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Hadith is the main way of life for Muslims besides the Qur'an whose can be applied in everyday life. Hadith also contains all the words or deeds of the Prophet Muhammad which are used as a source of the law of Islam. Therefore, many readers, especially Muslims, are interested in studying hadith. However, the large number of hadiths makes it difficult for readers or those who are still unfamiliar with Islam to read them. Therefore, we conducted a study to classify hadith textually based on the type of teaching, so that readers can get an overview or other reference in reading and searching for hadith based on the type of teaching more easily. This study uses KNN and chi-square methods as feature selection. We also carried out several test scenarios, including implementing stopword removal modifications in preprocessing and experimenting with selecting k values ​​for KNN to determine the best performance. The best performance was obtained by using the value of k = 7 on KNN without implementing chi-square and with stopword removal modification with a hammer loss value of 0.1042 or about 89.58% of the data correctly classified.

Diabetes (diabetes) was a metabolic disorder caused by high levels of sugar in the blood caused by disorders of the pancreas and insulin. According to data from the Ministry of Health of the Republic of Indonesia, Diabetes was the third-largest cause of death in Indonesia with a percentage of 6.7%. The high rate of death from diabetes encouraged this study, with the aim of early detection. This research used a Machine Learning approach to classify the data. In this paper, a comparison of Support Vector Machine (SVM) and Modified Balanced Random Forest (MBRF) was discussed for classifying diabetes patient data. Both methods were chosen because it was proven in previous studies to get high accuracy, so that the two methods are compared to find the best classification model. Several preprocessing methods were used to prepare the data for the classification process. The entire combination of preprocessing steps will be carried out on the two classification methods to produce the same dataset. The evaluation was carried out using the Confusion Matrix method. Based on the experimental results in the process of testing the system being built, the maximum performance results were 87.94% using SVM and 97.8% using MBRF.

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The second fundamental source of law for Moslems is the Hadith. The Hadith can be used to explain Quranic texts. However, Hadith still needs to be translated according to each national language to easily understand its meaning [1]. In Indonesia Hadith more usually refers to a special class of relevance to more particular religious concern [1]. Base on that, this research will Classify the translation Hadith Text into three classes: Obligation, Prohibition, and Information. From previous research, the Back Propagation Neural Network (BPNN) showed good performance in classifying hadith text. Therefore, BPNN was used to solve the problem of hadith text classification in this study. However, the dataset has a huge number of varied bag-of-words, which are features that will be used in the classification process. Hence, Information Gain (IG) was utilized to select influential features, and as the sequential process before the classification process. To measure the performance of this system, the Macro F1-Score was used. The F1-Score enables one to observe exactness from precision and completeness from recall. The Macro F1-score is also needed for the performance evaluation of more than two classes. Based on the experiment conducted, the system was able to classify hadith text using BPNN, IG, and without stemming, yielding the highest F1-score of 84.63%. However, the system performance that included the stemming process yielded an F1-score of 80.92%. This shows that the stemming process could decrease classification performance. This decreasing performance is due to some influential words merging with more noninfluential words.