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Fitriyani, Dede
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Computer Science & IT Social Sciences

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IMPLEMENTASI ALGORITMA K-MEANS UNTUK KLASTERISASI DALAM PENGELOLAAN PERSEDIAAN OBAT (STUDI KASUS : APOTEK NAZA) Fitriyani, Dede; Jajuli, Mohamad; Garno, Garno
Jurnal Informatika dan Teknik Elektro Terapan Vol 12, No 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.4921

Abstract

Apotek Naza plays an important role in providing medicines to the community. This study utilizes sales data from Apotek Naza for the period of July to December 2023. The K-Means algorithm is used to cluster the medicine data into clusters representing different sales patterns. The Elbow Method is employed to determine the optimal number of clusters (K) based on the Sum of Square Error (SSE). Evaluation is conducted using the Silhouette Coefficient (SC) to measure the quality of the resulting clusters. The analysis results show that the distribution of medicines in each cluster is as follows: 13.7% or 70 items are classified in the high-usage cluster (Cluster 0 - High), 57.5% or 294 items are classified in the medium-usage cluster (Cluster 1 - Medium), and 28.8% or 147 items are classified in the low-usage cluster (Cluster 2 - Low). This indicates a dominance of medium-usage medicines in the Apotek Naza dataset. The obtained Silhouette Score is 0.520, indicating that the clustering is well performed. According to Table 2.1 on the criteria for measuring clustering based on the Silhouette Coefficient (SC), this score indicates that the resulting clusters are fairly compact and well-separated from each other. Keywords: Medicine Inventory, Data Mining, K-Means, KDD, Elbow Method, Silhouette Coefficient
PERSEPSI SISWA TERHADAP LAYANAN BIMBINGAN DAN KONSELING DI SMP NEGERI 1 MALAUSMA Mahmudah, Mahmudah; Fitriyani, Dede
Jurnal Jembatan Efektivitas Ilmu dan Akhlak Ahlussunah Wal Jama'ah Vol 4 No 01 (2023): Maret
Publisher : LPPM UNU CIREBON

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52188/ja.v4i01.413

Abstract

Penelitian ini berjudul Persepsi Siswa Terhadap Layanan Bimbingan dan Konseling di SMPN 1 Malausma Kabupaten Majalengka. Permasalahan dalam penelitian ini adalah peran konselor dalam Bimbingan dan Konseling di sekolah dipandang sebagai guru khusus untuk siswa yang bermasalah saja dan masih tetap melekat disebagian besar siswa. Siswa berpersepsi bahwa setiap siswa yang dipanggil ke dalam ruang  Bimbingan dan Konseling merupakan siswa yang bermasalah. Adapun  tujuan dari penelitian ini adalah untuk mejelaskan persepsi siswa terhadap layanan Bimbingan dan Konseling di sekolah yang baik dan benar. Metode yang digunakan guru bimbingan dan konseling serta upaya yang dilakukan guru bimbingan dan konseling dalam memberikan pemahaman tentang layanan Bimbingan dan Konseling pada siswa SMPN 1 Malausma Kabupaten Majalengka. Metode yang digunakan dalam penelitian ini adalah metode deskriptif dengan pendekatan kualitatif, sedangkan tekhnik pengumpulan data yang digunakan dalam penelitian ini adalah observasi, wawancara, dan dokumentasi, dengan subjek penelitiannya adalah siswa SMPN 1 Malausma Kabupaten Majalengka. Hasil penelitian menunjukan bahwa persepsi siswa terhadap layanan Bimbingan dan Konseling siswa  beranggapan bahwa guru bimbingan konseling sering marah-marah (negatif) dan beragapan bahwa guru bimbingan konseling memberikan arahan terhapad siswa yang memiliki masalah (positif), hal ini dikarenakan tingkat pelayanan bimbingan konseling terhadap siswa yang rendah.
IMPLEMENTASI ALGORITMA K-MEANS UNTUK KLASTERISASI DALAM PENGELOLAAN PERSEDIAAN OBAT (STUDI KASUS : APOTEK NAZA) Fitriyani, Dede; Jajuli, Mohamad; Garno, Garno
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 3 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3.4921

Abstract

Apotek Naza plays an important role in providing medicines to the community. This study utilizes sales data from Apotek Naza for the period of July to December 2023. The K-Means algorithm is used to cluster the medicine data into clusters representing different sales patterns. The Elbow Method is employed to determine the optimal number of clusters (K) based on the Sum of Square Error (SSE). Evaluation is conducted using the Silhouette Coefficient (SC) to measure the quality of the resulting clusters. The analysis results show that the distribution of medicines in each cluster is as follows: 13.7% or 70 items are classified in the high-usage cluster (Cluster 0 - High), 57.5% or 294 items are classified in the medium-usage cluster (Cluster 1 - Medium), and 28.8% or 147 items are classified in the low-usage cluster (Cluster 2 - Low). This indicates a dominance of medium-usage medicines in the Apotek Naza dataset. The obtained Silhouette Score is 0.520, indicating that the clustering is well performed. According to Table 2.1 on the criteria for measuring clustering based on the Silhouette Coefficient (SC), this score indicates that the resulting clusters are fairly compact and well-separated from each other. Keywords: Medicine Inventory, Data Mining, K-Means, KDD, Elbow Method, Silhouette Coefficient
Co-Authors garno, Garno Mahmudah Mahmudah