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All Journal Dinamik JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Ilmiah Wahana Pendidikan Jurnal Informatika Polinema (JIP)

Yuyun Umaidah

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Author-ID : 2211623

Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Library & Information Science Social Sciences Other

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Clustering Tingkat Kedisiplinan Warga Bekasi Dalam Menjalankan Protokol Kesehatan Di Masa Pandemi Covid-19 Dengan Algoritme K-Means Andri Dwi Noviandi; Tesa Nur Padillah; Yuyun Umaidah
Jurnal Ilmiah Wahana Pendidikan Vol 7 No 4 (2021): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

Health protocols during the Covid-19 pandemic are very necessary because health protocols can speed up breaking the chain of spreading the Covid-19 virus. Violations that are often found in the Bekasi city environment are related to health protocols, namely maintaining distance, wearing masks and washing hands, or using hand sanitizer. There are still many who do not comply with the rules of the health protocol. The purpose of knowing the cluster level of discipline towards health protocols into five clusters spread by the number of respondents in various sub-districts in the city of Bekasi with the categories of discipline, somewhat disciplined, rarely disciplined, less disciplined, and undisciplined. Data mining is the process of extracting data to obtain new information. The technique used in this research is simple random sampling. This study using the CRIPS-DM methodology. This study calculates the k-means algorithm by obtaining a value of k = 2. The results of the test using the RapidMiner Studio 9.3 tools obtained two clusters or 2 categories of discipline levels against health protocols, namely cluster 0 with a percentage of 55.08% which is categorized as the most disciplined level, and cluster 1 with a percentage of 44.92% which is categorized as the least disciplined level. The results of clustering are evaluated by using the Silhouette Coefficient with the best cluster, k = 2 with a value of 0.926989, which is the best cluster.

Penerapan Data Mining untuk Klasifikasi Penjualan Baju Muslim Dimasa Pandemi Covid-19 Menggunakan Metode Algoritma C4.5 Chella Aprianti; Muhammad Faishal; Yuyun Umaidah
Jurnal Ilmiah Wahana Pendidikan Vol 8 No 1 (2022): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

During this Covid-19 pandemic, it has become a global pandemic that continues to experience an increase in daily infection rates in Indonesia. The most obvious example is the decline in the income turnover of MSME actors, this impact requires a special strategy to increase sales during the pandemic. Garaya Collection store is a store that is engaged in selling Muslim clothes, but from the various types of clothes that are sold, not all of them are necessarily sold and are not selling well. Sales data, purchases of goods, incoming goods, and unexpected goods expenditures at the Garaya Collection Store are not well structured, so the data only functions as a store archive and is not used for developing sales strategies. Therefore, it is necessary to apply the classification using the C4.5 algorithm data mining method at the Garaya Collection Store. The C4.5 algorithm can be applied to the Garaya Collection Store to determine the sales of clothes that are selling very well, selling well, and not selling well. Application of the C4.5 Algorithm method at the Garaya Collection Store, namely the classification of sales data stock. Then using the C4.5 Algorithm method on rapidminer, it is done by entering data on categories, brands of goods, prices, selling times, and selling well. Storage of data taken through MS.EXCEL, the data is connected to the rapidminer tools and will be processed and in the form of a decision tree. After that, rapidminer will generate which ones are sold, sold, and not sold well.

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

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

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

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.

Analisis Sentimen Pada Pembelajaran Daring Menggunakan Metode K-Nearest Neightbour Alfina Novi Yanti; Yuyun Umaidah; Rini Mayasari
Jurnal Ilmiah Wahana Pendidikan Vol 8 No 12 (2022): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

Corona Virus Disease-2019 has been rampant throughout the world, including in Indonesia. Covid-19 has greatly affected several sectors, one of which is the education sector. The Indonesian government itself implements a policy of courageous learning or distance learning which is carried out from their respective homes. SMA Negeri 3 Cikampek is one of the schools that implements bold learning, this bold learning affects the achievement of learning outcomes. Various students from this bold learning, there are those who agree that this bold learning has an effect on the achievement of learning outcomes and some even give a response that does not agree because it has no effect. For this reason, data mining is applied, especially text mining with the K-Nearest Neighbor algorithm to analyze various student responses to bold learning. The data used is a questionnaire data as much as 592 data. Before the data mining stage, the data is divided into 80% of the training data and 20% of the testing data. The classification with K-Nearest Neighbor quality is 85.35% accuracy, 81.19% precision, 92.42% recall and Auc is 0.902. Based on the quantity of negative classes which are more than positive classes, it is known that students will not agree to bold learning because it affects the achievement of learning outcomes. Keyword: Text mining, Online Learning , Covid-19, K-Nearest Neightbour.

Pemetaan Daerah Produksi Perkebunan Kelapa Sawit Pada Provinsi Indonesia Menggunakan Algoritma K-medoids Fatma Eka Zulfiakhoir; Yuyun Umaidah; Purwantoro Purwantoro
Jurnal Ilmiah Wahana Pendidikan Vol 8 No 16 (2022): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

In the National Leading Plantation Statistics yearbook issued by the Directorate General of Plantation, Ministry of Agriculture of the Republic of Indonesia, it is stated that Indonesia is the world's number one producer of palm oil and also the owner of the largest oil palm plantation area in the world. Indonesia's palm oil production reached 43.5 million tons, with an average growth percentage of 3.61%. The Coordinating Minister for Economic Affairs considered that the palm oil sector had a major role in the economy during the Covid-19 pandemic. The palm oil sector is able to maintain 16.2 million workers who depend on it in the midst of the pandemic. Therefore, it is necessary to map oil palm plantations from each province of Indonesia so that it can be seen which areas have the potential to produce oil palm. Thus, later the area can be maximized again productivity. Then, to find out also which provinces have a low potential level of palm oil production so that later the productivity of the area can be assisted by analyzing what patterns can be found from the highest potential level of oil palm production. In this research, mapping will be carried out using clustering techniques in data mining, using the k-medoids algorithm. The results showed that there were 3 clusters, namely cluster 1 (category of provinces with low production areas of oil palm), cluster 2 (category of provinces with high production areas of oil palm), and cluster 3 (category of provinces with medium production areas of oil palm). ). The silhouette coefficient evaluation result in the model calculation using this technique is 0.64. This range value is included in the medium structure criteria (good cluster structure).

Analisis Sentimen Pengguna Twitter Terhadap Grup Musik BTS Menggunakan Algoritma Support Vector Machine Tiara Safitri; Yuyun Umaidah; Iqbal Maulana
Journal of Applied Informatics and Computing Vol 7 No 1 (2023): July 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i1.5039

Twitter is often used as a source of public opinion and sentiment data for analysis, where the data can be used to understand public opinion about a topic. Sentiment analysis is widely used in various fields, one of which is in the marketing field. a company can carry out a sentiment analysis of the public figures they want to make Brand Ambassadors (BA), which later these sentiments can be taken into consideration for them to be able to determine the BA of their products. Sentiment analysis can also be used to distinguish the attitude of customers, users or followers towards a brand, topic, or product with the help of their reviews. Based on this, this study will analyze the sentiments of Twitter users towards music group BTS, using the Knowledge Discovery Database (KDD) research methodology, with 5 stages namely Data Selection, Data Preprocessing, Data Transformation, Text Mining and Evaluation. By using the Support Vector Machine (SVM) algorithm with a linear kernel, this study will do 3 scenarios with the distribution of training data and testing data 90:10 in scenario 1, 80:20 in scenario 2, and 70:30 in scenario 3. Confusion Matrix is used to evaluate the performance of the algorithm used and the results show that the best performance of the model formed is in scenario 1 and scenario 2.

Analisis Daftar Pemilih Tetap Pemilihan Gubernur dan Wakil Gubernur menggunakan Algoritma K-Means Syahrul Dwi Hilda; Apriade Voutama; Yuyun Umaidah
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 10 No 3 (2023): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Lembaga Penelitian dan Pengabdian pada Masyarakat (LPPM) STMIK Global Informatika MDP

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v10i3.4921

Permanent voters list is a very large collection of data in an election. Permanent voters list data is very important for an institution related to this, one of which is a village. Lack of understanding of Data Mining at the village level is a bad thing, because Data Mining is very much needed when associated with a lot of data because one of them can make it easier to group data or clustering with the k-means algorithm which is an effective choice for clustering. In this study using the k-means algorithm clustering method supported by the RapidMiner application. Processing of permanent voter list data will be needed by institutions or related. The results of this research assisted by the rapidminer application with the k-means algorithm clustering method successfully grouped or clustering of the permanent voters list with age and address variables in institutions or those in need, especially in this study in Curug Village, Klari District, Karawang Regency.

PENERAPAN METODE CRISP-DM UNTUK ANALISA PENDAPATAN BERSIH BULANAN PEKERJA INFORMAL DI PROVINSI JAWA BARAT DENGAN ALGORITMA K-MEANS Farras Salsabila; Ika Fitrianti; Yuyun Umaidah; Nono Heryana
Dinamik Vol 28 No 2 (2023)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v28i2.9454

Tujuan dari penelitian ini adalah untuk mengenali pola pendapatan dan karakteristik pekerja informal dengan lebih baik dengan menggunakan clusterisasi Algoritma K-Means sehingga pemerintah dapat mengarahkan sumber daya dan program dengan lebih tepat sasaran. Dalam penelitian ini, Indeks Davies Bouldin (DBI) akan digunakan sebagai metode untuk mengoptimalkan jumlah cluster dalam proses pengelompokkan wilayah Kabupaten/Kota di Jawa Barat berdasarkan pendapatan bersih pekerja informal. Data yang diguanakan adalah data pendapatan dari para pekerja Informal di Jawa Barat . Pengolahan data dilakukan menggunakan tools RapidMiner dengan pemodelan clustering dan untuk metode penelitian yang digunakan adalah CRISP-DM . Hasil pengujian dan pemodelan yang telah adalah terdapat indikasi adanya perbedaan yang cukup besar dalam pendapatan bulanan bersih pekerja informal di daerah kabupaten/kota di Jawa Barat. Cluster 0 menunjukkan pendapatan rendah, Cluster 1 menunjukkan pendapatan sedang, sementara Cluster 2 menunjukkan pendapatan tinggi

Co-Authors Alfanda Novebrian Maharadja Alfina Novi Yanti Andri Dwi Noviandi Apriade Voutama Aries Suharso Chella Aprianti Farras Salsabila Fatma Eka Zulfiakhoir Genta Triyandana Heryana, Nono Ika Fitrianti Iqbal Maulana Jaman, Jajam Haerul Lala Aprianti Putri Muhammad Faishal Purwantoro Purwantoro Rijal Abdulhakim Rika Nursyahfitri Rini Mayasari Riva Arsyad Farissa Stevanus Ertito Pramudja Syahrul Dwi Hilda Tesa Nur Padillah Tiara Safitri Wahyuningrum Ayu

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