine-sampling. Analisis yang dilakukan pada penelitian ini adalah mengetahui bagaimana pengaruh metode combine sampling yang digunakan terhadap akurasi prediksi data churn dengan melakukan penghitungan akurasi model churn prediction yang dinyatakan dalam bentuk lift curve, top decile dan gini coefficient serta f-measure untuk penghitungan akurasi prediksi data sebagai data yang imbalance. Hasil yang didapat dari penelitian menunjukkan bahwa metode combine sampling belum sesuai diterapkan pada data churn, karena cenderung masih menghasilkan nilai top decile yang kecil. Tetapi secara umum metode combine sampling ini mampu meningkatkan akurasi untuk memprediksi data minor. Dengan penerapan metode combine sampling, data churn yang memiliki tingkat imbalance yang besar dapat diklasifikasi tanpa mengorbankan data minor yang menjadi fokus penelitian. Metode combine sampling yang digunakan juga memiliki hasil evaluasi yang berbeda terhadap dataset sebagai data churn dan sebagai data imbalance