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All Journal Teknika Techno.Com: Jurnal Teknologi Informasi MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Prosiding SNATIKA Vol 01 (2011) Benefit: Jurnal Manajemen dan Bisnis Register: Jurnal Ilmiah Teknologi Sistem Informasi Dinamika Teknik Mesin : Jurnal Keilmuan dan Terapan Teknik Mesin Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Journal of Honai Math JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal RESISTOR (Rekayasa Sistem Komputer) Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika Jurnal Pengabdian Al-Ikhlas Jurnal Informatika dan Rekayasa Elektronik Jurnal Chart Datum Soeropati: Journal of Community Service IICS Bulletin of Computer Science Research Jurnal Informatika Teknologi dan Sains (Jinteks) WELFARE Jurnal Ilmu Ekonomi AKSIOMA : Jurnal Manajemen Jurnal Inovasi Pengabdian dan Pemberdayaan Masyarakat Dedikasi Saintek Jurnal Pengabdian Masyarakat Jurnal Krisnadana Jurnal Manajemen Dan Bisnis Ekonomi Information Technology Education Journal Nusantara Journal of Computers and its Applications Jurnal Inovasi Teknologi Terapan Journal of Information Technology and Cyber Security Engagement: Jurnal Pengabdian Kepada Masyarakat CYBER-Techn(Jurnal Informatika dan Industri) DEDIKASI SAINTEK Jurnal Pengabdian Masyarakat
Lukman Hakim
Universitas Yudharta Pasuruan
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering 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 Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Other

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SEGMENTASI CITRA WAYANG DENGAN METODE OTSU Misbach Munir; M. Ikmal Farih; Lukman Hakim
CYBER-TECHN Vol. 11 No. 01 (2017): CYBER-Techn
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM)

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Abstract

Wayang merupakan warisan budaya nusantara sekaligus warisan budaya dunia. UNESCO yang menetapkan wayang sebagai world herritage pada 7 Nopember 2003. Namun, pengakuan tersebut belum direspon oleh negara dalam mengembangkan dan melestarikan wayang sebagai budaya tradisi. Alhasil, wayang semakin ditinggalkan generasi muda yang lebih gandrung dengan budaya massa. Dibutuhkan pelestarian Wayang Kulit dengan mengembangkan media yang menarik dan mendidik, salah satu proses penting dalam mengembangkan media adalah segmentasi. Segmentasi adalah adalah salah satu teknik pengolahan citra digital yang mendasari berbagai aplikasi nyata, seperti pengenalan pola, penginderaan jarak-jauh melalui satelit atau pesawat udara, dan machine vision. Segmentasi memiliki beberapa metode salah satunya metode otsu. Metode Otsu merupakan salah satu metode segmentasi dengan menggunakan nilai ambang secara otomatis, yakni mengubah citra digital warna abu-abu menjadi hitam putih berdasarkan perbandingan nilai ambang dengan nilai warna piksel citra digital. Penelitian ini segmentasi dengan metode otsu pada 10 citra wayang kulit dengan ISO berbeda, mampu melakukan segmentasi citra wayang kulit dengan baik, yaitu dengan akurasi rata-rata 94,43%.
Hybrid PSO-XGBoost Model for Accurate Flood Risk Assessment Nabilah, Lailatun; Hakim, Lukman
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11094

Abstract

Flood risk prediction is a crucial step in disaster mitigation. This study optimizes the Extreme Gradient Boosting (XGBoost) algorithm using the Particle Swarm Optimization (PSO) method to improve prediction accuracy. The process includes data cleaning, normalization, and classification of risk levels into low, medium, and high. The XGBoost model is trained both before and after parameter optimization of n_estimators, max_depth, and learning_rate. Before optimization, the model achieved 93% accuracy but struggled to identify minority classes. After optimization with PSO, accuracy increased to 97%, with the recall for the low-risk class improving from 21% to 57%. The optimized model also demonstrated more stable performance compared to Support Vector Machine (SVM) and Random Forest. These findings indicate that the combination of XGBoost and PSO can provide more accurate and efficient flood risk predictions.
Lightweight Convolutional Neural Network Based on Modified LeNet for Retinal Pathology Classification in High-Resolution Fundus Imaging Mu’awanah, Cahyatul; Hakim, Lukman
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.796

Abstract

Eye disease are visual impairments that can lead to blindness if not detected early. Fundus imaging is one of the most effective methods for identifying abnormalities in the eye. With the advancement of deep neural network technologies, particularly Convolutional Neural Network (CNN), the classification of fundus image can now be performed efficiently. LeNet is a well-known CNN architecture commonly used in image classification tasks, however it has limitation when processing images with complex visual features with high resolution, such as fundus images. This study proposes a modification to the LeNet architecture to enhance it’s a ability to extract important features from images with high resolution. The modification involves adding convolutional layers and adjusting image resolution to optimize the models performance in detecting eye disease in fundus images. The dataset used consists of 4,217 fundus images, classified into four categories: normal, cataract, glaucoma, and diabetic retinopathy. Experimental result show that the original LeNet-5 achieved an accuracy 0f 76%, while the modified LeNet architecture improved the accuracy to 86%. The main contibution of this research lies in the development of a modified and lighweight LeNet architecture, which is capable of handling high-resolution fundus images while maintainig computational efficiency and producing better classification performance compared to the original LeNet.
Water Quality Classification Using SVM with PSO-Based Parameter Optimization Trisna Seviya; Lukman Hakim
Information Technology Education Journal Vol. 4, No. 3, August (2025)
Publisher : Jurusan Teknik Informatika dan Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/intec.v4i3.9746

Abstract

This study investigates the use of Support Vector Machine (SVM) enhanced with Particle Swarm Optimization (PSO) for water quality classification. Conventional SVM models often underperform when parameters are selected manually, resulting in reduced predictive accuracy. To overcome this limitation, PSO was applied to automatically optimize the SVM kernel parameters, enabling more reliable and robust classification. The research employed a quantitative experimental framework consisting of data preprocessing, model training, optimization, and performance evaluation. The dataset included physical and chemical attributes of water quality, which were normalized and prepared before classification. Evaluation was based on standard metrics such as accuracy, precision, recall, and F1-score. The results show that the PSO-optimized SVM consistently outperformed the baseline SVM model, producing more accurate and stable classifications. This confirms the potential of metaheuristic optimization in strengthening machine learning approaches for environmental data analysis. The main contribution of this study lies in applying a PSO–SVM framework to water quality classification, a domain where such integration has been rarely explored despite its importance for sustainable resource management. The findings provide both theoretical implications for advancing metaheuristic applications in environmental informatics and practical benefits for improving decision support in water quality monitoring and management.
Co-Authors Achmad Afifuddin Adrianto, Dian Agus Prianto Ahfa, Mokhamad Isna Marzuki Ahwan, Zainul Ainiyah, Roisatul Aliansyah, Zulhaj Amang Fathurrohman Amelia, Resti Aminatuz Zuhriyah Aminatuz Zuhriyah As'idah, Fizriyatul Awang Rivaldo Bachtiar, Mochammad Alvin Tri Bagus Hari Sugiharto Bagus Hari Sugiharto Baroah, Hamida Maulana Lailatal Cahya Bagus Sanjaya Candra Adi Lesmana Deby Rizky Prihatini E Ernawati, E Ernawati Evy Kamilah Ratnasari Fafit Rahmat Aji Firda Nur Aisyah Firdaus, Lukman Fitrianawati, Meita Hapsari Titi Palupi Imam Syafi’i Imam Wahyudi Farid Irwan Abdullah Khoilil Fitria Kholid Murtadlo Lilis Trisnawati M Dayat M. Daimul Abror M. Ikmal Farih Miftahul Huda Miftahul Huda Misbach Munir Moch. Lutfi Mochamad Hasyim Mohamad Zein Saleh Muh. Aniar Hari Swasono Muhammad Fahmy Mayadi Muhammad Faishol Amrulloh Muhammad Imron Rosadi Muhammad Imron Rosadi Mulyono Wobisono Mu’awanah, Cahyatul Nabilah, Lailatun Novitasari, Tri Wahyu Nur Aisyah, Firda Nur Robiah Nofikusumawati Peni Nurus Sa'adah Okariawan, I.D.K. Prayoga, Resdi Hadi Prayoga, Resdi Hadi Rahayu, Silvia Indah Ramli Ramli Resdi Hadi Prayoga Ridha, Faishal Ananta Roisatul Ainiyah Siti Mutrofin Sugiyarto Sugiyarto Sujita, Sujita Sulhan, M. Syarwani, Muhammad Syukur Sugeng Apriwiyanto Tiara Meylinda S Trisna Seviya Widodo S Pranowo Yusebla, Agil