Sistem Otomasi Perpustakaan dengan Barcode SLTPI Al Azhar 8, Kemang Pratama

Kesadaran orang akan pentingnya layanan prima yang nyaman bagi pelanggan, menuntut tersedianya sistem yang efisien dan handal. Perpustakaan SLTPI Al Azhar 8 sangat menyadari pentingnya sistem yang dapat meningkatkan kinerja karyawannya dengan cara meningkatkan efisiensi dan meminimalkan kerja manual untuk menghindari kesalahan manusia. Dalam penelitian ini, sistem perpustakaan dirancang dengan menggunakan barcode scanner sebagai alat bantu input data, untuk dapat mempercepat kerja layanan dan meningkatkan akurasi data. Sistem Perpustakaan ini dirancang sesuai dengan proses bisnis yang ada dengan dilengkapi beberapa fitur baru yang teridentifikasi dalam tahap analisa kebutuhan seperti pengecekan terhadap resources yang dimasukkan, perpindahan data antar pengguna dilakukan melalui sistem, sehingga dapat meningkatkan efisiensi proses bisnis yang berjalan saat ini.Recently people starts to realize the importance of service excellence and demand an efficient and reliable system. SLTPI Al Azhar 8 Library awares of the needs of new system to improve its performance through maximizing staffs performance and minimizing manual operations to prevent human error. In this research, library system was designed to use barcode scanner as input tool, to shorten the service and increase data accuracy. This system was designed to fit the existing business process with additional features identified in the user requirement analysis stage, such as resource checking, automatic data transfer, to improve the existing business process

Dimensionality Reduction using PCA and K-Means Clustering for Breast Cancer Prediction

Breast cancer is the most important cause of death among women. A prediction of breast cancer in early stage provides a greater possibility of its cure. It needs a breast cancer prediction tool that can classify a breast tumor whether it was a harmful malignant tumor or un-harmful benign tumor. In this paper, two algorithms of machine learning, namely Support Vector Machine and Extreme Gradient Boosting technique will be compared for classification purpose. Prior to the classification, the number of data attribute will be reduced from the raw data by extracting features using Principal Component Analysis. A clustering method, namely K-Means is also used for dimensionality reduction besides the Principal Component Analysis. This paper will present a comparison among four models based on two dimensionality reduction methods combined with two classifiers which applied on Wisconsin Breast Cancer Dataset. The comparison will be measured by using accuracy, sensitivity and specificity metrics evaluated from the confusion matrices. The experimental results have indicated that the K-Means method, which is not usually used for dimensionality reduction can perform well compared to the popular Principal Component Analysis

Fuzzy Reasoning House of Risk to Manage Supply Chain Risk in Wooden Toys Industries

An unexpected event in the supply chain will disturb material flow and activities in the whole. The occurrence of such interference would have caused losses at time and cost. In order to anticipate this situation, it needs proper supply chain risk management. Research was done in wooden toy industry case study which has wide marketing range. Fuzzy Reasoning House of Risk (FRHOR) are used to solved the problem. FRHOR is combination of fuzzy reasoning risk assessment model and house of risk. House of Risk Method is used to identify the most potential risk agents, while the fuzzy reasoning risk assessment model is used to determine the risk severity by risk agents. Based on the analysis, it is found that risk agent stock out of the product is the most potential risk agent. To reduce the impact, recommended mitigation strategies that can be used for stock out product risk agents in werehouse are flexible supply base, safety stock, internal coordination, as well as create and control production schedules

Analisa Genotype dan Serotype Virus Hepatitis B Potensial sebagai Kandidat Sekuens Vaksin Hepatitis B secara Bioinformatika Berbasis Web

Abstrak - Virus hepatitis B sudah menjadi masalah kesehatan publik terbesar ketiga dari populasi dunia yang dibuktikan dengan hasil serological infeksi kronis yang terjadi. Saat ini terdapat 10 jenis genotype virus hepatitis B, dari A hingga J, dan 4 jenis serotype yaitu adw, adr, ayw, dan ayr. Penelitian ini bertujuan untuk menentukan genotype dan serotype virus hepatitis B yang memiliki potensi sebagai kandidat vaksin hepatitis B. Salah satu metode yang efektif saat ini adalah bioinformatika, suatu multidisplin ilmu biologi berbasis web yang dapat mengeksplorasi berbagai urutan dan melihat filogeni. Hasil penelitian menunjukkan bahwa data sekuens yang dapat menjadi kandidat untuk desain vaksin hepatitis B yaitu virus hepatitis B genotype B dengan subgenotype B3, genotype C dengan subgenotype C6 untuk lingkup Indonesia, sedangkan untuk lingkup dunia diperoleh potensi dari serotype Adw. Abstrak - Hepatitis B virus has become the largest public health problem to three of the world’s population as evidenced by the results of serological chronic infections that occur. In the world, there are 10 types of hepatitis B virus genotypes present, from A to J, and 4 types of serotype, adw, adr, ayw, and ayr. In connection with various genotype and serotype, genotype and serotype candidate required potential so that the sequence can be used as a vaccine candidate sequence of hepatitis B. One of the effective methods currently is a web-based bioinformatics that could explore a variety of sequence and see the phylogeny, nor the annotations and predictions. Analysis of the sequence data predict can be candidate for the design of the hepatitis B vaccine hepatitis B virus genotype B with subgenotype B3, subgenotype C6 from genotype C with scope for Indonesia, while tha scope of the world to obtain the potential of serotype adw