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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 Almantiq
Nita Cahyani
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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 Library & Information Science Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Implementasi Machine Learning Model sebagai Sistem Prediksi Penyakit Breast Cancer Cahyani, Nita; Irsyada, Rahmat; Kartini, Alif Yuanita
Digital Transformation Technology Vol. 4 No. 2 (2024): Periode September 2024
Publisher : Information Technology and Science(ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/digitech.v4i2.5209

Abstract

Breast Cancer atau Kanker payudara adalah penyakit yang paling umum ditemukan pada wanita di seluruh dunia. Setiap perkembangan untuk prediksi dan diagnosis penyakit kanker merupakan modal penting untuk hidup sehat. Sehingga, akurasi tinggi dalam prediksi kanker penting untuk memperbarui aspek pengobatan dan standar kelangsungan hidup pasien. Teknik Machine Learning (ML) merupakan aplikasi dari Artificial Intelligence (AI) yang dapat memberikan kontribusi besar pada proses prediksi dan diagnosis dini kanker payudara, dan telah terbukti sebagai teknik yang kuat. Dalam penelitian ini, diterapkan algoritma Machine Learning yaitu metode single: Support Vector Machine (SVM), Random Forest, Logistic Regression, dan K-Nearest Neighbors (KNN) dan metode ensemble yaitu SMOTE-Boosting dan SMOTE-Bagging pada dataset Breast Cancer di Bojonegoro. Tujuan dari penelitian ini Mendaptakan ketepatan klasifikasi atau prediksi breast cancer khususnya studi kasus di Bojonegoro dengan tingkat kinerja yang lebih baik. Nilai akurasi yang terbaik pada metode single yaitu model Random Forest (RF) sebesar 95,65% untuk data testing, 100% untuk data training sedangkan untuk metode ensembel SMOTE-Boosting Random Forest (RF) sebesar 100% untuk data testing, 100% untuk data training dan SMOTE-Bagging RF sebesar 97% untuk data training dan 100% untuk data testing. Sehingga SMOTE-Boosting RF dapat dijadikan analisis prediksi yang terbaik dalam penelitian ini. Hasil ini dapat digunakan di masa depan untuk memprediksi penyakit lainnya.
Implementation of Least Square Method to Predict Crime in Indonesia Based on the Web Cahyani, Nita; Irsyada, Rahmat; Anggi, Diva
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.
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.
Co-Authors Agus Sulistiawan Alfiyatul, Siti Nur Ana Alfina Anggi, Diva Budiman S, M. Arif Denny Nurdiansyah Destri Susilaningrum Erick Febriyanto Firman, Azharil Hanifah, Aisyah Hariyanto, Sidiq Husna, Ovilia Iin Emy Prastiwi, Iin Emy Inayaturohmat, Fatuh Irsyada, Rahmat Kartini, Alif Yuanita Khoiriyah, Nuraini Lailatul Badriyah 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 Pratama, Gilar Budi Putri, Wardiana Adinda Putrye Aufia Indah Lestari Qibtiyah, Mariyatu Saidah, Saniyatus Sanjaya, Ucta Pradema Suttrisno Umi Kholifah