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Optimizing Investment: Combining Deep Learning for Price Prediction and Moving Average for Return-Risk Analysis Hastomo, Widi; Karno, Adhitio Satyo Bayangkari; Masriyanda, Masriyanda; Sestri, Ellya; Kardian, Aqwam Rosadi; Azis, Nur; Dewanto, Ignatius Joko; Rasyiddin, Ahmad; Sundoro, Aries; Kamilia, Nada
Jurnal Teknik Elektro Vol 14, No 2 (2022): Jurnal Teknik Elektro
Publisher : Jurusan Teknik Elektro, Fakultas Teknik, Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jte.v14i2.45002

The ability to analyze predictions marks something going up or down, as well as the level of possible risk taken into account by much-needed stock investors. In a study, this analysis of risk and correlation between shares was calculated using the method of moving averages (MA). Besides that, a dataset of 4 stocks (Apple, Google, Microsoft, and Amazon) also performed prediction mark stock in period time next (future) with the use of the neural network method (deep learning) Long Short-Term Memory (LSTM) model. The result of programming in the Python language is several visualizations for easy graph-reading information. This article presents new research that aims to fill the gap in understanding investment analysis for beginners by visualizing risk and return analysis on shares. The results reveal that changes in stock sales volume did not occur significantly, although the short and long-term MA charts for the four stocks tended to fluctuate, offering new insights into investment analysis and providing a basis for future development. The best accuracy results were on MSFT shares, with an achievement of 0.9532 and a loss value of 0.0014. Thus, MSFT shares can be used as a priority for investment. Therefore, this research adds a new dimension to the literature and paves the way for further investigations in risk and return analysis and stock prediction using deep learning.

ANALISIS PERFORMA KRIPTOGRAFI RSA PADA WINDOWS 11 DENGAN PYTHON DAN APLIKASI NZXT CAM Gunawan, Yusuf Atha; Yuda, Marutha Wira; Bintari, Salsabila Wahyu; Kardian, Aqwam Rosadi
TEKTRIKA Vol 8 No 2 (2023): TEKTRIKA Vol.8 No.2 2023
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/tektrika.v8i2.6930

This research aims to analyze the performance of RSA cryptography on the Windows 11 operating system, implemented using Python and the NZXT CAM application. A quantitative approach was employed, utilizing a processor with specifications of 2.80GHz, 8 cores, and 16 GB RAM. Data were collected through the NZXT CAM benchmark application, measuring CPU and memory usage during RSA key generation, encryption, and decryption processes. The findings reveal a direct correlation between the length of the RSA key and both CPU load and the time required for cryptographic processes. It was found that longer RSA keys result in increased computational resource usage. For instance, a 1024-bit RSA key averages 10% CPU load and 53.1 MB memory, while a 2048-bit key increases to 11.6% CPU load and 53.4 MB memory. A 4096-bit key requires 10.6% CPU load with similar memory usage, but the processing time increases from 2.5 seconds to 27.7 seconds. The conclusion of this study provides significant insights for the development of security applications on Windows 11, particularly in balancing key length and resource usage. Further research is recommended to evaluate RSA performance on various hardware configurations. Key Words: RSA Cryptography, Windows 11, Python, NZXT CAM, Resource Utilization

Komparasi Kinerja CPU dan Memori dalam Proses Klasifikasi Malware Menggunakan Algoritma Random Forest pada Sistem Operasi Kali Linux 64-bit dan Ubuntu 64-bit Hindami, Achmad Luthfan Aufar; Firmansyah, Dimas Rifqi; Anggoman, Christopher Ralin; Kardian, Aqwam Rosadi
CESS (Journal of Computer Engineering, System and Science) Vol 9, No 1 (2024): January 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v9i1.53994

Machine learning telah menjadi aspek krusial dalam keamanan siber, khususnya dalam deteksi intrusi dan klasifikasi malware. Namun, penerapan teknik ini memerlukan alokasi sumber daya komputasi yang signifikan. Dalam konteks ini, sistem operasi memiliki peran krusial berkaitan dengan kemampuannya dalam mengelola sumber daya komputasi. Penelitian ini bertujuan untuk mengevaluasi dan membandingkan performa CPU dan memori dari dua sistem operasi populer, yaitu Kali Linux dan Ubuntu, dalam konteks komputasi klasifikasi malware menggunakan teknik dan algoritma machine learning untuk mengetahui sistem operasi dengan performa yang lebih baik. Keduanya diuji menggunakan model machine learning dan variasi dataset yang sama untuk klasifikasi malware menggunakan algoritma Random Forest. Analisis dilakukan dengan membandingkan persentase konsumsi CPU dan memori antar kedua sistem operasi. Berdasarkan hasil pengujian, ditemukan bahwa sistem operasi Kali Linux memiliki rata-rata penggunaan CPU yang lebih rendah sekitar 19,64%, dan penggunaan memori yang lebih rendah sekitar 0,06% dibandingkan dengan sistem operasi Ubuntu. Dengan demikian, dapat disimpulkan bahwa sistem operasi Kali Linux memiliki performa yang lebih baik daripada sistem operasi Ubuntu dalam hal konsumsi CPU dan memori dalam komputasi klasifikasi malware menggunakan teknik dan algoritma machine learning.

Remote Code Execution (RCE) pada Windows 10 dengan Berkas .docx Menggunakan Framework Metasploit (CVE-2021-40444) Marbun, Jonathan Sebastian; Siddiq, Syubbanul; Giffari, Rizal Abie; Kardian, Aqwam Rosadi
CESS (Journal of Computer Engineering, System and Science) Vol 9, No 1 (2024): January 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v9i1.54091

Komputer menjadi salah satu kebutuhan masyarakat sekarang. Keberadaannya sudah merebak di berbagai tempat. Sebagian besar pengguna komputer menggunakan Windows sebagai sistem operasi mereka. Windows dinilai memiliki tampilan tatap muka yang atraktif dan mudah untuk digunakan. Namun, karena bukan merupakan sistem operasi yang open-source dan beragamnya latar belakang pengguna Windows, termasuk hacker, Windows memiliki beberapa kerentanan yang tergolong kritis. Salah satu kerentanannya adalah remote code execution (RCE). Kerentanan tersebut terdokumentasi secara resmi pada common vulnerabilities and exposures (CVE) dengan kode CVE-2021-40444. Kerentanan tersebut menjelaskan bahwa seseorang mampu memperoleh akses terhadap shell Windows menggunakan fail berekstensi .docx. Fail tersebut berisi skrip berbahaya yang dibangkitkan melalui beberapa proses menggunakan framework Metasploit dengan sistem operasi Linux (Ubuntu). Pemerolehan akses tersebut disebabkan usangnya aplikasi yang masih digunakan (Microsoft Office 2016). Penelitian ini menyiratkan makna akan pentingnya menggunakan aplikasi dengan versi mutakhir atau yang paling baru.

Perbandingan Resident Set Size dan Virtual Memory Size Algoritma Machine Learning dalam Analisis Sentimen Yudhanegara, Reza Ardiansyah; Hana, Nisrina Aliya; Mahfiridho, Syahrizal Yonanda; Kardian, Aqwam Rosadi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7201

In the rapidly advancing era of digital transformation, where textual data abounds from various online sources such as social media, forums, and product reviews, sentiment analysis has become a critical component in understanding public opinions and consumer behavior. Sentiment analysis employs machine learning, natural language processing, and computational linguistics to comprehend the feelings and opinions of others. The machine learning algorithms investigated in this paper include K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), Naive Bayes, ID3, and C4.5. The sentiment analysis process requires significant computational resources to handle the complexity and scale of data. This research aims to examine the differences in resource usage among these algorithms and determine which algorithm is best suited for sentiment analysis in this context. The research methodology employed is quantitative, focusing on the collection and numerical analysis of datasets. Testing is conducted using the Anaconda Library in the Python programming language to measure the usage of Resident Set Size (RSS), Virtual Memory Size (VMS), execution time, and the accuracy of each algorithm. The test results indicate that the Support Vector Machine (SVM) algorithm with an accuracy rate of 96% and the Naive Bayes algorithm with an accuracy rate of 97% are the best choices for use in the context of sentiment analysis. When considering the context of Resident Set Size (RSS) and Virtual Memory Size (VMS) usage in a single execution, ID3 is the algorithm with the smallest resource usage, with an accuracy rate of 92%. The average resources used by ID3 are 8.318.566,4 bytes for Resident Set Size (RSS) and 7.965.900,8 bytes for Virtual Memory Size (VMS) with an execution time of 2,619 seconds.

Expert system for diagnosing female reproductive disorders using forward chaining Aulia, Nur Rizky; Rere, Laode Mohammad Rasdi; Kardian, Aqwam Rosadi
Science Midwifery Vol 13 No 3 (2025): August: Health Sciences and related fields
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/midwifery.v13i3.2045

Early diagnosis increases the chances of successful treatment and prevents the disease from worsening. However, not all women feel comfortable consulting a doctor about their condition. This study aims to develop a web-based expert system capable of diagnosing diseases of the female reproductive system with a user-friendly interface and high accuracy. This application enables women to evaluate the likelihood of potential diseases based on their symptoms and consult a doctor for appropriate treatment. This expert system combines the forward chaining and fuzzy Mamdani methods. Forward chaining identifies possible diseases based on selected symptoms, while fuzzy Mamdani confirms the diagnosis. The disease and symptom data used in this study were gathered through interviews with two obstetricians and gynecologists. The final results of this study show a comparative accuracy level of diagnostic results between the system and experts of 88%.

Komparasi Kinerja CPU dan Memori dalam Proses Klasifikasi Malware Menggunakan Algoritma Random Forest pada Sistem Operasi Kali Linux 64-bit dan Ubuntu 64-bit Hindami, Achmad Luthfan Aufar; Firmansyah, Dimas Rifqi; Anggoman, Christopher Ralin; Kardian, Aqwam Rosadi
CESS (Journal of Computer Engineering, System and Science) Vol. 9 No. 1 (2024): January 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v9i1.53994

Machine learning telah menjadi aspek krusial dalam keamanan siber, khususnya dalam deteksi intrusi dan klasifikasi malware. Namun, penerapan teknik ini memerlukan alokasi sumber daya komputasi yang signifikan. Dalam konteks ini, sistem operasi memiliki peran krusial berkaitan dengan kemampuannya dalam mengelola sumber daya komputasi. Penelitian ini bertujuan untuk mengevaluasi dan membandingkan performa CPU dan memori dari dua sistem operasi populer, yaitu Kali Linux dan Ubuntu, dalam konteks komputasi klasifikasi malware menggunakan teknik dan algoritma machine learning untuk mengetahui sistem operasi dengan performa yang lebih baik. Keduanya diuji menggunakan model machine learning dan variasi dataset yang sama untuk klasifikasi malware menggunakan algoritma Random Forest. Analisis dilakukan dengan membandingkan persentase konsumsi CPU dan memori antar kedua sistem operasi. Berdasarkan hasil pengujian, ditemukan bahwa sistem operasi Kali Linux memiliki rata-rata penggunaan CPU yang lebih rendah sekitar 19,64%, dan penggunaan memori yang lebih rendah sekitar 0,06% dibandingkan dengan sistem operasi Ubuntu. Dengan demikian, dapat disimpulkan bahwa sistem operasi Kali Linux memiliki performa yang lebih baik daripada sistem operasi Ubuntu dalam hal konsumsi CPU dan memori dalam komputasi klasifikasi malware menggunakan teknik dan algoritma machine learning.

Remote Code Execution (RCE) pada Windows 10 dengan Berkas .docx Menggunakan Framework Metasploit (CVE-2021-40444) Marbun, Jonathan Sebastian; Siddiq, Syubbanul; Giffari, Rizal Abie; Kardian, Aqwam Rosadi
CESS (Journal of Computer Engineering, System and Science) Vol. 9 No. 1 (2024): January 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v9i1.54091

Komputer menjadi salah satu kebutuhan masyarakat sekarang. Keberadaannya sudah merebak di berbagai tempat. Sebagian besar pengguna komputer menggunakan Windows sebagai sistem operasi mereka. Windows dinilai memiliki tampilan tatap muka yang atraktif dan mudah untuk digunakan. Namun, karena bukan merupakan sistem operasi yang open-source dan beragamnya latar belakang pengguna Windows, termasuk hacker, Windows memiliki beberapa kerentanan yang tergolong kritis. Salah satu kerentanannya adalah remote code execution (RCE). Kerentanan tersebut terdokumentasi secara resmi pada common vulnerabilities and exposures (CVE) dengan kode CVE-2021-40444. Kerentanan tersebut menjelaskan bahwa seseorang mampu memperoleh akses terhadap shell Windows menggunakan fail berekstensi .docx. Fail tersebut berisi skrip berbahaya yang dibangkitkan melalui beberapa proses menggunakan framework Metasploit dengan sistem operasi Linux (Ubuntu). Pemerolehan akses tersebut disebabkan usangnya aplikasi yang masih digunakan (Microsoft Office 2016). Penelitian ini menyiratkan makna akan pentingnya menggunakan aplikasi dengan versi mutakhir atau yang paling baru.

Stacked LSTM-GRU Long-Term Forecasting Model for Indonesian Islamic Banks Sujatna, Yayat; Karno, Adhitio Satyo Bayangkari; Hastomo, Widi; Yuningsih, Nia; Arif, Dody; Handayani, Sri Setya; Kardian, Aqwam Rosadi; Wardhani, Ire Puspa; Rere, L.M Rasdi
Knowledge Engineering and Data Science
Publisher : citeus

Show Abstract | Download Original | Original Source | Check in Google Scholar

The development of the Islamic banking industry in Indonesia has become a significant concern in recent years, with rapid growth in the number of banks operating based on Sharia principles. To face emerging challenges and opportunities, a deep understanding of the long-term financial behavior of Islamic banks is becoming increasingly important. This study aims to predict the share price of PT Bank Syariah Indonesia Tbk, over 28 days using the LSTM-GRU stack. The observation stage includes importing the dataset, data separation, model variations, the training process, output, and evaluation. Observations were conducted using 10 model variations from 4 stacks of LSTM and GRU. Each model performs the training process in four epochs (200, 500, 750, and 1000). The results of observations in this study show that long-term predictions (28 days ahead) using four stacks of LSTM-GRU and daily training accumulation techniques produce better accuracy than the general method (using multiple outputs). From the observations we have made for predictions for the next 28 days, the model with the LGLG stack arrangement (LSTM-GRU-LSTM-GRU) produces the best accuracy at epoch 750 with an MSE LSTM-GRU 63.43762863. This study will undoubtedly continue in order to achieve even better precision, either by utilizing a new design or by further improving the technology we are now employing.

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