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All Journal Jurnal Pendidikan Fisika dan Teknologi Journal of Computing Theories and Applications
Muhamad Akrom
Dian Nuswantoro University
Computer Science & IT Decision Sciences, Operations Research & Management Education Physics

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Hybrid Quantum Key Distribution Protocol with Chaotic System for Securing Data Transmission De Rosal Ignatius Moses Setiadi; Muhamad Akrom
Journal of Computing Theories and Applications Vol. 1 No. 2 (2023): JCTA 1(2) 2023
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jcta.v1i2.9547

Abstract

This research proposes a combination of Quantum Key Distribution (QKD) based on the BB84 protocol with Improved Logistic Map (ILM) to improve data transmission security. This method integrates quantum key formation from BB84 with ILM encryption. This combination creates an additional layer of security, where by default, the operation on BB84 is only XOR-substitution, with the addition of ILM creating a permutation operation on quantum keys. Experiments are measured with several quantum measurements such as Quantum Bit Error Rate (QBER), Polarization Error Rate (PER), Quantum Fidelity (QF), Eavesdropping Detection (ED), and Entanglement-based detection (EDB), as well as classical cryptographic analysis such as Bit Error Ratio (BER), Entropy, Histogram Analysis, and Normalized Pixel Change Rate (NPCR) and Unified Average Changing Intensity (UACI). As a result, the proposed method obtained satisfactory results, especially perfect QF and BER, and EBD, which reached 0.999.
Predictive Modeling of Thermal Stability in Zn-MOF Using Multilayer Perceptron Muhammad Diva Irnanda; Harun Al Azies; Muhamad Akrom; Ananta Surya Pratama; Taufiqul Umam
Jurnal Pendidikan Fisika dan Teknologi (JPFT) Vol 12 No 1 (2026): January-June
Publisher : Department of Physics Education, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jpft.v12i1.11342

Abstract

Indonesia's heavy reliance on fossil fuels, which account for approximately 80% of its national energy supply, poses a significant obstacle to achieving Net Zero Emissions (NZE) by 2060. Metal-Organic Frameworks (MOF) have emerged as promising innovative materials for sustainable energy applications; however, their limited thermal stability at elevated temperatures remains a major challenge. This study aims to develop a Multilayer Perceptron (MLP -based predictive model for the thermal stability of zinc-based MOF (Zn-MOF) using four structural descriptors nZn, nN, Lig, and Het  derived from a dataset of 151 Zn-MOF compounds. Three hidden-layer configurations with 3, 6, and 9 neurons were evaluated using 10-fold cross-validation and three regression metrics: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²). The 9-neuron configuration achieved the highest predictive accuracy, with MAE = 0.0020, RMSE = 0.0022, and R² = 0.9991. SHAP analysis identified nN and Het as the most influential descriptors for thermal stability prediction. These results demonstrate that the MLP architecture effectively captures nonlinear structure–property relationships in Zn-MOFs, offering a computationally efficient tool to accelerate the design of thermally stable materials for sustainable energy applications.
Impact of SMOTE Oversampling on Classifying Band Gap Types in Imbalanced ABO₃ Perovskite Oxides Desvita Maharani; Johana Oktavia Ramadhani; Aliyah Zahratu Rizqi; Muhamad Akrom
Jurnal Pendidikan Fisika dan Teknologi (JPFT) Vol 12 No 1 (2026): January-June
Publisher : Department of Physics Education, Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jpft.v12i1.11479

Abstract

This study investigates the impact of the Synthetic Minority Over-sampling Technique (SMOTE) on the classification of direct and indirect band gap types in imbalanced ABO₃ perovskite oxide datasets. In the dataset used, the direct band gap class constitutes approximately 84% of the samples, while the indirect class represents only 16%, leading conventional classification models to become biased toward the majority class. To address this issue, SMOTE was employed to balance the class distribution, and its performance was evaluated using several machine learning algorithms, including Multi-Layer Perceptron (MLP), Extra Trees, CatBoost, and Gradient Boosting. Model performance was assessed using 5-fold stratified cross-validation, with particular emphasis on F1-macro and recall metrics to ensure adequate evaluation of the minority class. The results show that although SMOTE did not significantly improve overall accuracy (baseline: 0.89; SMOTE: 0.88), it enhanced the models’ ability to recognize the minority class. Notable improvements in F1-macro were observed, increasing from 0.76 to 0.78 for MLP and from 0.75 to 0.78 for CatBoost. These findings highlight the importance of using F1-macro as a more informative evaluation metric than accuracy for imbalanced datasets and provide methodological insights for developing more robust predictive models in materials informatics.
Co-Authors Aliyah Zahratu Rizqi Ananta Surya Pratama De Rosal Ignatius Moses Setiadi Desvita Maharani Harun Al Azies Johana Oktavia Ramadhani Muhammad Diva Irnanda Taufiqul Umam