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Rofiqoh Dewi
Universitas Satya Terra Bhinneka
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Rancang Bangun Sistem Informasi Pemasaran Barang Antik Berbasis Mobile Web Rofiqoh Dewi; Abdul Muis
Computer Science Research and Its Development Journal Vol. 15 No. 3 (2023): October 2023
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In this era of digitalization, the use of technology in developing a product is very commonly used to introduce products, sell products and other things related to recognizing product needs such as product information, product functionality, price, or uniqueness that a product has but is not found in other products. like antiques. Currently, it is very rare to find the marketing of antiques in modern and traditional markets. The need to buy antiques is also very rare, but that doesn't mean it has disappeared with time because there are still some people who are interested in antiques if they appear displayed in a window at the market. Based on this, to make it easier for people to access antiques, technology is needed to be able to process data well to make it more efficient and effective. Based on this, the direction of the research carried out by the researcher aims to introduce antiques to all levels of society and build a mobile website to market these antiques so that people can get to know the types and benefits of antiques, the history of antiques and the prices of these antiques.
Analisis Perbandingan Algoritma Klasifikasi Terhadap Data Problem Mesin ATM Dengan Rapidminer Dahriani Hakim Tanjung; Rofiqoh Dewi; Fujiati Fujiati; Rinrin Meilani Salim
Computer Science Research and Its Development Journal Vol. 16 No. 2 (2024): June 2024
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid.16.2.2024.188-200

Abstract

The aim of the proposed research is to compare and test the accuracy of data mining classification algorithms. Comparing algorithms that depend on different parameters of a given data set. There are learning and classification algorithms that are used to analyze, study and classify the available data. However, the problem is finding the best algorithm and the desired results with the highest level of accuracy in predicting future values ​​or events from a data set. Where the classification models used are the C4.5 and Naïve Bayes algorithms. Testing and validation using k-fold Cross Validation as well as evaluating the performance of the prediction model using the ROC-AUC graph with graphic visualization. The data used as samples were taken from ATM machine problem data with a total of approximately 250 samples. Testing was carried out with the help of the Rapidminer tool with operators and parameters used in creating models of the algorithms being compared. The tests that have been carried out prove that the C4.5 algorithm has the best performance with an average accuracy value of 96.00%, a recall value of 97.78% and a precision value of 92.14%, while the naïve Bayes algorithm produces an accuracy value of 83. 00%, the recall value is 76.40% and the precision value is 84.82%. Apart from that, evaluation and validation in this test is also seen based on the ROC curve called AUC (Area Under the ROC Curve) where for the C4.5 algorithm the value is 0.931 while naïve Bayes is 0.894 so the C4.5 algorithm is categorized as Very Good Classification because it has a value between 0.90-1.00. These results show that the C4.5 algorithm is proven to be a potentially effective and efficient classification algorithm.
ENHANCING MACHINE LEARNING ALGORITHM PERFORMANCE FOR PCOS DIAGNOSIS USING SMOTENC ON IMBALANCED DATA Rofiqoh Dewi; Ratna Sri hayati; Alfa Saleh; Dahri Yani Hakim Tanjung; Abwabul Jinan
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 1 (2025): JITK Issue August2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i1.6676

Abstract

Polycystic Ovarian Syndrome (PCOS) is one of the most frequently occurring endocrine disorders in women of reproductive age, distinguished by disruptions in hormonal regulation that can impact menstrual cycles, fertility, and physical appearance. Despite its high prevalence, PCOS is often diagnosed late and inaccurately, leading to inappropriate treatment and long-term health issues for patients. Machine learning can serve as an effective solution to enhance the accuracy of PCOS diagnosis. However, one of the primary challenges encountered is the class imbalance in the dataset, where the number of positive case data (PCOS) is often significantly lower than the negative case data. This imbalance can result in a biased model that is less effective in predicting the actual condition of patients. In this study, the Synthetic Minority Over-sampling Technique for Nominal and Continuous (SMOTENC) method is recommended to address the issue of imbalanced data, thereby improving the performance and accuracy of the machine learning model employed. The evaluation matrix test results clearly demonstrate that the accuracy of each machine learning model improved after applying the SMOTENC method. Specifically, the accuracy of the K-Nearest Neighbors (KNN) algorithm increased from 81.6% to 89.8%, the Support Vector Machine (SVM) algorithm from 90.6% to 92.5%, the Naive Bayes algorithm from 70% to 82.3%, and the C4.5 algorithm from 99.6% to 99.7%. This research provides a substantial contribution to advancing the development of diagnostic methods thatare both more precise and efficient.
Analisis Kinerja Algoritma Klasifikasi terhadap Dataset Penerimaan Pegawai Outsourcing Firman Syahputra; Dahri Yani Hakim Tanjung; Wiwi Verina; Ok.Muhammad Ihsan; Rofiqoh Dewi; Andi Sanjaya
Jurnal Minfo Polgan Vol. 14 No. 1 (2025): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v14i1.14729

Abstract

Penelitian ini bertujuan untuk menganalisis dan membandingkan kinerja tiga algoritma klasifikasi, yaitu Decision Tree, Naive Bayes, dan K-Nearest Neighbors (K-NN), dalam memprediksi kelayakan calon pegawai outsourcing berdasarkan data historis. Evaluasi dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score. Dari hasil pengujian yang telah dilakukan dimana nilai akurasi dari algoritma C4.5 yang dihasilkan adalah 75,00%. Nilai akurasi ini lebih besar daripada model algoritma klasifikasi K-NN sebesar 73,00% dan naïve bayes sebesar 64,00%, namun nilai performa algoritma KNN ini memiliki keunggulan nilai performa akurasi dibandingkan dengan Naïve bayes. Hasil analisis menunjukkan bahwa algoritma Decision Tree memiliki kinerja terbaik dibandingkan dua algoritma lainnya, baik dari sisi akurasi maupun keseimbangan antara presisi dan recall. Hal ini menunjukkan bahwa model Decision Tree cukup efektif dalam menangani data campuran dan menghasilkan prediksi yang andal dalam konteks klasifikasi calon pegawai outsourcing. Dengan hasil ini, diharapkan model klasifikasi berbasis Decision Tree dapat diterapkan dalam sistem pendukung keputusan untuk meningkatkan efisiensi dan akurasi dalam proses rekrutmen tenaga kerja outsourcing. Penelitian ini diharapkan dapat menjadi acuan untuk penelitian lebih lanjut di bidang penerapan data mining dalam manajemen sumber daya manusia.
Co-Authors Abdul Muis Abwabul Jinan Alfa Saleh Andi Sanjaya Dahri Yani Hakim Tanjung Dahriani Hakim Tanjung Firman Syahputra Fujiati, Fujiati Ok.Muhammad Ihsan Ratna Sri hayati Rinrin Meilani Salim Wiwi Verina