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All Journal SOSIOEDUKASI : JURNAL ILMIAH ILMU PENDIDIKAN DAN SOSIAL JITCoS : Journal of Information Technology and Computer System
Khairul Arifin
Universitas Islam Negeri Sumatera Utara
Computer Science & IT Control & Systems Engineering Education Social Sciences Other

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DA'WAH MANAGEMENT OF RELIGIOUS ACTIVITIES AT THE DARUL FALAH ISLAMIC BOARDING SCHOOL, AEK SONGSONGAN DISTRICT, ASAHAN REGENCY Khairul Arifin; Ali Akbar
SOSIOEDUKASI Vol 15 No 3 (2026): SOSIOEDUKASI : JURNAL ILMIAH ILMU PENDIDIKAN DAN SOSIAL
Publisher : Fakultas Keguruan Dan Ilmu Pendidikan Universaitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/sosioedukasi.v15i3.9070

Abstract

Da'wah management is the process of managing religious preaching activities, encompassing planning, organizing, implementation, and evaluation to achieve da'wah objectives effectively and efficiently. This study aims to describe the implementation of da'wah management in religious activities at Darul Falah Islamic Boarding School, Aek Songsongan, Asahan Regency, and its role in improving the quality of religious development within the community. This research employed a qualitative approach using a descriptive method. Data were collected through observation, interviews, and documentation and were analyzed descriptively. The findings indicate that the implementation of da'wah management functions effectively supports various religious activities, including regular Islamic studies, majelis taklim, Islamic holiday commemorations, mosque youth development, and community-based da'wah programs. The success of these activities is supported by well-planned programs, clear task distribution, appropriate use of da'wah media, and continuous evaluation. Furthermore, the implementation of da'wah management enhances community participation, strengthens religious values, fosters noble character, and reinforces social relationships between the Islamic boarding school and the surrounding community. Therefore, da'wah management plays a vital role in creating well-organized, sustainable religious activities that have a positive impact on community life.
Implementation of Color Feature Extraction and KNN Algorithm for Oil Palm Fruit Ripeness Classification Haidar Al-mutawalli; M. Miko Sahputra Sembiring; Khairul Arifin; Fadila Rahma Sari
Journal of Information Technology and Computer System Vol. 2 No. 1 (2026): June
Publisher : CV. Multimedia Teknologi Kreatif

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65230/jitcos.v2i1.117

Abstract

Determining the ripeness level of oil palm fresh fruit bunches (FFB) is crucial for Crude Palm Oil (CPO) quality, but current manual inspection in the field remains highly subjective and inconsistent. This study aims to develop and evaluate an automatic classification system for oil palm fruit ripeness using global HSV color feature extraction and the K-Nearest Neighbor (K-NN) machine learning algorithm. The research utilized a comprehensive public dataset comprising 4,728 images categorized into five ordinal ripeness classes (unripe, underripe, ripe, overripe, and decayed). The dataset was split into 80% training and 20% testing data. Images were resized, and global average values of the Hue, Saturation, and Value channels were extracted without prior background segmentation. The classification was executed using the K-NN algorithm optimized with the Euclidean Distance metric. Experimental results demonstrated that the optimal model configuration was achieved at a neighbor value of k=7, yielding an overall accuracy of 54.86%. The system showed the highest reliability in identifying the ripe class with an F1-score of 64.66% due to its prominent red-orange characteristics. However, utilizing global color averages without image segmentation led to misclassifications, particularly in transition stages and due to background noise. Therefore, it is highly recommended for future implementations to apply automatic image segmentation techniques to eliminate background interference and to combine HSV features with texture descriptors like GLCM to significantly enhance classification accuracy.
Co-Authors Ali Akbar Fadila Rahma Sari Haidar Al-Mutawalli M. Miko Sahputra Sembiring