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All Journal Semantik Techno.Com: Jurnal Teknologi Informasi Prosiding SNATIF Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Scientific Journal of Informatics Seminar Nasional Informatika (SEMNASIF) Journal of Information System Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Information Technology and Computer Science Jurnal Pilar Nusa Mandiri Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control IKRA-ITH Informatika : Jurnal Komputer dan Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Pengabdian UNDIKMA Jurnal Pengabdian Literasi Digital Indonesia Indonesian Journal of Health Information Management Services (IJHIMS) Community: Jurnal Pengabdian Pada Masyarakat Prosiding Seminar Riset Mahasiswa Jurnal Informatika: Jurnal Pengembangan IT Green Engineering: Journal of Engineering and Applied Science
Fahmi Amiq
Fakultas Ilmu Komputer, Universitas Dian Nuswantoro, Semarang 50131
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management 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 Nursing Public Health Social Sciences Other

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Spatial Epidemiological Typology of Dengue Risk in Semarang City: A K-Means Clustering Approach Based on Incidence and Fatality Rates Fahmi, Amiq; Anggit Wicaksono, Natanael
Journal of Information Technology and Computer Science Vol. 11 No. 1: April 2026
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.111793

Abstract

Dengue Hemorrhagic Fever (DHF) control strategies in urban Indonesia often rely on uniform interventions that fail to account for the spatial heterogeneity of disease outcomes. While Incidence Rate (IR) is commonly used to map risk, it overlooks the clinical severity represented by the Case Fatality Rate (CFR). This study creates a novel spatial epidemiological typology by integrating both IR and CFR using an unsupervised machine learning approach. analyzing data from 16 sub-districts in Semarang City (2016–2024), we constructed a dual-indicator clustering model. The analysis reveals three distinct risk typologies: (1) High Transmission Zones (High IR), driven by population density; (2) High Mortality Zones (High CFR, Low IR), indicating "silent" risks and potential clinical management gaps; and (3) Controlled Zones. Unlike traditional single-indicator mapping, this proposed typology offers a precise, data-driven framework for decision-makers, enabling the separation of vector control priorities from clinical system strengthening.  
Deteksi Edema Paru Pada Citra Chest X-ray Menggunakan YOLOv5n Dengan Optimasi Hyperparameter Berbasis Grey Wolf Optimizer Nasrudin Affandi Prasetyo; Cinantya Paramita; Amiq Fahmi
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 2 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i2.10375

Abstract

Pulmonary edema is a lung disorder characterized by fluid accumulation in the alveolar and interstitial spaces, which disrupts gas exchange and reduces oxygen levels in the blood. Chest X-ray (CXR) imaging is commonly used for pulmonary edema assessment because it is fast and widely available; however, its interpretation still depends heavily on radiologist expertise and may lead to diagnostic variability, particularly in healthcare facilities with limited radiology resources. This study aims to develop an automated pulmonary edema detection system based on deep learning to support more consistent analysis of CXR images. The novelty of this study lies in the integration of Grey Wolf Optimizer (GWO) for hyperparameter optimization of the YOLOv5n model specifically for pulmonary edema detection in Chest X-ray images. The proposed method employs YOLOv5n as a lightweight object detection architecture because of its computational efficiency and suitability for resource-constrained environments. To improve detection performance and training stability, the hyperparameters of YOLOv5n are optimized using GWO. The model is trained and evaluated using annotated CXR images, and its performance is measured using precision, recall, mAP@0.5, and mAP@0.5–0.95. Experimental results show that the YOLOv5n + GWO model achieved a precision of 0.906, recall of 0.936, mAP@0.5 of 0.963, and mAP@0.5–0.95 of 0.737, indicating improved detection performance compared with the baseline YOLOv5n configuration. The proposed framework demonstrates potential as a decision-support tool for assisting medical personnel in early pulmonary edema screening through efficient and consistent analysis of CXR images.
Implementation of Discrete Wavelet Transform and Directed Acyclic Graph SVM for Batik Pattern Recognition Edi Sugiarto; Fikri Budiman; Amiq Fahmi; MY Teguh Sulistyono; Asih Rohmani
JOINS (Journal of Information System) Vol 10 No 1 (2025): Edisi Mei 2025
Publisher : Fakultas Ilmu Komputer, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/joins.v10i1.12576

Abstract

Batik as a heritage of the ancestors of the Indonesian nation certainly needs to be preserved so that it continues to be recognized from generation to generation, one of which is by introducing the diversity of its patterns. Efforts to introduce batik patterns can be made, one of which is by implementing technology that can recognize batik patterns automatically based on batik patterns, namely pattern recognition technology. This study aims to optimize batik pattern recognition using the discrete wavelet transform (DWT) and directed acyclic graph SVM (DAGSVM) methods. The stages start from preprocessing, feature extraction, and classification. The study used 310 batik images of 7 different patterns and divided into 240 images for training data and 70 for testing data. DWT method is used in the feature extraction stage while DAG SVM is used in the classification stage. The study was conducted by comparing the accuracy between standard DAG SVM and DAG SVM that has been optimized with DWT and the results of the accuracy test can be proven that adding the DWT method with DAG SVM can increase accuracy by 3%.
Application of Green Hydrogen Technology for Industrial Decarbonization: Techno-Economic and Environmental Assessment Amiq Fahmi; Raden Arief Nugroho; Muljono Muljono; Noorsidi Aizuddin Bin Mat Noor
Green Engineering: International Journal of Engineering and Applied Science Vol. 1 No. 2 (2024): April: Green Engineering: International Journal of Engineering and Applied Scie
Publisher : International Forum of Researchers and Lecturers

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70062/greenengineering.v1i2.261

Abstract

This study explores the application of green hydrogen technology for industrial decarbonization, focusing on its techno-economic and environmental feasibility. A quantitative approach was used, incorporating system modeling of a solar-based hydrogen production system combined with electrolyzers. The techno-economic assessment involved calculating the Levelized Cost of Hydrogen (LCOH), estimating capital and operational expenditures (CAPEX and OPEX), and evaluating the system's energy efficiency and hydrogen output. The environmental impact was analyzed using Life Cycle Assessment (LCA), comparing the carbon footprint of green hydrogen with fossil-based hydrogen. The results reveal that green hydrogen can reduce carbon emissions by up to 60% compared to fossil hydrogen, primarily due to the use of renewable energy for production. Additionally, the study found significant improvements in energy efficiency as electrolyzer performance and solar capacity increased. The LCOH is expected to decrease steadily as solar panel and electrolyzer prices continue to fall, enhancing the competitiveness of green hydrogen in the energy market. The findings also highlight the potential for heavy industries, such as cement and steel production, to transition from fossil fuels to green hydrogen, contributing to a cleaner industrial energy mix. This transition presents both environmental and economic benefits, with long-term savings from reduced fossil fuel dependency and lower production costs.
Strengthening Responsible AI Literacy through TPACK and Prompt Engineering Training for High School Teachers to Enhance Digital Pedagogical Competence at SMA At Thohiriyyah, Semarang Amiq Fahmi; Edi Sugiarto; Y. Tyas Catur Pramudi; Edy Mulyanto
Jurnal Pengabdian UNDIKMA Vol. 7 No. 3 (2026): August
Publisher : LPPM Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jpu.v7i3.20779

Abstract

This community service program aims to enhance the Responsible AI Literacy of high school teachers at SMA At Thohiriyyah, Semarang City, in alignment with the Indonesian Joint Decree of Seven Ministers (SKB 7 Menteri), which emphasizes strengthening teachers' digital and pedagogical competencies in the era of AI-driven education. The program was implemented through In-House Training (IHT) based on the Technological Pedagogical and Content Knowledge (TPACK) framework and Prompt Engineering, involving 18 teachers. The implementation consisted of three stages—preparation, implementation, and evaluation—and employed pre-test and post-test instruments complemented by qualitative observations. Data were analyzed using quantitative and qualitative approaches, with triangulation applied to enhance the validity of the findings. The results showed a substantial improvement, with the average pre-test score increasing from 72.3 to 93.6 in the post-test, representing a 29.4% improvement. Qualitative findings indicated that participants actively engaged in discussions, hands-on practice, and reflective activities, and successfully developed AI-assisted teaching modules, student worksheets, and assessment instruments that support the deep learning paradigm. These findings suggest that TPACK- and Prompt Engineering-based In-House Training was effective in strengthening teachers' Responsible AI Literacy across technical, pedagogical, and ethical dimensions and has the potential to be adopted as a sustainable professional development strategy for educators in the digital era.
Mapping the Persistent Danger Zones of Dengue Hemorrhagic Fever in Semarang City: A Spatio-Temporal Analysis Based on INLA Natanael Anggit Wicaksono; Amiq Fahmi
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12928

Abstract

This study maps the spatio-temporal risk dynamics of Dengue Hemorrhagic Fever (DHF) across 16 districts in Semarang City (2016–2025). Traditional epidemiological approaches using raw incidence rates often ignore spatial autocorrelation and struggle with overdispersion anomalies. To address this, we implemented a Hierarchical Bayesian framework using Integrated Nested Laplace Approximations (INLA) with a Negative Binomial distribution and a Besag-York-Mollié (BYM2) spatial architecture. We specified the spatial topology through a manually validated binary adjacency matrix to minimize subjectivity in defining regional boundaries. Our structured model improved computational performance significantly, reducing the Deviance Information Criterion (DIC) by 34.31% and the Root Mean Square Error (RMSE) by 15.30% compared to a baseline Poisson regression model. Using Geopandas and NetworkX for visualization, we identified Tembalang and Banyumanik districts as absolute Epicenter Nodes with an Exceedance Probability of 1.000. Spatial spillover network analysis demonstrated the propagation of epidemiological pressure from these epicenters to surrounding buffer zones, synchronized with the seasonal peak in the first quarter. This framework provides a precise computational foundation for vector control strategies, shifting from localized reactive approaches to preventive cluster mitigation.
Sentiment Classification of Health Education YouTube Comments Using IndoBERT Embeddings with Logistic Regression and Naïve Bayes Andre Septa Wijaya; Amiq Fahmi; Yuventius Tyas Catur Pramudi
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.13016

Abstract

Class imbalance is a common issue in sentiment classification of social media data, particularly in mental health–related discussions where certain sentiment classes are underrepresented. This study focuses on sentiment classification of mental health–related YouTube comments by utilizing IndoBERT as a pre-trained language model to generate contextual text embeddings. Sentiment classification is subsequently performed using conventional machine learning algorithms, namely Logistic Regression and Naïve Bayes. The research framework includes data collection through the YouTube Data API, text preprocessing, semi-manual sentiment labeling into positive, neutral, and negative classes, and dataset partitioning using an 80:20 train–test split. To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied exclusively to the training data to prevent data leakage. Feature representation is obtained from IndoBERT embeddings with a dimensionality of 768. Model performance is evaluated using accuracy, precision, recall, and F1-score. Experimental results show that Logistic Regression outperforms Naïve Bayes, achieving an accuracy of 78%, compared to 56% for Naïve Bayes. This indicates that Logistic Regression is more effective in handling dense contextual embeddings generated by transformer-based models. Overall, the findings demonstrate that combining contextual embeddings with data balancing techniques can improve sentiment classification performance in mental health–related social media analysis, particularly in low-resource language settings.
Co-Authors -, Suhariyanto Abdul Rohim, Abdul Abu Salam Agus Winarno Agus Winarno Agus Winarno, Agus Al zami, Farrikh Alif, Moh. Fachri Alzami, Farrikh Andre Septa Wijaya Anggit Wicaksono, Natanael Apriyanti Apriyanti Ardianda Aryo Prakoso Ariel Bagus Nugroho Asih Rohmani Asih Rohmani, Asih Astuti, Yani Parti Budi Harjo Budiono Budiono Candra Irawan Catur Supriyanto Cinantya Paramita Ciputra, Indramawan Diana Purwitasari Edi Faisal Edi Sugiarto Edi Sugiarto Edi Sugiarto Edi Sugiarto Edi Sugiarto Edi Sugiarto Edi Sugiarto Edy Mulyanto Egia Rosi Subhiyakto, Egia Rosi Erlin Dolphina Etika Kartikadarma Fhaldian, Wahyu Fikri Budiman Fikri Budiman Hadi, Heru Pramono Harun Al Azies Husna, Farida Amila Indra Gamayanto ISWAHYUDI ISWAHYUDI Karis Widyatmoko Kurnia Desita, Raafi Lalang Erawan Laurensius Tokan, Geraldinho Lintang Mekar Tanjung Mauridhi Hery Purnomo Megantara, Rama Aria Moch. Eko Rustiyono Muhammad Fais Ramadhani Muhammad Hilmy Munsarif Muhammad Naufal Muljono, - Mulyanto, Edy Muslih Muslih MY Teguh Sulistyono MY. Teguh Sulistyono Nasrudin Affandi Prasetyo Natanael Anggit Wicaksono Noorsidi Aizuddin Bin Mat Noor Nova Rijati Novi Hendriyanto, Novi Prasetya, Rakan Shafy Pujiono Pujiono Pujiono Pujiono Pujiono Putra, Wahyu Bagus Wicaksono Raden Arief Nugroho Ramadhan Rakhmat Sani Respati Wulandari Ridha Rahmawati Ridho Pambudi Rizky Adrianto Salsabila, Rizka Mars Sidharta, Bayu Adjie Sihombing, Drigo Alexander Sri Winarno Sudibyo, Usman Suharnawi Suharnawi Suryo Adi Nugroho Syifa Sofia Wibowo Tacharri, Chusnuut Tsani, Maulida Aristia Utomo, Danang Wahyu Y. Tyas Catur Pramudi Yumna Huwaida, Imtiyaz Yuventius Tyas Catur Pramudi Zaenal Arifin Zahro, Azzula Cerliana