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All Journal Dinamik Jurnal Informatika dan Teknik Elektro Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Antivirus : Jurnal Ilmiah Teknik Informatika Generation Journal Jurnal Ilmiah Wahana Pendidikan Jurnal Penelitian Rumpun Ilmu Teknik Infomans: Jurnal Ilmu-ilmu Informatika dan Manajemen Jurnal Informatika Polinema (JIP) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Yuyun Umaidah
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Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Social Sciences Other

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

 1 
Perbandingan Algoritma K-Means dan K-Medoids Untuk Pengelompokkan Data Obat dengan Silhouette Coefficient di Puskesmas Karangsambung Riva Arsyad Farissa; Rini Mayasari; Yuyun Umaidah
Journal of Applied Informatics and Computing Vol 5 No 2 (2021): December 2021
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v5i1.3237

Abstract

Puskesmas merupakan unit pelaksana fungsional yang berperan sebagai pusat pembangunan kesehatan, pusat partisipasi masyarakat bidang kesehatan dan pusat pelayanan kesehatan primer. Masalah yang dialami puskesmas ini adalah perecanaan kebutuhan obat yang tidak efektif dan efisien. Penggunaan data mining ini dapat mengendalikan stok obat agar tidak terjadi penumpukan stok serta kehabisan stok obat. Clustering adalah teknik pengelompokan record dalam database berdasarkan kondisi tertentu. Metode yang akan digunakan untuk clustering data obat-obatan adalah algoritma K-Means dan K-Medoids yang merupakan metode clustering non hirarki yang mempartisi data ke dalam cluster sehingga data yang memiliki karakteristik yang sama akan dikelompokkan ke dalam cluster yang sama. Tujuan dari penelitian ini adalah untuk mengelompokkan data obat-obatan di Puskesmas Karangsambung yang dapat digunakan sebagai referensi untuk perencanaan obat yang akan datang di puskesmas tersebut. Pengelompokkan data dibagi menjadi tiga yaitu lambat, sedang dan cepat. Hasil yang didapatkan yaitu kedua algoritma tersebut menunjukan bahwa algoritma K-Means mendapatkan hasil Silhouette Coefficient lebih tinggi yaitu sebesar 0,627 sedangkan K-Medoids sebesar 0,536.
Optimasi Support Vector Machine Berbasis Particle Swarm Optimization Untuk Mendeteksi Hate Speech Pilkada Karawang Wahyuningrum Ayu; Rijal Abdulhakim; Yuyun Umaidah; Jajam Haerul Jaman
Journal of Applied Informatics and Computing Vol 5 No 2 (2021): December 2021
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v5i2.3473

Abstract

The rise of hate speech on social media can harm various parties, including the candidate for regional head of Karawang Regency in 2020, but because of the large number of comments, the sanctions given to violators are not evenly distributed. To make it easier for Bawaslu to give sanctions to violators and to provide a deterrent effect to the Karawang community so that hate speech does not occur again. Therefore, this study was conducted by classifying positive and negative comments. The methodology used is Knowledge Discovery in Database (KDD) by dividing the data into 4 scenarios. The results obtained state that the Support Vector Machine (SVM) Algorithm with scenario "2" on a linear kernel gets the highest accuracy value of "72.66%". Then the results of the 4 scenarios were optimized by Particle Swarm Optimization which got the highest accuracy value, namely the linear and polynomial kernels in the 4th scenario with 90:10 data sharing of "78.00%". Other evaluation values ​​also experienced the same increase, starting from precision, recall, and f1-score. It can be concluded that the Support Vector Machine algorithm optimized with Particle Swarm Optimization can increase the accuracy value.
Penerapan Data Mining Pengelompokan Menu Makanan dan Minuman Berdasarkan Tingkat Penjualan Menggunakan Metode K-Means Genta Triyandana; Lala Aprianti Putri; Yuyun Umaidah
Journal of Applied Informatics and Computing Vol 6 No 1 (2022): July 2022
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v6i1.3824

Abstract

Data mining can be used to find solutions in making sales decisions to increase sales. Sales data storage stores many sales transaction records, where each document provides products purchased by customers in each sales transaction. A problem began to arise with an excess stockpiling of materials. The number of fluctuating sales causes the stock of available materials to be unstable and can directly impact consumers. Mistakes in predicting sales caused the coffee shop to buy large quantities of material stock, which were not widely used or sold out, so the supply of these materials swelled in the warehouse. One way to be implemented is by applying data mining because there are ways and methods to meet needs, one of which is the need for extensive information, then the information that we can use to determine quality in determining a decision. Therefore, it is hoped that this research can help Dpom Coffee minimize material stock inventory management cases such as shortages and excesses and make policies to increase sales by grouping menus based on sales levels using the K-means algorithm. Based on the results of processing the sales dataset at Dpom Coffee, it produces 3 clusters, namely Cluster 1 with eight menus with low sales levels, cluster 2 with 40 menus with moderate sales levels, and cluster 3 with seven menus with high sales levels. The accuracy or performance of the k-means algorithm results in a Davies Bouldin index value of 0.457.
Co-Authors Alfanda Novebrian Maharadja Alfina Novi Yanti Andri Dwi Noviandi Apriade Voutama Chella Aprianti Dicki Pajri Euis Saraswati Farras Salsabila Fatma Eka Zulfiakhoir Genta Triyandana Heryana, Nono Ika Fitrianti Jaman, Jajam Haerul Khofifah Wulandari Lala Aprianti Putri Mentari Hasibuan Mohamad Jajuli Muhammad Faishal Muhammad Fitra Fajar Rusamsi Naibaho, Syela Herdina Nailufar Farha Afifah Purwantoro Purwantoro Radi Alpiyanto Rafli Pasya Rijal Abdulhakim Rika Nursyahfitri Rini Mayasari Riva Arsyad Farissa Roni Merdiansah Sarwanti, Ruth Tika Syahrul Dwi Hilda Tesa Nur Padilah Tesa Nur Padillah Wahyuningrum Ayu