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Implementasi Algoritma K-Nearest Neighbor untuk Prediksi Penjualan Alat Kesehatan pada Media Alkes: Implementation of the K-Nearest Neighbor Algorithm to Predict Sales of Medical Devices in Medical Devices Nijunnihayah, Uktupi; Hilabi, Shofa Shofiah; Nurapriani, Fitria; Novalia, Elfina
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 2 (2024): MALCOM April 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i2.1326

Abstract

Media Alkes Perusahaan ini bergerak dalam bidang industri Alat Kesehatan. Perusahaan ini menyediakan berbagai produk seperti jarum kursi roda, alat infus, alat monitor tekanan darah, dan lain-lain. Media Alkes juga aktif menerapkan strategi bisnis untuk memenuhi kebutuhan pelanggan. Namun sering terjadi kekurangan stok dan barang menumpuk di dalam perusahaan ini. Peneliti telah mengelola dan menganalisis data penjualan yang ada untuk memahami kebutuhan pelanggan terhadap Alat Kesehatan. Dalam menghadapi tantangan tersebut, peneliti mengusulkan algoritma K-Nearest Neighbor untuk memprediksi penjualan Alat Kesehatan di Media Alat Kesehatan. Informasi mengenai jumlah penjualan Alat Kesehatan dengan kriteria Sangat laris, Cukup laris dan Kurang laris dapat dilihat melalui data penjualan tahun 2020 hingga tahun 2022 pada Media Laporan Penjualan Alat Kesehatan. Penelitian dilakukan dengan menerapkan metode K-Nearest Neighbor (KNN) baik dengan perhitungan secara manual maupun menggunakan sistem RapidMiner. Hasil dari prediksi yang menggunakan sistem RapidMiner menunjukkan tingkat akurasi sebesar 95,00% dari data yang disebut penjualan. Dengan hasil prediksi yang didapat yang Sangat bagus tersebut, metode ini dapat dijadikan sebagai acuan dalam merencanakan penjualan di masa depan. Dengan menerapkan prediksi ini, perusahaan dapat mengelola stok barang dengan secara efisien dan menghindari kehabisan stok serta memuat barang yang tidak diinginkan.
Peningkatan Minat Digital Skill Menggunakan Algoritma K-Medoids Clustering Pada Karyawan Zulfiana, Rizka; Hilabi, Shofa Shofiah; Nurapriani, Fitria; Huda, Baenil
Journal of Information System Research (JOSH) Vol 5 No 3 (2024): April 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i3.4994

Abstract

General Company for Printing Money of the Republic of Indonesia is one of the state-owned enterprises that prints banknotes and other official documents. Perum Peruri also wishes to gain more insight into the technology implemented in the company. The demand for a workforce skilled in the use of technology in the work environment continues to increase over time. Perum Peruri has 16 Digital Skill categories, each of these categories has a high, medium to the lowest interest. In this problem, the data taken has not been grouped, so there is a lack of information about the number of categories that have the highest to lowest interest. By analyzing the specialization data, it will help determine which categories need improvement. The categories in Digital Skills specialization can then be improved by using this information as a reference for designing improvement strategies. Research was conducted using clustering to determine the number of categories that Perum Peruri personnel are interested in. In this study, sales data in Excel format was analyzed, and clusters based on product sales data were created using the K-Medoids approach. Sales information obtained from secondary data that manages employee specialization. Using RapidMiner, the accuracy for the three clusters designated as highest, middle, and lowest based on the clustering results was ascertained. The first cluster of 16 items analyzed consisted of 7 items with the highest ranking, the second cluster had 5 items categorized as medium, and the third cluster had 4 items classified as the lowest. Based on the results, 4 items were categorized as low, indicating the need for a socialization approach to increase interest in the Digital Skill.
Sistem Pemilihan Supplier Obat Menerapkan Metode Additive Ratio Analysis (ARAS) Al Khadzik, Fahmi; Huda, Baenil; Novalia, Elfina; Hilabi, Shofa Shofiah
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 5 No. 3 (2024): Maret 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v5i3.7499

Abstract

Qita Sehat pharmacy provides a wide range of medicines that are supplied by more than 30 suppliers and 100 buyers every month, but not all suppliers can meet the criteria set by pharmacies and suppliers are often late in the process of supplying drugs to pharmacies so that the stock in pharmacies is running low. From these problems, a solution is made, namely a drug supplier selection system is made by determining the priority order of drug suppliers with several criteria that match the availability of drugs at Qita Sehat pharmacies. The method used is the method of ARAS (Additive Ratio Analysis). The criteria considered are price, quality, lead time, communication systems, performance history and repair services. The result of this method is the order of priority of drug suppliers and knowing the results of the questionnaire through the sensitivity test that is the influence of changes in the value of the importance of the criteria. From the data generated in research using the ARAS method, the results obtained are that PT Javas Karya is the best supplier with the first rank of alternative A6 with a total value of 0.120.