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All Journal Jurnal Ilmiah Kursor Teknika: Engineering and Sains Journal JPMI (Jurnal Pendidikan Matematika Indonesia) Applied Technology and Computing Science Journal Building of Informatics, Technology and Science ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M) Informatics, Electrical and Electronics Engineering SCIENCE ELECTRO Jurnal Sistem Informasi dan Informatika The Indonesian Journal of Computer Science Galois : Jurnal Penelitian Pendidikan Matematika PERFECT: Journal of Smart Algorithms
Fawaidul Badri
Universitas Islam Malang
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences

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Journal : ILKOMNIKA: Journal of Computer Science and Applied Informatics

 1 
Implementasi Metode K-Means Clustering Analysis pada Pengelompokan Pengangguran di Indonesia sebagai Dampak dari Pandemi Covid-19 Fawaidul Badri; Anang Habibi
ILKOMNIKA: Journal of Computer Science and Applied Informatics Vol 4 No 2 (2022): Volume 4, Nomor 2, Agustus 2022
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v4i2.471

Abstract

Penelitian ini bertujuan untuk mengenali hasil persebaran seluruh provinsi di indonesia berlandaskan data dari tingkat pengangguran terbuka dengan memanfaatkan clustering analysis. Algoritma yang digunakan dalam prosedur penelitian ini yaitu K-Mean Clustering. Hasil analisa dari K-Mean Clustering ini menunjukkan dari seluruh 34 provinsi di indonesia dibagi menjadi 2 kelompok, pertama adalah dengan kelompok pengangguran yang tinggi dan yang kedua kelompok pengangguran rendah. Kelompok dengan jenjang pengangguran yang tinggi terdiri dari 10 kelompok, sementara itu kelompok kedua dengan jenjang pengangguran yang rendah terdiri dari 24 kelompok. Sehingga hasil dari K-Mean Clustering yaitu kelompok satu dengan kreteria tingkat pengangguran tinggi dan kelompok dua dengan kreteria pengangguran rendah menghasilkan nilai Mean Square kurang dari 0,05 maka terbukti bahwa kluster yang terbentuk antara kluster 1 dan kluster 2 adalah menunjukkan hasil signifikan yang cukup baik.
Integration of Knowledge-Based CNN Model for Breast Cancer Histopathology Image Classification Badri, Fawaidul; Patmanthara, Syaad; Zaeni, Ilham Ari Elbaith
ILKOMNIKA Vol 7 No 3 (2025): Volume 7, Number 3, December 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i3.801

Abstract

This study examines the integration of a knowledge-based Convolutional Neural Network (CNN) model for breast cancer histopathology image classification through ontological and epistemological perspectives. Ontologically, the research focuses on the digital representation of histopathological breast tissue images as entities representing benign and malignant conditions, establishing a stable and comprehensive mapping of tissue morphological characteristics. Epistemologically, the study employs a deep learning approach using a CNN model to acquire and validate knowledge about cancer cell morphology patterns from image data, constructing robust epistemic claims regarding tissue differentiation. The BreakHis dataset comprises 7,909 images resized to 224×224 pixels that underwent preprocessing normalization and image augmentation to enhance data quality. The CNN model was designed with Adam and SAM optimizers, learning rates of 0.0001 and 0.003, and a three-epoch warm-up phase to maintain training stability. Experimental results achieved training accuracy of 0.8432, testing accuracy of 0.8481, AUC of 0.8318, precision of 0.8124, and recall of 0.8966, demonstrating excellent model performance in recognizing cancer tissue patterns without overfitting. The integration of this knowledge-based CNN model contributes theoretically to the advancement of artificial intelligence and biomedical science, while demonstrating practical relevance as a reliable decision-support system for breast cancer diagnosis.
Co-Authors ABDULLAH FAQIH Adimas Ryandanu Ahmad Murtaqi Alawi, Ahmad Albarady, Muhammad Adiestha Alvilda Delsyia Putri Alvin Setiawan Anang Habibi Anastasia Lidya Maukar Anik Vega Vitianingsih Annisa, Faradilla Nur Ardiansyah Siregar Ardiansyah Siregar Awang Andhyka Azzahra, Morra Fatya Gisna Nourielda Azzaro, Nabila Bambang Minto Budiarti, Rizqi Putri Nourma Deny Rusdianto Dujjah, Nurul Ilmi Badrun DWI CAHYONO Efendi S Wirateruna Eko Mulyanto Yuniarno Erina Hanifah Sari Fandisya Rahman Faradilla Nur Annisa Fatimat Uzahro Ferdyanto Hartoko, Rafif Pudyo Hawia, Siti Imam Rosadi Khusnul Khotimah Lina Dwi Novita Sari M. Taqijuddin Alawiy Madia, Niswatul Maulani, Maghfira Izzani Moh Ridwan Mohamat Imron Mohammad Agustian Mohammad Jasa Afroni Muhammad Farih Al Habib Muhammad Taqiyyuddin Alawiy Muhammad Yusuf Niqris Nabila Azzaro Nabilatul Fikriyah Ngatmari Nila Nur Pratiwi Niqris, Muhammad Yusuf Nopia, Rambu Ade Novita Sari Nurullah, Zulfa Putria Nury Maela Adhima Oktrison, Oktrison Pradina Dyah Widyawan Putra, Rikko Nur Alif Hidayah Permana Qolbi Firmansyah Rafif Pudyo Hartoko Rambu Ade Nopia Ridwan Maulana Riski Mono Sari Rofi, Ahmad Nafiur Saputra, Herdian Saputri , Nanda Sari, Lina Dwi Novita Sari, Nur Farhania Silva, Virginia Amelia Dos Santos Sipahutar , Erwinsyah Siti Hawia Sulistya Umie Ruhmana Sari, Sulistya Umie Ruhmana Syaad Patmanthara Trisna Wati Sakka Uzahro, Fatimat Whardana, Dicky Kusuma Zaeni, Ilham Ari Elbaith Zulfa Putria Nurullah