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All Journal TeknoIS : Jurnal Ilmiah Teknologi Informasi dan Sains Pelita Teknologi : Jurnal Ilmiah Informatika, Arsitektur dan Lingkungan SIGMA: Information Technology Journal Jurnal Lentera Pengabdian Proceeding International Pelita Bangsa SAINTEK
Nanang Tedi Kurniadi
Universitas Pelita Bangsa
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Social Sciences

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Perbandingan Dalam Memprediksi Penyakit Liver Menggunakan Algoritma Naïve Bayes Dan K-Nearest Neighbor al fiyan; Muhamad Fatchan; Nanang Tedi Kurniadi; Edy Widodo
Jurnal Pelita Teknologi Vol 16 No 1 (2021): Maret 2021
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (292.728 KB) | DOI: 10.37366/pelitatekno.v16i1.309

Abstract

Along with the rapid development of information technology, and also the increasing need for information in various fields including health sector. Based on data from the World Health Organization (WHO), chronic hepatitis B attacks 300 million people in the world including Southeast Asia and Africa which causes the death of more than 1 million people each year. So far, a lot of data in the hospital has not been used, even though this data can be used to predict liver disease if used. The purpose of this study was to determine the comparison of the accuracy value of the Naïve Bayes algorithm and K-Nearest Neighbor. One of the classifications is to use the Naïve Bayes and K-Nearest Neighbor algorithms and use the Rapid Miner tools in the tests used. The results of this study indicate that the Naïve Bayes algorithm has a higher accuracy rate of 84.00% in diagnosing liver disease compared to the K-Nearest Neighbor algorithm which only gets a value of 80.57%. From this research it can be concluded that the Naïve Bayes algorithm is 3.43% greater than K-Nearest Neighbor.
Optimasi Metode Naïve Bayes Particle Swarm Optimization Analisis Sentimen Formula E Jakarta Pada Twitter Donny Maulana; Hasim Budi Jatmiko; Nanang Tedi Kurniadi
Jurnal SIGMA Vol 13 No 1 (2022): Maret 2022
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

The city of Jakarta plans to hold a Formula E racing event to promote electric cars as the vehicle of the future. The Covid-19 pandemic that hit Jakarta forced the plan to be postponed. The postponement caused a polemic in the community on social media due to the condition of Jakarta being hit by Covid-19 but the Jakarta city government still wants to hold Formula E by paying commitment money to the organizers which is not small. This difference of opinion on social media is used as material for sentiment analysis using the Naive Bayes classification method. The Naive Bayes method, which has a weakness in feature selection, is optimized by applying the Particle Swarm Optimization (PSO) feature selection. The results of the application of PSO optimization on the Naive Bayes method show an increase in performance with an accuracy value of 89.16%, precision 91.10%, recall 86.81% and AUC 0.690. Keywords: Naive Bayes, Particle Swarm Optimization, Sentiment Analysis, Jakarta E-Prix.
Pengembangan Sistem Informasi Penjualan Berbasis Web Menggunakan Metode Prototyping Pada Toko Bay Sticker Nanang Tedi Kurniadi
Jurnal SIGMA Vol 11 No 3 (2020): September 2020
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

The Bay Sticker shop is engaged in the cutting sticker business in its sales depending on regular consumers and consumers in the area around the shop, recording reports is still manual so that data search difficulties and data loss often occur and because the Bay Sticker Shop is related to graphic design, the delivery of information often occurs. The purpose of this research is to produce a new sales system at the Bay Sticker Shop by using a web-based application using the prototyping method and the application program design modeling using UML (Unified Modeling Language), and using PHP as a programming language and MySQL as a database. This research produces a sales information system that is fully managed by an administrator in controlling all information related to product data management, consumer data, sales report data, and a special menu for consumers to obtain information and be able to make online purchase transactions. Keywords : Bay Sticker Shop, Website, Prototyping, UML, PHP, MySQL.
Sistem Pendukung Keputusan Penerimaan Bantuan Beasiswa Pada Siswa SMK Menggunakan Metode SMART Nanang Tedi Kurniadi; Karsito Karsito; Alfiyan Alfiyan; Wisnu Dicky Prahara
TeknoIS : Jurnal Ilmiah Teknologi Informasi dan Sains Vol 13, No 1 (2023)
Publisher : Universitas Binaniaga Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36350/jbs.v13i1.185

Abstract

Processing of assessment data for determining acceptance of student scholarships at SMK Lentera Bangsa Karawang is still carried out conventionally, namely curriculum, the principal determines several points of assessment criteria for determining students who are eligible to receive a scholarship program, in determine the criteria still using the form and recapitulated by the staff later it will be calculated manually the entire value of the specified criteria points has been determined so that there is often a loss of archives used to make reports. With these problems, it is necessary to have a student assessment information system so that the data collection and recapitulation of student criteria values can be facilitated staff in recapitulating student grades making student grade reports. The research method used to determine the scholarship decision system this is the SMART Method. The SMART method is the method used to assess actions associated with a comparison of importance weights between factors and comparison of several alternative choices. This method will give the results of the weighting of each alternative choice accordingly with many defined criteria, namely income parents, number of siblings, average grades and status of orphans. Alternative choice with the largest weight, is the alternative choice that becomes a recommendation to be eligible to receive the scholarship program. The result of this research is a decision support system for scholarship recipients already has the ability to provide convenience in staff determine the results of the process of calculating the criteria for scholarship recipients.
Penanaman Mangrove sebagai Upaya Pencegahan Abrasi di Pesisir Pantai Bahagia Cabang Bungin Muara Gembong wening ken widodasih; Kurbandi Satpatmantya Budi Rochayata; Nanang Tedi Kurniadi
Lentera Pengabdian Vol. 1 No. 01 (2023): Januari 2023
Publisher : Lentera Ilmu Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59422/lp.v1i01.12

Abstract

Ketik Tanaman mangrove sangat berperan dalam mempertahankan lingkungan pesisir dan pantai agar tetap terjaga ekosistemnya, namun ancaman abrasi yang saat ini sudah mencapai lebih dari 1 km dari bibir pantai merupakan ancaman yang serius untuk segera ditindaklanjuti agar ekosistem pada lingkungan pesisir pantai Bahagia, Cabang Bungin , Muara Gembong dapat diselamatkan. Langkah strategis pada pengabdian masyarakat yang berada di pesisir pantai adalah upaya melakukan reduksi dengan penanaman mangrove sebagai tanggul alami. Mahasiswa S1 yang tergabung dalam organisasi Mahasiswa Pecinta Alam Universitas Pelita Bangsa Cikarang kabupaten Bekasi, berinisiatif untuk melakukan penanaman mangrove sebagai upaya untuk mencegah abrasi pantai serta meningkatkan kepedulian di lingkungan pesisir pantai. Pelaksanaan penanaman melibatkan 30 mahasiswa, 3 instruktur yang merupakan dosen penggiat lingkungan hidup,manajemen dan teknologi informasi, serta 12 panitia yang merupakan tim pengabdian kepada masyarakat penanaman pohon mangrove. Kegiatan dilaksanakan dengan membersihkan lingkungan pesisir dan pantai dari sampah, menanam, dan merawat 600 pohon mangrove. Mahasiswa merasakan manfaat dalam hal mengasah karakter peduli lingkungan pesisir dan pantai dengan melakukan praktek langsung dan mengkaitkan dengan beberapa mata kuliah terkait. Kegiatan tersebut dijadikan role model dan kegiatan rutin bagi perusahaan dalam penyaluran CSR sehingga memiliki dampak kepada lingkungan sekitar.
Penerapan Data Mining Untuk Prediksi Pola Pembelian Pelanggan Menggunakan Algoritma Apriori (Studi Kasus: Toko Jihan) Ratna Arista; Agung Nugroho; Nanang Tedi Kurniadi
Jurnal SIGMA Vol 14 No 3 (2023): September 2023
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract

Determining the combination of items and the layout of goods based on consumer purchasing trends is one solution for Toko Jihan in developing marketing strategies so as to increase sales at the store. The algorithm that can be used to find any combination of items that are often purchased together at a time is the Apriori Algorithm, the apriori algorithm is a market basket analysis algorithm used to generate association rules, with an "if then" pattern. In the apriori algorithm, frequent itemset-1, frequent itemset-2, and frequent itemset-3 are determined to obtain association rules from previously selected data. To get the frequent itemset, each data that has been selected must meet the minimum support and minimum confidence requirements. In this study using different minimum support and minimum confidence comparisons based on existing transaction data using a minimum support of 20% and a minimum confidence of 80% resulted in four association rules. One example is if the consumer buys cooking oil, coffee then 87% (certainty of consumers in buying items) will buy eggs. Keywords: Association Rule Mining, Apriori Algorithm, Support, Confidence.
Comparison of K-Means and K-Medoid Algorithms in Classifying Village Status (Case Study: Gorontalo Province) Aswan Supriyadi Sunge; Nanang Tedi Kurniadi; Edy Widodo
Proceeding International Pelita Bangsa Vol. 1 No. 01 (2023): September 2023
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/pipb.v1i01.2675

Abstract

In the national development process, the village occupies a very important position. This is because it is the smallest government structure and has direct contact with the community. Seeing the importance of its role in national development, one of which is Gorontalo Province, based on directions from the central government, is trying to implement the Village Fund Allocation (ADD) policy for all villages in Gorontalo Province. In distributing the allocation of funds, it is necessary to map the status of the Village to find out the amount that must be given. This test uses the average execution time and the Davies Bouldin Index (DBI). After testing it is known that the K-Medoid Algorithm has a better DBI value than the K-Means Algorithm with the DBI value of the K-Medoid Algorithm being 0.050. On the other hand, the K-Means Algorithm has a better average execution time than the K-Medoid Algorithm, where the average execution time is 1 second.
Penerapan Algoritma K-Medoids Dalam Klasterisasi Penjualan Laptop Abizar Ar Rifa’i Rifa’i; Muhamad Fatchan; Nanang Tedi Kurniadi
Prosiding Sains dan Teknologi Vol. 1 No. 1 (2022): Seminar Nasional Sains dan Teknologi (SAINTEK) ke 1 - Juli 2022
Publisher : DPPM Universitas Pelita Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37366/SAINTEK0101.147158

Abstract

Technological developments have made the use of laptops a basic necessity that must be owned to assist in completing a job as a substitute for a PC (Personal Computer). Along with technological advances, many laptop brands have sprung up, from each brand to launch laptops with various advantages. This has resulted in more and more emerging various types of laptop brands that compete with each other to be able to meet the needs of today's consumers. Therefore there must be a system that can provide advice or laptop recommendations in searching for references. This study aims to classify laptop sales data using the k-medoids clustering algorithm data mining method. laptop sales data are grouped based on the similarity of the data so that data with the same characteristics will be in one cluster. Based on the calculations that have been carried out by researchers on 1000 sample data, it can be categorized into 3 clusters. cluster 1 is a category of low laptop sales, which is 137 data, then cluster 2 is a category of high laptop sales, which is 669 data, and cluster 3 is a category of medium laptop sales, which is 194 data from 1000 categories of laptop sales. It can be concluded that the grouping of laptop sales data in cluster 2 is the most widely sold because the specifications and prices of laptops are more affordable than cluster 1 and cluster 3. And it has been tested using the Rapid Miner application with the same results as manual calculations using Microsoft Excel. Keywords: K-Medoids, Clustering, Laptop Sales
Co-Authors Abizar Ar Rifa’i Rifa’i Agung Nugroho al fiyan Alfiyan Alfiyan Aswan Supriyadi Sunge Donny Maulana Edy Widodo Hasim Budi Jatmiko Karsito Karsito Muhamad Fatchan R. R. Wening Ken Widodasih Ratna Arista Wisnu Dicky Prahara