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All Journal Jurnal Sains dan Seni ITS BAREKENG: Jurnal Ilmu Matematika dan Terapan Journal of Computer Networks, Architecture and High Performance Computing Jurnal Statistika dan Aplikasinya Journal of Mathematics Education and Science Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Journal Of Sharia Banking Brilliance: Research of Artificial Intelligence Mafaza : Jurnal Pengabdian Masyarakat Digital Transformation Technology (Digitech) Jurnal Statistika dan Komputasi (STATKOM) AL-UMRON: Jurnal Pengabdian kepada Masyarakat Jurnal Matematika Integratif Jurnal Pengabdian Masyarakat dan Riset Pendidikan Journal of Innovative and Creativity Almantiq Jurnal Pengabdian Masyarakat Indonesia
Nita Cahyani
Universitas Padjadjaran
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Predicting Heart Failure Status Using Binary Logistic Regression with Clinical and Demographic Factors: Penelitian Cahyani, Nita; Irsyada, Rahmat
Jurnal Pengabdian Masyarakat dan Riset Pendidikan Vol. 4 No. 3 (2026): Jurnal Pengabdian Masyarakat dan Riset Pendidikan Volume 4 Nomor 3 (Januari 202
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jerkin.v4i3.5189

Abstract

The aim of this study was to identify clinical and demographic characteristics associated with heart failure and develop an interpretable risk model using binary logistic regression on hospital patient data. Early detection of heart failure is expected to support timely intervention and clinical decision-making based on routine measurements. This study analyzed 130 anonymized patient data with heart failure status as a binary outcome. The initial logistic regression model included all candidate predictors and was then simplified to improve stability and calibration. Results are presented as odds ratios with 95% CIs. Performance evaluation included ROC–AUC, classification metrics, the Hosmer–Lemeshow test, calibration plots, and 5-fold cross-validation. The final model was significant (LR p = 1.0×10⁻⁵; McFadden R² = 0.222) with an accuracy of 81.54%, sensitivity of 89.41%, specificity of 66.67%, AUC of 0.811, and a Brier score of 0.164. Cross-validation showed an average AUC of 0.774 and an accuracy of 0.762. Significant predictors included BMI, serum creatinine, serum potassium, and total cholesterol, with acceptable calibration (p = 0.0767). This model has potential use as an interpretive screening tool, although external validation is still needed.
Transformasi Digital UMKM: Pengembangan Marketplace BANGKIT (Belanja UMKM Kreatif, Inovatif, dan Komplit) untuk Ekspansi Penjualan Produk Lokal UMKM Kabupaten Subang Rahmat Irsyada; Lani Nurlani; Abd Rachman Mildan; Arnov Abdillah Rahman; Rachmad Augy; Nita Cahyani
JURNAL PENGABDIAN MASYARAKAT INDONESIA Vol. 4 No. 3 (2025): Oktober : Jurnal Pengabdian Masyarakat Indonesia (JPMI)
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jpmi.v4i3.6021

Abstract

Limitations in the adoption of digital technology are a major challenge for Micro, Small, and Medium Enterprises (MSMEs) in Subang Regency, which is reflected in the still-manual production process, conventional business management, and limited marketing reach on a local scale. On the other hand, the general public also faces low digital literacy which hinders participation in the modern economy. This community service program aims to address these problems through the design and development of an integrated marketplace platform called BANGKIT (Creative, Innovative, and Complete MSME Shopping). The program implementation method uses a participatory approach that includes three main stages: (1) development of the marketplace platform as a digital showcase for local products; (2) intensive training and mentoring for MSME actors regarding online store management, product photography, and digital marketing strategies; and (3) facilitation of the onboarding process for MSME products into the platform. The results of this activity are the realization of a functional digital economic ecosystem, increased capacity and empowerment of MSME partners, and expanded market reach for local products. This program not only provides concrete solutions for MSMEs, but also supports the achievement of the Key Performance Indicators (KPI) of higher education through the active involvement of lecturers and students in providing direct benefits to the community. Program outputs are disseminated through publications in community service journals, mass media, activity videos, poster works and reports on increasing the level of partner empowerment: management aspects.
Implementation of the K-Nearest Neighbor Method in a Web-Based Creditworthiness Decision Support System in Employee Cooperatives Nita Cahyani; Rahmat Irsyada; Hidayah Maulida
Journal of Innovative and Creativity Vol. 5 No. 3 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

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

Abstract

The Republic of Indonesia Employees' Cooperative (KPRI) was established as a legal entity based on the principles of family and people's economy, with a primary mandate to improve the welfare of its members. In its operations, savings and loan units are a crucial service. However, the cooperative's financial sustainability often faces serious challenges in the form of the risk of losses due to bad debts from debtors. This problem indicates that conventional methods for assessing prospective borrowers are often inaccurate and risk subjective, necessitating the need for stronger and more systematic criteria as a basis for decision-making. This research aims to address these issues by developing a Decision Support System (DSS) for loan eligibility. Through literature review and the collection of historical member transaction data, this research implements the K-Nearest Neighbor (K-NN) algorithm. This method was chosen for its ability to classify new loan eligibility based on similarity patterns (shortest distance) to previous customer data. The research results show that integrating the K-NN algorithm into the decision support system has a significant positive impact. The system has proven capable of providing classification recommendations that assist cooperative staff in processing loan applications according to predetermined criteria. System testing yielded a feasibility rate of 88%, indicating excellent performance. Overall, it can be concluded that the implementation of the K-NN method in KPRI loan approval processes makes the selection process more objective, accurate, and time-efficient compared to manual methods. This system is suitable for implementation as a strategic solution to minimize the risk of bad debt and maintain the financial stability of cooperatives.
Profit Prediction for Skincare Resellers Using the Exponential Smoothing Method Nita Cahyani; Rahmat Irsyada; Azharil Firman; Fatuh Inayaturohmat; Retta Farah Pramesti
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6585

Abstract

This research elucidates the application of the exponential smoothing method in forecasting profit figures for Lutfia MS Glow Skincare. This method was chosen due to its capability to adapt data using the alpha value, along with continual refinement based on exponentially smoothed historical averages. With an explanatory purpose, the study collected profit data from 2020 to 2022 at Lutfia MS Glow Skincare. The single exponential smoothing technique was employed to develop a profit prediction system, enabling the identification of sales trends and evaluation through metrics like Mean Absolute Error (MAE) and Mean Squared Error (MSE). The approach offers simplicity in implementation while providing relatively accurate results, especially for short-term forecasting. This makes it particularly useful in retail and skincare business contexts, where sales figures can be volatile due to seasonal demands or market fluctuations. By utilizing exponential smoothing, the research helps reduce forecasting errors and provides actionable insights for business planning. The result of the analysis showed a reasonably low error margin with a Mean Absolute Percentage Error (MAPE) of 3.65%, indicating high prediction accuracy. The research outcomes furnish skincare resellers and decision-makers with practical guidance in planning inventory, managing supply chains, and executing marketing strategies, ultimately supporting better data-driven decisions in a competitive industry.
Implementation of Least Square Method to Predict Crime in Indonesia Based on the Web Nita Cahyani; Rahmat Irsyada; Diva Anggi
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7424

Abstract

This study was initiated by the need to apply the Least Square method to project the number of crimes in Indonesia using historical data from 2018 to 2022. Crime is a crucial issue in maintaining public security and supporting law enforcement, so accurate prediction results can assist the government in formulating public policies and optimizing resource use. The main problem of this study is how to apply the Least Square method to predict various categories of crimes in Indonesia, such as crimes against life, physical violence, morality, individual freedom, property rights with or without violence, and narcotics crimes. The purpose of this study is to develop a prediction model that can provide an accurate picture of future crime trends. The Least Square method was chosen because it can minimize prediction errors and process data with diverse variations, resulting in more stable and reliable estimates. The data used covers various types of crimes within the study period, with accuracy checked through the Mean Absolute Percentage Error (MAPE) value. The results show that the Least Square method is able to produce highly precise predictions with a MAPE value of 1.21%, thus proving effective in predicting crime rates in Indonesia with a very low error rate.
Co-Authors Abd Rachman Mildan Agus Sulistiawan Alfiyatul, Siti Nur Ana Alfina Arnov Abdillah Rahman Azharil Firman Budiman S, M. Arif Denny Nurdiansyah Destri Susilaningrum Diva Anggi Erick Febriyanto Fatuh Inayaturohmat Gilar Budi Pratama Hanifah, Aisyah Hariyanto, Sidiq Hidayah Maulida Husna, Ovilia Iin Emy Prastiwi, Iin Emy Irsyada, Rahmat Kartini, Alif Yuanita Khoiriyah, Nuraini Lailatul Badriyah Lani Nurlani Lestari, Putrye Aufia Indah Mahmuda, Rahmawati Muhammad Arif Budiman Muhammad Sjahid Akbar Mu’afa , M Rif’an Fawajul Nilna Himawati Nur Inda Nafa Natalia Nuraini Khoiriyah Nurhana, Inka Ayu Perdana , Chepy Pramesti, Retta Farah Putri, Wardiana Adinda Putrye Aufia Indah Lestari Qibtiyah, Mariyatu Rachmad Augy Rahmat Irsyada Rahmat Irsyada Retta Farah Pramesti Saidah, Saniyatus Sanjaya, Ucta Pradema Suttrisno Umi Kholifah