Data from the Ministry of Civil Works and Public Housing (Kementrian PUPR) in 2019 shows that around 81 million millennials do not own houses. Government Regulation Number 25 of 2020 on the Implementation of Public Housing Savings, commonly called PP 25 Tapera 2020, is one of the government's efforts to ensure that Indonesian people can afford houses. Tapera is a deposit of workers for house financing, which is refundable after the term expires. Immediately after enaction, there were many public responses regarding the ordinance. We investigate public sentiments commenting on the regulation and use Support Vector Machine (SVM) in the study since it has a good level of accuracy. It also requires labels and training data. To speed up labeling, we use the lexicon-based method. The issue in the lexicon-based lies in the dictionary component as the most significant factor. Therefore, it is possible to update the dictionary automatically by combining lexicon-based and SVM. The SVM approach can contribute to lexicon-based, and lexicon-based can help label datasets on SVM to produce good accuracy. The research begins with collecting data from Twitter, preprocessing raw and unstructured data into ready-to-use data, labeling the data with lexicon-based, weighting with TF-IDF, processing using SVM, and evaluating algorithm performance model with a confusion matrix. The results showed that the combination of lexicon-based and SVM worked well. Lexicon-based managed to label 519 tweet data. SVM managed to get an accuracy value of 81.73% with the RBF kernel function. Another test with a Sigmoid kernel attains the highest precision at 78.68%. The RBF kernel has the highest recall result with a value of 81.73%. Then, the F1-score for both the RBF kernel and Sigmoid is 79.60%.

Distribution of midwife practice pomegranate (quality of service) in Cirebon is difficult to know where the location of the practice because of the vast area of Cirebon. Then, the number of pregnant women who are less get help quickly (giving birth without medical assistance) because of ignorance location midwife practice pomegranate (quality of service) nearby. And the number of midwives pomegranate (quality of service) has not cooperated with the insurance BPJS to perform payment transactions. This study uses a clustering method, which can segment data clustering method, which is used to facilitate information retrieval midwife pomegranate (quality of service). Clustering methods have representation stage pattern, the selection traits or characteristics, pattern proximity, distance measurement, data obtained from IBI (Indonesian Midwives Association) and the tools used: phpMyAdmin, notepad ++, xampp, GoogleMapApi, Dreamwaver. This system can be expected to map the location of the practice of midwives pomegranate (quality of service) in the district of Cirebon, can find the nearest location midwife pomegranate (quality of service), can find pomegranate midwives who work with BPJS to perform payment transactions. Then, hopefully it can help people in handling pregnant women rapidly. And, is expected to reduce maternal and child mortality.

Waste is goods / materials that have no value in the scope of production, where in some cases the waste is disposed of carelessly and can damage the environment. The Indonesian government in 2019 recorded waste reaching 66-67 million tons, which is higher than the previous year, which was 64 million tons. Waste is differentiated based on its type, namely organic and anorganic waste. In the field of computer science, the process of sensing the type waste can be done using a camera and the Convolutional Neural Networks (CNN) method, which is a type of neural network that works by receiving input in the form of images. The input will be trained using CNN architecture so that it will produce output that can recognize the object being inputted. This study optimizes the use of the CNN method to obtain accurate results in identifying types of waste. Optimization is done by adding several hyperparameters to the CNN architecture. By adding hyperparameters, the accuracy value is 91.2%. Meanwhile, if the hyperparameter is not used, the accuracy value is only 67.6%. There are three hyperparameters used to increase the accuracy value of the model. They are dropout, padding, and stride. 20% increase in dropout to increase training overfit. Whereas padding and stride are used to speed up the model training process.

Kesehatan merupakan hak asasi manusia sekaligus investasi bagi keberhasilan pembangunan bangsa Indonesia. Salah satu faktor penting di dunia kesehatan adalah tersedianya obat-obatan untuk nantinya disalurkan ke seluruh wilayah Indonesia melalui badan organisasi kesehatan milik pemerintah secara merata dan berkelanjutan. Fungsi obat yaitu sebagai upaya pencegahan, penyembuhan, maupun peningkatan kesehatan bagi manusia. Obat juga merupakan bahan yang diatur oleh pemerintah dalam hal ini adalah Badan Pengawasan Obat dan Makanan (BPOM). Di era modern seperti saat ini, kita mengenal dengan istilah Data Mining. Dalam perkembangannya, data mining berhubungan erat dengan analisa data, maka dari itu data mining mampu mengolah dan mengelompokan data dalam jumlah yang besar berdasarkan kesamaan dalam sekumpulan data. Algoritma K-Means merupakan metode pengelompokan yang mudah digunakan. Pada proses penentuan titik pusat klaster (centroid) awal merupakan kelemahan bagi K-Means karena sifatnya yang acak. Algoritma Hierarchical Clustering (HCC) Single Linkage pada penentuan titik pusat klaster (centroid) memiliki sifat yang konsisten dan kompleks. Dari 204 data dan variabel yang akan diolah, kedua algoritma tersebut akan  mendapatkan klaster optimal data pada kelompok klaster C1 yaitu obat dengan pemakaian lambat dan klaster C2 yaitu obat dengan pemakaian cepat dan membandingkan nilai validitasnya. Hasil dari penelitian ini menunjukan bahwa algoritma HCC Single linkage mampu memberikan hasil yang terbaik dengan validitas Sillhoutte Index (SI) sebesar 0.8629 sedangkan algoritma K-Means mendapatkan nilai validitas SI sebesar 0.8414.

The problem that occurs in the application of K-Nearest Neighbors as a classification algorithm is the frequent occurrence of overfitting in data processing. This can be overcome by using cross-validation techniques in evaluating the algorithm model and minimizing overfitting. Then the performance of diabetes prediction accuracy is unknown using the K-Nearest Neighbors algorithm with cross-validation technique. The data used comes from the National Institute of Digestive and Kidney Diabetes in 2021. The case study in this study is to find out the initial screening for diabetes is supported by the results of algorithm accuracy and real time application of streamlit-based users. The purpose of this study was to optimize the accuracy results with a cross validation technique supported by the k-nearest neighbors algorithm in the study of diabetes data. The method used is the k-nearest neighbors algorithm which is supported by cross validation technique for optimal accuracy results. Then the application of a streamlit-based interactive web application for testing the accuracy results used by the user to see the probability that the user has diabetes. The results showed that the optimization of the Cross Validation technique supported by the KNearest Neighbors algorithm model worked well. The results of the confusion matrix using the cross validation technique are more accurate in terms of the advantages of using the cross-validation technique itself. So that the classification report which has a value of 95% is more accurate than the accuracy which is worth 92% because of the use of cross-validation techniques that can minimize overfitting in addition to considerations of the accuracy value and the implementation of streamlit-based interactive web applications for user testing is going well.