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All Journal Transmisi: Jurnal Ilmiah Teknik Elektro Prosiding SNATIF Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Abdimasku : Jurnal Pengabdian Masyarakat Moneter : Jurnal Keuangan dan Perbankan Community: Jurnal Pengabdian Pada Masyarakat
Ifan Rizqa
Fakultas Ilmu Komputer Universitas Dian Nuswantoro Semarang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences

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Pelatihan Pemanfaatan Google Sites Untuk Pembuatan Media Pembelajaran Berbasis Website Untuk Guru Dan Dosen Pada Perkumpulanprofesi Multimedia Dan Teknologi Informasi (PPMULTINDO) Jatmoko, Cahaya; Rakasiwi, Sindhu; Widya Laksana, Deddi Award; Erawan, Lalang; Rizqa, Ifan; Astuti, Erna Zuni
Community : Jurnal Pengabdian Pada Masyarakat Vol. 4 No. 2 (2024): Juli : Jurnal Pengabdian Pada Masyarakat
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/community.v4i2.535

Abstract

Conveying appropriate information to be understood quickly and accurately is very important in various areas of life, both academic and non-academic. A teacher or lecturer is a teacher whose job is to educate and provide instruction to students or students. Data visualization is one way that can be used to present data. The advantage of this method is the availability of statistical graphics which can enrich the display of information so that the results are more interactive for the audience. Google Sites is a service owned by the Google company that can be used for e-learning. That way, the information becomes more appropriate to understand quickly and accurately.
Explainable Machine Learning-Based Decision Tree Model for Early Detection of Hypertension Risk Sofiani, Hilda Ayu; Maulana, Isa Iant; Alzami, Farrikh; Naufal, Muhammad; Azies, Harun Al; Rizqa, Ifan; Santoso, Dewi Agustini; Nugraini, Siti Hadiati
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15585

Abstract

Hypertension is one of the leading causes of cardiovascular disease and is often referred to as a “silent killer” because it typically remains asymptomatic until serious complications, such as stroke or kidney failure, occur. Early detection of hypertension risk is therefore essential to enable timely intervention and prevention. This study aims to develop an explainable machine learning–based Decision Tree model for early detection of hypertension risk using clinical and lifestyle data. The balanced dataset includes variables such as age, body mass index (BMI), blood pressure, family history, smoking habits, stress levels, and sleep duration. The dataset used in this study was obtained from the “Hypertension Risk Prediction Dataset” available on the Kaggle platform, consisting of 1,985 patient records and 11 main features covering variables such as age, body mass index (BMI), systolic and diastolic blood pressure, family history, smoking habits, stress level, physical activity, and sleep duration. The dataset is balanced between the hypertension and normal categories, enhancing the reliability of the classification results. The model was constructed using a Decision Tree Classifier implemented in Scikit-learn and validated through cross-validation to minimize overfitting. Model performance was assessed using accuracy, precision, recall, F1-score, and AUC-ROC metrics. The results indicate that the model achieved an accuracy of 96% and an AUC of 0.9645, demonstrating excellent classification performance. The motivation behind this research lies in the growing need for interpretable artificial intelligence models in healthcare, where transparency and explainability are critical for clinical trust and ethical decision-making. Unlike black-box models, the Decision Tree approach allows clinicians to trace each prediction path, understand contributing variables, and apply insights in real-world medical settings. The primary advantage of this model lies in its transparency, as each prediction can be interpreted through explicit decision rules. Overall, this explainable and high-performing model shows strong potential as a clinical decision support tool for early hypertension screening and prevention programs.
Co-Authors Abu Salam Alpiana, Vika Alzami, Farrikh Astuti, Yani Parti Budi, Setyo Cahaya Jatmoko Candra Irawan Dewi Agustini Santoso Diana Aqmala Erna Zuni Astuti Etika Kartikadarma Fauzi Adi Rafrastara Firmansyah, Muhammad David Habib Mustofa Harun Al Azies Heribertus Himawan Ika Novita Dewi Lalang Erawan Lesmarna, Salsabila Putri Maulana, Isa Iant Megantara, Rama Aria Muhammad Naufal Muslih Muslih Nova Rijati Nugraini, Siti Hadiati Nurhindarto, Aris Purwanto Purwanto Ramadhan Rakhmat Sani Ricardus Anggi Pramunendar Rini Anggraeni Sindhu Rakasiwi Sofiani, Hilda Ayu Sugiyanto - Wibowo, Isro' Rizky Widya Laksana, Deddi Award Widyatama, Thierry