Article Per Year (5 Year)
p-Index From 2021 - 2026
2.155
P-Index
This Author published in this journals
All Journal METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi JURSIMA (Jurnal Sistem Informasi dan Manajemen) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Sistem Informasi dan Teknologi (SINTEK) Jurnal Manajemen Informatika & Teknologi JURSIMA Jurnal Manajemen Dan Bisnis Ekonomi AMMA : Jurnal Pengabdian Masyarakat Sintek Kuwera
Nining Rahaningsih
Unknown Affiliation
Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Nursing Social Sciences

Published : 14 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 14 Documents
Search

 1   2 
Model Machine Learning Untuk Prediksi Risiko Penyakit Liver Dengan Random Forest Teroptimasi Rizky Andrea Arifa; Nana Suarna; Agus Bahtiar; Nining Rahaningsih; Willy Prihartono
Jurnal Sistem Informasi dan Teknologi Vol 6 No 1 (2026): Jurnal Sistem Informasi dan Teknologi (SINTEK)
Publisher : LPPM STMIK KUWERA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56995/sintek.v6i1.204

Abstract

Penyakit liver merupakan salah satu kondisi kronis dengan tingkat mortalitas tinggi, sehingga diperlukan pendekatan prediksi yang akurat untuk mendukung deteksi dini. Penelitian ini bertujuan mengembangkan model machine learning untuk memprediksi risiko penyakit liver menggunakan algoritma Random Forest yang dioptimalkan dengan RandomizedSearchCV. Dataset yang digunakan terdiri dari 1.700 entri yang mencakup variabel klinis dan gaya hidup, termasuk usia, jenis kelamin, BMI, konsumsi alkohol, kebiasaan merokok, riwayat genetik, aktivitas fisik, diabetes, hipertensi, serta hasil Liver Function Test. Proses penelitian meliputi preprocessing, normalisasi skala, pembagian data menggunakan train-test split 80:20, pembangunan model baseline, dan optimasi hiperparameter. Hasil eksperimen menunjukkan bahwa optimasi menghasilkan peningkatan performa model, dengan akurasi 0.91, peningkatan recall sebesar 3.20%, dan AUC-ROC mencapai 0.96. Analisis feature importance menunjukkan bahwa LiverFunctionTest, BMI, dan AlcoholConsumption merupakan fitur paling berpengaruh terhadap prediksi risiko penyakit liver. Dengan demikian, Random Forest teroptimasi terbukti efektif dalam menghasilkan model prediksi yang akurat dan dapat digunakan sebagai alat pendukung keputusan dalam deteksi dini penyakit liver.
Application of Decision Tree Algorithms to Classify the Sales Results of Kangen Kripik Sme Products Adila G Khiqmatiar Muchsin; Nining Rahaningsih; Irfan Ali; Dadang Sudrajat; Saeful Anwar
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1854

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a vital role in strengthening the national economy; however, many still face challenges in managing and analyzing sales data effectively. This study aims to classify product sales results at UMKM Kangen Kripik Mang Acep by applying the Decision Tree algorithm as a data classification method based on machine learning. A quantitative experimental approach was employed to evaluate the model’s performance using one-year sales data, including attributes such as product variants, sales volume, sales channels, and marketing regions. Data processing was conducted using RapidMiner software following the Knowledge Discovery in Databases (KDD) framework, which includes data selection, preprocessing, transformation, data mining, and model evaluation. The results indicate that the Decision Tree algorithm successfully classified sales regions (Garut, Bandung, and Sumedang) with an accuracy rate of 96.48%, identifying “Units Sold (pcs)” as the most influential attribute for distinguishing marketing areas. These findings demonstrate that the Decision Tree method is not only effective in improving data analysis efficiency but also provides valuable strategic insights for data-driven business decision-making in MSMEs
ALGORITMA RANDOM FOREST UNTUK PREDIKSI STATUS PINJAMAN BERDASARKAN SKOR KREDIT Attaufiqqurrohman, Hadit; Ade Irma Purnamasari; Denni Pratama; Nining Rahaningsih; Willy Prihartono
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 1 (2026): Volume 12 Nomor 1 Tahun 2026
Publisher : Universitas Methodist Indonesia

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

Abstract

The rapid development of financial technology has encouraged financial institutions to adopt data-driven credit scoring systems in order to minimize the risk of default. However, many loan eligibility prediction models still face challenges such as data imbalance (class imbalance) and the limited capability of traditional models to capture non-linear relationships among variables. This study aims to develop a loan status prediction model using the Random Forest algorithm combined with the Synthetic Minority Oversampling Technique (SMOTE) and One-Hot Encoding (OHE) to improve model accuracy and generalization capability. The data used in this study are secondary data obtained from the public Kaggle platform, consisting of 45,000 records with 14 demographic and financial attributes. The research method employs a supervised learning approach with several stages, including data acquisition and preprocessing (data cleaning, normalization, encoding, and data balancing), Random Forest model training, and performance evaluation using accuracy, precision, recall, F1-score, and AUC metrics. The results show that the combination of Random Forest, SMOTE, and OHE achieves high predictive performance, with an accuracy of 94.8%, precision of 95.6%, recall of 93.7%, F1-score of 94.6%, and an AUC value of 0.972. The most influential variables in loan status prediction are credit_score, person_income, and loan_amnt. This approach is proven to be effective in addressing data imbalance issues and improving classification accuracy in identifying creditworthy and non-creditworthy borrowers.
Design and Construction of a Web-Based Information System for Product Performance Monitoring and Inventory Management at John Store Farras Fadhlur Rohman; Nining Rahaningsih; Bani Nurhakim
Jurnal Manajemen Informatika & Teknologi Vol. 6 No. 1 (2026): Mei : Jurnal Manajemen Informatika & Teknologi
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/q8y5kn82

Abstract

It must be able to stand alone since abstracts are frequently given apart The utilization of information technology has developed into a vital instrument in enhancing operational efficiency and business competitiveness, particularly in the medium-scale retail sector. Accurate inventory management is the primary foundation for business sustainability to maintain consistent customer satisfaction. However, John Store currently faces complex operational obstacles as it relies entirely on manual recording methods. The absence of a structured internal database system causes significant data synchronization issues between administrative records and physical stock in the warehouse, thereby increasing the risk of sudden stockouts. The most fundamental and crucial issue lies in managing bundling category products, where a single package transaction often fails to accurately deduct the stock balance of individual components due to the high risk of human error in manual calculation. To address this inefficiency, this research aims to build the web-based John Store Information System (SIJOHNS) as an integrated internal back-office solution. System development applies the Waterfall method systematically, covering requirements analysis, system design through Flowmap, Data Flow Diagram (DFD), and Entity Relationship Diagram (ERD), to implementation using PHP programming language and MySQL database. Key system features include the automation of bundling stock mapping and multi-role access rights management. Based on Black Box Testing, the system is proven valid. The implementation of SIJOHNS successfully transforms inventory management into a digital format, ensures the accuracy of stock deduction in real-time, and presents analytical data visualization on the dashboard to facilitate the store owner in monitoring sales trends for more strategic and targeted business decision-making
Co-Authors Ade Irma Purnamasari Ade Purnamasari Purnamasari Adila G Khiqmatiar Muchsin agus bahtiar Aldri Frinaldi Attaufiqqurrohman, Hadit Bani Nurhakim Bayu Ryanto Dadang Sudrajat Denni Pratama Edi Tohodi Euis Fadilah Fadhil M. Basysyar Farah Nur Farida Farras Fadhlur Rohman Handreyan Rizki Pratama Irfan Ali Kaslani Kaslani Nana Suarna Rizky Andrea Arifa Rudi Kurniawan Saeful Anwar Saeful Bakri Willy Prihartono