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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA JURIKOM (Jurnal Riset Komputer) KLIK: Kajian Ilmiah Informatika dan Komputer
M Afdal
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Journal : JURNAL MEDIA INFORMATIKA BUDIDARMA

 1 
Analisis Sentimen Terhadap Pemindahan Ibu Kota Negara Menggunakan Algoritma Naive Bayes Classifier dan K-Nearest Neightbors Dedi Pramana; M Afdal; Mustakim Mustakim; Inggih Permana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6523

Abstract

The relocation of Indonesia's capital city is a hot topic of discussion at the moment. So that this government policy reaps a lot of reactions from various parties, especially the general public in Indonesia. Various reactions were shown with various expressions on various social media. One of the social media that has become a place for people to express themselves in responding to this government policy is Instagram. The comments poured by the community on posts on Instagram are very diverse ranging from positive, negative, and neutral comments. If these comments are processed properly, they can be used as evaluation material for the relocation of the State capital. Seeing this, a sentiment analysis is needed which is intended to classify the various comments so that they can be presented into information which will be intended to help the government make considerations in carrying out policies towards moving the national capital. In this study, data processing was carried out with the Naive Bayes Classifier and K-Nearest Neightbors algorithms with Instagram comment data on posts related to moving the national capital. Where the amount of data used is 2,404 comments. It was found that the accuracy of the NBC algorithm was 63.09% and K-Nearest Neightbors was 69.23% so it can be concluded that KNN is better than NBC. In addition, the popularity of public sentiment towards the relocation of the National Capital was also obtained with a positive sentiment of 28% totaling 643 comments, a neutral sentiment of 42% totaling 1025 comments, and a negative sentiment of 30% totaling 730 comments.
Penerapan Algoritma Fuzzy C-Means Pada Segmentasi Pelanggan B2B dengan Model LRFM Aufa Zahrani Putri; M Afdal; Siti Monalisa; Inggih Permana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6150

Abstract

PT. XYZ is one of the major pharmaceutical industries in Indonesia by marketing its products through B2B (Business to Business) customers. PT. XYZ doesn't understand what customers need. PT. XYZ also implements a cashback system for B2B customers. This study aims to determine customer segmentation, analysis of customer characteristics, firmgration and proposed strategies provided by researchers to PT. XYZ. Loyalty and customer characteristics are very influential on a company. To show which customers are loyal to the company, the Fuzzy C-Means algorithm is used to cluster and the Davies Bouldien Indeks (DBI) is used for the clustering algorithm results. The algorithm used is according to the Length, Recency, Frequency and Monetary (LRFM) model to classify purchasing behavior. It can be seen from the frequency variable which customers are loyal to which companies are not. Then determine the firmography using the attributes of business entity type, customer type, and location. After determining loyal and non-loyal customers, the analysis of customer characteristics is divided into 4 parts, namely the Superstar Segment or the best customer, which is located in cluster 2 where customers in cluster 2 can have a long-term relationship with the company, then the Golden Segment or which has the second highest value (monetary) is located in cluster 4, then the Average Value Segment or the customer who has the average value of all segments is located in cluster 5 and the Dormant Segment or the lowest customer is located in cluster 3 where customer 3 has little relationship with the company.
Co-Authors Anofrizen Arief Rahman Aufa Zahrani Putri Dedi Pramana Desy Herlina Citra Inggih Permana Maisarah Assa'diyah Muhammad Jazman Mustakim Mustakim Restu Ramadhan Siti Monalisa Tengku Khairil Ahsyar