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All Journal Jurnal PkM MIFTEK Journal of Intelligent Systems Technology and Informatics
Salsabila, Rosa
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Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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PENINGKATAN KESADARAN BAHAYA KECANDUAN GAWAI DAN CYBER BULLYING MELALUI PROGRAM MABIT DI DESA MEKARSARI KECAMATAN CILAWU Mubarok, Muhammad Syauqi; Riki Mutakin, Ripan; Syamsudin A, Khoir; Ahmad Taufik, Asep; Azkia, Azka; Ashabil Haqdu, Gezant; Agustiansyah, Yoga; Amiludin, Ikbal; Yasa Riswanda, Dede; Ruli Cahyadi, Deden; Maulana Fazri, Moch Nashrull; Salsabila, Rosa; Hanafiah, Moch Rochmat; Dhiyaaul Haq, Harish; Permata Sari, Riani; Nauri, Shilan; Subekti, Aldi; Mardiana Muttaqin, Rhama; Salman Alfarizi, Muhammad
Jurnal PkM MIFTEK Vol 5 No 2 (2024): Jurnal PkM MIFTEK
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/miftek/v.5-2.2072

Abstract

Kegiatan Malam Bina Iman dan Takwa (MABIT) yang dikombinasikan dengan literasi digital tentang bahaya penggunaan gawai berlebihan dan cyber bullying dilaksanakan sebagai bagian dari program Kuliah Kerja Nyata (KKN) mahasiswa Institut Teknologi Garut di Desa Mekarsari, Kecamatan Cilawu, Kabupaten Garut. Kegiatan ini bertujuan untuk memberikan edukasi kepada masyarakat, khususnya remaja, mengenai pentingnya menjaga akhlak terhadap orang tua dan meningkatkan kesadaran akan dampak negatif teknologi, seperti kecanduan gawai dan cyber bullying. Metode yang digunakan dalam kegiatan ini adalah Penelitian Tindakan Partisipatif, di mana peserta dilibatkan secara aktif dalam ceramah keagamaan, diskusi, jurit malam, dan sesi literasi digital. Hasil kegiatan menunjukkan adanya peningkatan pemahaman peserta tentang bahaya kecanduan gawai dan cyber bullying. Sebagian besar peserta menyatakan bahwa mereka baru sadar akan bahayanya penggunaan gawai secara berlebihan, dan banyak diantaranya menyatakan baru memahami pentingnya menjaga etika di dunia digital setelah mengikuti kegiatan ini. Program ini diharapkan dapat berkelanjutan melalui kolaborasi dengan tokoh agama dan lembaga pendidikan setempat, sehingga literasi digital dan penguatan nilai-nilai moral dapat terus ditingkatkan.
Detection of Pneumonia Disease on Chest X-ray Images Using Convolutional Neural Network Salsabila, Rosa; Lea Saumi
Journal of Intelligent Systems Technology and Informatics Vol 1 No 3 (2025): JISTICS, November 2025
Publisher : Aliansi Peneliti Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64878/jistics.v1i3.18

Abstract

Pneumonia is a critical lung infection and a leading cause of morbidity and mortality worldwide. Early and accurate diagnosis is essential to ensure effective treatment and improved patient outcomes. Chest X-ray imaging, as a widely accessible diagnostic tool, presents challenges in manual interpretation due to overlapping anatomical structures and inter-observer variability. To address this, this study investigates the application of Convolutional Neural Networks (CNN) for automated pneumonia detection from chest X-ray images. The dataset used in this research consists of 5,863 labeled grayscale pictures obtained from the Kaggle repository, comprising 4,273 pneumonia and 1,583 normal cases. Preprocessing steps included image resizing, normalization, and class balancing through augmentation. The CNN model was trained using the augmented dataset and evaluated using various performance metrics. The proposed model achieved an overall accuracy of 79% on the test set, with a precision of 0.84, a recall of 0.79, and an F1-score of 0.78. The class-wise analysis revealed strong performance in detecting normal cases (F1-score = 0.82) but lower recall in pneumonia cases (Recall = 0.60), indicating a need for further improvement. In conclusion, CNN-based approaches demonstrate promising potential for aiding pneumonia diagnosis in clinical settings. However, additional work is necessary to enhance model reliability, particularly in detecting complex patterns of pneumonia. Future research may explore ensemble models and attention mechanisms to improve classification performance.
Co-Authors Agustiansyah, Yoga Ahmad Taufik, Asep Amiludin, Ikbal Ashabil Haqdu, Gezant Azkia, Azka Dhiyaaul Haq, Harish Hanafiah, Moch Rochmat Lea Saumi Mardiana Muttaqin, Rhama Maulana Fazri, Moch Nashrull Muhammad Syauqi Mubarok, Muhammad Syauqi Nauri, Shilan Permata Sari, Riani Riki Mutakin, Ripan Ruli Cahyadi, Deden Salman Alfarizi, Muhammad Subekti, Aldi Syamsudin A, Khoir Yasa Riswanda, Dede