Diabetes is a chronic disease that can cause serious illness. Women are four times more likely to develop heart problems caused by diabetes. Women are also more prone to experience complications due to diabetes, such as kidney problems, depression, and decreased vision quality. Nearly 200 million women worldwide are affected by diabetes, with two out of five affected by the disease being women of reproductive age. This paper aims to predict women with at least 21 years of age having diabetes based on eight diagnostic measurements using the statistical learning methods; Multinomial Naive Bayes, Fisher Discriminant Analysis, and Logistic Regression. Model validation is built based on dividing the data into training data and test data based on 5-fold cross-validation. The model validation performance shows that the Gaussian Naïve Bayes is the best method in predicting diabetes diagnosis. This paper’s contribution is that all performance measures of the Multinomial Naïve Bayes method have a value greater than 93 %. These results are beneficial in predicting diabetes status with the same explanatory variables.
The University of California Irvine Heart disease dataset had missing data on several attributes. The missing data can loss the important information of the attributes, but it cannot be deleted immediately on dataset. To handle missing data, there are several ways including deletion, imputation by mean, mode, or with prediction methods. In this study, the missing data were handled by deletion technique if the attribute had more than 70% missing data. Otherwise, it were handled by mean and mode method to impute missing data that had missing data less or equal 1%. The artificial neural network was used to handle the attribute that had missing data more than 1%. The results of the techniques and methods used to handle missing data were measured based on the performance results of the classification method on data that has been handled the problem of missing data. In this study the classification method used is Artificial Neural Network, Naïve Bayes, Support Vector Machine, and K-Nearest Neighbor. The performance results of classification methods without handling missing data were compared with the performance results of classification methods after imputation missing data on dataset for accuracy, sensitivity, specificity and ROC. In addition, the comparison of the Mean Squared Error results was also used to see how close the predicted label in the classification was to the original label. The lowest Mean Squared Error wasobtained by Artificial Neural Network, which means that the Artificial Neural Network worked very well on dataset that has been handled missing data compared to other methods. The result of accuracy, specificity, sensitivity in each classification method showed that imputation missing data could increase the performance of classification, especially for the Artificial Neural Network method.
This paper discussed about optimization production and distribution problem of tempe using Production Routing problem with Perishable Inventory (PRPPI) models with First Produce, First Deliver (FPFD) and First Produce First Selling (FPFS) inventory management policy. Tempe is a food made from soy fermentation and distributed from the depot to retailers. There are three retailers, namely Perumnas market, Sekip market and Kebon Semai market respectively as retailer 1, 2 and 3, and Ana depot is defined as retailer 0. An exact Branch and Bound algorithm was developed and solved by Lingo 17.0 Software. The results of minimum cost production and distribution retailer 0 was 69,174 rupiah, optimal amount of tempe production 34 pieces. The shipping routes starting from retailer 0 to retailer 1, proceed to retailer 2 and then to retailer 3 finally return to retailer 0.
In the inventory system is given operational policies relating to product inventory control, such as how much is ordered, when to order with the aim of minimizing storage and ordering costs. Customer demand and lead time affect the inventory system. This study aims to determine the densitas distribution of demand data, total inventory costs and optimal coconut supply for the two order periods using the stochastic inventory model. Stochastic inventory models can be used if there is variable uncertainty. This paper discusses the optimization of coconut inventories using a stochastic model with uncertainty about demand and lead time. It is known that demand data is uniformly distributed, based on the realization value of random variable requests can be formed in 49 scenarios. Obtained an optimal total inventory cost for the two planning periods is Rp. 45,672,910, optimal supply for period one was 26031 coconuts. Inventory levels in two period for any scenarios are
Penelitian Tindakan Kelas  (PTK) merupakan penelitian yang dapat dilakukan guru dalam rangka memperbaiki proses pembelajaran untuk mencapai tujuan tertentu. Hal ini menunjukkan bahwa sangat penting bagi guru untuk melakukan PTK. Peningkatan jenjang jabatan dan golongan bagi para guru memerlukan beberapa karya ilmiah yang dipublikasikan. Karya ilmiah dapat dihasilkan dari kegiatan PTK, oleh sebab itu perlu adanya tambahan wawasan bagi para guru bagaimana menuangkan hasil PTK ke dalam makalah ilmiah. Pelaksanaan kegiatan pengabdian ini bertujuan untuk memberikan tambahan wawasan akan pentingnya PTK dalam proses pembelajaran dan membantu para guru dalam menulis serta mempublikasikan hasil PTK pada jurnal nasional. Diharapkan dari kegiatan ini, para guru dapat melakukan PTK dan menulis makalah ilmiah dari hasil PTK yang sesuai metode ilmiah. Kegiatan pengabdian dilakukan dengan dua tahap, yaitu tahap pelaksanaan dan tahap pendampingan. Tahap pelaksanaan meliputi penyampaian materi PTK dan Penulisan ilmiah. Berdasarkan hasil kuesioner diketahui bahwa terdapat peningkatan  mengenai pemahaman konsep PTK dan publikasi ilmiah antara sebelum penyampaian materi dan sesudah penyampaian materi. Tahap yang kedua adalah tahap pendampingan. Pada tahap ini dilakukan pendampingan penulisan dalam bentuk review draf artikel.
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