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Sistem Informasi Pembelian Dengan Metode Waterfall Pada PT. Koyorad Jaya Indonesia Bekasi Safar, Suryani; Achyani, Yuni Eka
INFORMATION MANAGEMENT FOR EDUCATORS AND PROFESSIONALS Vol 3 No 1 (2018): INFORMATION MANAGEMENT FOR EDUCATORS AND PROFESSIONALS (Desember 2018)
Publisher : Penelitian dan Pengabdian Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (448.045 KB)

Abstract

Abstrak: Dalam Era Moderisasi yang sudah berkembang secara cepat , dunia bisnis menuntut kinerja dari semua aspek supaya bekerja secara efisian dan sistematis, sistem dalam sebuah perusahaan bukan hanya seperti sebuah kebutuhan tetapi juga merupakan bagian dari perusahaan itu sendiri. PT Koyorad Jaya Indonesia merupakan salah satu perusahaan yang cukup perkembang di dunia internasional. Sistem dalam perusahaan ini sudah menggunakan sistem komputerisasi yang baik, tapi system ini belum mencakup semua aspek perusahaan. Maka penulis mencoba untuk membuat tugas akhir ini untuk mengajukan system baru dalam perusahaan ini yang masih menggunakan sistem manual. Pada bagian pembelian PT Koyorad Jaya Indonesia menerapkan dua sistem. Yaitu sistem pembelian secara kredit / tempo dimana sistem membayaran menggunakan invoicing dan yang ke dua adalah sistem pembelian secara tunai / kas bon yang biasanya di butuhkan disaat ada kebutuhan pembelian barang keperluan perusahaan yang bersifat mendesak. Dari dua system pembelian tersebut, sistem pembelian secara tunai pada PT Koyorad Jaya Indonesia masih menggunakan pencatatan manual dalam form kasbon dan pelaporan yang masih terkesan seadanya. Hal ini akan menjadi masalah terutama pada bagian keuangan atau finance. Penulis mengharapkan mampu mengusulkan sebuah program yang akan menyempurnakan kinerja sistem yang terdapat pada PT Koyorad Jaya Indonesia. Sehingga perusahaan ini dapat lebih berkembang dan maju dari sebelumnya.   Kata kunci: Sistem Informasi, Pembelian Barang   Abstract: In Moderisasi Era that has been growing rapidly, the business world demands that the performance of all aspects of fuel-efficient and systematic work, the system in a company is not only a necessity but also a part of the company itself. PT Koyorad Jaya Indonesia is one company that is growing interest in the international community. enterprise system is already using a computerized system that well, but this system does not yet include all aspects of the company. the author tries to make this final task to propose a new system at these companies are still using manual systems. On the purchase of PT Koyorad Jaya Indonesia apply two systems. purchase credit system / tempo in which the provider payment system using the invoice and the second is the system of billing purchases cash / treasury are usually required when purchasing an urgent need consumer goods company. Purchase of two systems, the system of cash purchase in PT Koyorad Jaya Indonesia still use manual recording in the form of cash and reporting still deceptively simple. It will be a problem, especially in finance or finance. The author hopes to propose a program that will improve system performance at PT Koyorad Jaya Indonesia. So that these companies can be more developed and advanced than ever before.   Keywords: Information System, purchase of goods
Prediksi Pemasaran Langsung Menggunakan Metode Support Vector Machine Achyani, Yuni Eka
JURNAL TEKNIK KOMPUTER Vol 3, No 2 (2017): JURNAL TEKNIK KOMPUTER AMIK BSI
Publisher : AMIK BSI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (406.171 KB) | DOI: 10.31294/jtk.v3i2.1719

Abstract

Abstract— Direct marketing is a typical strategy to increase business. The company uses direct marketing when targeting customer segments with their contact to fulfill a specific purpose. Direct marketing is one way that can be used to predict potential customers who open deposits at the bank. Direct marketing became a very important application in data mining today. Data mining is widely used in direct marketing to identify potential customers for new products, using the purchase history data, predictive models can be used to measure that customers will respond to a given promotion or offer. One method that is most widely used method of support vector machine. In this study will be used method of support vector machine for prediction of direct marketing. After testing the results obtained is a support vector machine produces an accuracy value of 88.71%, 89.47% and a precision value AUC value of 0.896 with a value of classification accuracy was very good (excellent clasification). Based on these results it can be concluded that the use of support vector machine method can be used for precise and accurate prediction of direct marketing. Keywords : Prediction, Direct Marketing, Support Vector Machine. Abstrak— Pemasaran langsung merupakan strategi yang khas untuk meningkatkan bisnis. Perusahaan menggunakan pemasaran langsung bila menargetkan segmen pelanggan dengan menghubungi mereka untuk memenuhi tujuan tertentu. pemasaran langsung merupakan salah satu cara yang dapat digunakan untuk memprediksi nasabah yang berpotensi membuka simpanan deposito pada bank tersebut. Pemasaran langsung menjadi aplikasi yang sangat penting dalam data mining saat ini. Data mining  secara luas telah digunakan dalam pemasaran langsung untuk mengidentifikasi calon pelanggan untuk produk baru, dengan menggunakan data histori beli, model prediktif dapat digunakan untuk mengukur bahwa pelanggan akan menanggapi promosi atau tawaran yang diberikan. Salah satu metode yang paling banyak digunakan adalah metode  support vector machine. Dalam penelitian ini akan digunakan  metode  support vector machine untuk prediksi pemasaran langsung. Setelah dilakukan pengujian maka hasil yang didapat adalah support vector machine menghasilkan nilai akurasi sebesar 88,71 %, nilai precision 89,47%   dan nilai AUC sebesar 0,896 dengan nilai akurasi klasifikasi sangat baik (excellent clasification). Berdasarkan hasil tersebut dapat disimpulkan bahwa penggunaan metode support vector machine dapat digunakan secara tepat dan akurat untuk prediksi pemasaran langsung. Kata Kunci— Prediksi, Pemasaran Langsung,  Support Vector Machine.
SISTEM INFORMASI PENDAPATAN JASA PADA KOPERASI PDAM TIRTA PATRIOT BEKASI Achyani, Yuni Eka; Arviana, Eni
JURNAL TEKNIK KOMPUTER Vol 4, No 1 (2018): JURNAL TEKNIK KOMPUTER AMIK BSI
Publisher : AMIK BSI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1181.461 KB) | DOI: 10.31294/jtk.v4i1.2377

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Development of information technology is increasingly rapidly makes the need for information is increasing. Information is very important in supporting the course of a company to achieve the desired goal. With the data processing program, and the information will be more rapid, precise and accurate in its presentation. Employees Cooperative PDAM Tirta Patriot is a business entity that is engaged in trade and services. By using the Web program can perform inputting and storing data quickly and can be easier to find the data that we want, so as to reduce the mistakes that often occur. The data collection method used by the authors is the direct observation, interviews, and literature. The authors purpose of research on web-based revenue service information system on Employee Cooperation Tirta Patriot PDAM in the hope to overcome the obstacles that have occurred in the system of service revenue manually, and can assist in making reports service revenue.
Penerapan Metode Particle Swarm Optimization Pada Optimasi Prediksi Pemasaran Langsung Achyani, Yuni Eka
Jurnal Informatika Vol 5, No 1 (2018): Jurnal INFORMATIKA
Publisher : LPPM Universitas BSI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (590.919 KB) | DOI: 10.31311/ji.v5i1.2736

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Abstrak Dalam persaingan ketat saat ini, promosi yang baik dapat memberikan kredibilitas untuk produk baru. Promosi perlu mendapat perhatian lebih dan serius, karena dalam kehidupan sehari-hari timbul produk unggulan, jika tidak mengetahuinya, kemungkinan produk yang ditawarkan kepada konsumen kurang ditanggapi oleh pasar, oleh karena itu perusahaan harus mengupayakan produknya, meyakinkan dan mempengaruhi konsumen untuk menciptakan permintaan akan produk ini. Langkah yang bisa dilakukan oleh perusahaan untuk melakukannya adalah dengan melakukan pemasaran langsung. Peningkatan akurasi prediksi pemasaran langsung dapat dilakukan dengan cara melakukan seleksi terhadap atribut, karena seleksi atribut mengurangi dimensi dari data sehingga operasi algoritma data mining dapat berjalan lebih efektif dan lebih cepat. Dalam penelitian ini akan digunakan metode support vector machine dan akan dilakukan seleksi atribut dengan menggunakan particle swarm optimization untuk prediksi pemasaran langsung. Setelah dilakukan pengujian maka hasil yang didapat adalah support vector machine menghasilkan nilai akurasi sebesar 88,71 %, nilai precision 89,47% dan nilai AUC sebesar 0,896. Kemudian dilakukan seleksi atribut dengan menggunakan particle swarm optimization dimana atribut yang semula berjumlah 16 variabel prediktor terpilih 12 atribut yang digunakan. Hasil menunjukkan nilai akurasi yang lebih tinggi yaitu sebesar 89,38%, nilai precision 89,89% dan nilai AUC sebesar 0,909 dengan nilai akurasi klasifikasi sangat baik (excellent clasiffication). Sehingga dicapai peningkatan akurasi sebesar 0,67 %, dan peningkatan AUC sebesar 0,013. Kata Kunci: Particle Swarm Optimization, Pemasaran Langsung, Seleksi Atribut Abstract In the current intense competition a good promotion can provide credibility for a new product. Promotion needs to get more attention and serious, because in everyday life arise a prime product, if not find out, the possibility of products offered to consumers less responded by the market, therefore the company should strive for its products. , convincing, and influencing consumers to create demand for these products. Steps that can be done by the company to do so is to do direct marketing. Increased accuracy of direct marketing predictions can be done by selecting attributes, because of the selection. Data mining can run more effectively and quickly. In this study the method to be used is. With particle swarm optimization for direct marketing prediction optimization. After testing, the results obtained are support vector engine yield value of 88.71%, precision value 89.47% and AUC value of 0.896. Then the attribute selection is done using particle swarm optimization where the original attribute uses 16 predictor variables selected 12 attributes used. The results showed a higher value of 89.38%, 89.89% accuracy and AUC value of 0.909 with very good fair value (excellent classification). The price increase is 0.67%, and the increase of AUC is 0,013. Keywords: Particle Swarm Optimization, Direct Marketing, Selection Attributes.
PENERAPAN METODE WATERFALL PADA SISTEM INFORMASI MANAJEMEN BUKU PERPUSTAKAAN BERBASIS WEB Achyani, Yuni Eka; Saumi, Sela
Jurnal SAINTEKOM Vol 9 No 1 (2019): Maret 2019
Publisher : STMIK Palangka Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1630.896 KB) | DOI: 10.33020/saintekom.v9i1.84

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The development of increasingly advanced information technology can provide many benefits for completing work quickly and accurately. One example that requires the delivery of information quickly and accurately is the field of library, this is in accordance with the function of the library which is the heart of education. Most libraries are still many who adhere to a conventional system, of course this will result in disruption of the continuity of the process of managing books in the library. Therefore, the author takes the theme of this study regarding Library Book Management Information Systems Based on Websites by using the waterfall method on software development as well as methods of observation and literature on data collection. This Information System is the best solution for problem solving in managing library books. With the use of managed computer data technology becomes faster, reducing inefficient time and reducing the occurrence of errors in processing data.
Optimasi Metode Particle Swarm Optimization (PSO) Pada Prediksi Penilaian Apartemen Nilawati, Lala; Achyani, Yuni Eka
Paradigma - Jurnal Komputer dan Informatika Vol 21, No 2 (2019): Periode September 2019
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (129.53 KB) | DOI: 10.31294/p.v21i2.6159

Abstract

One property that is currently being glimpsed by investors is an apartment. Property consulting companies as one of the service provider companies that become a link between apartment owners and apartment enthusiasts, have an important task in terms of providing information about the assessment of the offered institutions. This study will conduct a trial on the accuracy of apartment assessment predictions using the Support Vector Machine (SVM) method, then will be compared again with the results of the accuracy of the assessment method Support Vector Machine (SVM) combined using the optimization method Particle Swarm Optimization (PSO). The results of the combination of the application of SVM and PSO are used to optimize attribute selection in apartment valuation to improve the accuracy of using the SVM method. This study shows that the Particle Swarm Optimization (PSO) Support Vector Machine (SVM) method is a pretty good method of data classification, because it can seen from the increase in accuracy of 2.84% and AUC of 0.003. Subjects (attributes) that affect apartment valuation are seen from rent prices (price range), city (apartment location), size (area), furnisihing (equipment), bedroom (number of bedrooms), bathroom (number of bathrooms) and maids badroom (number of maid rooms). The results of the attribute testing showed that city attributes (apartment locations), furnisihing (equipment) and maid badroom (number of maid rooms) greatly influenced the valuation of an apartment.