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All Journal International Journal of Electrical and Computer Engineering Sainteks Jurnal Teknik Elektro Jurnal Edukasi dan Penelitian Informatika (JEPIN) Semesta Teknika Jurnal Ilmiah KOMPUTASI Knowledge Engineering and Data Science Jurnal Pilar Nusa Mandiri CogITo Smart Journal Indonesian Journal of Information System Justek : Jurnal Sains Dan Teknologi Jurnal ULTIMA InfoSys Jurnal Teknologi Sang Pencerah: Jurnal Ilmiah Universitas Muhammadiyah Buton Infotekmesin RADIAL: JuRnal PerADaban SaIns RekAyasan dan TeknoLogi JIKA (Jurnal Informatika) Journal of Telecommunication, Electronics and Control Engineering (JTECE) Insearch: Information System Research Journal KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Jurnal Nasional Teknik Elektro dan Teknologi Informasi Malcom: Indonesian Journal of Machine Learning and Computer Science J-Icon : Jurnal Komputer dan Informatika Journal of Informatics and Information Security Prosiding SeNTIK STI&K RADIAL: Jurnal Peradaban Sains, Rekayasa dan Teknologi
Bayangkari Karno, Adhitio Satyo
Universitas Gunadarma
Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Other

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Journal : Journal of Informatics and Information Security

 1 
Optimizing Random Forest Models for Early Detection of Defects in Steel Tri Surawan; Adhitio Satyo Bayangkari Karno; Widi Hastomo; Reza Fitriansyah; Ahmad Eko Saputro; Indra Bakti
Journal of Informatic and Information Security Vol. 5 No. 1 (2024): Juni 2024
Publisher : Program Studi Informatika, Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/amqxgn78

Abstract

In the manufacturing sector, steel plate defects are a severe issue that may result in significant losses for a company's finances and image. The purpose of this study is to evaluate how well three machine learning algorithms detect steel plate flaws. The accuracy, area under the ROC curve (ROC-AUC), and Log-Loss of the method were used to assess its performance using a dataset that was downloaded from www.kaggle.com. Based on the findings, the Random-Forest algorithm performed best overall, having the lowest Log-Loss of 0.9327, an accuracy of 0.6722, and an AUC value of 0.9222. Research using other algorithms is still very open to be carried out to get better results. Research utilizing other algorithms is still very much open to be conducted in order to get better outcomes.
Prediksi Data Time Series Saham Bank BRI Dengan Mesin Belajar LSTM (Long ShortTerm Memory) Karno, Adhitio Satyo Bayangkari
Journal of Informatic and Information Security Vol. 1 No. 1 (2020): Juni 2020
Publisher : Program Studi Informatika, Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/jiforty.v1i1.133

Abstract

Abstract This study aims to measure the accuracy in predicting time series data using the LSTM (Long Short-Term Memory) machine learning method, and determine the number of epochs needed to produce a small RMSE (Root Mean Square Error) value. The result of this research is a high level of variation in RMSE value to the number of epochs needed in the data processing. This variation is quite difficult to obtain the right epoch value. By doing an iteration of the LSTM process on the number of different epochs (visualized in the graph), then the number of epochs with a minimum RMSE value will be easier to obtain. From the research of BBRI's stock data prediction, a good RMSE value was obtained (RMSE = 227.470333244533). Keywords: long short-term memory, machine learning, epoch, root mean square error, mean square error. Abstrak Penelitian ini bertujuan untuk mengukur ketelitian dalam memprediksi data time series menggunakan metode mesin belajar LSTM (Long Short-Term Memory), serta menentukan banyaknya epoch yang diperlukan untuk menghasilkan nilai RMSE (Root Mean Square Error) yang kecil. Hasil dari penelitian ini adalah tingkat variasi yang tinggi nilai rmse terhdap jumlah epoch yang diperlukan dalam proses pengolahan data. Variasi ini cukup menyulitkan untuk memperoleh nilai epoch yang tepat. Dengan melakukan iterasi dari proses LSTM terhadap jumlah epoch yang berbeda (di visualisasikan dalam grafik), maka jumlah epoch dengan nilai RMSE minimal akan lebih mudah diperoleh. Dari penelitan prediksi data saham BBRI diperoleh nilai RMSE yang cukup baik yaitu 227,470333244533. Kata kunci: long short-term memory, machine learning, epoch, root mean square error, mean square error.
A Breakthrough in Viral Pneumonia Detection: Unveiling Insights with ResNet-152 Widi Hastomo; Adhitio Satyo Bayangkari Karno; Nani Kurniawati; Harini Agusta
Journal of Informatic and Information Security Vol. 4 No. 2 (2023): Desember 2023
Publisher : Program Studi Informatika, Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/1zcjsb83

Abstract

Viral pneumonia is one of the most serious health issues. The key problem in providing early detection and rapid mitigation through the use of chest X-ray imaging has become the ability to identify accurately. The ResNet-152 convolutional neural network approach will be used in this study to predict viral pneumonia. The input dataset was obtained from Kaggle.com. The accuracy findings from this investigation obtained a substantial value, namely 0.99, indicating that the model used performed admirably. The model used can efficiently distinguish between the viral pneumonia dataset and other datasets. It is intended that the findings of this study will be used to inform early decisions in related medical sectors.
Diagnosa COVID-19 Chest X-Ray Menggunakan Arsitektur Inception Resnet Adhitio Satyo Bayangkari Karno; Dodi Arif; Indra Sari Kusuma Wardhana; Eka Sally Moreta
Journal of Informatic and Information Security Vol. 2 No. 1 (2021): Juni 2021
Publisher : Program Studi Informatika, Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/abbs9m42

Abstract

The availability of medical aids in adequate quantities is very much needed to assist the work of the medical staff in dealing with the very large number of Covid patients. Artificial Intelligence (AI) with the Deep Learning (DL) method, especially the Convolution Neural Network (CNN), is able to diagnose Chest X-ray images generated by the Computer Tomography Scanner (C.T. Scan) against certain diseases (Covid). Inception Resnet Version 2 architecture was used in this study to train a dataset of 4000 images, consisting of 4 classifications namely covid, normal, lung opacity and viral pneumonia with 1,000 images each. The results of the study with 50 epoch training obtained very good values for the accuracy of training and validation of 95.5% and 91.8%, respectively. The test with 4000 image dataset obtained 98% accuracy testing, with the precision of each class being Covid (99%), Lung_Opacity (97%), Normal (99%) and Viral pneumonia (99%).
Co-Authors Agita Tunjungsari Ahmad Eko Saputro Aji Digdoyo Aji Digdoyo Aji Digdoyo Arif, Dody Aryo Nur Utomo Azis, Nur Bakti, Indra Deon Strydom Diana Yusuf Digdoyo, Aji Dodi Arif Dodi Arif Dodi Arif Dody Arif Dyah Ruri Irawati Eka Sally Moreta Eka Sally Moreta Elliya Sestri Eva Karla, Eva Fahrul Razi Faqihudin Faqihudin Handayani, Sri Setya Harini Agusta Holmes Rolandy Kapuy Ignatius Joko Dewanto, Ignatius Joko Indra Bakti Indra Sari Kusuma Wardhana Indra Sari Kusuma Wardhana Indra Sari Kusuma Wardhana Ire Puspa Wardhani Iwan Setiawan Kalbuana, Nawang Kamilia, Nada Kardian, Aqwam Rosadi Kasoni, Dian Kusuma Wardhana, Indra Sari Linda Wahyu Widianti LM Rasdi Rere LM Rasdi Rere Lussiana ETP Masriyanda, Masriyanda Meika Syahbana Rusli Nada Kamilia Nada Kamilia Nada Kamilia Nani Kurniawati Nia Yuningsih Nia Yuningsih Nisfiani, Ervina Nur Aini Nurdina Widanti Rachmat Nursalam Rasyiddin, Ahmad Rere, L.M Rasdi Reza Fitriansyah Reza Fitriansyah Rudy Yulianto Rudy Yulianto Sestri, Elliya Sestri, Ellya Setiawati, Popong Shevti Arbekti Arman Stevianus Stevianus Sudarto Usuli sudjiran Sudjiran Sukardi, Sukardi Sundoro, Aries Surawan, Tri Sutarno Sutarno Sutarno Sutarno Tri Surawan Tri Surawan Vany Terisia Widi Hastomo Yasin Effendi Yayat Sujatna, Yayat Yuningsih, Nia YUSUF, DIANA