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All Journal Enrichment: Journal of Multidisciplinary Research and Development Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Gunawan Abdillah
Universitas Jenderal Achmad Yani, Indonesia
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Engineering Health Professions Social Sciences

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Deteksi Objek Bahasa Isyarat Huruf Bisindo Menggunakan SSD-Mobilenet Gunawan Abdillah; Ridwan Ilyas
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 1 (2024): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i1.295

Abstract

Automatic sign language recognition systems help the hearing-impaired community communicate better. The pure sign language system developed by deaf Indonesians is called BISINDO. BISINDO is used by deaf friends based on their knowledge of their environment. SSD is an abbreviation for Single Shot MultiBox Detector, a method of detecting objects in images using a neural network in one stage. With SSD, objects of any size and shape can be identified easily and accurately without the need for object suggestions or complex resampling steps. This research uses SSD Mobile Net to identify bisindo sign language for the Letter category. The evaluation results show that the best model is SSD Mobilenet V1 FPN 640 x 640.
Product Layout Determination System Using the Association Rules Method Using the Equivalence Class Transformation Algorithm Ahmed Haikal; Yulison Herry Chrisnanto; Gunawan Abdillah
Enrichment: Journal of Multidisciplinary Research and Development Vol. 1 No. 6 (2023): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v1i6.52

Abstract

Competition in the business world, specifically in the sales industry, requires companies to analyze the purchases made by customers during transactions in order to find effective business strategies. In the competitive fashion industry, merchants devise marketing strategies to increase sales. One strategy that can attract consumer interest is by organizing and arranging product displays, placing them in perfect layouts that align with customers' buying habits, making it easier for them to find and purchase products. Layout arrangement significantly influences customer satisfaction and purchase intent. The algorithm used in this study is Equivalence Class Transformation (ECLAT). The data used consists of transactional data from Aufco Clothing, specifically fashion products. A total of 1041 transactions were analyzed, using variables such as order number and items sold. The data was processed using JavaScript, with a minimum support of 0.2 and a minimum confidence of 0.7, resulting in 16 rules. The rules ranged from a min. confidence of 70% to a maximum confidence of 100%, forming 6 rules with 9 combinations of items.
Identification of Hoax News in the Using Community TF-RF and C5.0 Tree Decision Algorithm Enrico Budi Santoso; Yulison Herry Chrisnanto; Gunawan Abdillah
Enrichment: Journal of Multidisciplinary Research and Development Vol. 1 No. 6 (2023): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v1i6.58

Abstract

News has a great influence on social and political conditions, and the rapid circulation of information through social media increases the risk that people receive and redistribute hoax news. Identifying hoax news is therefore important to support the circulation of reliable information, particularly political news. This research aims to create a system for identifying hoax news using TF-RF feature weighting and the C5.0 Decision Tree algorithm and to evaluate its classification performance. The study uses 1,000 news data obtained by web scraping with the keywords "election 2024", "politics", and "checkfaktapilkadamafindo" from Turnbackhoax.id and Detik.com. The processing stages include preprocessing, TF-RF word weighting, division of training and test data, C5.0 classification, and evaluation using a confusion matrix. Three training/test scenarios were evaluated. The 70/30 scenario produced 79.33% accuracy, 80.50% precision, and 97.01% recall; the 80/20 scenario produced 79.50% accuracy, 81.32% precision, and 95.48% recall; and the 90/10 scenario produced 72.00% accuracy, 74.39% precision, and 89.71% recall. Among the tested scenarios, the 80/20 split provided the highest accuracy. These findings show that the combination of TF-RF weighting and C5.0 can be implemented as an automatic classification approach for political hoax-news identification, while performance remains dependent on the composition of the training and testing data
Consumer segmentation using K-Medians algorithm on transaction data based on LRFMP (length, recency, frequency, monetary, periodecity) Akbar Dena Maulana; Ade Kania Ningsih; Gunawan Abdillah
Enrichment: Journal of Multidisciplinary Research and Development Vol. 1 No. 8 (2023): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v1i8.70

Abstract

Consumer loyalty plays a crucial role for companies, particularly under intense competition among firms, and successfully retaining loyal customers is decisive for sustained profitability. For this reason, customer-loyalty analysis is needed to identify each customer's level of engagement with the company. Within this analysis, consumer segmentation is an essential step for grouping customers with similar characteristics so that the marketing-management process can be targeted more effectively. This study aims to segment e-commerce customers according to their behavioural loyalty and to characterise each resulting segment as a basis for differentiated marketing strategies. The segmentation employs the LRFMP model (Length, Recency, Frequency, Monetary, Periodicity), which represents customer purchasing patterns through relationship length, the recency of the last transaction, transaction frequency, total monetary value, and purchase regularity. Clustering is performed with the K-Medians algorithm, which uses coordinate-wise medians and Manhattan distance and is therefore robust to the outliers and skewness that are common in transaction data. The dataset comprises the purchase-transaction history of an e-commerce platform spanning 373 days, from which 4,712 unique customers were obtained after preprocessing. Applying LRFMP analysis with K-Medians produced four clusters, containing 1,183, 1,221, 1,206, and 1,102 customers, respectively. Interpretation of the LRFMP profiles indicates that 25.1% and 25.6% of customers (Clusters 1 and 3, jointly 50.7%) show high loyalty potential, 23.4% show medium potential, and 25.9% show low loyalty potential. The four-cluster solution attained an average silhouette coefficient of 0.608, indicating reasonably well-separated clusters.
Co-Authors Ade Kania Ningsih Ahmed Haikal Akbar Dena Maulana Enrico Budi Santoso Ridwan Ilyas Yulison Herry Chrisnanto