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Journal : Faktor Exacta

IMPLEMENTASI ALGORITMA COLLABORATION FILTERING DALAM WEBSITE E-COMMERCE (STUDI KASUS TOKO INDRI COLLECTION) Fajar Rizky; Wawan Gunawan
Faktor Exacta Vol 15, No 1 (2022)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v15i1.11741

Abstract

teknologi informasi dan ilmu pengetahuan mengalami pengembangan dan semakin maju, Saat ini yang berkembang adalah  situs belanja online, situs belanja online lebih popular dengan sebutan E-Commerce. Perkembangan situs belanja online membuat pelaku usaha memulai menjual barangnya melalui situs belanja online. E-Commerce merupakan tempat suatu kegiatan jual-beli yang di lakukan secara online antara pelanggan dan penjual tanpa harus bertemu langsung melalui situs atau website.Toko Indri Collection memasarkan dan menjual barang menggunakan media sosial, memasarkan dan menjual barang di sosial media sangat tidak aman untuk penjual dan pelanggan. Toko Indri Collection masih melakukan pencatatan secara manual dengan cara mencatat di buku. Cara seperti ini membutuhkan waktu yang lama untuk mencari data transaksi penjualan dan juga merekomendasikan barang kepada pelanggan. Dengan pembuatan website dan menggabungkan algoritma Collaborative Filtering dapat membantu penjualan agar dapat menginformasikan barang berkualitas berdasarkan rating pada produk. Dengan collaborative filtering menghasilkan prediksi tertingi yaitu dengan angka 5 dan 3.966
Penerapan Fuzzy Sugeno Orde Satu dalam Prediksi Pembelian Devi Fitrianah; Wawan Gunawan; Anggi Puspita Sari
Faktor Exacta Vol 14, No 4 (2021)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v14i4.11268

Abstract

Given the rapid advancement of information technology has a great influence in the fields of industry and services. This brings changes in competition between companies, so that company players must always create various techniques to survive. This study aims to assist SMEs in making purchases of the products they sell so that there is no excess stock. This research is calculated using the Fuzzy Sugeno algorithm with a system inference method that can be applied to determine the prediction of the number of purchases of goods. The prediction generated for the test data at week 30 is 60 pcs and this is less when compared to the real data, namely 70 pcs so that it can avoid overstock. Furthermore, the prediction results from the test data at week 21 to week 30 are tested to determine the error rate using the MAPE method, so that the result is 31.67%, and that means that the test is considered reasonable (reasonable).
Analisa Perbandingan Penerapan Metode SARIMA dan Prophet dalam Memprediksi Persediaan Barang PT XYZ Wawan Gunawan; Misbah Ramadani
Faktor Exacta Vol 16, No 2 (2023)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v16i2.13803

Abstract

Determining the right level of inventory is very important because it relates to the flow of money and can affect the performance of an organization. Too much inventory of goods can cause accumulation of storage space (warehouse) and reduce capital. The research will use data on sales of tires and wheels to be predicted using the SARIMA and Prophet methods, then the results will be compared for accuracy using RMSE. Based on the research results, it can be concluded that SARIMA (0, 0, 0)x(0, 1, 1, 12) with an RMSE evaluation result of 3.61 is superior to Prophet in predicting Dunlop product sales with an RMSE evaluation result of 4.02. SARIMA has the advantage in predicting because in the process there are features to find the best parameters to be implemented in the model.
Kombinasi algoritma base64 dan caesar cipher pada aplikasi Devianto, Yudo; Gunawan, Wawan; Sukowo, Bambang; Susafaati, Susafaati
Faktor Exacta Vol 17, No 1 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i1.20680

Abstract

Digital information systems must also pay attention to data security because it is confidential, there are many problems with data security which result in loss of data or damage caused by irresponsible parties. This research will combine the BASE64 and CAESAR CIPHER algorithms in applications to maintain the security of financial data so that it cannot be seen by users who do not have access to the application. The system development in this research looks like in Figure 1 which uses the Extreme Programming method. The testing carried out was using Black box and white box testing which produced the same cyclomatic complexity value, namely 4. So it can be concluded that the system is running well because the testing produces the same value
Pemanfaatan Chi Square dan Ensemble Tree Classifier pada Model SVM, KNN dan C4.5 dalam Penjualan Online Indriyanti, Prastika; Gunawan, Wawan
Faktor Exacta Vol 17, No 3 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i3.24149

Abstract

This research aims to assist MSMEs in overcoming problems in online sales. Currently, sellers only prepare stock without knowing how well the products are sold in their market segment. In the city of Tangerang alone, there are 222,602 MSMEs with various product categories. Therefore, besides utilizing offline sales, business actors should also engage in online sales. This research conducts feature selection using the Chi-Square method and Ensemble Tree Classifier to select the top 6 and 10 features. The SVM, KNN, and C4.5 algorithms are used to build prediction models based on the selected features. Using feature selection, it was found that the influential features are Estimated Shipping Cost, Shipping Cost Paid by Buyer, Total Product Price, and Estimated Shipping Cost Discount. The evaluation results using the three algorithms, SVM, KNN, and C4.5, indicate that the highest accuracy value is obtained when using the C4.5 model with data from the ensemble tree classifier with 6 features at 0.86%, followed by the C4.5 model with 10 features, KNN with 6 features, and KNN with 10 features, all of which source data from the ensemble tree classifier with an accuracy value of 0.85%.
Pemodelan Penentuan Pupuk Menggunakan Metode AHP dan SAW Eliyani, Eliyani; Gunawan, Wawan; Wahyuningram, Nugroho; Triyono, Gandung
Faktor Exacta Vol 17, No 3 (2024)
Publisher : LPPM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/faktorexacta.v17i3.24580

Abstract

The dataset used in this study comprises criteria and fertilizer brands for rice, based on assessments conducted by farmers. The criteria were: price (C1), product (C2), quality (C3), quantity (C4), recommendation (C5), effectiveness (C6), and suitability (C7). Each criterion is weighted as Very Good (4), Good (3), Fair (2), and Poor (1). The evaluated fertilizers consisted of 15 brands: Nitrea, Caping Tani, Pertiphos, NPK padi kuning, SP 36, Meroke, Pusri, Nitroku, Ponska, ZA, Urea, and NPK Pak Tani. The assessments were carried out by distributing questionnaires to 50 farmers who shop at Cv. Sari Alam Tani Store, where farmers could select more than one brand of fertilizer. The most chosen fertilizer by the farmers was Urea. This study aims to verify if Urea is indeed the best fertilizer using the AHP and SAW algorithms based on the established criteria. The results indicate that NPK Padi Kuning was ranked first with a score of 1.72, followed by NPK Tawon and NPK Pak Tani with scores of 1.61 and 1.40, respectively. Urea, despite being the most chosen by farmers, ranks fourth with a score of 1.25.