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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 Journal of Computing Theories and Applications 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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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.
Penerapan K-Nearest Neighbors (KNN) untuk Klasifikasi Aset dalam Upaya Menentukan Aset Wakaf Produktif Edi Sugiarto; Amiq Fahmi; Muslih Muslih; Novi Hendriyanto
Jurnal Transformatika Vol. 19 No. 2 (2022): January 2022
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v19i2.3356

Abstract

aset wakaf berupa tanah yang tersebar di Indonesia terbilang cukup besar, sehingga aset wakaf yang besar ini perlu dikelola dengan baik agar tidak menimbulkan banyak permasalahan yang pada akhirnya tanah wakaf tidak sesuai dengan tujuannya dan tidak dapat digunakan untuk kepentingan umat. Instrument pengamanan aset wakaf telah memenuhi, namun masih banyak muncul persoalan mengenai aset wakaf seperti menguapnya bondo wakaf, sengketa, alih fungsi, dll, sehingga dalam hal ini menunjukan bahwa banyak persoalan terkait pengelolaan aset wakaf yang harus dipecahkan. potensi wakaf sangat besar, bahkan diperkirakan potensi tanah wakaf di indonesia mencapai lima kali luas singapura, namun saat ini belum dikelola secara profesional dan lebih produktif. Penggunaan tanah wakaf di indonesia masih identik dengan masjid dan makam, padahal wakaf dapat juga dikelola menjadi aset-aset ekonomi yang menghasilkan keuntungan sehingga hasil dari wakaf produktif tersebut dapat digunakan untuk kepentingan umat. K-Nearest Neighbors (KNN) merupakan algoritma klasifikasi yang didasarkan pada analogi yaitu membandingkan data uji dengan data latih yang berada dekat dengan dan memiliki kemiripan dengan data uji tersebut, dalam penelitian ini KNN digunakan sebagai metode untuk klasifikasi aset wakaf guna mengidentifikasi aset wakaf tersebut berpotensi produtif atau tidak produktif. Penelitian dilakukan dengan menggunakan 57 data aset wakaf yang diperoleh dengan membagi menjadi 45 data untuk training dan 12 untuk testing. Hasil pengujian yang telah dilakukan membuktikan metode KNN ini memiliki akurasi yang baik untuk klasifikasi aset wakaf yaitu mencapai 93% pada data training dan 83% pada data testing.
Peningkatan Fitur Ekstraksi Berbasis Discrete Wavelet Transform dan Principal Component Analysis Pada Pengenalan Citra Batik Edi Sugiarto; Fikri Budiman; Muslih Muslih; Zaenal Arifin; Amiq Fahmi; Novi Hendriyanto
Jurnal Transformatika Vol. 20 No. 2 (2023): January 2023
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v20i2.5613

Abstract

Pengenalan pola batik menjadi penting karena batik sebagai warisan budaya bangsa perlu dilestarikan kepada generasi ke generasi. Salah satu upaya untuk memperkenalkan pola batik ini yaitu dengan memperkenalkan keragaman motif atau polanya. Penelitian ini bertujuan untuk mengoptimalisasi metode fitur ekstraksi dengan menggunakan metode Discrete Wavelet Transform (DWT) dan Principal Component Analysis (PCA) untuk mereduksi hasil fitur ekstraksi yang diperoleh dari DWT berdasarkan fitur-fitur yang memiliki korelasi yang baik. Tahapan dilakukan dengan menggunakan 310 data berupa citra batik yang terdiri dari 7 motif dengan komposisi 240 untuk data training dan 70 untuk data testing. Pada tahap fitur ekstraksi dengan menambahkan metode PCA pada DWT mampu mereduksi fitur dari 20 menjadi 5 fitur. Selanjutnya fitur tersebut diuji dengan melakukan klasifikasi menggunakan metode KNN dan SVM. Hasil dari klasifikasi dapat dibuktikan bahwa dengan menggunakan metode PCA dan DWT pada tahap fitur ekstraksi mampu meningkatkan klasifikasi hingga 5%.
Clinically Weighted Feature Fusion for Unsupervised Identification of Postpartum Depression and Anxiety Risk Subgroups Among Breastfeeding Mothers Eka Prasetyaningrum; Amiq Fahmi; Purwanto Purwanto
Journal of Computing Theories and Applications Vol. 4 No. 2 (2026): JCTA 4(2) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.17824

Abstract

Postpartum depression and anxiety often go undetected, and breastfeeding experience is among the factors most strongly linked to both. However, previous research has largely been supervised, dependent on biomarkers, and rarely captures the heterogeneity of risk profiles among mothers with similar symptom levels. This study proposes Clinically Weighted Feature Fusion (CWFF), a clustering framework that weights clinical composite features based on their out-of-fold association with EPDS and GAD-7 scores, to map postpartum depression and anxiety risk subgroups among 2,010 breastfeeding mothers in the United Kingdom. As a foundation, 22 composite features representing five clinical domains were compared with 30 raw variables using K-Means (k = 4). The engineered approach with equal weighting (equal-weight) showed better clustering quality, marked by a 28.2% increase in the Silhouette Score, a 21.8% decrease in the Davies–Bouldin Index, and a 26.8% increase in the Calinski–Harabasz Index; all significant based on bootstrap testing (p < 0.0001). Ablation analysis of leave-one-domain-out and leave-one-interaction-term-out (Friedman χ² = 794.15; p < 0.001) shows that this improvement mainly comes from Interaction Features and Mental Health History, not from all domains uniformly. This advantage is also only consistent for K-Means and Agglomerative Clustering, not for Gaussian Mixture Model (GMM) or HDBSCAN. Then, applying CWFF, the results show that CWFF raises Silhouette from 0.1537 to 0.2418 while also raising clinical validity (η²_EPDS from 0.071 to 0.096; η²_GAD-7 from 0.090 to 0.123), with all improvements significant (bootstrap 95% CI does not include zero). These findings indicate that clinically informed weighting improves risk-subgroup identification compared to equal-weight, potentially supporting low-cost postpartum screening in primary healthcare 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 Eka Prasetyaningrum 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 Muslih Muslih MY Teguh Sulistyono MY. Teguh Sulistyono Nasrudin Affandi Prasetyo Natanael Anggit Wicaksono Noorsidi Aizuddin Bin Mat Noor Nova Rijati Novi Hendriyanto Novi Hendriyanto Prasetya, Rakan Shafy Pujiono Pujiono Pujiono Pujiono Pujiono Purwanto Purwanto 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 Zaenal Arifin Zahro, Azzula Cerliana