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All Journal CogITo Smart Journal CSRID (Computer Science Research and Its Development Journal) Jurnal Ilmiah Sinus Jurnal Abdimas Mahakam Indonesian Journal of Data and Science Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Jurnal Ilmiah Dan Karya Mahasiswa International Journal of Artificial Intelligence in Medical Issues Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika
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 Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Public Health Social Sciences

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ANALISIS PRIORITAS PASIEN COVID-19 UNTUK RAWAT INAP MENGGUNAKAN LOGISTIC REGRESSION DAN AHP Prasetio, Dimaz Arno; Zein, Hamada; Kusrini, Kusrini; Supriatin, Supriatin
CSRID (Computer Science Research and Its Development Journal) Vol 13, No 3 (2021): CSRID OKTOBER 2021
Publisher : Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid.13.3.2021.149-157

Abstract

Kondisi bertambahnya pasien covid-19 ini tidak sebanding dengan jumlah kamar yang tersedia di rumah sakit atau tempat khusus yang ditunjuk sebagai tempat isolasi pasien covid-19. Kekurangan ruang isolasi dan fasilitas juga dialami oleh rumah sakit lainnya diseluruh wilayah Indonesia. Sedikitnya jumlah kamar yang tersedia pada rumah sakit dan terbatasnya jumlah dan tenaga para dokter dan perawat maka diperlukan sebuah sistem untuk membantu dokter memberikan rekomendasi perangkingan pasien yang dapat masuk sebagai pasien rawat inap sehingga pasien, penggunaan ruangan, fasilitas serta tenaga yang ada menjadi lebih efisien serta mampu menolong semua pasien yang benar-benar membutuhkan. Dengan pemodelan yang diajukan AHP dapat membantu memberikan keputusan yang cepat dan dengan algoritma logistic regression mampu membantu mempercepat keputusan dari salah satu kriteria pada AHP yang digunakan dengan tingkat akurasi pengklasifikasian kondisi paru paru pasien sebesar 97.14%.
Analisis Seleksi Tingkat Kecocokan Gambar pada MDID Multimedia Database Dengan Menggunakan Metode ImageDNA Jimmy Moedjahedy; Hamada Zein; Isdayani B; Erfan Tongalu; Kusrini Kusrini; M. Syukri Mustafa
CogITo Smart Journal Vol 6, No 1 (2020): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (798.177 KB) | DOI: 10.31154/cogito.v6i1.223.50-59

Abstract

Dengan semakin tersedianya pilihan informasi digital saat ini, definisi multimedia yang umum diterima adalah kombinasi dari berbagai media seperti teks, gambar, suara, video, animasi. Dalam teoris basisdata, multimedia basisdata mulai dikenalkan yaitu kumpulan data multimedia terkait. Basisdata yang dipilih untuk optimasi dalam penelitian ini adalah MDID (Multiply Distorted Image Database) yang terdiri dari 20 gambar referensi dan 1600 gambar yang sudah diberikan distorsi. Basidata 1600 gambar tersebut akan diuji kecocokan dengan 20 gambar referensi dengan menggunakan metode ImageDNA. Nilai ImageDNA kemudian dilakukan uji data pencilan, sehingga gambar yang nilai ImageDNAnya ekstrim akan dikeluarkan dari basisdata MDID. Hasil dari penelitian ini adalah ada 100 gambar yang dikeluarkan
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
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 : Pusat Riset dan Inovasi Nasional

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.
Rice Leaf Disease Classification with Machine Learning: An Approach Using Nu-SVM Setiawan, Rudi; Zein, Hamada; Azdy, Rezania Agramanist; Sulistyowati, Sulistyowati
Indonesian Journal of Data and Science Vol. 4 No. 3 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i3.114

Abstract

This study explores the application of machine learning for classifying rice leaf diseases, employing the Nu-Support Vector Machine (Nu-SVM) algorithm, analyzed through a 5-fold cross-validation approach. The research focuses on distinguishing between healthy leaves and those affected by BrownSpot and LeafBlast diseases. The dataset, comprising segmented rice leaf images processed using Sobel edge detection and Hu Moments feature extraction, is utilized to train and test the model. Results indicate a moderate level of accuracy (52.12% to 53.81%) across the cross-validation folds, with precision and recall metrics demonstrating variability and highlighting the challenges in precise disease classification. Despite this, the model maintains a consistent ability to identify true positives. The study contributes to the field of precision agriculture by showcasing the potential and limitations of using machine learning for plant disease diagnosis. It underscores the need for advanced image processing techniques and diverse feature extraction methods to enhance model performance. The findings are pivotal for developing more effective, automated diagnostic tools in agriculture, thereby aiding in better disease management and potentially improving crop yields. This research serves as a foundational step towards integrating machine learning in agricultural disease detection, emphasizing its importance in sustainable farming practices.
Advancements in Agricultural Automation: SVM Classifier with Hu Moments for Vegetable Identification Waluyo Poetro, Bagus Satrio; Maria, ⁠⁠Eny; Zein, Hamada; Najwaini, Effan; Zulfikar, Dian Hafidh
Indonesian Journal of Data and Science Vol. 5 No. 1 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i1.123

Abstract

This study investigates the application of Support Vector Machine (SVM) classifiers in conjunction with Hu Moments for the purpose of classifying segmented images of vegetables, specifically Broccoli, Cabbage, and Cauliflower. Utilizing a dataset comprising segmented vegetable images, this research employs the Canny method for image segmentation and Hu Moments for feature extraction to prepare the data for classification. Through the implementation of a 5-fold cross-validation technique, the performance of the SVM classifier was thoroughly evaluated, revealing moderate accuracy, precision, recall, and F1-scores across all folds. The findings highlight the classifier's potential in distinguishing between different vegetable types, albeit with identified areas for improvement. This research contributes to the growing field of agricultural automation by demonstrating the feasibility of using SVM classifiers and image processing techniques for the task of vegetable identification. The moderate performance metrics emphasize the need for further optimization in feature extraction and classifier tuning to enhance classification accuracy. Future recommendations include exploring alternative machine learning algorithms, advanced feature extraction methods, and expanding the dataset to improve the classifier's robustness and applicability in agricultural settings. This study lays a foundation for future advancements in automated vegetable sorting and quality control, offering insights that could lead to more efficient agricultural practices.
Multi-Object Classification of Sports Equipment Images with Texture and Line Features Zein, hamada; Priyambada, Galih; Hida Sakti MZ, Siti Puspita
Jurnal Ilmiah SINUS Vol 22, No 2 (2024): Vol. 22 No. 2, Juli 2024
Publisher : STMIK Sinar Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30646/sinus.v22i2.835

Abstract

mage recognition of sports equipment is often carried out for detection purposes, balls as sports equipment are detected in the form of round objects for various purposes such as goalkeeping robots, ball collection, or monitoring the position of the ball in a match. Classification of various types of sports balls with various shapes located in various areas in one image has not been widely done. This research aims to classify several sports balls, namely soccer balls, hockey balls and shuttlecocks in images with various backgrounds where the images used are images taken randomly from Google Images. Pre-processing in this research starts from changing the image size, changing the GRB image to gray and removing noise using a Gaussian filter. Feature extraction in this research uses two different methods, namely Hough to get line features and LBP (Local Binary Pattern) to get texture features. The feature extraction results were then classified using several classification algorithms with the highest results using Random Forest and LBP feature extraction, namely 70% and the lowest accuracy results using KNN with LBP feature extraction at 43%.
Optimizing Neurodegenerative Disease Classification with Canny Segmentation and Voting Classifier: An Imbalanced Dataset Study Sinra, A.; Waluyo Poetro, Bagus Satrio; Angriani, Husni; Zein, Hamada; Musdar, Izmy Alwiah; Taruk, Medi
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 2 (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.v1i2.97

Abstract

This study explores the efficacy of a Voting Classifier, combining Logistic Regression, Random Forest, and Gaussian Naive Bayes, in the classification of neurodegenerative diseases, focusing on Alzheimer's Disease (AD), Parkinson’s Disease (PD), and control groups. Utilizing a dataset pre-processed with Canny segmentation and Hu Moments feature extraction, the research aimed to address the challenges posed by imbalanced datasets in medical image classification. The classifier's performance was evaluated through a 5-fold cross-validation approach, with metrics including accuracy, precision, recall, and F1-Score. The results revealed a consistent recall rate of approximately 46% across all folds, indicating the model's effectiveness in identifying cases of neurodegenerative diseases. However, the precision and F1-Score were notably lower, averaging around 22% and 29%, respectively, underscoring the difficulties in achieving accurate classification in imbalanced datasets. The study contributes to the understanding of machine learning applications in medical diagnostics, specifically in the challenging context of neurodegenerative disease classification. It highlights the potential of using advanced image processing techniques combined with machine learning ensembles in enhancing diagnostic accuracy. However, it also draws attention to the inherent challenges in such approaches, particularly regarding precision in imbalanced datasets. Recommendations for future research include exploring data balancing techniques, alternative feature extraction methods, and different machine learning algorithms to improve the precision and overall performance. Additionally, applying the model to a broader and more diverse dataset could provide more generalizable and robust findings. This study is significant for researchers and practitioners in medical imaging and machine learning, offering insights into the complexities and potential of automated disease classification
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.
Classification of Mushroom Edibility Using K-Nearest Neighbors: A Machine Learning Approach Admojo, Fadhila Tangguh; Radhitya, Made Leo; Zein, Hamada; Naswin, Ahmad
Indonesian Journal of Data and Science Vol. 5 No. 3 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i3.199

Abstract

This study investigates the use of the K-Nearest Neighbors (KNN) algorithm for the binary classification of mushroom edibility using a cleaned version of the UCI Mushroom Dataset. The dataset underwent pre-processing techniques such as modal imputation, one-hot encoding, z-score normalization, and feature selection to ensure data quality. The model was trained on 80% of the dataset and evaluated on the remaining 20%, achieving an overall accuracy of 99%. Evaluation metrics, including precision, recall, and F1-score, confirmed the model's effectiveness in distinguishing between edible and poisonous mushrooms, with minimal misclassification errors. Despite its high performance, the study identified scalability as a limitation due to the computational complexity of KNN, suggesting that future research should explore alternative algorithms for enhanced efficiency. This research underscores the importance of pre-processing and hyperparameter optimization in building reliable classification models for food safety applications.
Co-Authors Abdul Rahim Achmad Farid Adia Lestari Admojo, Fadhila Tangguh Angriani, Husni Any Sawheri Gading 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 Febri Ananda Chairi Ferdi Iwanda Fitriyati Agustina Hery Kurniawan Highness Mailani Putri Ilhan Firanda izmy alwiah musdar Jimmy H Moedjahedy Joni Saputra Khusnul Khotimah Kusrini Kusrini Kusrini, Kusrini Lidya Sari Lilis Sagita M. Syukri Mustafa Manda Rela Istiantoko Maria, ⁠⁠Eny Melisa Nur Aini Muhamad Wahyu Tirta Muhammad Aditya Pratama Muhammad Fariz Ijlal Rafi Muhammad Khumaidi Nursyarif Muhammad Taufiq Sumadi Mukminatul Munawaroh Naswin, Ahmad Novia Hidayati Ramadhani Poetro, Bagus Satrio Waluyo Prasetio, Dimaz Arno Priyambada, Galih Radhitya, Made Leo Raenald Syaputra Raihan Nabil Reyka Luna Karalo Ridha Anisa Soldzu Parnga Ririn Wahyuni Rita Yulfani Rudi Setiawan Seftiani Nur Sigiet Haryo Pranoto Sinra, A. Siti Muawwanah Siti Patimah Siti Puspita Hida Sakti MZ Sri Mar’ati Sholikhah Sulistyowati Sulistyowati Supriatin Supriatin Tarigan, Thomas Edyson Taruk, Medi Tri Wahyudi Vito Junivan Rivaldo