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All Journal Jurnal Simantec JURNAL REKAYASA TEKNOLOGI INFORMASI JURNAL EDUCATION AND DEVELOPMENT IJISTECH (International Journal Of Information System & Technology) JUTEKIN (Jurnal Manajemen Informatika) METIK JURNAL Seminar Nasional Teknologi Informasi Komunikasi dan Administrasi [SEMINASTIKA] Jurnal Abdi Insani IJISTECH Jurnal Computer Science and Information Technology (CoSciTech) Jurnal Teknosains Kodepena (Kodepena Journal of Technoscience) JAMINFOKOM Multica Science and Technology Jurnal Teknosains Kodepena (Kodepena Journal of Technoscience) Jurnal Manajemen Informatika & Teknologi JUPADAI : Jurnal Pengabdian Kepada Masyarakat LOFIAN: Jurnal Teknologi Informasi dan Komunikasi Asian Journal of Science, Technology, Engineering, and Art
Nariza Wanti Wulan Sari
Universitas Mulia
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Physics Social Sciences Other

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Journal : Multica Science and Technology

 1 
MARKET BASKET ANALYSIS METHOD ON SALES DATA USING FP-GROWTH ALGORITHM Marcelino Irawan, Kenny; Tri Wulansari, Tina; Wanti Wulan Sari, Nariza
Multica Science and Technology Vol 1 No 2 (2021): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v1i2.239

Abstract

Product promotion is a way for business owners to increase sales of existing goods. Business owners could use association rules as a consideration of product promotion policies. Determination of association rules can be determined using the market basket analysis method with the fp-growth algorithm. From the research, there are 248 association rules for goods that are often purchased simultaneously with minimum support of 0.01 and minimum confidence of 0.1. Of the 248 rules, there are seven rules that have a confidence value of more than 0.5. Of the seven rules, flour appeared five times, and the cake mat and cake box had the highest confidence value of 0.62 and a lift of 5.54. Therefore, we recommend shop owners to place wheat flour on the main display close to items often purchased together, such as sweetened condensed milk, sugar, powdered margarine, and margarine. In addition, shop owners can also promote bundling cake boxes and cake mats.
APPLICATION OF WEB-BASED APRIORI ALGORITHM FOR DRUG INVENTORY AT KHAIRI FARMA PHARMACY ZIDAN, Muhamad Nur Zidan; Rika Ismayanti; Nariza Wanti Wulan Sari
Multica Science and Technology Vol 2 No 2 (2022): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/mst.v2i2.365

Abstract

Inventory has a very important role in increasing sales and service to consumers. The purpose of this study was to determine the information and sales patterns in the form of association rules at a certain period that can provide advice to the pharmacy in managing drug inventory. The algorithm used in this study is a priori to determine the results of sales patterns in the form of association rules. Association rules are obtained by implementing an apriori data mining algorithm to a website-based system using laravel and the resulting calculation results are in the form of Drug Association rules purchased simultaneously. With a minimum support value of 2, there are 214 items in 1 – the itemset that passes the minimum support and 9 association rules formed from all transactions of 519 data with a confidence value of more than 30%. From the resulting Association rules, there are Association rules with the highest confidence value of 66.67% in the form of ketotifen and cupanol pairs purchased simultaneously.
IMPLEMENTATION OF NEURAL NETWORK IN PREDICTING STOCK PRICE OF PT BANK RAKYAT INDONESIA (PERSERO) TBK Nurmayanti, Wiwit Pura; Ni Luh Desvita Pratiwi; Nariza Wanti Wulan Sari; Desi Yuniarti; Erlyne Nadhilah Widyaningrum; Thesya Atarezcha Pangruruk
Multica Science and Technology (ACCREDITED-SINTA 5) Vol. 5 No. 1 (2025): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/dwkza342

Abstract

Forecasting involves estimating future outcomes by examining patterns in both historical and present data. A commonly used data type in forecasting is time series data, characterized by observations collected at consistent time intervals. One forecasting technique that has gained significant attention is the Neural Network, particularly through the Backpropagation method utilized in this study. In the context of the stock market, price fluctuations are influenced by a variety of factors, including shareholder rights, company performance, and the balance between supply and demand. Typically, a rise in stock prices leads to decreased demand, while a decline tends to stimulate it. Predicting stock prices, such as those of Bank Rakyat Indonesia (BRI), can support investors in making well-informed decisions. This research seeks to identify the optimal number of neurons in the hidden layer for forecasting BRI stock prices by minimizing error metrics such as MAPE, MSE, and MAE. The analysis revealed that forecasting the stock price of PT Bank Rakyat Indonesia (Persero) Tbk. using a neural network with one hidden neuron resulted in a MAPE of 1.22248 and an MAE of 61.30548.
TRAFFIC ACCIDENT VICTIM CLASSIFICATION IN BONTANG USING NW-KNN AND BACKWARD ELIMINATION Mangalik, Gerald; Nariza Wanti Wulan Sari; Surya Prangga; Wiwit Pura Nurmayanti; Ika Purnamasari
Multica Science and Technology (ACCREDITED-SINTA 5) Vol. 5 No. 1 (2025): Multica Science and Technology
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/yfbspb33

Abstract

Traffic accidents have been a serious problem caused by various factors such as road conditions, driver behavior, and weather. To understand the pattern of victim severity, a classification approach capable of handling imbalanced data and irrelevant features was needed. This study aimed to classify the status of accident victims using the Neighbor Weighted K-Nearest Neighbor (NW-KNN) method, equipped with backward elimination for feature selection. Backward elimination was employed to reduce insignificant features and improve accuracy.The case study for this research involved the status of accident victims in Bontang City, with a sample size of 93 cases. There were nine features in this study: accident victim status, accident time, road density, road function, road surface condition, speed limit at the location, road slope, and road status.The research results showed that the best parameter combination for classification using the NW-KNN method with backward elimination was K = 7 and E = 3. The "type of accident" feature was eliminated, leaving 8 features. Classification results using the NW-KNN method with backward elimination yielded an accuracy of 88.89%, demonstrating an improvement in classification performance for identifying the status of traffic accident victims. Thus, this method proved to be an effective approach for traffic accident analysis in Bontang City.
Co-Authors Abdul Rahim Abdurahman Achmad Asdori Achmad Solichin Agung Ranggono Aisyiyah Maulisa Nuur Anam, M Khairul Arief Hidayat Arista Riski, Nanda Bahriah, Ayu Budi, Ennesya Estya Dasril Aldo Dedy Mirwansyah Dedy Mirwansyah Desi Yuniarti Dody Novandi Dody Prasetia Purnama Erlyne Nadhilah Widyaningrum Fahrullah Fahrullah Fahrullah, Fahrullah Faldi Fanny Pratiwi Fardiansyah Ibrahim Faza Alameka Gubtha Mahendra Putra Hairah, Ummul Hambali, Nidaa Rifdah Rahima Heri Yansah Hidayanty, Nurul Ilma Ika Purnamasari Ika Purnamasari Indah Ayu Maharani Indra Dwi Pangestu Ivan Leonard Tandi Kosasih, Raditya Arya Mangalik, Gerald Marcelino Irawan, Kenny Maulidin Akbari Muhammad Muhammad Adryan Pratama Muhammad Bambang Firdaus Muhammad Ferdiansyah Muhammad Rayda Bazhrullah Muhammad Wisdan Pratama Putra Muhammad Yani Muhyiddin Muhyiddin Mutmainnah, Dalfa Diandra Natanael Dewanto Dewanto Ni Luh Desvita Pratiwi Nugraha, Pratama Yuly Nurmayanti, Wiwit Pura Palilu, Dryan Putra Pangruruk , Thesya Atarezcha Pangruruk Putriani Putriani Riawan, Tommy Andi Richki Hardi Rika Ismayanti Riyayatsyah Riyayatsyah Riyayatsyah Rizal Putra Aditya Sifriyani, Sifriyani Sigit Pangestu SITI MAHMUDAH Sri Wahyuningsih Sry Tualeka Sulfikar Rakita Dewa Surya Prangga Suyitno Suyitno Tejawati, Andi Thami Rusdi Agus Thesya Atarezcha Pangruruk Tina Tri Wulansari Tri Wulansari, Tina Wahyu Yanuartha Wiji Prima Fitriyanto Wiwit Pura Nurmayanti Wulansari, Tina Tri Yumami, Eva Yuniarti, Desi ZIDAN, Muhamad Nur Zidan