Sistem Antrian Konsultasi Dokter Praktik Menggunakan Single Channel Single Phase Berbasis Website

In digital era, development of information technology is unavoidable, almost all sectors have utilized  development of information technology to facilitate all human activities, including health agencies. One form of health agency is a clinic. Sometimes in clinics, there is still way to take the queue number manually, thus making queue so long. Based on these problems, queuing system is needed to solve all queuing problems. In this study, used Single Channel Single Phase where Single Channel is service system that has one service, while Single Phase is only having one service station so after receiving service, it can immediately exit queue system. Based on results of the study, conclusions were obtained: The system built has been successfully implemented Single Channel Single Phase method where this success parameter can be seen from system that provides information about the queue that is running so that the waiting time for patients is more efficient. The characteristics of queuing that occur in Mono Valensi clinic with one stage of service show that average queue in system is ± 2 people, average queue is ± 4 people, average waiting time in system is ± 63 minutes, average waiting time in queue is ± 108 minutes

ANALISIS METODE SIMPLE ADDATIVE WEIGHTING (SAW) DAN WEIGHTED PRODUCT DALAM PENERIMAAN KARYAWAN BARU

Hiring still involves subjective considerations to the making inappropriate decisions. In a decision support system, there are several methods determine the employee decision-making. In the decision, there are several methods that can be used, Weighted Product (WP) is a method by means of multiplication to connect rating attributes, where each branch must be raised to attribute weights, Simple Addative Weihgting (SAW) is a weighted summation method. Analysis Simple Addative Weighting (SAW) and Weighted Product (WP) have the same decision but different value calculation process, both methods have the same decision if the weight value the same attributes used

RANCANG BANGUN ARSITEKTUR DAN OTOMASI PERPUSTAKAAN MENGGUNAKAN LIBRARY MANAGEMENT SYSTEM PADA FAKULTAS TEKNIK UNIVERSITAS HALU OLEO

Perpustakaan merupakan bagian terpenting dari sebuah lembaga pendidikan tinggi begitupun di tingkat fakulta s, khusunya pada fakultas teknik universitas halu oleo, saat ini pengelolaan manajemen perpustakaan masih dilakukan secara manual sehingga dari manajemen buku, keanggotaan serta pengarsipan belum dapat berjalan dengan baik sehingga berpengaruh pada pelayanan kepada mahasiswa maupun anggota perpustakaan, penelitian ini merancang dan membangun sebuah sistem otomasi perpustakaan sebagai solusi untuk melakukan manajemen buku mulai dari pendataan buku dalam bentuk bibliografi, sirkulasi , keanggotaan dan inventarisasi, penelitian ini menggunakan senayan library management system (SliMS) yang memiliki fitur -fitur yang cukup lengkap untuk membangun otomasi perpustakaan, penelitian ini menghasilkan sistem berbasis web dengan kategori katalog buku maupun keanggotaan dibuat berdasarkan jurusan yang ada pada fakuktas teknik dan ditambahkan kategori umum untuk buku dan keanggotaan diuar bidang keteknikan selain itu sistem otomasi perpustakaan memudahkan dalam mengakses karena telah terhubung dengan jaringan intranet fakultas sehin gga dengan adanya otomasi perpustakaan ini dapat meningkatkan kualitas dan kuantitas bahan pustaka maupun kun jungan anggota perpustakaan

Deteksi Uang Palsu Rupiah dengan Menggunakan Metode Deteksi Tepi Laplacian of Gaussian (LoG) dan Algoritma K-Means Clustering

Abstract. Detection of Counterfeit Rupiah Using the Laplacian of Gaussian (LoG) Edge Detection Method and the K-Means Clustering Algorithm Counterfeit money is a severe problem that is increasing in every country. The reason is the ease of getting information on making counterfeit money and the development of technology such as color printers. This study used data from 20 images of authentic rupiah banknotes and 20 photos of fake rupiah banknotes. Data analysis in this study consisted of four stages: reading the image, converting the image to grayscale, image segmentation, and grouping image values. The dataset of real money images were taken with a cellphone camera, while counterfeit money images were obtained from the website. After the dataset retrieval process, the image conversion process was carried out into a grayscale image; then, the image segmentation process proceeded. The conclusion obtained from this study is that edge detection with Laplacian of Gaussian combined with the K-Means Clustering algorithm is quite effective in detecting an image to determine the picture as whether real money or counterfeit money.Keywords: Counterfeit Money, Laplacian of Gaussian, K-Means Clustering. Abstrak. Uang palsu adalah masalah serius yang semakin meningkat di setiap negara. Penyebabnya ialah kemudahan mendapatkan informasi cara pembuatan uang palsu serta perkembangan teknologi seperti printer warna. Penelitian ini menggunakan data 20 gambar uang kertas rupiah asli dan 20 gambar uang kertas rupiah palsu. Analisis data pada penelitian ini terdiri dari empat tahap, yaitu membaca gambar, mengubah gambar menjadi skala abu-abu, segmentasi gambar, dan pengelompokan nilai citra. Pengambilan dataset berupa uang asli dilakukan dengan kamera handphone dan gambar uang palsu didapatkan dari website. Setelah proses temu kembali dataset, dilakukan proses konversi citra menjadi citra grayscale, kemudian dilakukan proses segmentasi citra. Kesimpulan yang diperoleh dari penelitian ini adalah deteksi tepi dengan Laplacian of Gaussian yang dikombinasikan dengan algoritma K-Means Clustering cukup efektif mendeteksi suatu citra untuk menentukan gambar tersebut sebagai uang asli atau uang palsu.Kata Kunci: Uang Palsu, Laplacian of Gaussian, K-Means Clustering

Prosiding Seminar Nasional Pendidikan Tinggi Vokasi Indonesia 2018

Membangun Sinergi Penelitian Terapan Dengan Kebutuhan Pembangunan Daerah dan Dunia Industr

Internasional Conference Of Vocational Higher Education (ICVHE)

Internasional Conference Of Vocational Higher Education (ICVHE

Prosiding Seminar Nasional FPTVI 2017

Membangun Penelitian Terapan Berbasis Inovasi dan Sinergi Antara Pendidikan Tinggi Vokasi Industri Serta Pemerintah Menuju Indonesia yang Unggu

Implementasi Naïve Bayes Clasifier dalam Klasifikasi Jenis Berita

Sometimes the classification of news categories is still an obstacle. Classification can be wrong because it is still subjective. As a result, the selected category does not match the uploaded news description. Based on these problems, the authors feel the need to make Classification of News Types with the Naïve Bayes Classifier Algorithm. The importance of this system is to be able to classify news and help news seekers to get the news they want. Based on the test results, the Naïve Bayes Classifier algorithm has a good performance for the classification of news types. This is evidenced in testing using news data taken from www.kompasiana.com, then news is classified into four categories namely politics, economics, sports, and entertainment. The classification results using 16 test news obtained an accuracy of 87.5%