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All Journal Techno.Com: Jurnal Teknologi Informasi Bulletin of Electrical Engineering and Informatics Prosiding SNATIF JAIS (Journal of Applied Intelligent System) Journal of Information System Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Teknik Informatika (JUTIF) Journal of Soft Computing Exploration Prosiding Seminar Nasional Teknologi Informasi dan Bisnis Community: Jurnal Pengabdian Pada Masyarakat Jurnal Informatika Polinema (JIP) Kesejahteraan Bersama : Jurnal Pengabdian dan Keberlanjutan Masyarakat Masyarakat Berkarya: Jurnal Pengabdian dan Perubahan Sosial Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK)
Heru Lestiawan
Universitas Dian Nuswantoro
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Physics Public Health Other

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Pelatihan Pengembangan Media Pembelajaran Menggunakan Animaker Untuk Guru dan Dosen Pada PPMULTINDO Sindhu Rakasiwi; Heru Lestiawan; Suprayogi; Feri Agustina; Daurat Sinaga
Community : Jurnal Pengabdian Pada Masyarakat Vol. 4 No. 3 (2024): November : 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/vmqpnx34

Abstract

Dalam era digital saat ini, guru dan dosen diharapkan mampu mengintegrasikan teknologi dalam proses pembelajaran. Pelatihan pengembangan media pembelajaran menggunakan Animaker diadakan untuk membantu mereka dalam menciptakan konten pembelajaran yang menarik dan interaktif. Pelatihan ini dilaksanakan tim dosen UDINUS Semarang yang melibatkan 125 peserta yang terdiri dari guru dan dosen dari PPMULTINDO. Tujuan pelatihan ini agar bisa meningkatkan literasi digital dan keterampilan teknis peserta dalam membuat bahan ajar yang interaktif. Hasil dari kegiatan ini, para guru dan dosen mampu menghasilkan bahan ajar yang lebih inovatif, sehingga dapat meningkatkan motivasi belajar siswa. Dengan demikian, pelatihan ini menjadi langkah penting dalam mendukung transformasi pendidikan di era digital.
Pelatihan Desain Layout Buku Monograf dengan Canva untuk Guru dan Dosen pada PPMULTINDO Cahaya Jatmoko; Sindhu Rakasiwi; Feri Agustina; Daurat Sinaga; Heru Lestiawan
Masyarakat Berkarya : Jurnal Pengabdian dan Perubahan Sosial Vol. 2 No. 4 (2025): November : Masyarakat Berkarya : Jurnal Pengabdian dan Perubahan Sosial
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/karya.v2i4.2362

Abstract

In the midst of the demand to actively publish scientific papers, the ability to design is a significant added value for teachers and lecturers. This report outlines a monograph book layout design training with Canva held for academics at PPMULTINDO. The main purpose of this activity is to provide practical skills so that participants can independently produce professional book layouts. This training uses an interactive workshop method, where participants are guided from the introduction of Canva's features, the application of design principles, to the practice of preparing layout chapters by chapter. As a result, participants demonstrated a significant improvement in their ability to operate Canva for publication design needs. They are able to produce a structured, consistent, and visually appealing layout. Thus, this training has succeeded in becoming a practical solution for academics to efficiently improve the visual quality of their monograph books
Optimization of Heart Failure Classification on Imbalanced Data Using a Supervised Learning Approach Based on Logistic Regression, Random Forest, and K-Nearest Neighbor: Optimalisasi Klasifikasi Gagal Jantung pada Data Imbalanced Menggunakan Pendekatan Supervised Learning Berbasis Regresi Logistik, Random Forest, dan K-Nearest Neighbor agustina, feri; Irawan, Candra; Erawan, Lalang; Suprayogi; Award Widya Laksana, Deddy; Jatmoko, Cahaya; Sinaga, Daurat; Lestiawan, Heru
Jurnal Informatika Polinema Vol. 12 No. 1 (2025): Vol. 12 No. 1 (2025)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v12i1.9071

Abstract

Heart failure remains one of the leading causes of mortality worldwide, posing significant challenges for early diagnosis and patient management. One of the major obstacles in developing predictive models for heart failure is the class imbalance problem, where the number of surviving patients far exceeds those who experience death events. This imbalance often leads machine learning algorithms to bias toward the majority class, reducing sensitivity to critical minority cases. To address this issue, this study applies the Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset and improve model performance. Three supervised learning algorithms, namely Logistic Regression (LR), Random Forest (RF), and K-Nearest Neighbor (KNN), were implemented and compared on the UCI Heart Failure Clinical Records dataset containing 299 patient samples with 13 clinical attributes. Experimental results show that the Random Forest model achieved the highest performance with 90% accuracy, precision, recall, and F1-score, outperforming both LR and KNN. The findings demonstrate that combining data balancing with ensemble learning effectively enhances prediction accuracy and sensitivity toward minority classes. The main contribution of this research lies in optimizing supervised models for medical data with skewed class distributions, providing a more reliable and interpretable approach for early heart failure detection. Future research may extend this work by integrating advanced ensemble or hybrid deep learning models and expanding the dataset for multi-institutional validation
Co-Authors Abdussalam Abdussalam, Abdussalam Agustina, Feri Ardyan Pratama Ardytha Luthfiarta Aris Febriyanto Award Widya Laksana, Deddy Cahaya Jatmoko Candra Irawan Chaerul Umam Christy Atika Sari Daurat Sinaga Doheir, Mohamed Edi Sugiarto Eko Hari Rachmawanto Erna Zuni Astuti Fauzi Adi Rafrastara Feri Agustina Guruh Fajar Shidik Hadi, Heru Pramono Hussein, Jassim Nadheer Islam, Hussain Md Mehedul Jatmiko, Cahaya Lalang Erawan MARIA BINTANG Mohammad Rofi’udin Nur Rokhman Nur Rokhman Pulung Nurtantio Andono Purwanto Purwanto Putri, Ni Kadek Devi Adnyaswari Ramadhan Rakhmat Sani Reza Dwi Oktaf Purnama Saputro, Fakhri Rasyid Sinaga, Daurat Sinaga, Daurat Sindhu Rakasiwi Solichul Huda, Solichul suprayogi Suprayogi Suprayogi Syamsul Arifin Wellia Shinta Sari Wibowo Wicaksono Wildanil Ghozi Yaacob, Noorayisahbe Mohd