Claim Missing Document
Check
Articles

Found 12 Documents
Search

Transformasi Digital pada UMKM Luthfi Handcraft: Meningkatkan Daya Saing dan Branding Produk Melalui Pemasaran Digital Cahyana, Nur Heri; Simanjuntak, Oliver Samuel; Kusumo, Yudhy Widya; Utami, Yenni Sri; Prabowo, Agung
IKRA-ITH ABDIMAS Vol. 9 No. 3 (2025): Jurnal IKRAITH-ABDIMAS Vol 9 No 3 November 2025
Publisher : Universitas Persada Indonesia YAI

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Program Kemitraan Masyarakat (PKM) ini bertujuan untuk mengoptimalkan pemanfaatan kecerdasan buatan (Artificial Intelligence/AI) dalam mempercepat transformasi digital dan meningkatkan daya saing global pada UMKM Luthfi Handcraft di Desa Murtigading, Kabupaten Bantul. Permasalahan utama mitra meliputi rendahnya kualitas visual produk, lemahnya manajemen digital, dan belum optimalnya strategi pemasaran berbasis teknologi. Metode pelaksanaan dilakukan melalui lima tahapan, yakni sosialisasi, pelatihan, penerapan teknologi, pendampingan, dan evaluasi. Hasil kegiatan menunjukkan peningkatan signifikan dalam literasi digital mitra, kemampuan memproduksi konten visual profesional menggunakan AI, penyusunan SOP produksi, serta penguatan strategi digital marketing di marketplace dan media sosial. Program ini memberikan dampak positif berupa peningkatan kapasitas teknologi, kemandirian manajerial, dan perluasan jangkauan pasar. Kegiatan ini sejalan dengan tujuan Sustainable Development Goals (SDGs), sekaligus mendukung pengembangan UMKM berbasis teknologi yang berdaya saing global.
Implementation of Mel-Frequency Cepstral Coefficient as Feature Extraction using K-Nearest Neighbor for Emotion Detection Based on Voice Intonation Nawasta, Revanto Alif; Cahyana, Nur Heri; Heriyanto, Heriyanto
Telematika Vol 20 No 1 (2023): Edisi Februari 2023
Publisher : Jurusan Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v20i1.9518

Abstract

Purpose: To determine emotions based on voice intonation by implementing MFCC as a feature extraction method and KNN as an emotion detection method.Design/methodology/approach: In this study, the data used was downloaded from several video podcasts on YouTube. Some of the methods used in this study are pitch shifting for data augmentation, MFCC for feature extraction on audio data, basic statistics for taking the mean, median, min, max, standard deviation for each coefficient, Min max scaler for the normalization process and KNN for the method classification.Findings/result: Because testing is carried out separately for each gender, there are two classification models. In the male model, the highest accuracy was obtained at 88.8% and is included in the good fit model. In the female model, the highest accuracy was obtained at 92.5%, but the model was unable to correctly classify emotions in the new data. This condition is called overfitting. After testing, the cause of this condition was because the pitch shifting augmentation process of one tone in women was unable to solve the problem of the training data size being too small and not containing enough data samples to accurately represent all possible input data values.Originality/value/state of the art: The research data used in this study has never been used in previous studies because the research data is obtained by downloading from Youtube and then processed until the data is ready to be used for research.