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All Journal Journal of Innovation and Future Technology (IFTECH) DEDIKASI : Jurnal Pengabdian Kepada Masyarakat
Ade Christian
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Library & Information Science Social Sciences

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Optimalisasi Pelatihan Desain Grafis untuk Promosi Produk Bagi Remaja Masjid Islamic Center Jakarta Utara Supriyadi; Ibnu Rusdi; Ade Christian; Indah Suryani
Dedikasi : Jurnal Pengabdian Kepada Masyarakat Vol. 3 No. 1 (2024): Dedikasi : Jurnal Pengabdian Kepada Masyarakat
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah III DKI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53276/dedikasi.v3i1.161

Abstract

Dunia digital dan milenial di Indonesia berdampak pada persaingan di semua sektor industri, komersial dan nonkomersial, termasuk Remaja Islam Jakarta Islamic Centre (Madaris JIC). Permasalahan yang saat ini dihadapi oleh Madaris JIC adalah kurangnya pemahaman penggunaan aplikasi desain grafis yang diterapkan pada produk, kemasan dan poster yang diproduksi untuk mendukung pemberdayaan ekonomi dalam organisasi. Oleh karena itu, dosen dan mahasiswa Program Studi Informatika Universitas Nusa Mandiri (UNM) melakukan pengabdian masyarakat dengan tujuan membantu para remaja Madaris JIC memperkuat perekonomian organisasi dan anggotanya. Metode yang digunakan dalam pengabdian ini adalah ceramah persuasif. Adapun hasil dari kegiatan pendampingan ini diharapkan nantinya dapat meningkatkan keahlian dan pemahaman dalam bidang desain meningkat dikarenakan ada brandstorming terhadap materi desain grafis ini, semoga dapat mempengaruhi terhadap nilai promosi di lingkungan Madaris JIC. Kegiatan ini juga membantu penyebaran dakwah Madaris JIC melalui poster dakwah yang lebih kreatif dan inovatif sehingga berdampak pada brand awareness Madaris JIC di kalangan masyarakat umum di sekitar lingkungan Jakarta Islamic Center tersebut.
ANALISIS MACHINE LEARNING UNTUK PREDIKSI PENYAKIT PARU-PARU MENGGUNAKAN RANDOM FOREST Ade Christian; Hariyanto Hariyanto; Ahmad Yani; Sumanto Sumanto
Journal of Innovation And Future Technology Vol. 7 No. 1 (2025): Vol 7 No 1 (Februari 2025): Journal of Innovation and Future Technology (IFTECH
Publisher : LPPM Unbaja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/iftech.v7i1.3906

Abstract

Lung diseases, including COPD, lung cancer, and asthma, are serious global health issues, causing over seven million deaths annually. Advanced technologies, such as deep learning and the Random Forest algorithm, have been effectively utilized to detect and classify lung diseases from imaging data with high accuracy. This study aims to demonstrate the effectiveness of Random Forest in predicting lung diseases. The dataset used consists of 30,000 records with 11 attributes, collected from Kaggle and processed using Orange software version 3.36.2. The implementation of the Random Forest algorithm was conducted with 10 decision trees and six attributes considered at each split. The model was tested using Cross Validation with 10 folds. The testing results showed an AUC value of 0.993, indicating a very high level of accuracy. A confusion matrix was used to measure the model's performance through various metrics, including accuracy, precision, recall, F1-score, and AUC. This model achieved high accuracy, with ROC AUC values of 0.453 for predicting the presence of lung disease and 0.547 for predicting its absence. These results confirm that the Random Forest algorithm is an effective predictive tool for identifying lung diseases. This study makes a significant contribution to the development of more accurate and efficient diagnostic techniques, assisting medical professionals in identifying lung diseases in patients. With a deeper understanding of how this algorithm operates in the healthcare domain, it is expected to significantly enhance the quality of patient diagnosis and care.
Co-Authors Ahmad Yani Hariyanto Ibnu Rusdi Indah Suryani Sumanto Sumanto Supriyadi