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All Journal Journal Of Natural Science and Technology Adpertisi International Journal of Artificial Intelligence in Medical Issues
Maulidinnawati, Andi
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Computer Science & IT Dentistry Electrical & Electronics Engineering Energy Engineering Health Professions Medicine & Pharmacology Public Health

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Perancangan Sistem Informasi Pelayanan Organisasi Kemahasiswaan Universitas Teknologi Akba Makassar (Unitama) Berbasis Web Maulidinnawati, Andi; Syamsu, Suryadi; Tamrin, Fadli; Arafah, Muhammad; Yusuf , Andi M; Maslihatin, Tatik; Sumardin, Andi; Pasnur, Pasnur; Resha, Muhammad; Fauziah Suhardi , Rifdah Tami
JNSTA ADPERTISI JOURNAL Vol. 2 No. 2 (2022): Juli 2022
Publisher : Aliansi Dosen Perguruan Tinggi Swasta Indonesia (Adpertisi)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62728/jnsta.v2i2.340

Abstract

The campus has an organization to develop the potential of its students according to the student division therefore This study aims to create a web-based service information system for student organizations in order to facilitate the service and implementation of procedures for submitting proposals and activity reports. The data were obtained through observations, Correspondence documents, direct interviews with the campus and organizational management. This system is built using visual studio code and mysql as a database storage service. The system development method in this research uses the waterfall method with the stages, namely analysis, design, coding, testing and implementation (maintenance programs). The results of this study indicate that the student organization service information system can run well. Based on the results of testing using blackbox testing techniques obtained 81% results from respondents, so it is said to be suitable for use based on the tests carried out.
WP Sistem Pendukung Keputusan Penyedia Jasa Asisten Rumah Tangga Menggunakan Metode Weighted Product (WP): WP Anugraha, Nurhajar; Arifuddin, Nurul Afifah; Saputra , Febri Hidayat; Maulidinnawati, Andi; Pangayan, Yulianto
JNSTA ADPERTISI JOURNAL Vol. 3 No. 1 (2023): Januari 2023
Publisher : Aliansi Dosen Perguruan Tinggi Swasta Indonesia (Adpertisi)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62728/jnsta.v3i1.375

Abstract

Permasalahan yang terjadi adalah banyak keluarga kesulitan mendapatkan informasi tentang asisten rumah tangga karena keterbatasan informasi yang tersebar, sehingga calon pengguna jasa yang ingin mencari dan ingin menggunakan jasa asisten rumah tangga harus bertanya-tanya mengenai informasi asisten rumah tangga tersebut kepada teman atau keluarga. Maka dari itu dibutuhkan sebuah aplikasi sistem yang memudahkan pencarian penyedia jasa asisten rumah tangga. Penelitian ini bertujuan untuk merancang dan membangun sebuah Sistem Pendukung Keputusan Penyedia Jasa Asisten Rumah Tangga Menggunakan Metode Weighted Product. Data ini diperoleh melalui penelitian lapangan, penelitian Pustaka dan wawancara. Metode yang digunakan dalam penelitian ini adalah metode weighted product. Hasil penelitian ini menunjukkan bahwa Sistem Pendukung Keputusan Penyedia Jasa Asisten Rumah Tangga Menggunakan Metode Weighted Product berhasil diimplementasikan dan sangat baik digunakan dengan nilai kuesioner penelitian 85,6%.
Classification Optimization of Skin Cancer Using the Adaboost Algorithm Sumiyatun; Maulidinnawati, Andi
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 1 (2023): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v1i1.86

Abstract

Early detection of melanoma skin cancer is crucial in improving prognosis and saving lives. This research aimed to optimize the classification of melanoma images using the Adaboost algorithm. Employing a dataset of 10,000 melanoma images, the study combined the Canny method for image segmentation, Hu Moments for feature extraction, and the Adaboost algorithm for classification. The 5-fold cross-validation results revealed an average accuracy of 61.52%. While the precision consistently surpassed recall, indicating the model's conservative nature in predicting positive cases. The outcomes align with previous research, emphasizing the challenges in melanoma classification. This study contributes to the domain by showcasing the potential and areas of improvement for machine learning in early melanoma detection. Future research is recommended to explore hybrid models and diversify data sources for enhanced robustness and generalizability.
Co-Authors Andi Sumardin, Andi Fauziah Suhardi , Rifdah Tami Maslihatin, Tatik Muhammad Arafah, Muhammad Nurhajar Anugraha Nurul Afifah Arifuddin Pangayan, Yulianto Pasnur Pasnur Resha, Muhammad Saputra , Febri Hidayat Sumiyatun Suryadi Syamsu, Suryadi Tamrin, Fadli Yusuf , Andi M