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All Journal Komunita: Jurnal Pengabdian dan Pemberdayaan Masyarakat PREDATECS: Public Research Journal of Engineering, Data Technology and Computer Science
Khalifah, Habibah Dian
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Public Health Social Sciences

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Sentiment Analysis of Twitter Reviews on Google Play Store Using a Combination of Convolutional Neural Network and Long Short-Term Memory Algorithms Ningrum, Meriana Prihati; Mutia, Risma; Azmi, Habil; Khalifah, Habibah Dian
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 2 No. 2: PREDATECS January 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v2i2.1625

Abstract

In this era of rapidly evolving technology, the use of social media has become widespread and has become a major platform for sharinhabibahdian.khalifah@ogr.deu.edu.trg people's opinions and views. Google Play Store, as one of the main platforms for digital content, provides access to various applications including Twitter, which allows users to provide reviews and ratings. This research aims to conduct sentiment analysis of Twitter reviews on the Google Play Store using two algorithms namely Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The data used is 4999 reviews after the scraping process. From the experimental results, an accuracy value of 84.67%, recall of 81%, and precision of 84% were obtained on CNN, and an accuracy of 82.19% recall of 69%, and precision of 87% on LSTM. From these results, it can be seen that the highabibahdian.khalifah@ogr.deu.edu.trhest accuracy value is obtained in the CNN algorithm. Although the difference in accuracy is small, the CNN algorithm provides better results in classifying sentiment analysis data on Twitter reviews on the Google Play Store.
Deep Learning for Pneumonia Detection in Chest X-Rays using Different Algorithms and Transfer Learning Architectures Lestari, Danur; Mulya, Anggi; Tatamara, Aghnia; Haiban, Ryando Rama; Khalifah, Habibah Dian
Public Research Journal of Engineering, Data Technology and Computer Science Vol. 3 No. 1: PREDATECS July 2025
Publisher : Institute of Research and Publication Indonesia (IRPI).

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/predatecs.v3i1.1553

Abstract

Pneumonia is one of the lung conditions brought on by bacterial infections. An accurate diagnosis is necessary for successful treatment. A radiologist can typically diagnose the condition based on images from a chest X-ray. The diagnosis may be arbitrary for a variety of reasons, such as the indistinctness of certain diseases on chest X-ray pictures or the possibility of the illness being mistaken for another. Consequently, clinicians require guidance from computer-aided diagnosis tools. We diagnosed pneumonia using two algorithms CNN and GAN, as well as two architectures ResNet50V2 and InceptionV3. The test results show that the ResNet50V2 architecture is superior to the InceptionV3 architecture on the CNN algorithm with an accuracy of 94% versus 93%. In addition, the test results on the GANs algorithm show that the ResNet50V2 architecture is superior to the InceptionV3 architecture with an accuracy of 96%, while the InceptionV3 architecture achieves an accuracy of 92%.
Implementasi Aplikasi Cegah dan Rawat Stroke dalam Pencegahan dan Penanganan Stroke Berbasis Masyarakat Amila, Amila; Sembiring, Evarina; Gunawan, Immanuel Hormat; Zebua, Agus Briel Putra; Daulay, Zuraidah Mastura; Br. Ginting, Rahma Tria Rayani; Khalifah, Habibah Dian
KOMUNITA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol 4 No 4 (2025): November
Publisher : PELITA NUSA TENGGARA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60004/komunita.v4i4.262

Abstract

Kasus Penyakit Tidak Menular (PTM), khususnya stroke, terus meningkat di wilayah Puskesmas Medan Sunggal, Stroke dapat dicegah melalui pola hidup sehat, manajemen stres, dan pengobatan teratur. Namun, stroke sering menimbulkan kondisi gawat darurat dan berdampak jangka panjang pada kehidupan pasien pasca stroke. Kegiatan Pengabdian Kepada Masyarakat (PKM) ini bertujuan meningkatkan kapasitas dan kemandirian masyarakat dalam mencegah, menangani kondisi kegawatdaruratan, serta merawat pasien stroke secara umum serta sosialisasi sistem informasi “GaWat Stroke” sebagai sarana bantu dalam pencegahan dan penanganan stroke secara mandiri, mempermudah pencatatan dan pelaporan data pasien bagi kader puskesmas. Metode pelaksanaan mencakup pemeriksaan kesehatan, edukasi, dan sosialisasi aplikasi berbasis digital. Hasil kegiatan menunjukkan rata-rata TD sistolik dan distolik 126.38 mmHg dan 73.78 mmHg, IMT 24.96 kg/m2, KGD sewaktu 134.33 mg/dL, kolesterol total 192.22 mg/dL Risiko rendah stroke sebesar 80%. Rata-rata skor pre-test sebesar 54,27 meningkat menjadi 73,69 pada post-test. Kegiatan ini mendorong terbentuknya komunitas sadar stroke yang berkelanjutan dan berbasis teknologi, mempercepat deteksi dini serta menurunkan angka PTM di wilayah kerja Puskesmas Medan Sunggal.
Co-Authors Amila Amila, Amila Azmi, Habil Br. Ginting, Rahma Tria Rayani Daulay, Zuraidah Mastura Gunawan, Immanuel Hormat Haiban, Ryando Rama Lestari, Danur Mulya, Anggi Mutia, Risma Ningrum, Meriana Prihati Sembiring, Evarina Tatamara, Aghnia Zebua, Agus Briel Putra