Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Jurnal Abdimas Ekonomi Terapan
Dina Andayati
Universitas AKPRIND Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Optimalisasi Media Sosial dalam Meningkatkan Kualitas Komunikasi dan Pelayanan Publik di Kalurahan Imogiri Anggun Sulistyowati; Yoshua Ronaldo Primartono; Dina Andayati
Jurnal AbdiMas Ekonomi Terapan Vol. 4 No. 1 (2026): JURNAL ABDIMAS EKONOMI TERAPAN
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LPPM), Universitas Selamat Sri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51792/8tz80y63

Abstract

Pemanfaatan media sosial oleh pemerintah tingkat lokal merupakan salah satu strategi penting dalam meningkatkan kualitas pelayanan publik dan komunikasi dengan masyarakat. Namun, pemanfaatan media sosial di Kapanewon Imogiri masih tergolong rendah dan belum dikelola secara optimal. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk mengidentifikasi peluang dan tantangan pemanfaatan media sosial di Kapanewon Imogiri serta merumuskan rekomendasi strategis guna meningkatkan efektivitas pelayanan publik dan komunikasi pemerintahan. Metode yang digunakan adalah pendekatan deskriptif-partisipatif melalui observasi dan diskusi dengan perangkat kapanewon serta masyarakat. Hasil pengabdian menunjukkan bahwa rendahnya pemanfaatan media sosial disebabkan oleh keterbatasan literasi digital, kurangnya sumber daya manusia pengelola, serta belum adanya strategi konten yang terencana. Di sisi lain, kondisi tersebut membuka peluang besar dalam penguatan citra daerah, peningkatan partisipasi masyarakat, dan promosi potensi lokal. Pengabdian ini merekomendasikan pelatihan literasi digital, pembentukan tim pengelola media sosial, dan pengembangan konten berbasis potensi lokal secara berkelanjutan.
Evaluasi Information Gain pada Random Forest, Decision Tree, dan KNN Klasifikasi Tanaman Dina Andayati; Suraya; Muhammad Sholeh; Suparyanto
Jurnal Informatika Komputer, Bisnis dan Manajemen Vol 24 No 3 (2026): September 2026
Publisher : LPPM STMIK El Rahma Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61805/fahma.v24i3.262

Abstract

Selecting suitable crop types based on soil and environmental conditions is essential for improving agricultural productivity. Machine learning enables crop classification using soil and climate characteristics, but using all features may increase model complexity without necessarily improving performance. This study analyzes the effect of Information Gain-based feature selection on Random Forest, Decision Tree, and K-Nearest Neighbors (KNN) for crop classification. The farming.csv dataset contains 2,200 samples, seven input features—nitrogen (N), phosphorus (P), potassium (K), temperature, humidity, pH, and rainfall—and 22 crop classes. The experiment employed an 80:20 train-test split with random_state 42. Feature selection using mutual_info_classif with a threshold >1.0 reduced the features to six: humidity, K, rainfall, P, temperature, and N. Model performance was evaluated using accuracy, precision, recall, and F1-score. Using all features, Random Forest, Decision Tree, and KNN achieved accuracies of 99.32%, 98.64%, and 97.05%, respectively. After feature selection, Random Forest and Decision Tree achieved 99.09%, while KNN remained at 97.05%. These results indicate that Information Gain can reduce the feature set by one feature while maintaining nearly unchanged classification performance.
Co-Authors Anggun Sulistyowati Sholeh Muhammad Suparyanto Suraya Yoshua Ronaldo Primartono