Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Sistemasi: Jurnal Sistem Informasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOMLAI: Journal of Machine Learning and Artificial Intelligence
Siregar, Muhammad Rahmansyah
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
The Implementation of Rough Set Algorithm to Classify Student Comfort Level Using Rosetta Siregar, Muhammad Rahmansyah; Sugiandi, Jeni; Pahriza, Alpiki; Sitorus, Salomo Marudut Pandapotan
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 2 No. 3 (2023): September
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v2i3.2884

Abstract

Student comfort in the campus environment is an integral aspect in creating optimal learning conditions. Students who feel comfortable are more likely to be involved in academic and social activities. Several students were identified as frequently not attending class, and their interest in learning appeared to be lacking. This creates serious challenges in creating an optimal learning environment and meeting student needs. The research classifies student comfort levels and also provides a basis for developing more targeted campus policies. The data collection method uses a questionnaire method. The data processing method uses the Rough Set algorithm. Data processing uses Rosetta software. Based on the analysis carried out from 154 rules, the number of occurrences of the rest level attribute was 94 times, the class environment attribute was 110 times, the assignment difficulty level attribute was 114 times, the lecturer's teaching method attribute was 98 times, the campus facilities attribute was 136 times. So it can be seen that the campus facility attribute is the most influential because it has the highest number of occurrences. The next influential attribute after facilities is the level of difficulty of assignments, class environment, lecturer's teaching method and level of rest and reduce statistics show that campus facilities are a condition attribute that is very influential in student comfort levels, namely with an occurrence of 90,9%.
Analyzing Perceptron Algorithm for Global Gold Price Prediction using Quantum Computing Approach Solikhun; Siregar, Muhammad Rahmansyah
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 1, February 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i1.2024

Abstract

The price of gold has garnered significant attention in the world of finance and investment due to its role as a safe haven asset and an indicator of global economic stability. An inherent risk of investing in gold is the daily fluctuation in prices, which can rise, fall, or remain stable. Investors are constantly seeking accurate ways to predict gold price movements in order to make informed investment decisions. While classic algorithms like artificial neural networks have been used for gold price prediction, they often struggle with analyzing complex data and identifying the hidden patterns within large datasets. It is widely acknowledged that accurately and consistently predicting the gold price movements, exchange rate, and whether the gold price will rise or fall is very challenging. To address this challenge, this study explored the use of quantum perceptron algorithm for predicting global gold prices. This approach harnesses the principles of quantum computing to improve the efficiency and performance of neural network models. Quantum computers can perform multiple computations simultaneously, enabling the solution of problems that are difficult for classical computers. This study utilized global gold data from January 2018 to December 2022, with 80:20 split of training and testing data; data from January 2018 to December 2021 for training and data from January 2022 to December 2022 for testing. This study aims to offer insights into the potential and application of quantum algorithms in predicting gold prices. The research involved an analysis of global gold price predictions using the quantum perceptron algorithm and quantum computing.
Comparison of Manhattan and Chebyshev Distance Metrics in Quantum-Based K-Medoids Clustering Solikhun, Solikhun; Siregar, Muhammad Rahmansyah; Pujiastuti, Lise; Wahyudi, Mochamad; Kurniawan, Deny
Sistemasi: Jurnal Sistem Informasi Vol 14, No 4 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i4.5193

Abstract

Anemia is a condition characterized by a decrease in the number of red blood cells or hemoglobin levels in the bloodstream. It can lead to fatigue and reduced productivity. Clustering is a technique in data mining used to identify patterns that can support decision-making processes. In the case of anemia, clustering plays a crucial role in identifying various severity patterns and understanding the contributing factors behind the condition. Quantum computers, which utilize the principles of quantum mechanics for information processing, have made significant advancements over the past decade. Quantum computing is an advanced method of information processing that leverages qubits, enabling systems to exist in multiple states simultaneously. This technology offers the potential to solve complex problems at exponentially faster speeds than classical computers. In this study, researchers applied the K-Medoids clustering algorithm, calculated using quantum-based equations. The research compares two distance measurement methods: Chebyshev distance and Manhattan distance. The results show that the Manhattan algorithm performs better in medical contexts, particularly for detecting positive cases, with a recall of 0.57 and an F1-score of 0.695, although it has a slightly lower precision of 0.88. This makes it more suitable for medical applications where false negatives carry high risks, such as disease detection, despite its higher cost and mean squared error (MSE). On the other hand, Chebyshev distance achieved perfect precision (1.0) and higher accuracy (80%), but its low recall (0.33) indicates that many positive cases were missed. Therefore, Manhattan distance is more recommended for medical applications that require the detection of more positive cases, while Chebyshev is more efficient for scenarios that prioritize accuracy and cost.
Co-Authors DENY KURNIAWAN Mochamad Wahyudi Pahriza, Alpiki Pujiastuti, Lise Sitorus, Salomo Marudut Pandapotan Solikhun Solikhun Solikhun, Solikhun Sugiandi, Jeni