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All Journal International Journal of Electrical and Computer Engineering Teknika International Journal for Applied Information Management Journal of Data Science Methods and Applications
Fikri, Ruki Rizal Nul
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Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Social Sciences

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Alleviating cold start and sparsity problems in the micro, small, and medium enterprises marketplace using clustering and imputation techniques Lestari, Sri; Yulmaini, Yulmaini; Aswin, Aswin; Ma'ruf, Singgih Yulizar; Sulyono, Sulyono; Fikri, Ruki Rizal Nul
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3220-3229

Abstract

Recommendation systems are often implemented in e-commerce and micro, small, and medium enterprises (MSMEs) marketplaces to improve consumer services by providing product recommendations according to their interests. However, it still faces problems, namely sparsity and cold start, thus affecting the quality of recommendations. This research proposes clustering and imputation techniques to overcome this problem. The clustering technique used is k-means, while the missing value imputation method uses average values. The imputation results are then implemented in the k-nearest neighbor (KNN) and naïve Bayes algorithms and evaluated based on performance accuracy. Experimental results show an increase in accuracy of 16.48% in the KNN algorithm from 83.52% to 100%. Meanwhile, the naïve Bayes algorithm increased accuracy by 35.30% from 64.70% to 100%.
Job Clustering Based on AI Adoption and Automation Risk Levels: An Analysis Using the K-Means Algorithm in the Technology and Entertainment Industries Hasibuan, Muhammad Siad; Fikri, Ruki Rizal Nul; Dewi, Deshinta Arrova
International Journal for Applied Information Management Vol. 4 No. 2 (2024): Regular Issue: July 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i2.79

Abstract

This study explores job clustering based on AI adoption levels and automation risks in the technology and entertainment industries using the K-Means algorithm. By applying K-Means clustering, jobs were grouped into five clusters based on their AI adoption and susceptibility to automation. The analysis revealed that Cluster 1, with roles such as software engineers and data scientists, exhibited higher AI adoption and lower automation risks, making these positions more resilient to automation. In contrast, other clusters reflected varying degrees of AI integration and automation vulnerability, offering insights into workforce trends. Principal Component Analysis (PCA) and a heatmap of salary distributions further highlighted the economic implications of these clusters, with Cluster 3 representing the highest-paying roles. The findings suggest the importance of tailored upskilling and reskilling strategies to address the challenges of workforce displacement in AI-driven environments. This study provides actionable insights for workforce planning in industries facing rapid technological transformation.
Pengujian Blackbox pada Sistem Informasi Komunitas Pecinta Kucing di Bandar Lampung Fikri, Ruki Rizal Nul; Indera, Indera; Rahardi, Agus; Agus, Isnandar
TEKNIKA Vol. 18 No. 1 (2024): Teknika Januari - Juni 2024
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.10513105

Abstract

Penelitian ini merespon dengan pertumbuhan minat komunitas pecinta kucing di Bandar Lampung dengan menelusuri dan mengevaluasi keandalan Sistem Informasi Forum Komunitas Pecinta Kucing melalui pendekatan blackbox testing. Fokus penelitian ditujukan untuk mengamati fungsionalitas sistem, untuk menemukan kekurangan dari sistem yang sebelumnya tidak diketahui oleh komunitas. Melalui penerapan metode blackbox testing, kinerja sistem dievaluasi dengan skenario penggunaan uji fungsional. Hasil pengujian akan mengungkap sejumlah aspek terkait peforma sistem, serta menemukan bagian masalah yang dapat disarankan untuk diperbaiki. Dari 12 skenario pengujian yang digunakan, 75% pengujian menghasilkan hasil yang valid atau lulus uji, sementara 25% menghasilkan hasil yang tidak valid atau tidak valid, hal ini menandakan perlunya perbaikan dan tindak lanjut untuk meningkatkan pengalaman pengguna sistem. Dari penelitian ini dapat disimpulkan bahwa pengujian blackbox pada Sistem Informasi Komunitas Pecinta Kucing di Bandar Lampung memiliki peran yang sangat penting dalam menemukan masalah pada sistem tersebut, sehingga akan meningkatkan kewaspadaan pada komunitas pada sistem yang telah dibangun serta untuk pengembangan sistem pada masa yang akan datang.
Prediksi Survivabilitas Pasien Kanker Payudara dengan Penanganan Imbalance Data Menggunakan Algoritma Machine Learning Fikri, Ruki Rizal Nul; Prasetyo, Indra; Soleh, Ary Sofyan; Pratama, Reza Lintang Hana; Kurniawan, Hendra
Journal of Data Science Methods and Applications Vol. 2 No. 1 (2026)
Publisher : Program Studi Sains Data - Institut Informatika dan Bisnis Darmajaya

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

Abstract

Breast cancer is one of the leading causes of death among women worldwide. A major challenge in modeling patient survivability prediction is imbalanced data, where the number of surviving patients significantly outweighs the deceased ones. This study aims to compare the performance of three machine learning algorithms: Logistic Regression, Support Vector Classifier (SVC), and Gradient Boosting Classifier, in predicting patient survivability status. To address the class imbalance issue, Random Over Sampling (ROS) technique was applied during the data preprocessing stage. The methodology includes categorical data encoding, resampling, and model evaluation using accuracy, precision, recall, and F1-score metrics. Experimental results show that the application of ROS successfully balanced the class distribution. Among the three models tested, the Gradient Boosting algorithm demonstrated the best performance compared to linear and vector-based models. This study provides insights into the importance of handling imbalanced data to improve the accuracy of AI-based medical diagnoses.
Co-Authors Agus Rahardi, Agus Agus, Isnandar Aswin Aswin Dewi, Deshinta Arrova Hasibuan, Muhammad Siad Hendra Kurniawan Indera Indera Ma'ruf, Singgih Yulizar Prasetyo, Indra Pratama, Reza Lintang Hana Soleh, Ary Sofyan Sri Lestari Sulyono, Sulyono Yulmaini Yulmaini