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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JATI (Jurnal Mahasiswa Teknik Informatika) JPM: JURNAL PENGABDIAN MASYARAKAT
Solikhun
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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

 1 
Quantum Perceptron: A Novel Approach to Predicting Unemployment Levels in North Sumatra Province Solikhun; Trianda, Dimas
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 5 (2024): October 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i5.5815

Abstract

The application of Quantum Computing to improve the perceptron algorithm in unemployment prediction is a new aspect of this research. This study focuses on unemployment, which is a big challenge for the young generation in Indonesia, especially in the North Sumatra region. This research applies the quantum perceptron method to provide an alternative solution in predicting the unemployment rate. The data used in this analysis comes from the North Sumatra Central Statistics Agency and includes published unemployment rates (TPT) for individuals aged 15 years and over from 2017 to 2023. This research uses seven variables ranging from x1 to x7 to produce accurate data. Quantum perceptron methods offer specific advantages over traditional methods, including higher computing speeds and the ability to handle greater data complexity. This analysis aims to identify unemployment patterns and trends in North Sumatra and provide more accurate predictions by applying the quantum perceptron method. Although the results of this research are still limited to analysis, this research shows promising results and opens up opportunities for further, more in-depth research. This research is limited to predicting unemployment rates in North Sumatra. The use of quantum computing using the quantum perceptron method shows great potential for application to various other socio-economic problems in the future. This research contributes by introducing a new approach that utilizes quantum technology to improve prediction accuracy in economic analysis.
Comparison of Madaline and Perceptron Algorithms on Classification with Quantum Computing Approach Baidawi, Taufik; Solikhun
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 2 (2024): April 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i2.5502

Abstract

The fundamental problem in this research is to explore a more profound understanding of both performance and efficiency in quantity computing. Successful implementation of algorithms in computational computing environments can unlock the potential for significant improvements in information processing and neural network modeling. This research focuses on developing Madaline and Perceptron algorithms using a quantum approach. This study compares the two algorithms regarding the accuracy and epoch of the test results. The data set used in this study is a lens data set. There are four attributes: 1) patient age: young, prepresbyopia, presbyopia 2) eyeglass prescription: myopia, hypermetropia, 3) astigmatic: no, yes. 4) tear production rate: reduced, normal. There are three classes: 1) patients must have hard contact lenses installed, 2) patients must have soft contact lenses installed, and 3) patients cannot have contact lenses installed. The number of data is 24 data. The result of this research is the development of the Madaline and Perceptron algorithms with a quantum computing approach. Comparing these algorithms shows that the best accuracy is the Perceptron algorithm, namely 100%. In comparison, Madaline is 62.5%, and the smallest epoch is the Madaline algorithm, namely 4 epochs, while the smallest Perceptron epoch is 317. This research significantly contributes to the development of computing and neural networks, with potential applications extending from data processing to more accurate modeling in artificial intelligence, data analysis, and understanding complex patterns.
A Quantum Perceptron: A New Approach for Predicting Rice Prices at the Indonesian Wholesale Trade Level Solikhun; Yunita, Tri
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 4 (2024): August 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i4.5869

Abstract

The wholesale rice trade in Indonesia encounters various challenges in forecasting prices. These challenges are influenced by factors such as weather, government policies, global market conditions, and other economic variables. Accurate price predictions are crucial for informing government policy in a timely manner. This research introduces a new approach that utilizes the Quantum Perceptron algorithm to forecast rice prices. The algorithm, an innovative method in quantum computing, is expected to enhance the efficiency and effectiveness of price predictions. Although the research is still in the analytical stage, the use of Quantum Perceptron shows promise in dynamically addressing the complexity of market data and the variability of factors affecting rice prices. The method focuses on developing models that can leverage quantum computing to process information more effectively than classical methods. By harnessing the unique properties of quantum mechanics, such as superposition and entanglement, Quantum Perceptron can identify complex patterns and optimize predictions of future rice prices. The research describes the implementation of quantum algorithms in the context of the Indonesian rice wholesale market, including the technical challenges encountered and future development prospects. The research utilizes quantum computing along with the perceptron algorithm. The researchers focused on analyzing the quantum perceptron algorithm because of the limited availability of quantum computing devices. The findings of this research are confined to analysis. In order to advance this research, the author recommends that future studies employ quantum devices to achieve more accurate predictions
Manhattan Distance-based K-Medoids Clustering Improvement for Diagnosing Diabetic Disease Solikhun; Rahmansyah Siregar, Muhammad; Pujiastuti, Lise; Wahyudi, Mochamad
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 6 (2024): December 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i6.5894

Abstract

Diabetes is a metabolic disorder characterized by blood glucose levels above normal limits. Diabetes occurs when the body is unable to produce sufficient insulin to regulate blood sugar levels. As a result, blood sugar management becomes impaired and there is no cure for diabetes. Early detection of diabetes provides an opportunity to delay or prevent its progression into acute stages. Clustering can help identify patterns and groups of diabetes symptoms by analyzing attributes that indicate these symptoms. In this study, researchers are using K-Medoid and Quantum K-Medoid methods for clustering diabetes data. Quantum computing utilizes quantum bits, or qubits, which can represent multiple states at the same time. Compared to classical computers, quantum computing has the potential for an exponential speedup in problem-solving. Researchers conducted a comparison between two methods: the classic K-Medoids method and the K-Medoids method utilizing quantum computing. The researchers found that both Quantum K-Medoid and Classic K-Medoid achieved the same clustering accuracy of 91%. In testing with the Quantum K-Medoids algorithm, it was found that the cost value in the 8th epoch showed a significant decrease compared to the Classical K-Medoids algorithm. This demonstrates that Quantum K-Medoid can be considered a viable alternative for clustering purposes.
Optimizing Multilayer Perceptron for Car Purchase Prediction with GridSearch and Optuna Ginanti Riski; Dedy Hartama; Solikhun
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6328

Abstract

Multilayer Perceptron (MLP) is a powerful machine learning algorithm capable of modeling complex, non-linear relationships, making it suitable for predicting car purchasing power. However, its performance depends on hyperparameter tuning and data quality. This study optimizes MLP performance using GridSearch and Optuna for hyperparameter tuning while addressing data imbalance with the Synthetic Minority Over-sampling Technique (SMOTE). The dataset comprises demographic and financial attributes influencing car purchasing power. Initially, the dataset exhibited class imbalance, which could lead to biased predictions; SMOTE was applied to generate synthetic samples, ensuring a balanced class distribution. Two hyperparameter tuning approaches were implemented: GridSearch, which systematically explores a predefined parameter grid, and Optuna, an adaptive optimization framework utilizing a Bayesian approach. The results show that Optuna achieved the highest accuracy of 95.00% using the Adam optimizer, whereas GridSearch obtained the best accuracy of 94.17% with the RMSProp optimizer, demonstrating Optuna's superior ability to identify optimal hyperparameters. Additionally, SMOTE significantly improved model stability and predictive performance by ensuring adequate class representation. These findings offer insights into best practices for optimizing MLP in predictive modeling. The combination of SMOTE and advanced hyperparameter tuning techniques is applicable to various domains requiring accurate predictive analytics, such as finance, healthcare, and marketing. Future research can explore alternative optimization algorithms and data augmentation techniques to further enhance model robustness and accuracy.
Co-Authors Agus Perdana Windarto Dedy Hartama Ginanti Riski Mochamad Wahyudi Nasution, Zulaini Masruro Pujiastuti, Lise Putrama Alkhairi Rahmansyah Siregar, Muhammad Sibarani, Horainim Siregar, Muhammad Rahmansyah Taufik Baidawi, Taufik Trianda, Dimas Veithzal Rivai Zainal Wanto, Anjar Widodo Saputra Yunita, Tri