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PERANCANGAN APLIKASI PENJADWALAN DAKWAH MUBALIGH MENGGUNAKAN METODE PIECES Sainlia, Ahmad Fauzan; Belluano, Poetri Lestari Lokapitasari; Azis, Huzain
Buletin Sistem Informasi dan Teknologi Islam (BUSITI) Vol 5, No 3 (2024)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v5i3.2039

Abstract

Dalam hal pengaturan jadwal dakwah, metode PIECES digunakan untuk mengatur sinkronisasi jadwal, durasi dakwah, memberikan notifikasi ketika mubaligh sudah memasuki waktu dakwah dan mengatur jadwal dakwah para mubaligh dengan baik.  Analisa kinerja dalam PIECES yaitu menyediakan sistem yang dapat mengatur jadwal mubaligh, dalam analisa informasi yaitu memberikan informasi ke mubaligh ketika mendekati waktu dakwahnya, dalam analisa ekonomi memberikan informasi mengenai transparansi honorarium pada mubaligh, dalam analisa efisiensi memberikan durasi waktu bagi para mubaligh dan dalam analisa pelayanan memberikan notifikasi ketika mubaligh sudah memasuki waktu dakwah. Aplikasi Penjadwalan Dakwah Mubaligh menggunakan Metode PIECES dirancang untuk dapat membantu petugas masjid dalam menyusun jadwal para mubaligh serta memberikan informasi terkait durasi dakwah bagi tiap mubaligh sehingga para mubaligh tidak lupa dengan jadwal dakwah mereka serta dapat mengatur tranparansi pendapatan tiap mubaligh. Sistem aplikasi berbasis mobile dapat diakses melalui perangkat android, dimana sistem dilengkapi notifikasi aktif yang dapat diakses setiap saat oleh para pengguna sesuai kebutuhan, sehingga mubaliqh dapat mengatur durasi dakwah dengan jelas, dan tidak ada lagi jadwal yang berbentura antar mubaligh satu dengan lainnya .
Optimizing Javanese Numeral Recognition Using YOLOv8 Technology: An Approach for Digital Preservation of Cultural Heritage Syafie, Lukman; Azis, Huzain; Admojo, Fadhila Tangguh
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.239

Abstract

Introduction: The preservation of Javanese script as part of Indonesia’s cultural heritage is increasingly urgent in the digital era, especially due to declining literacy among younger generations. This study aims to explore the effectiveness of YOLOv8, an advanced object detection algorithm, for recognizing handwritten Javanese numerals to support efforts in cultural digitization and education. Methods: A dataset of 2,790 handwritten Javanese numerals (0–9) was collected from 93 respondents. Each numeral was manually annotated using bounding boxes via the MakeSense.ai platform. The YOLOv8 model was trained using 80% of the data and validated on the remaining 20%. Training was performed in the PyTorch framework with data augmentation techniques to increase robustness. Model performance was evaluated using precision, recall, F1-score, and mean Average Precision (mAP), along with visualization through confidence curves and confusion matrices. Results: The model achieved a high validation precision of 88.3%, recall of 89.1%, and mAP of 0.88 at IoU 0.90. F1-score peaked at a confidence threshold of 0.89, while certain numerals like 'six' and 'nine' achieved near-perfect detection. Visualizations confirmed the model’s ability to accurately classify and localize characters in both training and unseen data. Minor misclassifications occurred between visually similar numerals. Conclusions: YOLOv8 demonstrates high effectiveness in recognizing handwritten Javanese numerals and holds significant potential for digital heritage preservation. Future work should focus on expanding the dataset, improving generalization under varied conditions, and integrating this model into educational tools and augmented reality applications for interactive learning.
Transformasi Digital Dan Keselamatan Online: Workshop Interaktif Untuk Siswa Internasional As'ad, Ihwana; Salim, Yulita; Azis, Huzain
Open Community Service Journal Vol. 4 No. 1 (2025): Open Community Service Journal
Publisher : Research and Social Study Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33292/ocsj.v4i1.109

Abstract

Anak-anak dan remaja sering menggunakan media sosial dan bermain game secara berlebihan, yang dapat berdampak negatif terhadap kesehatan dan prestasi akademik. Kurangnya kesadaran akan keamanan digital dan etika penggunaan teknologi menjadikan mereka rentan terhadap ancaman siber. Kegiatan pengabdian ini bertujuan untuk meningkatkan literasi digital siswa Sekolah Kebangsaan Syeikh Mohd Idris Al-Marbawi di Malaysia melalui workshop interaktif. Metode pelaksanaan meliputi analisis kebutuhan, penyusunan dan pelaksanaan materi pelatihan, kepada 20 siswa kelas 5. Hasil kegiatan menunjukkan peningkatan pemahaman siswa terhadap penggunaan gadget yang bijak dan etika digital. Kegiatan ini juga mendorong pengembangan kemampuan interpersonal dan kesadaran terhadap keamanan siber.
Imbalanced Text Classification on Tourism Reviews using Ada-boost Naïve Bayes Suzanti, Ika Oktavia; Kamil, Fajrul Ihsan; Rochman, Eka Mala Sari; Azis, Huzain; Suni, Alfa Faridh; Rachman, Fika Hastarita; Solihin, Firdaus
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 1 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i1.1496

Abstract

Hidden paradise is a term that aptly describes the island of Madura, which offers diverse tourism potential. Through the Google Maps application, tourists can access sentiment-based information about various attractions in Madura, serving both as a reference before visiting and as evaluation material for the local government. The Multinomial Naïve Bayes method is used for text classification due to its simplicity and effectiveness in handling text mining tasks. The sentiment classification is divided into three categories: positive, negative, and mixed. Initial analysis revealed an imbalance in sentiment data, with most reviews being positive. To address this, sampling techniques—both oversampling and undersampling—were applied to achieve a more balanced data distribution. Additionally, the Adaptive Boosting ensemble method was used to enhance the accuracy of the Multinomial Naïve Bayes model. The dataset was split into training and testing sets using ratios of 60:40, 70:30, and 80:20 to evaluate the model’s stability and reliability. The results showed that the highest F1-score, 84.1%, was achieved using the Multinomial Naïve Bayes method with Adaptive Boosting, which outperformed the model without boosting, which had an accuracy of 76%.