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All Journal Jurnal Computech & Bisnis (e-Journal) Informatics Management, Engineering and Information System Journal Electronic, Business, Management and Technology Journal Journal of Economics, Management, and Entrepreneurship Jurnal Pengabdian Masyarakat: Bisnis dan Iptek (JPMBISTEK) Majalah Bisnis & IPTEK
Cahyo Hermanto
STMIK Mardira Indonesia, Bandung
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Industrial & Manufacturing Engineering Library & Information Science Social Sciences Other

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Implementation of ResNet 101 Architecture in Convolutional Neural Network (CNN) Algorithm to Detect Diseases in Chili Plant Leaves Based on Image Processing Feri Alpiyasin; Cahyo Hermanto; Naura Khoirunnisa; Chintya Rahmadanti; Nida Mutmainah
Majalah Bisnis & IPTEK Vol. 18 No. 2 (2025): Majalah Bisnis & IPTEK
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat (P3M) STIE Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55208/bistek.v18i2.407

Abstract

Chili plants (Capsicum annuum L.) constitute a significant horticultural commodity in Indonesia, with production on the rise.  Chili producers frequently encounter difficulties, including insufficient knowledge regarding diseases that impact chili plant foliage and the application of technology.  Identified major diseases include leaf spot, curly top, and gemini, which can diminish chili output.  The application of the ResNet 101 architecture in Convolutional Neural Networks (CNN) for disease detection in plant pictures presents a viable approach.  The ResNet 101 architecture is employed to recognize and classify various illnesses on chili leaves, facilitating early symptom detection and diagnosis.  The residual architecture (ResNet 101) acquires intricate elements in images to improve disease diagnosis precision. This research examines the utilization, challenges, and benefits of disease detection systems through various methodologies, including observations and field investigations.  The process entails gathering picture models from various disease categories (leaf spot, curly top, gemini) and healthy leaves to construct a dataset, in addition to seeking expert consultation on illness classifications and employing the ResNet 101 architecture for modeling.  The use of the ResNet 101 architecture in the CNN model for disease detection on chili leaves, utilizing a dataset of 1,518 images categorized into four groups (leaf spot, curly top, gemini, and healthy leaves), yielded substantial outcomes, achieving an overall accuracy of 0.9382.  The established architecture must be evaluated against alternative designs to enhance the model's outcomes and to expand the dataset utilized to improve accuracy substantially.
Earth at Your Fingertips Socialization: Getting to Know Digital Maps and Google Earth Regarding Lembang Fault Mitigation at SMP Negeri 2 Lembang Cahyo Hermanto; Deni Sopiyan; Aisah Nur Endah Sari; Rr. Isni Anisah; Khairan Fathan Hubainallah; Rifky Andriansyah Angela; Sutanto Dwi Putra; Ahmad Fauzan Abdurrohman; Willy Radiansyah; Arya Dutha Ramadhan; Muhamad Rendi Mulana; Revansa Zahrandhika; Wildan Kurniawan; Wahyu Alvan
Jurnal Pengabdian Masyarakat: Bisnis dan Iptek (JPMBISTEK) Vol. 3 No. 1 (2026): Jurnal Pengabdian Masyarakat: Bisnis dan Iptek (JPMBISTEK)
Publisher : LPPM STMIK Mardira Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56447/jpmbistek.v3i1.05

Abstract

Lembang, located near a fault line, demonstrates significant susceptibility, underscoring the need for proactive disaster literacy initiatives. This community service initiative seeks to enhance students' understanding of earthquake disaster mitigation by using digital maps and Google Earth to pinpoint areas, assess potential risks, and formulate disaster risk reduction measures. The implementation technique is a participatory-educational strategy that includes presentations of materials, demonstrations of digital maps, practical exploration of the Lembang Fault area in Google Earth, and interactive conversations. The participants, students from SMP Negeri 2 Lembang, acquired a thorough understanding of fundamental Geographic Information System (GIS) principles, the identification of high-risk areas, and basic visual-spatial analysis simulations. The findings indicate an enhancement in students' understanding of disaster prevention principles and their capacity to analyze digital spatial data. This activity demonstrates that incorporating digital technology into disaster education effectively enhances risk awareness from a school-age perspective.
Co-Authors Adel Adel Ahmad Fauzan Abdurrohman Ai Fitri Alia Tri Utami Alvin Nurrochman Alvin Nurrochman Arie Arie Aris Dianto Arya Dutha Ramadhan Asep Sudrajat Chintya Rahmadanti Dadi Rosadi Deni Sopiyan Emalia, Lilis Feri Alpiyasin Hilga Rizky Nugraha, Hilga Rizky Ikbal Jamaludin Jaelani, Jaelani Khairan Fathan Hubainallah M. Yogi Riyantama Isjoni Mohammad Hamdani, Mohammad Muhamad Rendi Mulana Nasrulloh, Wildan Khotibul Umam Naura Khoirunnisa Nida Mutmainah Novi Novi Oktavia Oktavia Rahmat Anggit Wahyudi Revansa Zahrandhika Rifky Andriansyah Angela Rima Melati Risma Risma Rismawati Rismawati Rohmat Nur Ibrahim Rohmat Nur Ibrahim Rr. Isni Anisah Salsabilal Salsabilal Sarah Azhar Sari, Aisah Nur Endah Septi Septi Siti Dini Sutanto Dwi Putra Tay, En Wahyu Alvan Wahyudi, Rahmat Anggit Wildan Kurniawan Willy Radiansyah Yunisva Yunisva