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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Teknologi INFORMAL: Informatics Journal Fountain of Informatics Journal JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research CogITo Smart Journal JURNAL PENGABDIAN KEPADA MASYARAKAT MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer SISFO JURTEKSI Jurnal ICT : Information Communication & Technology METIK JURNAL Jurnal Abdidas Jurnal Teknik Informatika (JUTIF) Jurnal Pengabdian dan Kewirausahaan Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Jurnal Abdimastek (Pengabdian Masyarakat Berbasis Teknologi) Jurnal Pengabdian Masyarakat Nasional
Hadi Santoso
Teknik Informatika, Universitas Mercubuana
Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Other

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A Hybrid Model for Dry Waste Classification using Transfer Learning and Dimensionality Reduction Santoso, Hadi; Hanif, Ilham; Magdalena, Hilyah; Afiyati, Afiyati
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1943

Abstract

The categorization of waste plays a crucial part in efficient waste management, facilitating the recognition and segregation of various waste types to ensure appropriate disposal, recycling, or repurposing. With the growing concern for environmental sustainability, accurate waste classification systems are in high demand. Traditional waste classification methods often rely on manual sorting, which is time-consuming, labor-intensive, and prone to errors. Hence, there is a need for automated and efficient waste classification systems that can accurately categorize waste materials. In this research, we introduce an innovative waste classification system that merges feature extraction from a pretrained EfficientNet model with Principal Component Analysis (PCA) to reduce dimensionality. The methodology involves two main stages: (1) transfer learning using the EfficientNet-CNN architecture for feature extraction, and (2) dimensionality reduction using PCA to reduce the feature vector dimensionality. The features extracted from both the average pooling and convolutional layers are combined by concatenation, and subsequently, classification is performed using a fully connected layer. Extensive experiments were conducted on a waste dataset, and the proposed system achieved a remarkable accuracy of 99.07%. This outperformed the state-of-the-art waste classification systems, demonstrating the effectiveness of the combined approach. Further research can explore the application of the proposed waste classification system on larger and diverse datasets, optimize the dimensionality reduction technique, consider real-time implementation, investigate advanced techniques like ensemble learning and deep learning, and assess its effectiveness in industrial waste management systems.
IMAGE CLASSIFICATION OF HOUSEHOLD BENEFICIARIES OF DIRECT CASH ASSISTANCE USING EFFICIENTNET IN DKI JAKARTA PROVINCE Adam, Dzikri; Santoso, Hadi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2121

Abstract

This study investigates the application of the EfficientNet architecture for image classification to determine eligible recipients of direct cash assistance among households in Jakarta Province. As government efforts to provide aid to citizens increase, it becomes essential to have a system that can accurately recognize and classify eligible populations. Misallocation of aid remains a prevalent issue, often leading to undeserving individuals receiving assistance, which has detrimental consequences. The primary focus is on leveraging deep learning, specifically EfficientNet, to address these challenges. The dataset used consists of house images categorized into two classes: "Mampu" and "Tidak Mampu," which were collected through personal photography and web scraping from Google. The research aims to develop an algorithm that accurately classifies and analyzes the types and eligibility of residential buildings within the general population. Data collection and processing challenges are addressed to ensure the training of high-quality, representative image datasets. The model has demonstrated a high accuracy rate of approximately 95.03% on the validation data.
DIGITAL LITERACY PROGRAM DAILY LIFE WITH AI TOOLS Bambang Jokonowo; Hadi Santoso; Afiyati Afiyati
Jurnal Pengabdian Masyarakat Nasional Vol 4, No 2 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/pemanas.v4i2.29638

Abstract

The "Digital Literacy Program: Daily Life with AI Tools" is a community service initiative aimed at enhancing digital literacy by integrating artificial intelligence (AI) tools into daily routines. Conducted at Rumah Pertubuhan Masyarakat Indonesia (PERMAI) in Pulau Pinang, Malaysia, this program seeks to democratize access to AI technologies, fostering a foundational understanding that bridges the gap between complex AI concepts and their practical applications in everyday life. By equipping participants with the skills to utilize AI tools effectively, the program not only improves efficiency in personal and professional activities but also empowers individuals with the knowledge to navigate the evolving digital landscape. The innovative approach of this program is its focus on making AI accessible to a broader audience, promoting digital inclusivity and literacy. Through hands-on workshops and real-world applications, participants learn to integrate AI into tasks such as time management, data organization, and problem-solving, leading to enhanced productivity and informed decision-making. This initiative ultimately contributes to the broader goal of fostering a digitally literate society capable of leveraging emerging technologies for personal and collective advancement.
SOSIALISASI PENGENALAN APLIKASI STUNTING DAN TUMBUH KEMBANG BALITA PADA DESA CIPUTRI –KABUPATEN CIANJUR Hakim, Lukman; Santoso, Hadi; Yusuf, Mohamad
Jurnal Pengabdian dan Kewirausahaan Vol 9, No 1 (2025): Jurnal Pengabdian dan Kewirausahaan
Publisher : Universitas Bunda Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30813/jpk.v9i1.8190

Abstract

Government Policies on Improving Nutritious Eating as a Priority in Combating Stunting The government’s policy to enhance nutritious eating is currently a priority in addressing stunting, as outlined in Presidential Regulation No. 72 of 2021 on Stunting Reduction. Stunting is a growth impairment condition caused by recurrent malnutrition. Ciputri Village, located in Pacet District, Cianjur Regency, with an area of 6.36 hectares and comprising four hamlets, still has a stunting prevalence of 1.03% among toddlers. A community service program involving LLDIKTI 3, in collaboration with the Cianjur Regency and several universities in Jakarta, was conducted. The purpose of the program was to provide understanding and socialization on the impacts and factors contributing to stunting in toddlers, as well as the use of a stunting application for monitoring the growth and development history of toddlers. The community service activities included preparatory observations and implementation on November 13-14, 2024. The program involved the presentation of the stunting and growth monitoring application and explanations of facial recognition for accessing the application. The program was attended by 25 participants, and the results were evaluated through a questionnaire. Based on the questionnaire, the expectation score was 3.52, while the reality score was 3.48 on a scale of 1-4, indicating overall satisfaction with the community service program.
Co-Authors Adam, Dzikri Ade Septryanti Ade Septryanti Ade Septryanti Afiyati, Afiyati Agus Harjoko Agus Harjoko Aruriansyah, Syariz Nadhifa Arvina Destiyani Bambang Jokonowo Cherid, Anis Dita Meliansari Elly Yanuarti Eza Budi Perkasa Hakim, Lukman Hanif, Ilham Helna Wardhana Henny Rahayuningsih Hensceh Gunawan Hilyah Magdalena Hilyah Magdalena Hilyah Magdalena Hilyah Magdalena Hilyah Magdalena Hilyah Magdalena Irene Chrismisel Tella Jokonowoa, Bambang Karina Rochmayani Karina Rochmayani Litha Leonita Lukman Hakim Misni, HM Mohamad Yusuf Nursuci Aprilianiza Oktoranda oktoranda Rochmah, Dwi Anindyani Wahyuni Sahara Yusuf, Mohamad