Study and Observation of the Variation of Accuracies of KNN, SVM, LMNN, ENN Algorithms on Eleven Different Datasets from UCI Machine Learning Repository

Machine learning qualifies computers to assimilate with data, without being solely programmed [1, 2]. Machine learning can be classified as supervised and unsupervised learning. In supervised learning, computers learn an objective that portrays an input to an output hinged on training input-output pairs [3]. Most efficient and widely used supervised learning algorithms are K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Large Margin Nearest Neighbor (LMNN), and Extended Nearest Neighbor (ENN). The main contribution of this paper is to implement these elegant learning algorithms on eleven different datasets from the UCI machine learning repository to observe the variation of accuracies for each of the algorithms on all datasets. Analyzing the accuracy of the algorithms will give us a brief idea about the relationship of the machine learning algorithms and the data dimensionality. All the algorithms are developed in Matlab. Upon such accuracy observation, the comparison can be built among KNN, SVM, LMNN, and ENN regarding their performances on each dataset.Comment: To be published in the 4th IEEE International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018

ADBSCAN: Adaptive Density-Based Spatial Clustering of Applications with Noise for Identifying Clusters with Varying Densities

Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm which has the high-performance rate for dataset where clusters have the constant density of data points. One of the significant attributes of this algorithm is noise cancellation. However, DBSCAN demonstrates reduced performances for clusters with different densities. Therefore, in this paper, an adaptive DBSCAN is proposed which can work significantly well for identifying clusters with varying densities.Comment: To be published in the 4th IEEE International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018

Study and Observation of the Variations of Accuracies for Handwritten Digits Recognition with Various Hidden Layers and Epochs using Neural Network Algorithm

In recent days, Artificial Neural Network (ANN) can be applied to a vast majority of fields including business, medicine, engineering, etc. The most popular areas where ANN is employed nowadays are pattern and sequence recognition, novelty detection, character recognition, regression analysis, speech recognition, image compression, stock market prediction, Electronic nose, security, loan applications, data processing, robotics, and control. The benefits associated with its broad applications leads to increasing popularity of ANN in the era of 21st Century. ANN confers many benefits such as organic learning, nonlinear data processing, fault tolerance, and self-repairing compared to other conventional approaches. The primary objective of this paper is to analyze the influence of the hidden layers of a neural network over the overall performance of the network. To demonstrate this influence, we applied neural network with different layers on the MNIST dataset. Also, another goal is to observe the variations of accuracies of ANN for different numbers of hidden layers and epochs and to compare and contrast among them.Comment: To be published in the 4th IEEE International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018

Optimasi Perencanaan Produksi Menggunakan Linear Programming Dan Perencanaan Persediaan Bahan Baku Di PT. Sandy Globalindo (SND)

PT. Sandy Globalindo (SND) merupakan salah satu produsen spare part dan aksesoris otomotif di Indonesia khususnya kendaraan roda 2 (dua). Produk pertama PT. Sandy Globalindo (SND) adalah foot step untuk kendaraan roda 2 (dua) dan produk unggulannya saat ini adalah Exhaust/ Knalpot untuk kendaraan roda 2 (dua). Salah satu produk yang diproduksi di PT. Sandy Globalindo (SND) adalah Exhaust Kawasaki KLX 150. Pada produksi Exhaust Kawasaki KLX 150 terdapat produksi yang tidak sesuai terget sehingga perlu adanya perencanaan produksi dan perencanaan persediaan bahan baku khususnya Head Silencer KLX 150 yang memiliki lead time 1 minggu. Agar produksi berjalan sesuai target maka perlu adanya perencanaan produksi dari produk Exhaust Kawasaki KLX 150 dan perencanaan persediaan bahan baku Head Silencer KLX 150. Perencanaan produksi menggunakan programa linier membutuhkan data permintaan, data kapasitas dan hari kerja dalam satu tahunnya. Programa linier pada studi kasus ini berfungsi untuk mengetahui jumlah produksi yang optimal dengan memperhatikan ongkos, kapasitas produksi dan demand. Output yang dihasilkan dari perhitungan ini adalah jumlah produksi yang optimal perbulannya. Perhitungan ini dibantu dengan softwere WinQSB, dimana softwere ini digunakan untuk memecahkan masalah Integer Linear Programming dengan menggunakan metode Big M. Output dari perhitungan ini adalah jumlah produksi yang optimum dari produk Exhaust Kawasaki KLX 150. Perencanaan persediaan menggunakan metode EOQ (Eqonomic Order Quantity) dengan Quantity Discount yang dimana perusahaan mendapat pilihan dari pihak supplier untuk membeli komponen Head Silencer KLX 150 dengan harga tertentu sesuai dengan kuantitas yang dipesan. Dalam perhitungan EOQ yaitu menghitung jumlah komponen yang akan dipesan dengan perbandingan harga sesuai interval harga yang ditetapkan oleh pihak supplier. EOQ ini menggambarkan jumlah yang optimum dari sekali pemesanan komponen kepada supplier. Perhitungan EOQ ini memperhatikan Total Cost dari interval harga yang ditawarkan oleh supplier. Setelah didapat Total Cost maka dapat dipilih jumlah pemesanan yang memiliki Total Cost yang terkecil dan juga menghitung frekuensi pemesanan, waktu interval pemesanan dan re-order point. Setelah dilakukan perhitungan perencanaan produksi menggunakan programa linier maka, dihasilkan jumlah produksi pada bulan Januari sampai dengan bulan Desember sebesar 0, 75, 125, 225, 225, 125, 225, 525, 325, 125, 225 dan 125. Pada bulan Januari produksi sebesar 0 artinya pada bulan Januari tidak adanya produksi. Perhitungan perencanaan persediaan menggunakan metode EOQ dengan Quantity Discount dihasilkan kuantitas pemesanan per sekali pesan adalah 101 unit komponen Head Silencer KLX 150 dengan total biaya Rp. 152.749.263 dan frekuensi pemesanan sebanyak 23 kali pemesanan dengan waktu interval pemesanan 0,04 tahun, pemesanan ulang/ re-order point yang harus dilakukan ketika komponen Head Silencer KLX 150 tinggal tersisa sebanyak 65 unit lagi dengan lead time pemesanan bahan baku/ komponen selama 7 hari. Kata Kunci: PT. Sandy Globalindo (SND), Programa Linier, Perencanaan Produksi, Perencanaan Persediaan, EOQ, Quantity Discount

ANALISA MANAJEMAN WAKTU MENGGUNAKAN METODE CPM(CRITICAL PATH METHOD) DAN PERT(PROGRAM EVALUATION AND REVIEW TECHNIQUE) PADA PROYEK PEMBANGUNAN RUANG KELAS BARU INSTITUT AGAMA ISLAM NAZHATUT THULLAB PULAU MADURA

Manajemen konstruksi adalah perencanaan, penjadwalan dan pengendalian proyek, untuk mencapai tujuan proyek tersebut tanpa adanya penyimpangan. Manajemen harus membandingkan biaya, waktu, dan kinerja dari program terhadap rencana anggaran biaya, rencana waktu dan kinerja dalam setiap aktifitas. Manajemen konstruksi dapat diatur sesuai dengan sumber daya yang telah direncanakan yaitu tenaga kerja (manpower), peralatan (machine), metode (method), bahan (material), uang (money), dan pemasaran (market) Metode yang di gunakan dalam Penelitian ini adalah CPM(Critical Path Method) dan PERT(Project Evaluation and Tecnique Review). Peneliti melakukan Pengoptimalan Waktu Pelaksanaan pada proyek Pembangunan Ruang Kelas Baru Institut Agama Islam Nazhatut Thullab  Pulau Madura  yang sedang mengalami keterlambatan waktu pengerjaannya. Dari hasil penelitian  Mendapatkan perhitungan waktu yang lebih optimal dengan menggunakan metode CPM dan PER

INDONESIA – CHINA BILATERAL COOPERATIONS: IMPACT OF CSP (COMPREHENSIVE STRATEGIC PARTNERSHIP) TOWARD INFRASTRUCTURE DEVELOPMENT UNDER JOKO WIDODO'S ERA

Abstract This research is the answer to how strategic partnerships affect infrastructure development. The researcher focuses on the comprehensive strategic partnership between Indonesia-China toward Indonesia's infrastructure development under Joko Widodo's era. Furthermore, the author will explain the benefits of the collaboration for China as Indonesia's partner country. In addition, the researcher will elaborate by using variables which are delivered in bilateral concepts, and also will use qualitative methods, in which data collections are from the study of literature - books, journals, documents that are taken from websites and trusted websites to strengthen researcher's argument. The results of this study indicate that the impacts of comprehensive strategic partnership on Indonesia's infrastructure development are realizing Indonesia's national connectivity agenda which was listed in the MP3EI document with China's helping through investment. in addition, by helping Indonesia in developing infrastructure, China succeeded in realizing and expanding the 21st century silk road agenda. Keyword : Bilateral Relations; Comprehensive strategic Partnership; Infrastructure development

Antigen-Specific Memory B-cell Responses to Enterotoxigenic Escherichia coli Infection in Bangladeshi Adults

Background: Multiple infections with diverse enterotoxigenic E. coli (ETEC) strains lead to broad spectrum protection against ETEC diarrhea. However, the precise mechanism of protection against ETEC infection is still unknown. Therefore, memory B cell responses and affinity maturation of antibodies to the specific ETEC antigens might be important to understand the mechanism of protection. Methodology In this study, we investigated the heat labile toxin B subunit (LTB) and colonization factor antigens (CFA/I and CS6) specific IgA and IgG memory B cell responses in Bangladeshi adults (n = 52) who were infected with ETEC. We also investigated the avidity of IgA and IgG antibodies that developed after infection to these antigens. Principal Findings Patients infected with ETEC expressing LT or LT+heat stable toxin (ST) and CFA/I group or CS6 colonization factors developed LTB, CFA/I or CS6 specific memory B cell responses at day 30 after infection. Similarly, these patients developed high avidity IgA and IgG antibodies to LTB, CFA/I or CS6 at day 7 that remained significantly elevated at day 30 when compared to the avidity of these specific antibodies at the acute stage of infection (day 2). The memory B cell responses, antibody avidity and other immune responses to CFA/I not only developed in patients infected with ETEC expressing CFA/I but also in those infected with ETEC expressing CFA/I cross-reacting epitopes. We also detected a significant positive correlation of LTB, CFA/I and CS6 specific memory B cell responses with the corresponding increase in antibody avidity. Conclusion: This study demonstrates that natural infection with ETEC induces memory B cells and high avidity antibodies to LTB and colonization factor CFA/I and CS6 antigens that could mediate anamnestic responses on re-exposure to ETEC and may help in understanding the requirements to design an effective vaccination strategies

Multi-Operator Cell Tower Locations Prediction from Crowdsourced Data

Cell tower locations are not publicly available due to business interests of wireless providers. Very often wireless providers provide exaggerated coverage maps that may mislead the public. In addition to providing a neutral check on the coverage maps, prediction of cell tower locations hosting multiple operators’ access nodes could also be helpful in disaster communications and public safety in general. The localization of the disaster-affected towers can be very conducive to respond and reach to the victims. Further, victims’ devices could utilize this knowledge to initiate device-to-device (D2D) or unmanned aerial vehicular (UAV) communications as alternatives to the damaged cellular infrastructure. Publicly available crowdsourced cell (base station) locations and FCC’s sites can be used to predict the cell tower/site locations in the United States. In this work, we utilized a weighted k-means algorithm to predict cell tower locations from OpenCellid crowdsourced dataset and implemented a mapping algorithm to locate nearest physical towers. We map the predicted towers to two different sources of physical towers. Our comparison shows a significant accuracy in predicting tower locations regardless of sources of physical towers. The technique can be used to predict the tower locations in other countries as well