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All Journal Transmisi: Jurnal Ilmiah Teknik Elektro Prosiding SNATIF Jurnal Transformatika 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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Clustering IT Incidents Using K-Means: Improving Incident Response Time in Service Management Anggraeni, Rini; Alzami, Farrikh; Nurhindarto, Aris; Budi, Setyo; Megantara, Rama Aria; Rizqa, Ifan; Muslih, Muslih
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 2 (2025): Research Articles April 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i2.14822

Abstract

Incident management is one of the critical processes in Information Technology service management that aims to manage disruptions and minimize the impact of unexpected incidents on business services. This study applies the K-Means algorithm to cluster IT service incidents, aiming to enhance company operational efficiency. Utilizing a dataset from the UCI Machine Learning Repository comprising 141,712 events related to 24,918 incidents, this research analyzes incident patterns and characteristics for optimized handling. The data was analyzed through a series of preprocessing stages, and the elbow and silhouette methods were used to determine the optimal number of clusters. From the results, it was successfully grouped into 4 (four) clusters with a distortion score value of 964264294.569 and 0.52 silhouette score based on incident characteristics, such as urgency, priority, and number of reassignments. From this, the clustering results show that the K-Means algorithm effectively identifies incidents that require further handling, such as those with high urgency and priority, as well as helping the company focus resources to resolve incidents that have the most impact on the business sector. This research provides a data-driven solution to improve incident management and Service Level Agreement (SLA) fulfillment, while offering a framework for more effective and efficient IT incident analysis and resource allocation.
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.
Pemanfaatan Aplikasi Artificial Intelligence (AI) Generatif untuk Pembelajaran Kreatif Guru PAUD dan SD Sugiyanto, Sugiyanto; Astuti, Yani Parti; Rizqa, Ifan; Sutojo, Totok; Himawan, Heribertus; Nugraha, Adhitya; Santoso, Dewi Agustini
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 9, No 1 (2026): JANUARI 2026
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v9i1.3232

Abstract

Dalam perkembangan teknologi, setiap orang di dunia ini akan mengikutinya baik anak – anak maupun orang tua. Perkembangan teknologi juga tidak lepas dengan dunia Pendidikan dari berbagai jenjang. Jenjang pada dunia Pendidikan saat ini sudah dimulai sejak anak berusia 2 tahun yang disebut dengan Pendidikan Anak Usia Dini (PAUD). Setelah PAUD ada Kelompok Bermain(KB), kemudian Sekolah Dasar (SD) dan seterusnya yang semua orang sudah mengetahuinya. Dengan adanya Pendidikan di usia dini, maka seorang pendidik harus siap untuk mendampingi secara ekstra dalam pembelajarannya. Pendampingan ini dilakukan karena begitu pesatnya perkembangan teknologi yang sudah mempengaruhi anak – anak balita. Untuk itu seorang pendidik harus dibekali dengan pembelajaran yang dikaitkan dengan perkembangan teknologi. Teknologi yang dipakai yang saat ini dikenal dengan AI dan salah satunya Adalah AI Generatif. AI generatif ini bisa digunakan sebagai media pembelajaran karena menarik dan interaktif. Pada kegiatan ini akan dikenalkan berbagai macam AI Generatif sesuai dengan kebutuhannya. AI Generatif yang dimaksud diantaranya ChatGpt/Gemini/Copilot, Canva AI, DALL-E/Bing Image Creator/Leonardo.ai, Synthesia/Pictory dan MusicGen/Mubert/Soundraw. Dengan diadakannya pelatihan tentang AI Generatif diharapkan guru atau pendidik pada PAUD, KB dan juga SD bisa memberikan arahan dan pengetahuan kepada siswa agar pembelajaran lebih interaktif dan menarik.
Co-Authors Abu Salam Adhitya Nugraha Alpiana, Vika Alzami, Farrikh Astuti, Yani Parti Budi Widjajanto 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 Ida Farida 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 Retno Astuti Setijaningsih Ricardus Anggi Pramunendar Rini Anggraeni Setiawan, Aries Sindhu Rakasiwi Sofiani, Hilda Ayu Sugiyanto - T. Sutojo Wibowo, Isro' Rizky Widya Laksana, Deddi Award Widyatama, Thierry Zaenal Sugiyanto