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All Journal JurTI (JURNAL TEKNOLOGI INFORMASI) Journal of Information Systems and Informatics Infotek : Jurnal Informatika dan Teknologi Jurnal Ekonomi dan Teknik Informatika Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Abdi Wina : Jurnal Pengabdian Kepada Masyarakat Universitas Kristen Wira Wacana Sumba Jurnal Komputer, Informasi dan Teknologi Jurnal Publikasi Ilmu Komputer dan Multimedia
Winarnie
Universitas Satu
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Analisa Daerah Kebakaran Hutan Pulau Sumatera dengan K-Means Clustering Metode CRISP-DM Winarnie, Winarnie; Hery Oktafiandi; Pebriyanti Panjaitan; M. Fajar Ramadhan; Yohanes
Jurnal Publikasi Ilmu Komputer dan Multimedia Vol. 5 No. 1 (2026): Januari: Jurnal Publikasi Ilmu Komputer dan Multimedia
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupikom.v5i1.6198

Abstract

Kebakaran Hutan dan Lahan (Karhutla) sering terjadi di negara Indonesia. Karhutla pada wilayah Sumatera dan Kalimantan tahun 2015 menjadi sorotan dunia karena telah membawa polusi kabut asap ke negara tetangga. Informasi tentang pengelompokkan daerah yang terkena dampak kebakaran hutan digunakan untuk mengingatkan supaya masyarakat lebih waspada dan siap. Metode K-Mean Clustering dengan model data mining CRISP-DM, bisa dipakai sebagai salah satu cara untuk menganalisa daerah yang termasuk dalam daerah rawan terjadi karhutla. Penelitian ini dilakukan untuk membuat kelompok daerah dengan kemungkinan kebakaran hutan yang tinggi. dan daerah yang aman dari bencana kebakaran hutan pada kabupaten-kabupaten yang terdapat pada pulau Sumatera yang meliputi 9 provinsi di Pulau Sumatera., Kelompok daerah waspada kebakaran hutan dan lahan diwakili oleh beberapa cluster. Pada penelitian ini berhasil menghasilkan cluster yang baik dan optimal dapat dilihat dengan nilai silhouette bernilai positif dan mendekati 1. Cluster nilai terbaik adalah cluster 2 dengan nilai silhouette coefficient indeks terbesar yaitu 0,96 dengan luas karhutla sebanyak 6624 ha dan kemunculan titik api sebanyak 6006 titik.
Machine Learning Classification of SCD, CHF, and NSR Using 15-Minute ECG-Derived HRV Features Febriyanti Panjaitan; Win Ce; M. Fajar Ramadhan; Winarnie; Hery Oktafiandi
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1557

Abstract

Heart disease remains one of the leading causes of mortality worldwide, making early detection essential for effective intervention. Heart Rate Variability (HRV) is widely used as a non-invasive marker for assessing cardiac conditions, and machine learning has shown potential in classifying heart diseases such as Sudden Cardiac Death (SCD) and Congestive Heart Failure (CHF). This study evaluates the performance of Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbors (KNN) using 15-minute ECG signals comprising three 5-minute segments. The dataset consists of 53 subjects, generating 159 segments, including SCD, CHF, and Normal Sinus Rhythm (NSR). To prevent data leakage, a subject-wise split (80:20) is applied for training and testing. Two evaluation scenarios are considered: per-segment classification and combined 15-minute classification. Results indicate that SVM and DT achieve consistently high, stable performance with near-perfect accuracy, precision, recall, and F1-score, whereas KNN shows lower, more variable performance, particularly in segment-based analysis. The combined 15-minute approach provides more stable results, suggesting improved HRV representation and class separability. Although the results are promising, further validation with larger, more diverse datasets is required to ensure robustness and generalizability. This study highlights the potential of HRV-based machine learning while emphasizing the importance of appropriate temporal representation and rigorous evaluation design.
Co-Authors Anggit Dwi Hartanto Ayuningtyas, Pratika Febriyanti Panjaitan Febriyanti Panjaitan Hery Oktafiandi Hery Oktafiandi Hery Oktafiandi Juwahir, Amat Khairu Nisa, Nisrina Kusrini Kusrini M. Fajar Ramadhan M. Fajar Ramadhan Melani, Lestari Eka Mulyowati, Gandes Pebriyanti Panjaitan Putri, Farah Tisya Anindya Rahayu, Kurnia Puji Ramadhan, M. Fajar Sayid M. Raziq Olajuwon Wahid Nur Win Ce Yohanes Yohanes