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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) Jurnal Informatika dan Rekayasa Elektronik 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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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.
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.
PREDIKSI STATUS AKADEMIK MAHASISWA BERDASARKAN DATA PEMBAYARAN DENGAN NAIVE BAYES DAN PARTICLE SWARM OPTIMIZATION Rukiastiandari, Sinta; Rohimah, Luthfia; Mutia, Fara; Aprillia; Chodidjah, Chodidjah
Jurnal Informatika dan Rekayasa Elektronik Vol. 8 No. 2 (2025): JIRE November 2025
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/jire.v8i2.1756

Abstract

Pendidikan tinggi di Indonesia menghadapi tantangan dalam pengelolaan pembayaran mahasiswa, di mana keterlambatan dapat berdampak pada status akademik, termasuk risiko cuti atau pengunduran diri. Penelitian ini bertujuan mengembangkan model prediksi status akademik berbasis data pembayaran kuliah dengan metode Naive Bayes (NB) yang dioptimasi menggunakan Particle Swarm Optimization (PSO). Dataset berjumlah 15.697 record mahasiswa yang telah melalui pra-pemrosesan, termasuk penanganan nilai hilang dan pengkodean atribut kategorikal. Hasil menunjukkan bahwa model NB menghasilkan akurasi 98,83%, precision 98,21%, recall 65,09%, dan AUC 0,905. Optimasi dengan PSO meningkatkan recall menjadi 65,13% dan AUC menjadi 0,907, sementara akurasi dan precision tetap stabil. Analisis fitur mengindikasikan bahwa Jenis Kelamin, Jurusan SLTA, dan Kuliah Sambil Bekerja merupakan atribut paling berpengaruh, sedangkan Pekerjaan Ayah relatif kurang signifikan. Temuan ini menegaskan potensi NB-PSO sebagai pendekatan prediktif untuk mendukung pengelolaan administrasi akademik yang lebih efektif.
The Influence of Service Quality and Price on Alfamart Consumer Loyalty with Customer Satisfaction As Mediation Variables Herawaty, Mety Titin; Aprillia, Aprillia; Rahman, Aan; Rohimah, Luthfia; Taruna, Helmy Ivan; Styaningrum, Etik Dwi; Suleman, Dede
International Journal of Social and Management Studies Vol. 3 No. 2 (2022): International Journal of Social and Management Studies (IJOSMAS)
Publisher : IJOSMAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (544.63 KB) | DOI: 10.5555/ijosmas.v3i2.179

Abstract

This study aims to analyze the quality of service and price on consumer loyalty, with the variable customer satisfaction as a mediating variable. Data collected from 100 respondents of Alfamart minimarkets in Jakarta, Bogor, Depok, Tangerang and Bekasi. The distribution was carried out during December 2021, using the google form due to the pandemic conditions. The research method used is purposive sampling, namely people who shop at the Alfamart Minimarket in the last month and are willing to fill out the questionnaire that the researcher gave. The collected data were analyzed using Structural Equation Modeling with SmartPLS version 3.0 software. Hasil penelitian menunjukkan Service Quality has a positive and significant effect on Customer Satisfaction, Price has a positive and significant effect on Customer Satisfaction, Service Quality has a positive and significant effect on Customer Loyalty, Price has a no significant effect on Customer Loyalty, Customer Satisfaction has a no significant effect on Customer Loyalty, Customer satisfaction did not significantly mediate the service quality and price variables on consumer loyalty.
The Effect Of Product Quality And Promotion On Customer Purchase Decisions Of Pizza Hut Restaurant In The City Of Tangerang Selatan With Price As Intervening Variable Suleman, Dede; Saputra, Fendi; Sugiyah, Sugiyah; Aprillia, Aprillia; Martias, Andi; Rohimah, Luthfia; Herawaty, Mety Titin; Rulando, Refindo Pradikta
International Journal of Social and Management Studies Vol. 3 No. 6 (2022): International Journal of Social and Management Studies (IJOSMAS)
Publisher : IJOSMAS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5555/ijosmas.v3i6.256

Abstract

This study aims to examine and examine the effect of product quality, promotion and price on purchasing decisions by Pizza Hut customers in South Tangerang City. This research is quantitative where data is obtained by giving Likert scale questionnaires to respondents who bought Pizza Hut products in the last three months in South Tangerang City. The data collection method used purposive sampling. From the questionnaires distributed online using the google form link, of the 96 respondents who filled out, only 94 data were eligible for further processing using the SmartPLS version 3.0 software. The results showed that product quality, promotion and price had a significant effect on purchasing decisions. price is not significant as a mediating variable on the effect of product quality on purchasing decisions, while on the effect of promotion on purchasing decisions, price becomes a significant mediating variable
Co-Authors - UBSI, Aprillia Aan Rahman Adjat Sudradjat Amelia, Silvy Andi Martias Aprillia Aprillia, Aprillia Chodidjah Chodidjah Dede Suleman Dewi Yuliandari Fara Mutia Hari Sugiarto - UBSI Helmy Ivan Taruna Juarni Siregar Juarni Siregar Lilik Yuliawati Mety Titin Herawaty Muhammad Rizki Pradana Mutia, Fara Rikiwanto sinaga Rulando, Refindo Pradikta Saputra, Fendi Sartini Sartini Silvy amelia Sinta Rukiastiandari Styaningrum, Etik Dwi Sugiyah Sugiyah, Sugiyah Suhardjono S Supriatin Supriatin Yana Iqbal Maulana Yoraeni, Ani