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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Proceeding of the Electrical Engineering Computer Science and Informatics JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Resona : Jurnal Ilmiah Pengabdian Masyarakat Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) Jurnal Repositor
Sumadi, Fauzi Dwi Setiawan
Universitas Muhammadiyah Malang
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Environmental Science Public Health Social Sciences

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Rancang Bangun Document Management System (DMS) Kwartir Cabang Gerakan Pramuka Kota Malang Moch Rizky Wibowo; Ilyas Nuryasin; Fauzi Dwi Setiawan Sumadi
Jurnal Repositor Vol 5 No 2 (2023): Mei 2023 (In Press)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/repositor.v5i2.1462

Abstract

Penelitian ini berawal dari kendala yang terjadi dalam pendataan yang sering terbengkalai dalam waktu yang lama. Jika data gudep tersebut tidak segera diperbaiki dua sampai tiga tahun mendatang dan hal ini terus berlanjut maka akan menyebabkan data gudep tersebut hilang dan akan menimbulkan permasalahan baru. Oleh karena itu, untuk mengatasi masalah kearsipan tersebutLDocument Management System (DMS) adalah solusi alternatif untuk mengubah bentuk dokumen-dokumen dalam fisik kertas dengan harapan meminimalisir penggunaan kertas atau menjadi bentuk digital sehingga prosesddistribusi dari dokumen yang ada di Kwarcab Kota Malang menjadi lebih cepat dan mudah untuk diterapkan. Hasil penelitian menunjukkan bahwa DMS sangat efisien untuk diterapkan dalam kelola data gudep di tingkat Gerakan Pramuka Kwartir Cabang Kota Malang Sehingga diharapkan fitur ini memberi kemudahan dalam monitoring arsip dari data gudep serta dapat memberikan solusi tentang sebuah kearsipan yang baik. Sehingga dengan sistem ini akan membantu dalam kelola arsip yang mudah diakses dan dimonitoring dari setiap data yang masuk.
Low-rate distributed denial of service attacks detection in software defined network-enabled internet of things using machine learning combined with feature importance Muhammad Abizar; Muhammad Ferry Septian Ihzanor Syahputra; Ahmad Rizky Habibullah; Christian Sri Kusuma Aditya; Fauzi Dwi Setiawan Sumadi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1974-1984

Abstract

One of the main challenges in developing the internet of things (IoT) is the existence of availability problems originated from the low-rate distributed denial of service attacks (LRDDoS). The complexity of IoT makes the LRDDoS hard to detect because the attack flow is performed similarly to the regular traffic. Integration of software defined IoT (SDN-Enabled IoT) is considered an alternative solution for overcoming the specified problem through a single detection point using machine learning approaches. The controller has a resource limitation for implementing the classification process. Therefore, this paper extends the usage of Feature Importance to reduce the data complexity during the model generation process and choose an appropriate feature for generating an efficient classification model. The research results show that the Gaussian Naïve Bayes (GNB) produced the most effective outcome. GNB performed better than the other algorithms because the feature reduction only selected the independent feature, which had no relation to the other features.
Combination of Term Weighting with Class Distribution and Centroid-based Approach for Document Classification Sri Kusuma Aditya, Christian; Sumadi, Fauzi Dwi Setiawan
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 4, November 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i4`.1793

Abstract

A text retrieval system requires a method that is able to return a number of documents with high relevance upon user requests. One of the important stages in the text representation process is the weighting process. The use of Term Frequency (TF) considers the number of word occurrences in each document, while Inverse Document Frequency (IDF) considers the wide distribution of words throughout the document collection. However, the TF-IDF weighting cannot represent the distribution of words to documents with many classes or categories. The more unequal the distribution of words in each category, the more important the word features should be. This study developed a new term weighting method where weighting is carried out based on the frequency of occurrence of terms in each class which is integrated with the distribution of centroid-based terms which can minimize intra-cluster similarity and maximize inter-cluster variance. The ICF.TDCB term weighting method has been able to provide the best results in its application to SVM modeling with a dataset of 931 online news documents. The results show that SVM modeling had accuracy of 0.723, outperforming the use of other term weightings such as TF.IDF, ICF & TDCB.
Multipath Routing Implementation in SD-IoT Network Using OpenFlow-based Routing Metrics Atthariq, Muhammad Daffa; Hidayat, Rizky Fauzi Ari; Sadida, Medina Kaulan; Syafa'ah, Lailis; Sumadi, Fauzi Dwi Setiawan
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1691

Abstract

The implementation growth of the Internet of Things (IoT) may increase the complexity of the data transmission process between smart devices. The route generation process between available nodes on the network will burden the intermediary node. One of the possible solutions for resolving the problem is the integration of Software Defined Networks and IoT (SD-IoT) to provide network automation and management. The separation of networking control and data forwarding functions may provide a multipath delivery path between each node in the IoT environment. In addition, the controller can directly extract the resource usage of the intermediary devices, which can be utilized as the routing metric variable in order to maintain the resource utilization on the intermediary devices. Instead of using traditional routing, this paper aims to develop multipath routing based on Deep First Search (DFS) and Dijkstra algorithms for acquiring an efficient path using OpenFlow-based routing metrics. The traffic monitoring module delivered the metrics extraction process, which obtained the variables using Port and Aggregate Flow Statistic features. The metrics calculation aimed to provide the multipath, which was constructed based on switches resource usage. Each selected path was chosen based on the smallest cost and probability provided by the group table feature in OpenFlow. The results showed that the Dijkstra algorithm could create the multipath more swiftly than DFS with a time difference of 0.6 s. The Quality of Service (QoS) results also indicated that the proposed routing metric variables could maintain the transmission process efficiently.
Classification of Brain Tumors on MRI Images Using DenseNet and Support Vector Machine Eko Minarno, Agus; Setiyo Kantomo, Ilham; Setiawan Sumadi, Fauzi Dwi; Adi Nugroho, Hanung; Ibrahim, Zaidah
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.991

Abstract

The brain is a vital organ in the human body, performing various functions. The brain has always played a major role in the processing of sensory information, the production of muscular activity, and the performance of high-level cognitive functions. Among the most prevalent diseases of the brain is the development of aberrant tissue in brain cells, which results in the formation of brain tumors. According to data from the International Agency for Research on Cancer (IARC), more than 124,000 people worldwide were diagnosed with brain tumors in 2014, and more than 97,000 people died due to the condition. Current research indicates that magnetic resonance imaging (MRI) is the most effective means of detecting brain cancers. Because brain tumors are associated with significant mortality risk, a large number of brain tumor MRI imaging datasets were used in this research to detect brain cancers using deep learning techniques. To classify three forms of brain tumors, including glioma, meningioma, and pituitary, a deep learning model called DenseNet 201 paired with Support Vector Machines (SVM) was employed in this work included three types of brain tumors. Based on the results of the tests that were conducted, the best accuracy results obtained in this study were 99.65 percent, with a comparison ratio of 80 percent for training data and 20 percent for testing data, oversampled with the SMOTE method, with the best accuracy results obtained in this study being 99.65 percent.
Co-Authors - Syaifuddin Abyan Faishal Reza Achmad Irfani Nur Iman Agung Wahyu Purnadi Agus Eko Minarno Agus Eko Minarno Ahmad Akbar Maulana Ahmad Fahri Jamaluddin Ahmad Rizky Habibullah Akbi, Denar Regata Alrizal Rakhmat Widagdo Amrul Faruq Atthariq, Muhammad Daffa Basuki, Setio Chandranegara, Didih Rizki Christian Sri Kusuma Aditya Diah Risqiwati Edi H. Kalabo Flora Salfa Salsabila Hanung Adi Nugroho Hardianto Wibowo Haris Zulfikar Islam Hariyady Hidayat, Rizky Fauzi Ari Ibrahim, Zaidah Irfan, Muhammad Irmawati Feren Kilwalaga Jurdan Wahyu Adi Saputra Lailis Syafa’ah Lucky Nurfiqin Mahar Faiqurahman Mauridhi Hery Purnomo Mirza Maulana Azmi Moch Rizky Wibowo Muhamad Fathul Azis Muhammad Abizar Muhammad Ferry Septian Ihzanor Syahputra Muhammad Nur Ichsan Muhammad Zain Iqbal Muzakki Muhammad Zidan Najmuddin Tsaqib Nur Armita Nuryasin, Ilyas Oxicusa Gugi Housman Sadida, Medina Kaulan Said imam Baihaqqi Setiyo Kantomo, Ilham Sri Kusuma Aditya, Christian Syafa'ah, Lailis Syafaah, Lailis Syaifuddin Syaifuddin Syaifuddin Syaifuddin Syaifuddin, Syaifuddin Vera Suryani Wahyuli Dwiki Nanda Yoga Pamungkas Yuda Munarko Yusril Izza Rizqony Zamah Sari