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Penerapan Metode AHP-SAW Berbasis Web Untuk Menentukan Lulusan Terbaik Di Prodi Profesi Ners UMKT Any Sawheri Gading; Hamada Zein; Khusnul Khotimah; Adia Lestari; Aulia Khofifah Syamsuri; Siti Patimah; Tri Wahyudi; Joni Saputra; Ilhan Firanda; Achmad Farid; Ferdi Iwanda
Jurnal Ilmiah Dan Karya Mahasiswa Vol. 2 No. 1 (2024): FEBRUARI : JURNAL ILMIAH DAN KARYA MAHASISWA
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jikma.v2i1.1427

Abstract

In the nursing profession study program, has an important role in producing quality graduates and is ready to compete in the world of work. This research has a high urgency because it can make a real contribution in improving the quality of graduates of the UMKT Ners professional program. The main objective of this research is to implement a web-based AHP-SAW method to determine the best graduates in the UMKT Nursing Profession Program. The data collection method uses secondary data. Secondary data is obtained based on data from related agencies and sources, including the data that has been collected. This research uses multi-criteria, namely GPA, Study Period, Achievement, and Final Project KIAN. The AHP method is used to determine weights based on many criteria or multi criteria. The results of this study concluded that the implementation of the AHP-SAW method can help determine the best graduates in the UMKT Ners Professional Study Program. This system is equipped with features that can display all calculations in detail, this system also has a database that makes it easy for users to access LifeTime, other advantages can overcome the possibility of lost data. the author hopes that this system will be developed to be dynamic so that it can be used on all devices. As for the appearance of the system which is still basic, it can be developed to be more attractive, but still has to adjust the purpose of using the system.
Ensemble Learning Using KNN and Decision Tree for Virus Infection Classification in Mouse Study Dataset Wahyu Murdiyanto, Aris; Tarigan, Thomas Edyson; Zein, Hamada
International Journal of Artificial Intelligence in Medical Issues Vol. 3 No. 1 (2025): 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.v3i1.359

Abstract

In this study, we propose an ensemble learning approach to classify viral infection presence in mice using the Mouse Viral Infection Study Dataset. The dataset includes two numerical features—volumes of two administered medications—and a binary label indicating viral presence. To improve prediction performance, we combined K-Nearest Neighbor (KNN) and Decision Tree (DT) classifiers within a soft voting ensemble framework. Standardization was applied as a preprocessing step to ensure fair feature contribution, especially for the distance-sensitive KNN. The ensemble model underwent hyperparameter optimization using GridSearchCV with 5-fold cross-validation to fine-tune the number of neighbors for KNN and depth-related parameters for DT. The experimental results demonstrated that the ensemble classifier achieved perfect performance, with 100% accuracy, precision, recall, and F1-score on the test set. The confusion matrix showed no misclassifications, and the Receiver Operating Characteristic (ROC) curve achieved an Area Under Curve (AUC) of 1.00, indicating excellent separability between classes. These results suggest that the proposed ensemble effectively leverages the strengths of both KNN and DT, making it suitable for biomedical classification tasks where interpretability and reliability are critical. Although the model performed exceptionally well, the simplicity of the dataset, including balanced classes and clear feature boundaries, may have contributed to the ideal performance. Thus, while the findings are promising, further validation is necessary using more complex or noisy datasets. This study contributes a practical, interpretable, and effective ensemble learning framework for binary classification problems in experimental virology, and opens pathways for further research in preclinical biomedical data analytics using hybrid classification systems.
Pengembangan Kepribadian Islami Anak Usia Sekolah Dasar melalui Safari Ramadhan Muhammad Fariz Ijlal Rafi; Abdul Rahim; Hamada Zein; Muhammad Taufiq Sumadi
Jurnal Abdimas Mahakam Vol. 7 No. 02 (2023): Juli
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24903/jam.v7i02.2340

Abstract

Saat ini anak-anak menjadi pihak yang tidak luput dari gempuran informasi, padahal anak-anak merupakan usia emas dimana penyerapan informasi dan pengetahuan sedang berada dipuncaknya. Pendidikan karakter islami menjadi hal yang perlu dilakukan sejak dini untuk dapat membentuk masyarakat yang berakhlak islami dan mencegah prilaku buruk yang mungkin timbul dari informasi yang salah. Salah satu cara yang dapat dilakukan untuk membentuk karakter adalah dengan memberikan teladan yang baik sebagai contoh. Tentu saja teladan yang baik bukan hanya dari perilaku kita, namun juga bisa dari kisah-kisah Nabi dan Rasul. Pengabdian ini dilakukan dengan menceritakan kisah-kisah pada nabi dan Rasul dengan kemudian dilakukan mini games sebagai salah satu cara untuk melihat sejauh mana anak-anak memahami dan nilai apa yang dapat diambil dari kisah para Nabi dan Rasul ini. Jumlah peserta dalam kegiatan ini ada 17 anak dengan lama kegiatan adalah dua hari. Dari 17 anak yang menjadi peserta, sebanyak sembilan anak mampu menjelaskan apa saja nilai-nilai dalam kisah nabi dan Rasul yang diceritakan. Sedangkan enam anak hanya mampu menyebutkan setidaknya satu nilai baik dalam kisah yang diberikan
Optimalisasi Sirkulasi Oksigen dan Monitoring Kualitas Air untuk Peningkatan Budidaya Perikanan di Desa Makarti Kutai Kartanegara Sigiet Haryo Pranoto; Hamada Zein; Fitriyati Agustina; Arbansyah
Jurnal Abdimas Mahakam Vol. 9 No. 02 (2025): Juli
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24903/jam.v9i02.3619

Abstract

Kegiatan pengabdian kepada masyarakat ini dilaksanakan di Desa Makarti, Kabupaten Kutai Kartanegara, dengan tujuan untuk meningkatkan produktivitas budidaya ikan nila melalui penerapan teknologi tepat guna dan pemberdayaan masyarakat. Permasalahan utama yang dihadapi masyarakat adalah keterbatasan dalam mengelola kualitas air kolam serta kurangnya sarana pendukung budidaya. Untuk menjawab permasalahan tersebut, tim pengabdian merancang dan menerapkan sistem aerator berbasis Internet of Things (IoT) yang dilengkapi dengan sensor suhu dan pH, guna memantau kondisi air secara real-time. Selain itu, dilakukan pelepasan 1.500 ekor bibit ikan nila ke kolam warga sebagai bentuk dukungan nyata terhadap keberlanjutan budidaya. Hasil kegiatan menunjukkan bahwa suhu dan pH air kolam berada dalam kisaran optimal untuk pertumbuhan ikan nila, dan penggunaan teknologi monitoring berbasis IoT memberikan kemudahan bagi warga dalam melakukan kontrol kualitas air.