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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal Jupiter Bulletin of Electrical Engineering and Informatics Rekursif: Jurnal Informatika Jurnal Meteorologi Klimatologi dan Geofisika MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Buletin Konstitusi
Muhammad Fachrurrozi
Sriwijaya University
Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Mathematics Physics Social Sciences

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PEMANFAATAN DATA MODEL GLOBAL, CITRA SATELIT, DAN DATA OBSERVASI UDARA ATAS UNTUK IDENTIFIKASI KEJADIAN PUTING BELIUNG DAN WATERSPOUT DI KUPANG – NTT (STUDI KASUS TANGGAL 14 JANUARI 2011 DAN 18 JANUARI 2012) Fishwaranta, Alexandra; Kade Wida, Dewa Ayu; Fachrurrozi, Muhammad
Jurnal Meteorologi Klimatologi dan Geofisika Vol 4 No 2 (2017): Jurnal Meteorologi Klimatologi dan Geofisika
Publisher : Unit Penelitian dan Pengabdian Masyarakat Sekolah Tinggi Meteorologi Klimatologi dan Geofisika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (786.061 KB) | DOI: 10.36754/jmkg.v4i2.40

Abstract

Memasuki bulan hujan sering terjadi cuaca buruk di Kupang ? Nusa Tenggara Timur. Salah satunya bulan Januari dimana posisi Matahari berada di Belahan Bumi Selatan. Cuaca buruk yang sering terjadi adalah angin kencang dan hujan lebat. Namun di bulan Januari 2011 terjadi fenomena cuaca puting beliung dan di bulan Januari 2012 terjadi fenomena cuaca waterspout. Perbedaan antara puting beliung dan waterspout yakni pada tempat terjadinya. Puting beliung yang terjadi di tanggal 14 Januari 2011 menyebabkan kerugian materi sedangkan pada fenomena waterspout pada tanggal 18 Januari 2012 tidak menyebabkan kerugian materi sebab terjadi di Perairan sebelah Utara Pantai Pasir Panjang hingga Pantai Lasiana. Pentingnya pemanfaatan data observasi udara atas, data citra, dan data analisis model guna untuk mendapatkan data akurat dan tepat dalam memberikan informasi cuaca kepada masyarakat. Adanya indikasi terjadi angin kencang, tercapainya suhu konvektif, dan ketidakstabilan atmosfer dapat dilihat dari analisa data udara atas radiosonde Stasiun Meteorologi El Tari Kupang. Dalam citra satelit juga dapat terlihat dari keadaan tutupan awan dan timeseries suhu puncak awan pada waktu sebelum dan sesudah kejadian. Serta pada data model global didapat hasil output parameter suhu permukaan, kelembapan, kecepatan angin permukaan, dan vortisitas yang mendukung dalam prakiraan dan analisis fenomena cuaca buruk yang terjadi. Didapatkan hasil kesimpulan bahwa ketiga data tersebut mampu menginterpretasikan dengan baik kondisi alam dengan keadaan yang sebenarnya
Improving the performance for automated brain tumor classification on magnetic resonance imaging deep learningbased Fachrurrozi, Muhammad; Darmawahyuni, Annisa; Samsuryadi, Samsuryadi; Passarella, Rossi; Archibald Hutahaean, Jerrel Adriel
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1679-1686

Abstract

Brain tumor is an uncontrolled growth of abnormal cell in the brain. Early diagnosis of brain tumor has a crucial step in this type of cancer, which is fatal. Magnetic resonance imaging (MRI) is one of the examination tools to examine brain anatomy in clinical practice. The high resolution and clear separation of the tissue enable medical experts to identify brain tumor. The earlier of brain tumor is detected, the wider of treatment options. However, manually analysed of brain anatomy on MRI images are time-consuming. Computer-aided diagnosis with automated way is helpful solution to help management with unreliable degrees of automation to trace various tissue boundaries. This study proposes convolutional neural network (CNN) with its excellences to automated features extraction in convolution layer. The popular architectures of CNN, i.e., visual geometry group16 (VGG16), residual network-50 (resNet-50), inceptionV3, mobileNet, and efficientNetB7 in medical image processing are compared to brain tumor classification task. As the results, VGG16 outperformed other architectures of CNN in this study. VGG16 yields 100% accuracy, precision, sensitivity, specificity, and F1-score for testing set data. The results show the excellent performance in classifying brain tumor and no tumor from MRI images that demonstrate the efficiency of system suggested.
TINJAUAN YURIDIS TERHADAP KEWENANGAN MAHKAMAH KONSTITUSI DALAM PEMBUBARAN PARTAI POLITIK Fachrurrozi, Muhammad
BULETIN KONSTITUSI Vol 1, No 1 (2020): Vol. 1 No. 1
Publisher : BULETIN KONSTITUSI

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Abstract

Mahkamah Konstitusi adalah Lembaga Negara diranah Kekuasaan Kehakiman yang mempunyai kewenangan salah satunya ialah Pembubaran Partai Politik yang mana Partai Politik merupakan Organ Negara namun tidak secara eksplisit disebutkan dalam Undang-Undang Dasar 1945,namun mempunyai peran besar terhadap prinsip-prinsip demokrasi di Negara Republik Indonesia, Penelitian ini akan membahas bagaimana Partai Politik dapat dibubarkan oleh Mahkamah Konstitusi sesuai dengan Undang-Undang.Berdasarkan penelitian ini dipahami bahwa Mahkamah Konstitusi merupakan Lembaga Yudisial Inpenden dengan mengemban tugas dan wewenang yang sangat besar dan penting. Menurut pasal 24C Ayat (1) UUD 1945 Mahkamah Konstitusi berwenang mengadili pada tingkat pertama dan terakhir yang putusannya bersifat final untuk menguji undang-undang terhadap Undang-Undang Dasar,memutus sengketa kewenangan Lembaga negara yang kewenangannya diberikan oleh Undang-Undang Dasar ,memutus pembubaran partai politik,memutus perselisihan tentang hasil pemilihan umum. Dalam hal menjaga konsistensi daripada konstitusi negara republic Indonesia, Mahkamah Konstitusi harus memastikan agar terjaga dan terlindunginya Hukum Dasar negara tersebut agar kemurniannya tidak dikotori oleh kekuatan politik dalam negeri manapun, oleh karena itu kewenangannya menurut peraturan perundang-undangan haruslah jelas dan kuat. Mahkamah Konstitusi juga harus mampu menerjemah daripada dugaan dugaan kekuatan politik yang bersebrangan dengan ideologi maupun konstitusi.
Deteksi Struktur Jantung pada anak menggunakan CNN Arsitektur YOLO versi 5 Pratama, Jimiria; Nurmaini, Siti; Fachrurrozi, Muhammad
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 16 No 2 (2024): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.13762983

Abstract

 A major challenge in the medical field is detecting heart structures in children, which requires a high level of time and accuracy. To address this issue, the You Only Look Once version 5 (YOLO v5) method is employed to identify children's heart structures using a convolutional neural network (CNN). YOLO v5s, YOLO v5n, and YOLO v5x are three versions tested to identify children's heart structures. Standard evaluation metrics such as precision, recall, F1 score, mean average precision, and IoU threshold 0.5 (mAP_0.5) are used to assess the model's performance. Experimental results indicate that YOLO v5s demonstrates the best performance in detecting children's heart structures with high detection rates and accuracy. This model can effectively detect heart structures in various image positions and conditions, suggesting potential for more accurate and effective diagnostic use in identifying heart diseases in children. The development of heart structure detection models is highly relevant in the medical field. The deep learning model using YOLO v5s offers remarkable capabilities in various visual applications. This model can be an efficient and reliable solution in various fields, providing reliable and accurate performance to streamline data analysis processes and enhance work efficiency.  Keywords—Detection, Pediatric Cardiac Structures, Convolutional Neural Network, YOLO v5
Pengelompokan Kebutuhan Anggaran Negara Berdasarkan Indikator Ekonomi dan Kesehatan Menggunakan Fuzzy C-Means dan PCA Fachrurrozi, Muhammad; Muhammad, Fadzli; Sitepu, Delvi Nur Ropiq; Pratama, Reksi Hendra
Rekursif: Jurnal Informatika Vol 12 No 2 (2024): Volume 12 Nomor 2 November 2024
Publisher : Universitas Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekursif.v12i2.38096

Abstract

Clustering the national budgets based on the economic and health indicators is another strategic approach that has been used to improve the effective planning of budget allocation. In this study, FCM will be applied for clustering budget data based on economic and health indicators across the regions. To reduce high-dimensional data complexity, in this paper, pre-processing data analysis will be done using PCA. Basically, PCA works by reducing data dimensions through the extraction of major factors that provide the greatest contribution to the variance of the data, thereby making the process of clustering using FCM feasible. The results derived from the analysis will indicate that the integration of PCA into FCM derives more accurate and informative clustering results and helps policymakers in devising appropriate strategies for budget allocations. Consequently, such findings are envisioned as adding to the positive development of enhancing efficiency and effectiveness in national budget allocation.
Analisis Perbandingan Klasifikasi Intent Chatbot Menggunakan Deep Learning BERT, RoBERTa, dan IndoBERT Dwiyono, Aswin; Abdiansah, Abdiansah; Fachrurrozi, Muhammad
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.6051

Abstract

A chatbot is a software application to designed handle user inputs and generate appropriate replies based on those inputs, which are then communicated back to the user. In able to provide accurate responses, the chatbot must be able to understand the intent of the user accurately. An issue in the development of chatbots is how to accurate classify user intent. Incorrectly understanding user intent can result in irrelevant responses. In order to have a conversation with the user, the intent of the user needs to be classified correctly. This paper compares three state-of-the-art transformer-based models BERT (Bidirectional Encoder Representations from Transformers), RoBERTa (Robustly Optimized BERT Pretraining Approach), and IndoBERT (Indonesia Bidirectional Encoder Representations from Transformer) for the task of intent classification in chatbot systems. Various performance metrics, including accuracy, F1-score, precision, and recall, were analyzed to determine which model performs more effectively in the same parameter conditions. Performance metrics like accuracy and F1-score were compared to assess model BERT, RoBERTa and IndoBERT performs better in a University Chatbot Dataset in Indonesian language. The BERT model achieved an accuracy of 0.89, RoBERTa model achieved 0.84 and IndoBERT model achieved an accuracy of 0.94. The better performance of IndoBERT compared to BERT and RoBERTa is caused by more language-specific training, more relevant pretraining, and more effective adaptation to Indonesian context and structure.
Identification of Indonesian Authors Using Deep Neural Networks Firdaus; Fahreza, Irvan; Nurmaini, Siti; Darmawahyuni, Annisa; Sapitri, Ade Iriani; Rachmatullah, Muhammad Naufal; Lestari, Suci Dwi; Fachrurrozi, Muhammad; Afrina, Mira; Putra, Bayu Wijaya
Computer Engineering and Applications Journal (ComEngApp) Vol. 11 No. 1 (2022)
Publisher : Universitas Sriwijaya

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Abstract

Author Name Disambiguation (AND) is a problem that occurs when a set of publications contains ambiguous names of authors, i.e. the same author may appear with different names (synonyms) in other published papers, or author (authors) who may be different who may have the same name (homonym). In this final project, we will design a model with a Deep Neural Network (DNN) classifier. The dataset used in this final project uses primary data sourced from the Scopus website. This research focuses on integrating data from Indonesian authors. Parameters accuracy, sensitivity and precision are standard benchmarks to determine the performance of the method used to solve AND problems. The best DNN classification model achieves 99.9936% Accuracy, 93.1433% Sensitivity, 94.3733% Precision. Then for the highest performance measurement, the case of Non Synonym-Homonym (SH) has 99.9967% Accuracy, 96.7388% Sensitivity, and 97.5102% Precision.
Classification of Atrial Fibrillation In ECG Signal Using Deep Learning Fachrurrozi, Muhammad; Rachmatullah, Muhammad Naufal; Setiadi, Raihan Mufid
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 3 (2023)
Publisher : Universitas Sriwijaya

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Abstract

Atrial fibrillation is a type of heart rhythm disorder that most often occurs in the world and can cause death. Atrial fibrillation can be diagnosed by reading an Electrocardiograph (ECG) recording, however, an ECG reading takes a long time and requires specialists to analyze the type of signal pattern. The use of deep learning to classify Atrial Fibrillation in ECG signals was chosen because deep learning has 10% higher performance compared to machine learning methods. In this research, an application for classification of Atrial Fibrillation was developed using the 1- Dimentional Convolutional Neural Network (CNN 1D) method. There are 6 configurations of the 1D CNN model that were developed by varying the configuration on the learning rate and batch size. The best model obtained 100% accuracy, 100% precision, 100% recall, and 100% F1 Score.
Forecasting Of Intensive Care Unit Patient Heart Rate Using Long Short-Term Memory Firdaus; Fachrurrozi, Muhammad; Nurmaini, Siti; Tutuko, Bambang; Rachmatullah, Muhammad Naufal; Darmawahyuni, Annisa; Sapitri, Ade Iriani; Islami, Anggun; Maharani, Masayu Nadila; Putra, Bayu Wijaya
Computer Engineering and Applications Journal (ComEngApp) Vol. 12 No. 3 (2023)
Publisher : Universitas Sriwijaya

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Abstract

Cardiac arrest remains a critical concern in Intensive Care Units (ICUs), with alarmingly low survival rates. Early prediction of cardiac arrest is challenging due to the complexity of patient data and the temporal nature of ICU care. To address this challenge, we explore the use of Deep Learning (DL) models, specifically Long ShortTerm Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU), for forecasting ICU patient heart rates. We utilize a dataset extracted from the MIMIC III database, which poses the typical challenges of irregular time series data and missing values. Our research encompasses a comprehensive methodology, including data preprocessing, model development, and performance evaluation. Data preprocessing involves regularizing and imputing missing values, as well as data normalization. The dataset is partitioned into training, testing, and validation sets to facilitate model training and evaluation. Fine-tuning of hyperparameters is conducted to optimize each DL architecture's performance. Our results reveal that the GRU architecture consistently outperforms LSTM and BiLSTM in predicting heart rates, achieving the lowest RMSE and MAE values. The findings underscore the potential of DL models, particularly GRU, in enhancing the early detection of cardiac events in ICU patients.
Text clustering for analyzing scientific article using pre-trained language model and k-means algorithm Firdaus, Firdaus; Nurmaini, Siti; Yusliani, Novi; Rachmatullah, Muhammad Naufal; Darmawahyuni, Annisa; Kunang, Yesi Novaria; Fachrurrozi, Muhammad; Armansyah, Risky
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9670

Abstract

Text clustering is a technique in data mining that can be used for analyzing scientific articles. In Indonesia-accredited journals, SINTA, there are two languages used, Indonesian and English. This is the first research focusing on clustering Indonesian and English texts into one cluster. In this research, bidirectional encoder representations from transformers (BERT) and IndoBERT are used to represent text data into fixed feature vectors. BERT and IndoBERT are pre-trained language models (PLMs) that can produce vector representations that take care of the position and context in a sentence. To cluster the articles, the K-Means algorithm is implemented. This algorithm has good convergence and adapts to the new examples, which helps in improved clustering performance. The best k-value in the K-Means algorithm is defined by using the silhouette score, the elbow method, and the Davies-Bouldin index (DBI). The experiment shows that the silhouette score can produce the most optimal k-value in clustering the articles, which has a mean score of 0.597. The mean score for the elbow method is 0.425, and for the DBI is 0.412. Therefore, the silhouette score optimizes the performance of PLMs and the K-Means algorithm in analyzing scientific articles to determine whether in scope or out of scope.
Co-Authors Abdiansah, Abdiansah Archibald Hutahaean, Jerrel Adriel Armansyah, Risky Bambang Tutuko Bayu Wijaya Putra Darmawahyuni, Annisa Dwiyono, Aswin Fahreza, Irvan Firdaus Firdaus Firdaus Fishwaranta, Alexandra Islami, Anggun Kade Wida, Dewa Ayu Maharani, Masayu Nadila Mira Afrina Muhammad Ikhsan Muhammad Naufal Rachmatullah Muhammad Qurhanul Rizqie Muhammad, Fadzli Nazuli, Muhammad Furqan Novi Yusliani Pratama, Jimiria Pratama, Reksi Hendra Rossi Passarella Samsuryadi Samsuryadi Sapitri, Ade Iriani Setiadi, Raihan Mufid Sitepu, Delvi Nur Ropiq Siti Nurmaini Suci Dwi Lestari Yesi Novaria Kunang