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All Journal Techno.Com: Jurnal Teknologi Informasi JEECS (Journal of Electrical Engineering and Computer Sciences) NERO (Networking Engineering Research Operation)
Rahmawati Febrifyaning Tias
Universitas Bhayangkara Surabaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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Prediction of Skincare Sales Turnover Using the Support Vector Method at the Widya Msglow Sidoarjo Company Oktaviana Isbirotin; Wiwiet Herulambang; Rahmawati Febrifyaning Tias; Rangsang Purnama; Ahmadi
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 8 No. 2 (2023): JEECS (Journal of Electrical Engineering and Computer Sciences)
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v8i2.10

Abstract

Every entrepreneur will certainly follow technological developments in the business world. MsGlow is one of theskincare businesses. The skincare business is one of the businesses that must compete with rapid and complexchanges, and this very competitive makes business people have to think of strategies for business continuity in order to compete and also survive. One way that can be done is to utilize existing sales data. The importance of fast andprecise operational data processing, information system facilities can be an alternative to solving problems in dataprocessing, minimizing errors and accelerating the data processing process. As the number of sales transactionsincreases, there will be a buildup of data that has not been processed optimally. With the above problems, aforecasting system was created that can forecast skincare sales turnover using the Support Vector Machine (SVM)method. In this study, turnover in several areas will be forecasted. The kernel function variations used in SupportVector Machine (SVM) are RBF, Linear, and, Polynomial Degree 2. The results obtained from this research trialshow that the overall forecasting model is good. The accuracy of the three areas obtained with the RBF kernel has arelatively good MAPE. In the accuracy test to predict skincare sales turnover, the three areas got a fairly goodaccuracy value of 94.46%. In the Sidoarjo area, it is predicted that there will be a lot of decrease in turnover in 2023-2024.
Analysis of the Indonesian Tourist Destination Recommendation System Using User Profile-Based Collaborative Filtering Mas Nurul Hamidah; Rifki Fahrial Zainal; Rahmawati Febrifyaning Tias; Tio Kukuh Ardiansyah
JEECS (Journal of Electrical Engineering and Computer Sciences) Vol. 11 No. 1 (2026): June
Publisher : Fakultas Teknik Universitas Bhayangkara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54732/jeecs.v11i1.6

Abstract

Tourism recommendation systems in Indonesia are challenged by highly heterogeneous user preferences and severe rating sparsity, which undermine the effectiveness of conventional collaborative filtering methods. However, prior studies predominantly rely on rating-based interactions and often utilize generic datasets, limiting their ability to capture the contextual and behavioural diversity of Indonesian tourism. Although user profile information is known to influence preferences, its integration with latent factor models is still fragmented and rarely evaluated in a unified, context-aware framework. Consequently, existing approaches often produce suboptimal accuracy and lack robustness in sparse and imbalanced data environments. This study proposes a unified user profile-enriched collaborative filtering framework that integrates Singular Value Decomposition (SVD), Jaccard similarity, and K-Nearest Neighbor (KNN) to jointly model latent preferences and contextual user characteristics. This integration constitutes the main novelty of this work, enabling simultaneous mitigation of sparsity and enhancement of personalization in a single pipeline. Experiments are conducted on an Indonesian tourism dataset, with performance evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and execution time. The results show that the proposed method consistently outperforms the rating-based baseline, achieving lower MAE (1.6994 vs. 1.7355) and RMSE (2.0653 vs. 2.1148), while maintaining comparable computational efficiency. Furthermore, the model demonstrates greater stability across varying neighbor sizes, indicating improved scalability and robustness. Practically, this approach provides a scalable and context-aware recommendation framework that can support more adaptive and personalized tourism services in Indonesia, particularly in real-world scenarios characterized by sparse and heterogeneous data.
A Comparative Analysis of K-Nearest Neighbors and Random Forest Methods for Recommendations on Selecting Islamic Boarding Schools Based on Student Interest Profiles (primary and middle school students at xxx) Mas Nurul Hamidah; Rahmawati Febrifyaning Tias; Rifki Fahrial Zainal
NERO (Networking Engineering Research Operation) Vol 10, No 2 (2025): Nero - 2025
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v10i2.30548

Abstract

KNN and Random Forest are one of the classification methods, in this study will compare 2 methods in machine learning namely KNN and Random forest to recommend the type of Islamic boarding school based on student interests, the application of a comparison of 2 classification methods in the recommendation system for selecting the type of Islamic boarding school based on student interests at the Elementary and Middle School levels of Xxx, The types of Islamic boarding schools are salafi, khalafi and mixed, with attributes such as academic tendencies, religious interests, extracurricular involvement, and family background. application of machine learning methods to support decision making in selecting Islamic boarding schools that are in accordance with student character, which is still rarely found in Islamic educational institutions. Performance evaluation is carried out using the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics. The test results show that the Random Forest algorithm gives better results with an MAE of 0.23 and an RMSE of 0.57, compared to KNN which has an MAE of 0.6 and an RMSE of 0.96. Thus, Random Forest shown to be more effective in providing recommendations for selecting appropriate Islamic boarding schools, and can be used as a basis for developing a decision support system for Islamic boarding school-based schools.Keywords: KNN, Machine Learning, Random Forest, Islamic boarding schools
Co-Authors Adam Ridwan Ahmadi Akhmad Najmul Irfani Arif Arizal Ayu Dwi Safitri Bella Risky Nasution Efilah Risqi Maulana Eko Prasetyo Fardanto Setyatama M Mahaputra Hidayat M Mahaputra Putra M. ZAINAL ABIDIN Mas Nurul Hamidah Noer Firda Yuana Ridhawaty Oktaviana Isbirotin Putri Agustin Merditawati Rangsang Purnama Rani Purbaningtyas Rifki Fahrial Zainal Rifki Fahrial Zainal Tio Kukuh Ardiansyah Wiwiet Herulambang