JUHERMAN, EMAN (2019) PENGELOMPOKAN TOTAL KEMASAN YANG DIKIRIM DENGAN METODE K-MEANS CLUSTERING PADA PT. INDONESIA TOPPAN PRINTING BERDASARKAN DATA PENGIRIMAN. S1 thesis, Universitas Mercu Buana Bekasi.
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Abstract
ABSTRAK Nama : Eman Juherman NIM : 41515310033 Pembimbing TA : Giri Purnama, S.Pd., M.Kom. Judul : Pengelompokan Total Kemasan Yang Dikirim Dengan Metode K-Means Clustering Pada PT. Indonesia Toppan Printing Berdasarkan Data Pengiriman Algoritma K-means merupakan salah satu algoritma dengan partitional, karena KMeans didasarkan pada penentuan jumlah awal kelompok dengan mendefinisikan nilai centroid awalnya. Pada penelitian ini, algoritma K-Means diimplementasikan pada reporting tahunan yang dilakukan perusahaan PT. Indonesia Toppan Printing. Peng-implementasian algoritma ini bertujuan untuk memberikan gambaran jenis kemasan apa saja yang paling banyak dikirim. Proses yang berjalan saat ini adalah belum ada sistem atau cara yang digunakan untuk memberikan gambaran jenis kemasan apa saja yang paling banyak di kirim, sehingga nantinya gambaran tersebut dapat dijadikan pertimbangan serta prediksi pembelian material harus dilakuakan setelah ada PO yang masuk. Hal ini bisa berakibat pada proses produksi yang menjadi telat akibat menunggu material yang baru dipesan oleh purchasing dikarenakan harus menunggu PO masuk terlebih dahulu. Untuk mengatasi masalah tersebut, maka peneliti mengimplementasikan algoritma K-Means untuk dapat membantu memberikan gambaran jenis kemasan dan apa saja yang paling banyak dikirim, sehingga data tersebut dapat dipakai sebagai alat pendukung keputusan oleh staf purchasing untuk prediksi pembelian material. Jenis kemasan yang akan di teliti diantaranya adalah kemasan minyak 500 ml, kemasan minyak 1 liter, kemasan minyak 2 liter, kemasan obat-obatan, kemasan snack, kemasan minuman sachet, kemasan bumbu, kemasan untuk perlengkapan toilet, dll. Kata kunci: Algoritma, K-Means, Report, Purchasing, data minning ABSTRACT Name : Eman Juherman Student Number : 41515310033 Counsellor : Giri Purnama, S.Pd., M.Kom. Title : Grouping of Total Packaging Sent by the K-Means Clustering Method at PT. Indonesia Toppan Printing Based on Shipping Data K-means algorithm is one algorithm with partitional, because K-Means is based on determining the initial number of groups by defining the value of the centroid initially. In this study, the K-Means algorithm was implemented on monthly, quarter, and annual reporting at PT. Indonesia Toppan Printing. The implementation of this algorithm aims to predict what types of packaging are often produced along with the type of media / printed material. The current process is that there is no system or method used to predict the type of packaging and the type of printed material that is often used in production, so the purchase of material must be done after the PO has entered. This can result in production process that becomes late because must waiting for new materials ordered by purchasing due to having to wait for the PO to enter first. To solve this problem, the researcher implemented the K-Mean algorithm to be able to help provide the type of packaging and what was most sent, so that the data could be used as a decision support tool by purchasing staff. The types of packaging to be examined include 500 ml oil packaging, 1 liter oil packaging, 2 liter oil packaging, medicine packaging, snack packaging, sachet beverage packaging, seasoning packaging, toiletries packaging, etc. Key words: Algorithm, K-Means, Report, Purchasing, data minning
| Item Type: | Thesis (S1) |
|---|---|
| Call Number CD: | FIK/INFO 19 015 |
| NIM/NIDN Creators: | 41515310033 |
| Uncontrolled Keywords: | Algoritma, K-Means, Report, Purchasing, data minning |
| Subjects: | 000 Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 000. Computer Science, Information and General Works/Ilmu Komputer, Informasi, dan Karya Umum > 004 Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika |
| Divisions: | Fakultas Ilmu Komputer > Informatika |
| Depositing User: | siti maisyaroh |
| Date Deposited: | 02 Aug 2022 04:18 |
| Last Modified: | 02 Aug 2022 04:18 |
| URI: | http://repository.mercubuana.ac.id/id/eprint/66612 |
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