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All Journal INDONESIAN JOURNAL OF APPLIED PHYSICS Al-Ijtimā: Jurnal Pengabdian Kepada Masyarakat
Nazhifah, Dafina
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Religion Humanities Astronomy Economics, Econometrics & Finance Education Energy Materials Science & Nanotechnology Physics Other

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PENGENALAN DIGITAL MARKETING DALAM UPAYA OPTIMALISASI PEMASARAN PADA UMKM DI DUSUN GIRIMULYO A’yun, Efrida Qurotul; Piero, Alessandro Adrian; Nisa, Ayu Sofi Khairul; Nazhifah, Dafina; Orvala, Edelweis Zita; Erowati, Endang; Kinanthi, Gendis Surya; Ma’ruf, Muhammad Nur Fatah; Sidiq, Muhammad Rais; Billa, Shaqhirra Diva Salsa; Latifah, Emmy
Al-Ijtimā': Jurnal Pengabdian Kepada Masyarakat Vol 5 No 1 (2024): Oktober
Publisher : Lembaga Penelitian, Publikasi Ilmiah dan Pengabdian kepada Masyarakat (LP3M)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53515/aijpkm.v5i1.156

Abstract

With the rapid development of technology and the internet, the marketing world is experiencing a significant transformation. Optimizing and utilizing digital marketing helps in the product marketing activities of a business and the activities of tiny and medium enterprises. Realizing this potential, the UNS 336 KKN Team aims to help market UMKM in Girimulyo Hamlet, Trengguli Village, Jenawi District, Karanganyar, by adopting and optimizing digital marketing techniques to increase the visibility and sales of their products. The activities include socialization and training on marketing digitalization through various platforms such as e-commerce, Google Maps, e-wallet, and social media. The results of this program show a significant increase in the understanding and ability of UMKM players to utilize digital marketing tools. Several UMKM have succeeded in creating online business accounts, increasing visibility on Google Maps, and starting to use social media to promote their products. In conclusion, this marketing digitalization program has the potential to be an essential catalyst in local economic development in Girimulyo Hamlet, with recommendations for continued assistance and further training to ensure the continued adoption of digital technology by local UMKM.
Implementation of CNN-SVM with Index Pattern-Based Feature Selection on PPG Signals for Cuffless Hypertension Detection Nazhifah, Dafina; Wiharto, Wiharto; Muslim, Fajar
INDONESIAN JOURNAL OF APPLIED PHYSICS Vol 15, No 2 (2025): October
Publisher : Department of Physics, Sebelas Maret University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijap.v15i2.102421

Abstract

Hypertension is one of the leading causes of death worldwide and often goes undetected due to its minimal symptoms. Early detection is crucial, and one non-invasive method involves the use of photoplethysmogram (PPG) signals. However, PPG signals contain a large number of features, which can lead to information redundancy and decreased model performance. This study proposes a hypertension detection system based on a CNN-SVM combination, preceded by feature selection using position-based indices (odd, even, specific multiples) to reduce data dimensionality and accelerate computation. The PPG signal dataset was obtained from 216 patients at UNS Hospital. After preprocessing and feature selection, feature extraction was performed using a Convolutional Neural Network (CNN), followed by classification using a Support Vector Machine (SVM). The model was evaluated under three classification scenarios: binary classification (normal vs. prehypertensive-hypertension and normal-prehypertension vs. hypertension) and three-class classification (normal, prehypertension, hypertension). The best classification accuracy achieved was 93.10% for the normal vs. prehypertension-hypertension scenario, 88.38% for normal-prehypertension vs. hypertension, and 82.79% for the three-class classification. This approach demonstrates that the combination of CNN-SVM with simple feature selection can improve both accuracy and efficiency in PPG-based hypertension detection.
Co-Authors A’yun, Efrida Qurotul Billa, Shaqhirra Diva Salsa Emmy Latifah Erowati, Endang Kinanthi, Gendis Surya Ma’ruf, Muhammad Nur Fatah Muslim, Fajar Nisa, Ayu Sofi Khairul Orvala, Edelweis Zita Piero, Alessandro Adrian Sidiq, Muhammad Rais Wiharto Wiharto