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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Informasi dan Komputer Jurnal Ilmiah Wahana Pendidikan Jurnal Informatika Polinema (JIP)

Tesa Nur Padilah

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Author-ID : 1788356

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

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Articles

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Optimasi Algoritma K-Means Menggunakan Metode Elbow dalam Pengelompokan Penyakit Demam Berdarah Dengue (DBD) di Jawa Barat Dea Amelia; Tesa Nur Padilah; Asep Jamaludin
Jurnal Ilmiah Wahana Pendidikan Vol 8 No 11 (2022): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

Dengue hemorrhagic fever (DHF) is an acute febrile infectious disease that usually occurs in tropical and subtropical areas of the world and is caused by a virus transmitted by the Aedes mosquito, namely Aedes aegypti and Aedes albopictus. Dengue fever is one of the endemic diseases that almost occurs throughout the world. Indonesia is the country with the highest dengue fever cases in Southeast Asia. One of the provinces with the most cases of dengue fever is West Java. Every year cases of dengue fever have increased and decreased, so cases cannot be controlled properly. This must be a concern for the West Java Government in handling this Dengue Fever disease. To help the Government of West Java, this research conducted a grouping of dengue fever in West Java in 2016-2021. This research uses the Knowledge Discovery in Database (KDD) method. The algorithm used is k-means clustering with the help of the elbow method to get the optimal number of clusters, which is 2 clusters. Cluster 0 with low category consists of 22 regions, and cluster 2 with high category consists of 5 regions. The result of silhouette coefficient evaluation is 0.689 with standard structure criteria

Penerapan K-Means Clustering dalam Pengelompokan Kasus Tuberkulosis di Provinsi Jawa Barat Fadhlan Sulistiyo Hidayat; Rizma Berliana Putri Affandi; Virgaria Zuliana; Tesa Nur Padilah
Jurnal Ilmiah Wahana Pendidikan Vol 8 No 15 (2022): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

Tuberculosis is a very common infectious disease and is lethal in most of the cases. This is the background of this research, namely because there are many cases of Tuberculosis in Jawa Barat. According to data obtained from the Open Data Jabar, namely Tuberculosis Data in Jawa Barat Province, showing data that in 2020 all districts and cities in Jawa Barat had a number of Tuberculosis cases starting from 320 cases in Banjar Regency which was the lowest case, and 10,248 cases in Bogor Regency which is the highest case in Jawa Barat. The purpose of this study was to cluster TB cases into high and low categories based on gender. The data we use is data on the number of TB cases in Jawa Barat province in 2020 which consists of 27 districts/cities. In this study using the Clustering method with the K-Means algorithm. The results obtained based on the test, the clusters obtained were 2 with cluster 0 with 23 low TB cases and 4 clusters for high TB cases. Researchers hope that the results of this study can become knowledge for the government to reduce the number of TB in Jawa Barat

Perancangan Sistem Informasi Penjualan Produk Parfum Berbasis Web Pada Toko Rinas Mashel Bekasi Raynaldy Mahdi Putra; Tesa Nur Padilah; Carudin Carudin
Jurnal Ilmiah Wahana Pendidikan Vol 8 No 18 (2022): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

Advances in Information Technology have experienced very significant developments in various aspects of human life, including in the electronic sales industry (E-Commerce). To take advantage of this potential, an E-Commerce Website-based Sales Information System is useful for selling goods or services through electronic media so that the process becomes computerized and able to function in processing data and changing data to obtain accurate information. Rinas Mashel is a store that sells a variety of inspired perfume products in Bekasi. In the process of making transactions, collecting data on goods and making reports at the Rinas Mashel store, they still use the manual method by recording them in a book, so this is considered very ineffective because it is feared that errors will occur due to human error factors. Therefore, the perfume product sales information system is needed by Rinas Mashel store to make it easier to run business processes and provide the information needed accurately. The results of this study are in the form of designing and implementing a sales system using the PHP, JavaScript, CSS, HTML programming languages with MySQL database. While taking advantage of this research is to be able to help Rinas Mashel to improve service by making it easier for customers in the sales transaction process and being able to help relieve admin performance in data collection and minimize errors in making sales reports.

Analisis Sentimen Opini Publik Tentang Vaksin Booster Menggunakan Metode Support Vector Machine dan firefly Algorithm RIfky Pujianto; Dadang Yusup; Tesa Nur Padilah
Jurnal Ilmiah Wahana Pendidikan Vol 8 No 23 (2022): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

Coronavirus is a viral epidemic that has swept across the world. Indonesia is one of the countries affected by the virus, so the government is trying to prevent the spread of the coronavirus so that not many people are exposed to the coronavirus. One of the government's efforts to prevent the spread of this virus is to create a free vaccination program. In January 2022, the government issued the latest vaccine, namely the booster vaccine. The emergence of the booster vaccine is slightly doubted by the public because of the many hoax news about this booster vaccine. This study aims to analyze the sentiment of public opinion on the emergence of the booster vaccine. The data used for this analysis comes from public opinion on Twitter. The data is taken using the crawling method. This research uses the support vector machine (SVM) method as a process for classifying public opinion and the firefly algorithm as an optimization of SVM parameters. There are 3 class labels used for classifiers, namely positive, negative, and neutral. A lot of data used after the pre-processing process is 2223 data which is then split into as much as 80% training data and 20% testing before entering the classification stage using the SVM method. The results of the classification using SVM produce an accuracy of 85% on the default parameters and after being optimized using the firefly algorithm it produces an accuracy of 86% with parameters C = 1.0–3.0, = 0.1-1.0.

Co-Authors Asep Jamaludin Budi Arif Dermawan Carudin Carudin Dadang Yusup Dea Amelia Fadhlan Sulistiyo Hidayat Mochamad Riszky Sulaeman Nafis Naufal Anwari Nikita Salsabila Nina Sulistiyowati Novi Widiawati Purwantoro . Puwantoro . Raynaldy Mahdi Putra RIfky Pujianto Rizma Berliana Putri Affandi Virgaria Zuliana Wahyu Alfafisabil

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