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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab CogITo Smart Journal CSRID (Computer Science Research and Its Development Journal) Jurnal Ilmiah Sinus Indonesian Journal of Data and Science Jurnal Ilmiah Dan Karya Mahasiswa International Journal of Artificial Intelligence in Medical Issues Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Jurnal Abdimas Mahakam Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Hamada Zein
Universitas Muhammadiyah Kalimantan TImur
Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Public Health Other

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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.
PEMILIHAN PEMBIMBING SKRIPSI BERBASIS MACHINE LEARNING DAN KOMBINASI METODE MCDM: SELECTION OF THESIS SUPERVISORS BASED ON MACHINE LEARNING AND A COMBINATION OF MCDM METHODS Hamada Zein; Siti Hadijah Aspan
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6387

Abstract

The selection of thesis advisors is a critical step in supporting students’ academic success. This study proposes the hybrid model that combines the C4.5 classification method with the AHP-SAW-TOPSIS multi-criteria decision-making approach to provide objective and systematic advisor recommendations. In the initial phase, the first advisor is selected using four classification algorithms: Decision Tree (C4.5), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) with RBF kernel, and Naive Bayes. The C4.5 algorithm achieved the highest accuracy at 95%. The second advisor is determined using AHP to assign weights to four criteria: supervision load, academic rank, seminar involvement, and research interest alignment. These weights are applied in the SAW method for normalization and initial scoring, followed by TOPSIS to produce the final ranking. The top-ranked advisor is the sixth, followed by the second and tenth. The hybrid approach offers advantages over using either Machine Learning or MCDM alone. Machine Learning excels in identifying patterns from historical data for accurate predictions, while MCDM explicitly incorporates multiple criteria and institutional preferences. Their combination creates a recommendation system that is both precise and policy-aware. Sensitivity analysis shows stable results, indicating that the assigned weights are relevant. This model supports fair, data-driven decision-making and helps reduce the administrative burden of advisor assignments at the university level.
Analisis Perbandingan Penerapan Metode AHP-SAW dan AHP-TOPSES Dalam Pemilihan Mahasiswa Terbaik Prodi Ilmu Keperawatan Muhamad Wahyu Tirta; Muhammad Khumaidi Nursyarif; Hamada Zein; Rita Yulfani; Melisa Nur Aini; Farhan Akbar
Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 4 No. 1 (2024): Maret : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/teknik.v4i1.2672

Abstract

Student graduation becomes a separate assessment reference in a higher education institution. Things that are taken into consideration are efforts to carry out assessments to determine the best students. The data used comes from the graduation achievements of students from the Nursing Profession Study Program, Faculty of Nursing, Muhammadiyah University, East Kalimantan, which consists of four criteria. Based on these problems, the author conducted research aimed at analyzing the use of Decision Support System methods. The method used in this research uses a combination of AHP-SAW and AHP-TOPSIS. The results obtained explain that both methods obtain ranking results in the same order even though the Priority value of each method is different. Where rank 1 for each method is A1 with an AHP-SAW Priority Value of 100 and AHP-TOPSES of 1, likewise in the next ranking order.
Analisis Sistem Pendukung Keputusan Menggunakan Algoritma AHP Dan Topsis Untuk Menentukan Mahasiswa Lulusan Terbaik Mukminatul Munawaroh; Hamada Zein; Fajri Harits Muzaki; Febri Ananda Chairi; Lidya Sari; Bobi Zinaidin Zidan; Muhammad Aditya Pratama; Novia Hidayati Ramadhani; Reyka Luna Karalo; Ririn Wahyuni
Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika Vol. 2 No. 1 (2024): Januari : Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/jupiter.v2i1.37

Abstract

This research examines the application of the Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods in determining the best graduate students in the Ners Professional Study Program at Muhammadiyah University of East Kalimantan. The third step of the AHP method involves converting the values in the pairwise comparison matrix to decimal form, which is then normalized to calculate the priority weight of each criterion and sub-criteria. Next, checking the logic of the criteria and designing AHP-TOPSIS for ranking were carried out. The analysis showed that AHP resulted in 5 ranking changes with a percentage change of 4.4%, while TOPSIS resulted in 3 ranking changes with a percentage change of 3.9%. From these results, the AHP-TOPSIS method proved to have an accuracy of 83.00%. This article also presents a comparison between AHP, TOPSIS, and AHP-TOPSIS methods, where the best student selected is Dinda Ayu Framaisella. This research provides practical guidance for decision makers in solving multi-criteria problems and contributes to the selection of the best graduate students with a comprehensive and accurate approach.
Co-Authors Abdul Rahim Achmad Farid Adia Lestari Admojo, Fadhila Tangguh Angriani, Husni Any Sawheri Gading Arbansyah Arbansyah Ari Ahmad Dhani Aris Wahyu Murdiyanto Aulia Khofifah Syamsuri Azdy, Rezania Agramanist Azelina Zahra Riadini B, Isdayani Bima Satria Bobi Zinaidin Zidan Bulan Suci Cahayawati Dian Hafidh Zulfikar Effan Najwaini Erfan Tongalu Fajri Harits Muzaki Faldy Alfareza Pambudi Farhan Akbar Farhan Akbar Febri Ananda Chairi Ferdi Iwanda Fitriyati Agustina Galih Priyambada Hery Kurniawan Highness Mailani Putri Ilhan Firanda izmy alwiah musdar Jimmy H Moedjahedy Joni Saputra Khusnul Khotimah Kusrini Lidya Sari Lilis Sagita M. Syukri Mustafa Manda Rela Istiantoko Maria, ⁠⁠Eny Medi Taruk Melisa Nur Aini Melisa Nur Aini Muhamad Wahyu Tirta Muhamad Wahyu Tirta Muhammad Aditya Pratama Muhammad Fariz Ijlal Rafi Muhammad Khumaidi Nursyarif Muhammad Khumaidi Nursyarif Muhammad Taufiq Sumadi Mukminatul Munawaroh Naswin, Ahmad Novia Hidayati Ramadhani Poetro, Bagus Satrio Waluyo Prasetio, Dimaz Arno Radhitya, Made Leo Raenald Syaputra Raihan Nabil Reyka Luna Karalo Ridha Anisa Soldzu Parnga Ririn Wahyuni Rita Yulfani Rita Yulfani Rudi Setiawan Seftiani Nur Sigiet Haryo Pranoto Sinra, A. Siti Hadijah Aspan Siti Muawwanah Siti Patimah Siti Puspita Hida Sakti MZ Sri Mar’ati Sholikhah Sulistyowati Sulistyowati Supriatin Supriatin Tarigan, Thomas Edyson Tri Wahyudi Vito Junivan Rivaldo