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All Journal PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Teknik Komputer AMIK BSI JITK (Jurnal Ilmu Pengetahuan dan Komputer) JUTIM (Jurnal Teknik Informatika Musirawas) Specta Journal of Technology Infotek : Jurnal Informatika dan Teknologi International Journal of Social and Management Studies (IJOSMAS) SPEED - Sentra Penelitian Engineering dan Edukasi Paradigma International Journal of Accounting, Management, Economics and Social Sciences (IJAMESC)
Rohimah, Luthfia
Universitas Bina Sarana Informatika
Religion Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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Classifying Half-Unemployment Levels in Indonesian Provinces: A K-Means Approach for Informed Policy Decisions Suhardjono Suhardjono; Hari Sugiarto; Dewi Yuliandari; Adjat Sudradjat; Luthfia Rohimah
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol 11 No 2 (2023): September 2023
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v11i2.7390

Abstract

Half-level unemployment refers to individuals who work part-time and are not fully employed. Increasing the half-poverty rate from year to year can lead to challenges in the lives of these individuals. The issue arising with the rise in the half-poverty rate is the government's difficulty in prioritizing areas that require intervention to address these problems. Consequently, an increase in the half-poverty rate can have adverse consequences. Therefore, it is necessary to categorize underemployment rate data obtained from public sources, specifically from data.go.id, using the widely recognized clustering method known as K-Means. The purpose of this categorization is to identify and classify provinces with a significant prevalence of half-poverty levels. This classification will assist the government in making informed decisions when addressing individuals who meet the half-poverty criteria. The results were obtained by grouping the data from the first to the eighteenth iteration into three categories: 'large' (C1), 'medium' (C2), and 'small' (C3) in terms of half-poverty levels. Group C1 comprises 17 provinces with a high half-poverty rate, while C2 includes only 2 provinces, and C3 covers 16 provinces with a significant half-poverty rate. Based on these findings, it is advisable for the Indonesian government to consider implementing policies aimed at reducing the poverty level by half. Priority should especially be given to the C1 group when creating employment opportunities for the province's residents
Predicting Graduation Outcomes: Decision Tree Model Enhanced with Genetic Algorithm Rukiastiandari, Sinta; Rohimah, Luthfia; Aprillia, Aprillia; Mutia, Fara
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 1 (2024): March 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i1.3165

Abstract

This research aims to improve the accuracy of predicting student permit results in the digital era by utilizing machine learning techniques. The main focus is the use of a Decision Tree (DT) model optimized with a Genetic Algorithm (GA) to overcome the limitations of accuracy and testing of conventional methods. This research began with collecting student academic data, followed by preprocessing to eliminate incompleteness and organize the data format. The DT model is then built and optimized with GA, which is inspired by biological evolutionary processes to improve feature selection and parameter tuning. The results show a significant increase in prediction accuracy, from 86.19% to 87.68%, and an increase in the Area Under Curve (AUC) value from 0.755% to 0.788%. This research not only proves the effectiveness of GA integration in improving DT models, but also paves the way for the application of evolutionary techniques in educational data analysis and other fields. The main contributions of this research include the development of more accurate prediction models and practical applications in educational contexts, with the hope of assisting educational institutions in making more informed decisions for their students.
Model Hibrida K-Nearest Neighbors Berbasis Genethic Algorithm untuk Prediksi Penyakit Ginjal Kronis Rukiastiandari, Sinta; Rohimah, Luthfia; Aprillia, Aprillia; Chodidjah, Chodidjah; Mutia, Fara
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 1 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i1.27918

Abstract

Chronic Kidney Disease, which is often abbreviated as PGK, is a serious disease that is of major concern to society and the medical world. This disease can cause various serious complications if not treated properly and early. Therefore, accurate prediction of CKD is very important to support early intervention that can slow disease progression, prevent further complications, and increase the patient's chances of recovery. This research aims to increase the accuracy of PGK predictions by developing a hybrid model that combines the K-Nearest Neighbors (KNN) algorithm with optimization using the Genetic Algorithm (GA). In this approach, the KNN algorithm is used to build a prediction model, while GA acts as an optimization tool that improves model performance. The effectiveness of the optimized model is evaluated using key metrics such as accuracy, precision, recall, and area under the curve (AUC). The results show a significant increase in performance, with accuracy increasing by 17.75%, precision increasing by 23.84%, and recall increasing by 5.34%. This research makes an important contribution to the development of data mining technology for clinical applications and opens up opportunities for further improvements in the future in increasing the prediction accuracy of chronic diseases such as CKD
HOW PLATFORM FEATURES DRIVE CONSUMER BEHAVIOR ON OMNICHANNEL IN INDONESIA Sinta Rukiastiandari; Dede Suleman; Lilik Yuliawati; Fara Mutia; Luthfia Rohimah; Aprillia
International Journal of Accounting, Management, Economics and Social Sciences (IJAMESC) Vol. 1 No. 5 (2023): October
Publisher : ZILLZELL MEDIA PRIMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61990/ijamesc.v1i5.87

Abstract

This study investigates the relationship between platform features, customer engagement, satisfaction, purchase and repurchase intentions, and customer types in omnichannel retail. A survey of 250 participants utilized an online questionnaire, analyzed with statistical methods. Results show that platform convenience and advanced features drive satisfaction, purchase, and repurchase intentions. Impact on customer engagement varies by customer type. Ease of use indirectly affects satisfaction, purchase, and repurchase via engagement and customer type. The research underscores considering customer type when assessing ease of use and design impact on outcomes. It reveals intricate relationships, surpassing prior research by illustrating how platform features affect diverse customer engagement. Ease of use indirectly influences loyalty via engagement and customer type. This underscores a multifaceted loyalty formation. Businesses must factor engagement and customer type in platform refinement for target audience needs. Study underscores understanding platform features, engagement, and loyalty interplay for enhanced customer experiences and business success. It establishes a foundation for future research and practical omnichannel retail improvements.
Co-Authors - UBSI, Aprillia Aan Rahman Adjat Sudradjat Amelia, Silvy Andi Martias Aprillia Aprillia aprillia Aprillia, Aprillia Chodidjah Chodidjah Dede Suleman Dewi Yuliandari Etik Dwi Styaningrum Fara Mutia Fendi Saputra Hari Sugiarto - UBSI Helmy Ivan Taruna Juarni Siregar Juarni Siregar Lilik Yuliawati Mety Titin Herawaty Muhammad Rizki Pradana Mutia, Fara Refindo Pradikta Rulando Rikiwanto sinaga Sartini Sartini Silvy amelia Sinta Rukiastiandari Sugiyah Sugiyah Suhardjono S Supriatin Supriatin Yana Iqbal Maulana Yoraeni, Ani