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All Journal Infotekmesin Jurnal Puan Indonesia
Sarmini , Sarmini
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Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Public Health

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TRANSFORMASI DIGITAL DALAM PERPAJAKAN: PELATIHAN CORETAX UNTUK MENINGKATKAN KEPATUHAN PAJAK Laili, Nur Isra; Saputra , Muhammad Dio; Sarmini , Sarmini
PUAN INDONESIA Vol. 7 No. 1 (2025): Jurnal PUAN Indonesia Vol. 7 No. 1 Juli 2025
Publisher : ASOSIASI IDEBAHASA KEPRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37296/jpi.v7i1.425

Abstract

This community service program aims to improve digital tax literacy and tax compliance at Sekolah Nabila through training on the use of the Coretax application. The activity addresses challenges in digital tax administration, such as low digital literacy and the lack of training on Coretax usage. The training covers basic tax introduction, how to operate Coretax, and simulations of electronic tax reporting. The results of the program show that more than 75% of participants successfully operated the application and submitted their tax reports electronically. In addition, participants' awareness of the importance of tax compliance also increased significantly. This program has had a positive impact on enhancing participants' technical skills in managing their tax administration. With improved digital literacy and tax awareness, participants are expected to become more independent in fulfilling their tax obligations while reducing the risk of administrative errors that could lead to tax penalties. This program also serves as a model for other communities facing similar challenges in adopting digital taxation systems.
Pengembangan Model Machine Learning Berbasis Linear Discriminant Analysis (LDA) untuk Deteksi Gejala Penyakit Jantung Menggunakan Python Saputri, Inka; Raras Ajeng Widiawati, Chyntia; Sarmini , Sarmini; Yunita, Ika Romadoni
Infotekmesin Vol 16 No 2 (2025): Infotekmesin: Juli 2025
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v16i2.2377

Abstract

Heart disease is the leading cause of death globally and is often not detected early due to limited awareness and the high cost of medical diagnosis. This study aims to develop an accurate and efficient prediction model for heart disease using the Linear Discriminant Analysis (LDA) algorithm. The dataset, obtained from Kaggle, contains 1,024 patient records with 14 clinical attributes, including age, blood pressure, cholesterol, and ECG results. The preprocessing steps include handling outliers, duplicates, class imbalance using SMOTE, and feature standardization. The model was evaluated using cross-validation and learning curve analysis. Results show that the optimized LDA model, tuned with GridSearchCV, achieved an accuracy of 82.54%, a recall of 88.91%, a precision of 79.03%, and an F1-score of 83.54%. The model demonstrates balanced and stable performance, although some misclassification in the positive class remains. This study highlights LDA as a promising method for the early detection of heart disease based on structured clinical data.
Co-Authors Nur Isra Laili Raras Ajeng Widiawati, Chyntia Saputra , Muhammad Dio Saputri, Inka Yunita, Ika Romadoni