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All Journal JURNAL REKAYASA TEKNOLOGI INFORMASI Jurnal Kridatama Sains dan Teknologi Malcom: Indonesian Journal of Machine Learning and Computer Science Scientific Journal of Engineering Research
Fahmi, Miftahuddin
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Education Engineering Social Sciences

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Identifikasi Gerakan Shalat Menggunakan Model Klasifikasi Convolutional Neural Network dengan Pengolahan Citra Prewitt dan Morphology: Identification of Prayer Movements Using Convolutional Neural Network Classification Model and Prewitt and Morphology Image Processing Fahmi, Miftahuddin; Musthofa, Aziz; Pratama, Ardena; Syifasultana, Dhika; Al-mumtaz, Fatih
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 1 (2025): MALCOM January 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i1.1790

Abstract

Gerakan shalat menurut sunnah harus dilakukan dengan tepat. Gerakan shalat dapat dipelajari dengan guru agama agar gerakannya tepat, namun banyak orang yang membutuhkan waktu lebih lama atau mencari guru agama yang dapat mengajarkannya. Untuk itu, diperlukan suatu sistem pembelajaran yang dapat membantu mengenali gerakan shalat, khususnya gerakan takbir. Penelitian ini berfokus pada gerakan takbir berdasarkan kitab Fiqih Sholat Seperti Nabi karya Syeikh Albani. Penelitian ini berfokus pada peningkatan akurasi pendeteksian gerakan takbir menggunakan metode pengolahan citra berbasis Convolutional Neural Network (CNN) dengan operator Prewitt dan operasi morfologi. Pada tahap awal, operator Prewitt diterapkan untuk mendeteksi tepi gerakan pada citra grayscale, yaitu dengan menonjolkan kontur gerakan tangan saat takbir. Kemudian, dilakukan operasi morfologi seperti dilatasi dan erosi untuk menghaluskan citra dan mengurangi noise, sehingga memperjelas tepi gerakan yang terdeteksi. Citra yang dihasilkan menjadi input bagi model CNN yang dilatih menggunakan teknik transfer learning. Dengan pendekatan ini, model CNN memperoleh akurasi sebesar 89,2% dalam mendeteksi gerakan takbir. Hasil penelitian menunjukkan bahwa kombinasi operator Prewitt, operasi morfologi, dan CNN efektif meningkatkan akurasi klasifikasi gerakan sholat dan memberikan kontribusi baru pada pengenalan gerakan sholat menggunakan metode pemrosesan gambar
Classification for Waste Image in Convolutional Neural Network Using Morph-HSV Color Model Fahmi, Miftahuddin; Yudhana, Anton; Sunardi; Abdel-Nasser Sharkawy; Furizal
Scientific Journal of Engineering Research Vol. 1 No. 1 (2025): January
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjer.v1i1.2025.12

Abstract

Waste management is essential in preserving nature to be cleaner and more well-maintained. Waste management runs slower than the speed of waste accumulation. One reason is slow waste sorting. This problem can be overcome by building a learning machine that can sort the types of waste. The type of waste often separated in the first sorting is waste based on its type, namely organic and inorganic. The classification model used is the CNN with image processing Morph-HSV color model. The data obtained from Kaggle is collected and processed using Python. The processed image is trained using a CNN classification model. The results of this study are an accuracy of 99.58% and a loss of 1.57%. With this research, it is hoped that it can accelerate waste sorting performance using the most efficient ML based on image processing and its classification model.
Klasifikasi Citra Gerakan Takbir Berdasarkan Fikih Syaikh Al-Albani Menggunakan Model Hibrida CNN-SVM Fahmi, MIftahuddin; Widhi, Eko Prasetio; Fawait, Aldi Bastiatul; Syaifullah, Ahmad
Jurnal Rekayasa Teknologi Informasi (JURTI) Vol 9, No 3 (2025): Jurnal Rekayasa Teknologi Informasi (JURTI)
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jurti.v9i3.21327

Abstract

Penelitian ini mengklasifikasikan kebenaran gerakan takbir dalam salat berdasarkan parameter fikih Syaikh Al-Albani menggunakan pendekatan hibrida CNN dan SVM. Alur kerja mencakup prapemrosesan citra melalui deteksi tepi operator Prewitt dan operasi morfologi untuk pemurnian kontur, diikuti normalisasi. Fitur mendalam diekstraksi dengan VGG16 melalui transfer learning, sedangkan klasifikasi dilakukan menggunakan Support Vector Machine dengan penalaan hiperparameter serta mekanisme ambang (threshold) untuk penetapan keputusan. Dataset terdiri atas 184 citra beranotasi (146 benar, 38 tidak benar) dengan pembagian 80:20 untuk pelatihan dan pengujian. Evaluasi menggunakan akurasi, precision, recall, F1-score, dan confusion matrix. Model mencapai akurasi 95% pada data uji, menunjukkan bahwa kombinasi prapemrosesan berbasis tepi, ekstraksi fitur konvolusional, dan klasifikasi margin-maksimum efektif membedakan variasi halus pada postur takbir. Temuan ini berimplikasi pada pengembangan alat bantu pembelajaran dan koreksi gerakan salat, termasuk skenario umpan balik real-time. Keterbatasan meliputi ukuran serta ketidakseimbangan dataset dan rujukan fikih tunggal; penelitian lanjutan diarahkan pada perluasan data, validasi eksternal, dan pengujian pada perangkat nyata.
Analisis Clustering Persepsi Santriwati Akhir KMI Terhadap Pengabdian UNIDA Reguler Menggunakan Algoritma K-Means Fahmi, Miftahuddin; Fikrianti, Dian; Nurulita, Kharisma Zalza
Jurnal Kridatama Sains dan Teknologi Vol 7 No 02 (2025): Jurnal Kridatama Sains dan Teknologi
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/kst.v7i02.1866

Abstract

This study aims to categorise and analyse the perceptions of female students graduating from Kulliyatul Mu'allimat al-Islamiyyah (KMI) towards the UNIDA Regular community service programme using the K-Means Clustering algorithm. The categorisation was conducted to determine the level of acceptance among female students in an objective, efficient, and transparent manner as a basis for evaluation and development of future community service programmes. The research method used the CRISP-DM framework, which includes the stages of data understanding, data preparation, modelling, evaluation, and deployment. Primary data was obtained from a five-point Likert scale questionnaire completed by 561 respondents. The analysis was carried out through data cleaning and normalisation, modelling with the K-Means algorithm, determining the optimal number of clusters using the Elbow Method, and validation with the Silhouette Score. The results showed that the optimal number of clusters was three, namely positive (25%), neutral (41%), and negative (34%). The positive cluster had high motivation and attitude, the neutral cluster showed moderate scores with weak social aspects, while the negative cluster was low in almost all variables, especially motivation and experience. The Silhouette Score value of 0.61 indicates that the clustering quality is good. This study proves that the application of K-Means Clustering is effective in mapping female students' perceptions systematically and accurately. The results provide practical input for UNIDA to strengthen motivation, social support, and coaching and mentoring strategies so that the community service programme is received more positively
Co-Authors Abdel-Nasser Sharkawy Al-mumtaz, Fatih Aldi Bastiatul Fawait Fawait Anton Yudhana Eko Prasetio Widhi Fikrianti, Dian Furizal Musthofa, Aziz Nurulita, Kharisma Zalza Pratama, Ardena Sunardi Syaifullah, Ahmad Syifasultana, Dhika