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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Teknologi JUITA : Jurnal Informatika 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 SISFO JURTEKSI Jurnal ICT : Information Communication & Technology METIK JURNAL International Journal of Informatics and Computation Jurnal Abdidas Jurnal Teknik Informatika (JUTIF) Jurnal Pengabdian dan Kewirausahaan Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Jurnal Abdimastek (Pengabdian Masyarakat Berbasis Teknologi) Journal of Fuzzy Systems and Control (JFSC)
Hadi Santoso
Universitas Mercu Buana
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 Energy 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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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.
Brain Tumor Detection and Classification Using Fine-Tuned CNN with ResNet50 and EfficientNet Muhammad Ali Sultan; Christopher Marco Angelo; Muhammad Alkam Alfariz; Dinda Fatimah Kautsarina; Dhani Amanda Putra; Muhammad Sharjil Ashfaq; Hadi Santoso; Genoveva Ferreira Sores
International Journal of Informatics and Computation Vol. 6 No. 1 (2024): International Journal of Informatics and Computation
Publisher : University of Respati Yogyakarta, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/ijicom.v6i1.80

Abstract

Brain tumors have become a leading cause of mortality worldwide. Detecting and classifying brain tumors accurately at the initial stages is challenging due to their complex and varying structure. In this study, an improved fine-tuned model based on Convolutional Neural Networks (CNN) with ResNet50 and U-Net is proposed. The model works on the publicly available TCGA-LGG and TCIA dataset, which consists of 120 patients. The fine- tuned ResNet50 model outperforms the CNN model in brain tumor classification and detection using MRI images. Accurate and timely diagnosis of brain tumors is critical for successful treatment of the disease. Early detection not only aids in the development of better medication, but it can also save a life in the long run. The domain of brain tumor analysis has efficiently applied medical image processing ideas, particularly on MR images. This paper presents segmentation using Convolutional Neural Networks (CNN) architecture with ResNet50 and EfficientNet as backbones.
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.
Analisis Perbandingan Algoritma Machine Learning untuk Klasifikasi Potabilitas Air Tanah di Jakarta Diky Arianto Tarihoran; Hadi Santoso
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 3, November 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i3.27348

Abstract

Groundwater quality is a fundamental aspect of fulfilling clean water needs, particularly in urban areas such as Jakarta, which faces significant supply limitations due to severe contamination from domestic waste, chemical pollutants, industrial activities, and septic tank leakage. This study aims to compare the performance of nine machine learning algorithms in developing a classification model for groundwater feasibility based on physical parameters. Real-time data were collected from three administrative regions in Jakarta using Internet of Things (IoT) sensors, which monitored pH, temperature, total dissolved solids (TDS), and turbidity. Model evaluation involved hyperparameter tuning, cross-validation, feature importance analysis, LIME interpretation, and performance metrics including AUC, accuracy, precision, recall, and F1-score. The results indicate that CatBoost achieved the highest overall performance (AUC: 0.9448, accuracy: 0.9318, F1-score: 0.9209). LightGBM demonstrated competitive results with an F1-score of 0.9211 and AUC of 0.9431, while XGBoost recorded the highest recall at 0.9359. Random Forest and AdaBoost also exhibited consistent performance, with precision of 0.9094 and recall of 0.9327, respectively. In contrast, Support Vector Machine (SVM) yielded the lowest performance (AUC: 0.8860, accuracy: 0.8499). Based on a comprehensive evaluation, CatBoost model is recommended as the most suitable model for IoT-based groundwater quality classification systems.
Enhancing Geospatial House Price Prediction in Greater Jakarta Using XGBoost and ResNet18 Feature Fusion Hadi Santoso; Alif Biuti Anastasya
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i1.28582

Abstract

Precise house price predictions play a vital role in shaping housing policies and informing investment decisions in urban regions such as Greater Jakarta, encompassing Jakarta, Bogor, Depok, Tangerang, and Bekasi. Most models rely exclusively on structured data, ignoring spatial and environmental factors that influence property prices. This study proposes a multimodal machine learning framework that integrates structured property data with Sentinel-2 satellite imagery within a geospatial context. The baseline dataset consists of 17 tabular variables. The ResNet-18 algorithm extracts visual environmental information from satellite imagery. The integration of both modalities through a late fusion strategy results in improved predictive performance. The baseline XGBoost achieved R² scores of 0.81 (log scale) and 0.79 (Rupiah), with an error of about 184 million Rupiah. The image-only model achieved an R² of 0.43, indicating moderate explanatory capability. Late fusion further improved performance, achieving R² values of 0.94 (log scale) and 0.93 (Rupiah), while reducing prediction error by over 40%.
Dual-Stream MobileNetV2 and Light Mixer Fusion for Robust Weather Classification Muhammad Reza Alfatah; Hadi Santoso
Journal of Fuzzy Systems and Control Vol. 4 No. 2 (2026): Vol. 4 No. 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jfsc.v4i2.406

Abstract

This study proposes an image-based weather classification model designed to accurately recognize various sky conditions, with the aim of providing accessible weather information for elderly individuals and users with visual impairments. While existing lightweight models often struggle to effectively capture both fine-grained local textures and global contextual patterns, this study bridges the gap by proposing a hybrid dual-branch architecture. Specifically, the proposed model utilizes an early spatial feature-level fusion dual-branch architecture. The first branch combines MobileNetV2 with a Feature Pyramid Network (FPN) and incorporates selective attention mechanisms to capture multi-scale features. The second branch, referred to as the Mixer branch, improves visual feature representation through patch embedding and feature mixing techniques. Outputs from both branches are integrated using a fusion layer before being processed by a softmax classifier. The dataset includes five weather categories: cloudy, foggy, rainy, sunny, and sunrise, and is preprocessed through normalization, data augmentation, and partitioning into training, validation, and testing sets. Model training is conducted using TensorFlow and Keras with the Adam optimizer over a two-phase training schedule of 60 epochs (20 epochs for head-only pre-training and 40 epochs for whole-model fine-tuning). The experimental evaluation achieves a test accuracy of 0.975 (97.50%), with precision, recall, and F1-score reaching 0.976, 0.975, and 0.975, respectively, reflecting consistent and reliable classification performance. These results indicate that the proposed model has strong potential for integration with text-to-speech systems to improve accessibility of weather information for users with special needs.
Co-Authors Adam, Dzikri Ade Septryanti Afiyati, Afiyati Agus Harjoko Agus Harjoko Alif Biuti Anastasya Anis Cherid Aruriansyah, Syariz Nadhifa Arvina Destiyani Christopher Marco Angelo Dhani Amanda Putra Diky Arianto Tarihoran Dinda Fatimah Kautsarina Dita Meliansari Dwi Anindyani Rochmah Elly Yanuarti Eza Budi Perkasa Genoveva Ferreira Sores 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, Misni Mohamad Yusuf Mohamad Yusuf Muhammad Ali Sultan Muhammad Alkam Alfariz Muhammad Reza Alfatah Muhammad Sharjil Ashfaq Nursuci Aprilianiza Oktoranda oktoranda Wahyuni Sahara