Article Per Year (5 Year)
p-Index From 2021 - 2026
0.92
P-Index
This Author published in this journals
All Journal JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Jurnal Masyarakat Indonesia Worksheet : Jurnal Akuntansi Journal of Innovative and Creativity Horizon: Indonesian Journal of Multidisciplinary
Devanta Abraham Tarigan
Politeknik Negeri Medan
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Public Health Social Sciences Other

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Implementasi dan Perbandingan Algoritma Random Forest dan Support Vector Machine untuk Prediksi Penyakit Jantung menggunakan Dataset Heart Disease UCI Devanta Abraham Tarigan; Zakaria Sembiring; Efrata Tarigan; Alexander Sebayang
Journal of Innovative and Creativity Vol. 5 No. 3 (2025)
Publisher : Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joecy.v5i3.6942

Abstract

Heart disease is a leading cause of death in Indonesia, with a national prevalence of 0.85% in 2025 and cardiovascular-related deaths reaching 651,481 annually. This study aims to implement and compare Random Forest (RF) and Support Vector Machine (SVM) algorithms for heart disease prediction using the UCI Heart Disease dataset (Cleveland subset, 303 instances). Methods include preprocessing (missing value imputation, one-hot encoding, scaling), model training, and evaluation using accuracy, precision, recall, F1-score, and 5-fold cross-validation. Results show RF achieving 86.89% test set accuracy and 89% recall for Presence, while SVM reaches 85.25%. In cross-validation, SVM is more stable with a mean accuracy of 81.84% compared to RF's 80.18%. Feature importance from RF highlights thal_7.0 (0.129), thalach (0.126), and ca (0.110) as key predictors. Conclusion: RF excels in reducing false negatives for early detection, while SVM is more generalizable. The novelty lies in the comprehensive comparison with feature interpretation in the Indonesian health context, supporting AI-based prediction systems for heart disease prevention.
Eco-Reporting Kopi : Inovasi Laporan Keuangan UMKM Berbasis Dampak Sosial dan Lingkungan Untuk Usaha Berkelanjutan Pada CV. Mandiri Kopi Nurhaflah Soraya; Rahmadani Rahmadani; Khairi Anshor; Devanta Abraham Tarigan
Worksheet : Jurnal Akuntansi Vol 5, No 2 (2026)
Publisher : UNIVERSITAS DHARMAWANGSA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/wjs.v5i2.8316

Abstract

Penelitian ini bertujuan untuk mengembangkan dan menguji model Eco-Reporting sebagai inovasi pelaporan keuangan berbasis Environmental, Social, and Governance (ESG) pada CV. Mandiri Kopi, sebuah UMKM yang bergerak di sektor agribisnis kopi. Penelitian menggunakan pendekatan deskriptif kualitatif dengan teknik pengumpulan data melalui observasi, wawancara, dan dokumentasi. Hasil penelitian menunjukkan bahwa penerapan Eco-Reporting mampu menyajikan informasi keuangan yang lebih komprehensif dengan mengintegrasikan dimensi ekonomi, sosial, dan lingkungan. Dari sisi ekonomi, CV. Mandiri Kopi mencatat peningkatan pendapatan sebesar 250% dengan laba bersih Rp 1 Miliar. Pada dimensi lingkungan, perusahaan berhasil mengolah 5.000 kg limbah kopi menjadi pupuk organik serta menanam 2.000 bibit pohon. Sedangkan pada aspek sosial, pemberdayaan 120 petani mitra dan penyelenggaraan 240 jam pelatihan telah memberikan dampak positif bagi komunitas lokal. Hasil ini sejalan dengan teori legitimasi yang menekankan pentingnya pengungkapan informasi keberlanjutan untuk memperoleh dukungan publik. Penelitian ini menyimpulkan bahwa Eco-Reporting dapat meningkatkan transparansi, akuntabilitas, serta legitimasi sosial UMKM, sekaligus mendukung pencapaian Tujuan Pembangunan Berkelanjutan (SDGs). Penelitian selanjutnya disarankan untuk memperluas objek kajian ke berbagai sektor UMKM guna menguji konsistensi model ini pada konteks yang berbeda.
DIGITALISASI MANAJEMEN KEANGGOTAAN, ABSENSI, DAN TRANSAKSI KEUANGAN MELALUI PENGEMBANGAN APLIKASI MOBILE PADA RYU DOJO MEDAN Devanta Abraham Tarigan
Jurnal Masyarakat Indonesia (Jumas) Vol. 5 No. 01 (2026): Jurnal Masyarakat Indonesia (Jumas)
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jumas.v5i01.369

Abstract

Ryu Dojo Medan merupakan pusat latihan bela diri yang menaungi sejumlah cabang seperti Brazilian Jiu-Jitsu, Muay Thai, Boxing, Judo, hingga Mixed Martial Arts. Selama ini seluruh kegiatan administrasi, mulai dari pendataan anggota, pencatatan kehadiran, iuran keanggotaan, sampai penjualan di kantin, masih dikerjakan secara manual menggunakan buku dan catatan kertas. Cara ini menyulitkan pengelola dalam memantau jumlah anggota aktif, merekap kehadiran, serta mengetahui pemasukan harian secara cepat dan akurat. Kegiatan pengabdian kepada masyarakat ini bertujuan membantu Ryu Dojo Medan melakukan digitalisasi operasional melalui pengembangan sebuah aplikasi mobile berbasis Android. Aplikasi dibangun menggunakan kerangka kerja React Native dengan basis data Firebase, dan dirancang agar seluruh proses dapat dijalankan cukup melalui telepon pintar tanpa memerlukan komputer khusus. Tahapan kegiatan meliputi observasi dan analisis kebutuhan, perancangan sistem, pengembangan aplikasi, implementasi, pelatihan pengelola, serta evaluasi. Hasil kegiatan menunjukkan bahwa aplikasi mampu menggantikan pencatatan manual pada modul keanggotaan, absensi termasuk melalui pemindaian QR Code, pengelolaan kelas, kasir kantin dengan konsep tagihan berjalan, pembayaran membership, layanan drop-in, serta laporan keuangan harian. Pengelola dojo menilai aplikasi memudahkan pekerjaan administrasi dan membuat data tersimpan lebih rapi serta aman. Digitalisasi ini diharapkan menjadi fondasi bagi Ryu Dojo Medan untuk berkembang lebih profesional di era ekonomi digital.
Evaluasi Algoritma Random Forest dan KNN dalam Memprediksi Risiko Diabetes Berdasarkan Fitur Klinis Siti Jamilah Br Tarigan; Alyiza Dwi Ningtyas; Arif Hamied Nababan; Devanta Abraham Tarigan; Dini Rizqi Dwikunti Siregar
HORIZON: Indonesian Journal of Multidisciplinary Vol. 4 No. 3 (2026): HORIZON: Indonesian Journal of Multidisciplinary
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/hijm.v4i4.6400

Abstract

This study aims to demonstrate the performance of the Random Forest and K-Nearest Neighbors (KNN) algorithms in predicting diabetes risk based on numerical clinical data. The study used a dataset of 757 samples with eight clinical features, namely the number of pregnancies, glucose levels, blood pressure, skin thickness, insulin, body mass index (BMI), familial diabetes predisposition function, and age. The data was divided into 80% training data and 20% testing data, with data scale adjustments to support the classification process. The evaluation results showed that Random Forest produced better performance with an accuracy of 73.7% and an F1-Score of 0.623, compared to KNN with an accuracy of 72.4% and an F1-Score of 0.604. Comparison of classification results showed that Random Forest was able to provide more consistent predictions in distinguishing groups at risk of diabetes from healthy groups. The contribution of this study is to provide an empirical evaluation of the description of two classification algorithms commonly used on numerical clinical data and show that Random Forest is more suitable for the development of a decision support system for diabetes risk prediction. This research can be the basis for the development of more accurate prediction models through the use of broader datasets and other machine learning methods.
Revitalisasi UMKM Pengrajin Kayu Kota Medan untuk Penguatan Daya Saing melalui Optimalisasi Peralatan Produksi dan Efektivitas Biaya Produksi Nurhaflah Soraya; Rahmadani Rahmadani; Khairi Anshor; Devanta Abraham Tarigan; Rika Githamala Ginting
JURPIKAT Vol 6 No 4 (2025)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/jurpikat.v6i4.2729

Abstract

This community service program aims to enhance the competitiveness of woodcraft SMEs in Medan City through revitalization strategies focused on optimizing production equipment and improving cost-effectiveness. The main challenges faced by these SMEs are outdated technology, inefficient tools, and high operational costs, which result in low productivity and suboptimal product quality. Through technical training, mentoring, and the implementation of equipment improvement as well as cost management practices, the program successfully increased production efficiency, reduced unnecessary expenses, and improved the quality of wooden products to become more competitive in local and regional markets. The results indicate significant improvements in production capacity, cost efficiency, and entrepreneurial knowledge in sustainable business management, thereby strengthening the competitiveness of woodcraft SMEs amid an increasingly dynamic market environment.
Co-Authors Alexander Sebayang Alyiza Dwi Ningtyas Arif Hamied Nababan Dini Rizqi Dwikunti Siregar Efrata Tarigan Khairi Anshor Nurhaflah Soraya Rahmadani Rahmadani Rika Githamala Ginting Siti Jamilah Br Tarigan Zakaria Sembiring