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.

This paper proposes support vector machines (SVMs), which is currently one of the most popular algorithms in machine learning (ML), in order to classify the low birth weight (LBW) data. The main objectives of this study are to predict the classification of LBW data in Indonesia based on the SVMs andto compare the performance of the proposed SVMs with the binary logistic regression as the most common model for classification of LBW data. The obtained samples were based on the results of Indonesian Demographic and Health Survey in 2012. The results showed that SVMs with four kernel functions (linear, radial, polynomial and hyperbolic tangent) were fit well to the LBW data in Indonesia. Furthermore, the constructed SVMs based on linear kernel function had the best performance among the SVMs with the other proposed kernel functions. This research also concluded that the SVMs based on linear kernel competed well with thebinary logistic regression forclassification LBW data in Indonesia.

Cutting Stock Problem (CSP) is the determination of how to cut stocks into items with certain cutting rules. A diverse set of stocks is called multiple stock CSP. This study used Pattern Generation (PG) algorithm to determine cutting pattern, then formulated it into a Gilmore and Gomory model and solved by using Column Generation Technique (CGT). Set Covering model was generated from Gilmore and Gomory model. Based on the results, selected cutting patterns in the first stage can be used in the second stage. The combination of patterns generated from Gilmore and Gomory model showed that the use of stocks was more effective than Set Covering model.

ABSTRAKTujuan penelitian ini adalah untuk mengetahui faktor-faktor yang berpengaruh secara signifikan terhadap loyalitas konsumen pasar swalayan di Kota Palembang dan Kabupaten Ogan Ilir, Sumatera Selatan. Metode yang digunakan adalah Model Persamaan Struktural (MPS), karena peubah yang terlibat berupa peubah laten dan hubungan antara peubahnya bersifat langsung dan tak langsung. Hasil yang diperoleh adalah faktor kualitas harga memberikan pengaruh langsung terbesar secara signifikan terhadap kepuasan konsumen sebesar 0,51 dengan nilai statistik uji-t sebesar 5,91. Faktor kepuasan konsumen memberikan pengaruh total terbesar terhadap loyalitas konsumen sebesar 0,89 dengan nilai statistik uji-t nya sebesar 3,31.Kata kunci: Model Persamaan Struktural (MPS), Kepuasan dan loyalitas konsumen. ABSTRACT The aim of this research is to know factors having an influence by significant to self-service market consumer loyality in Palembang Town and Ogan Ilir Sub-Province, South Sumatra. There used a Structural Equation Models (SEM) analysis method, because variables in this research in the form of laten variable and causal relationship between a set of variables nor modestly Keywords:Structural Equation Models (SEM), Satisfaction and Loyality of Consumer PendahuluanMakin pesatnya perkembangan bisnis pasar modern Indonesia, khususnya di Provinsi Sumatera Selatan, ditandai dengan munculnya berbagai pusat perbelanjaan seperti minimarket dan pasar swalayan. Keberadaan pasar swalayan ini menjangkau ke berbagai pelosok daerah dan mampu menekan keberadaan pasar tradisional yang ada di sekitarnya, contohnya pasar swalayan Indomaret dan Alfamart yang sedang gencar dalam mengembangkan gerai bisnisnya.Agar mampu terus bersaing dan menjadi pilihan masyarakat dalam berbelanja, pihak pasar swalayan perlu terus menarik konsumen dan memberi kepuasan, serta menciptakan loyalitas konsumen. Loyalitas merupakan faktor penting dalam menunjang kemampuan bertahan hidup sebuah bisnis dan membantu menentukan kesuksesannya. Oleh karena itu perlu mengetahui faktor apa saja yang mempengaruhi loyalitas konsumen agar bisa mempertahankan keberadaannya, khususnya bagi pasar swalayan di Provinsi Sumatera Selatan.Faktor yang berpengaruh dapat diantaranya kualitas produk, kualitas harga, kualitas karyawan dan kualitas lokasi pasar swalayan. Kualitas produk dapat dilihat dari persepsi konsumen terhadap kemasan, kondisi, variasi, kelengkapan dan kesegaran produk yang pasarkan. Faktor kualitas harga dapat berupa kewajaran dan keekonomisan harga yang ditawarkan.Faktor kualitas karyawan dapat berupa interaksi karyawan pasar swalayan dengan konsumen. dan Faktor kualitas lokasi adalah mudah tidaknya pasar swalayan dijangkau oleh konsumen.

Cervical cancer is one of the deadliest female cancers. Early identification of cervical cancer through pap smear cell image evaluation is one of the strategies to reduce cervical cancer cases. The classification methods that are often used are SVM, MLP, and K-NN. The weakness of the SVM method is that it is not efficient on large datasets. Meanwhile, in the MLP method, large amounts of data can increase the complexity of each layer, thereby affecting the duration of the weighting process. Moreover, the K-NN method is not efficient for data with a large number of attributes. The ensemble method is one of the techniques to overcome the limitations of a single classification method. The ensemble classification method combines the performance of several classification methods. This study proposes an ensemble method with the majority voting that can be used in cervical cancer classification based on pap smear images in the Herlev dataset. Majority Voting is used to integrate test results from the SVM, MLP, and KNN methods by looking at the majority results on the test data classification. The results of this study indicate that the accuracy results obtained in the ensemble method increased by 1.72% compared to the average accuracy value in SVM, MLP, and KNN. for sensitivity results, the results of the ensemble method were able to increase the sensitivity increase by 0.74% compared to the average of the three single classification methods. for specificity, the ensemble method can increase the specificity results by 3.4%. From the results of the study, it can be concluded that the ensemble method with the most votes is able to improve the classification performance of the single classification method in classifying cervical cancer abnormalities with pap smear images.