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Siti Monalisa
Jurusan Sistem Informasi Fakultas Sains dan Teknologi UIN Suska Riau
Religion Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Physics Social Sciences Other

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Journal : Building of Informatics, Technology and Science

 1 
Analisis Sentimen Masyarakat Terhadap Penghapusan Honorer Berdasarkan Opini Dari Twitter Menggunakan Naïve Bayes Classifier Andriyani, Dwi Ratna; Afdal, M; Monalisa, Siti
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3541

Abstract

The removal of honorees is currently a hot topic throughout Indonesia. Sharing how honorary personnel do so that the honorary removal policy is not implemented. Most honorary personnel have served for several years, but the government has issued a circular on the abolition of honorees. Various pros and cons of society regarding the abolition of honorees, such as honorary workers can lose their jobs, not get income, and unemployment is increasing. The purpose of the study is that the government can provide strategies that must be carried out in the event of the removal of honorees, such as appointing all honorees to become Civil Servants or Government Employees with Work Agreements. So the removal of the honoree became one of the trending topics on Twitter social media in 2022. From the results of the analysis conducted, public opinion that uses Twitter is very influential for honorary workers by grouping opinions into three categories, namely positive opinions, neutral opinions, and negative opinions. So the study with text mining used the Naïve Bayes Classifier algorithm with data from Twitter tweets from January 2022 to December 2022 with 2,705 data. The results of this study obtained accuracy with 10 K-fold Cross Validation on K-10, which was 73.01%. And it was found that sentiment polarity against the removal of honorees on positive class sentiment by 10% against agreeing to remove honorees with 285 data tweets, neutral class sentiment by 67% against agreeing and disagreeing with the removal of honorees with 1,801 data tweets, and negative class sentiment by 23% against disagreeing with the removal of honorees with 619 data tweets
Penerapan Algoritma FP-Growth untuk Menentukan Strategi Promosi Berdasarkan Waktu dan Pembelian Produk Wilrose, Anandeanivha; Afdal, M; Monalisa, Siti; Munzir, Medyantiwi Rahmawita
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3577

Abstract

Sales is the main activity in every business. In making business decisions, sales patterns can be used to provide useful information such as strategies for promotion. Wandri Mart is a business engaged in the sale of products or goods commonly referred to as minimarkets in the city of Payakumbuh. In conducting promotional strategies, the owner of Wandri Mart does not know when to do promotions and what promotions are needed in order to increase sales. The purpose of this study is to obtain purchasing patterns related to the time of purchase and the type of goods purchased, so that a more effective promotional strategy can be developed. The method used by researchers is data mining techniques with the FP-Growth algorithm. The data used was taken as much as 5471 sales transaction data for 1 year. The results of this study indicate that the FP-Growth algorithm can be used to determine association rules using a minimum support of 1%, 2%, 3% and a minimum confidence of 10%. Experiments using Minimum Support 1% and Minimum Confidence 10% have the highest lift ratio value and produce more rules compared to other experiments so that it is obtained if on Tuesdays in August, customers buy instant noodles and packaged drinks with 6% and 5% support respectively and 50% and 45% confidence respectively with a lift ratio of 1.75 and 1.59 respectively. The lift ratio means that the rules have high association accuracy, and this also has a positive impact on sales and can be used as useful information for Wandri Mart to increase sales
Analisis Customer Lifetime Value Berdasarkan Produk Menggunakan Metode RFM/P dan Algoritma Fuzzy C-Means Rachmawati, Dyana; Monalisa, Siti; Muttakin, Fitriani
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6320

Abstract

212 Mart Soebrantas is a retail company based on a Sharia Cooperative. 212 Mart Soebrantas segments its customers in terms of monetary value, specifically customers who make many purchases. Currently, 212 Mart does not consider recency and frequency, because customers who make transactions of 50 thousand rupiahs receive 1 point. If the points accumulate to 200, they exchange them for a shopping voucher worth 50 thousand rupiah to shop at 212 Mart. 212 Mart Soebrantas needs to understand Customer Lifetime Value (CLV) to determine the customer categories worth keeping and profitable for 212 Mart. Therefore, 212 Mart needs to understand and know its customer segments based on product-based transactions or RFM/P. This research analyzes Customer Lifetime Value Based on Products Using the RFM/P Method and Fuzzy C-Means Algorithm at 212 Mart Soebrantas to help 212 Mart identify customer segment characteristics, and customer loyalty per product category, and provide strategic recommendations. The data used is customer transaction data from January 2023 to September 2023. The study uses products from 10 categories with 6 attributes: Member Code, Stock Name, Transaction Date, Quantity, Basic Price, and Department. The research shows that the best cluster is found in the Basic Material category with a DBI value of 0.4990, and it is a Superstar Customer based on Customer Portfolio Analysis (CPA).
Analisis Sentimen Komentar Perplexity AI di X Tentang Pendidikan Menggunakan Support Vector Machine Ardiansah, Yoga; Monalisa, Siti; Muttakin, Fitriani
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6396

Abstract

Chatbots with Artificial Intelligence are increasingly popular in everyday life. Due to its ability to reason and convey information expressively, Artificial Intelligence (AI) using Natural Language Processing (NLP) models can communicate like humans. Users find one of Perplexity's AI chatbots interesting because it can pinpoint sources of information. As time goes by and the number of Perplexity users increases, sentiment analysis is used to measure user happiness. This sentiment analysis serves as the data source for this research, helping understand how users react to social media X (Twitter). The Support Vector Machines (SVM) method was used in this study, where SVM maximises the distance (margin) between data groups to determine the ideal hyperplane. According to the survey, 90.11% of respondents expressed positive sentiments, 5.30% expressed negative opinions, and 4.69% expressed neutral sentiments. Using a ratio of 80% training data and 20% test data, the f1 score reached 96%, with accuracy and precision of 92% each.
Co-Authors Afilla, Dini Ahsyar, Tengku Khairil Ananda, Dea Putri Andriyani, Dwi Ratna Angraini Angraini Ardiansah, Yoga Bacin, Jukhri Syahputra Eki Saputra Fitri Amillia fitriano, ernesto Hartati, Misra M. Afdal MaySarah, M Megawati - Mona Fronita, Mona Muhammad Fadli Munzir, Medyantiwi Rahmawita Muttakin, Fitriani Nurahmah, Novia Rachmawati, Dyana Sarbaini Sarbaini Tengku Nurainun Wilrose, Anandeanivha Wiranti, Ririn Zarnelly Zarnelly