Content-based Filtering Movie Recommender System Using Semantic Approach with Recurrent Neural Network Classification and SGD

The application of recommendation systems has been applied in various types of platforms, especially applications for watching movies such as Netflix and Disney+. The recommendation system is purposed to make it easier for users, especially in choosing a movie because currently the number of movie productions is increasing every day. This research proposed a CBF movie recommendation system by comparing the performance of several semantic methods to be able to get the best rating prediction results. In order to improve the performance quality to get the best rating prediction results, this research  utilized semantic feature methods by comparing the performance of the evaluation results produced by the TF-IDF method and word embedding applications, such as BERT, GPT-2, RoBERTa, and implemented RNN model to classify the results of rating prediction. The data were used to generate the recommendation system by involving 854 data movie and 39 accounts with a total of 34,056 movie reviews on Twitter. This research has succeeded in getting a method that produced rating predictions, namely RoBERTa. In the classification process with the RNN model and SGD optimization, the measurement results with confusion matrix by classifying the RoBERTA rating prediction obtained an evaluation value of 0.6514 loss, 95.59% accuracy, and 0.6514 precision

Theoretical Studies on Redox Potential of Metalloproteins

13301甲第4817号博士（理学）金沢大学博士論文要旨Abstract 以下に掲載：Bulletin of the Chemical Society of Japan 91(9) pp.1451-1456 2018. Chemical Society of Japan. 共著者：Isman Kurniawan, Kazutomo Kawaguchi, Mitsuo Shoji, Toru Matsui, Yasuteru Shigeta, Hidemi Naga

Implementation of Ensemble Methods on Classification of CDK2 Inhibitor as Anti-Cancer Agent

Cancer is known as the second leading cause of death worldwide. About 7-10 million cases of death by cancer occur every year. The recent treatment to heal the cancer is chemotherapy. However, chemotherapy treatment is known to have side effects and cell resistance issues to certain drugs. Therefore, it is required to develop a new drug that can reduce the side effects and provide a better treatment effect. In general, anti-cancer drugs are developed by targeting Cyclin-Dependent Kinase 2 (CDK2) enzyme. Conventional drug design is not effective and efficient for obtaining new drug candidates because of no information about the biological activity before it is synthesized. In this study, we aim to develop a model to predict the activity of CDK2 inhibitors by using ensemble methods, i.e.,  XGBoost, Random Forest, and AdaBoost. The study was conducted by calculating several fingerprints, i.e., Estate, Extended, Maccs, and Pubchem, as feature variables. Based on the results, we found that Random Forest with Pubchem fingerprint gives the best result with the value of Matthews Correlation Coefficient (MCC) and Area Under the ROC Curve (AUC) values are 0.979 and 0.999, respectively. From this study, we contributed to revealing the potency of the ensemble with fingerprint in bioactivity prediction, especially CDK2 inhibitors as anti-cancer agents

Movie Recommender System with Cascade Hybrid Filtering Using Convolutional Neural Network

The current technological advancements have made it easier to watch movies, especially through online streaming platforms such as Netflix. Social media platforms like Twitter are used to discuss, share information, and recommend movies to other users through tweets. The user tweets from Twitter are utilized as a film review dataset. Film ratings can be used to build a recommendation system, incorporating Collaborative Filtering (CF) and Content-based Filtering (CBF). However, both methods have their limitations. Therefore, a hybrid filtering approach is required to overcome this problem. The filtering approach involves CF and CBF processes to improve the accuracy of film recommendations. No current research employs the Cascade Hybrid Filtering method, particularly within the context of movie recommendation systems. This study addresses this gap by implementing the Cascade Hybrid Filtering method, utilizing the Convolutional Neural Network (CNN) as the evaluative instrument. This research presents a significant contribution by implementing the Cascade Hybrid Filtering method based on CNN. This research uses several scenarios to compare methods to produce the most accurate model. This study's findings demonstrate that the application of Cascade Hybrid Filtering, incorporating CNN and optimized with RMSProp, yields a movie recommendation system with notable performance metrics, including an MAE of 0.8643, RMSE of 0.6325, and the highest accuracy rate recorded at 86.95%. The RMSprop optimizer, facilitating a learning rate of 6.250551925273976e-06, enhances accuracy to 88.40%, showcasing a remarkable improvement of 6.00% from the baseline. These outcomes underscore the significant contribution of the paper in enhancing the precision and effectiveness of movie recommendation systems

Movie Recommender System with Cascade Hybrid Filtering Using Convolutional Neural Network

The current technological advancements have made it easier to watch movies, especially through online streaming platforms such as Netflix. Social media platforms like Twitter are used to discuss, share information, and recommend movies to other users through tweets. The user tweets from Twitter are utilized as a film review dataset. Film ratings can be used to build a recommendation system, incorporating Collaborative Filtering (CF) and Content-based Filtering (CBF). However, both methods have their limitations. Therefore, a hybrid filtering approach is required to overcome this problem. The filtering approach involves CF and CBF processes to improve the accuracy of film recommendations. No current research employs the Cascade Hybrid Filtering method, particularly within the context of movie recommendation systems. This study addresses this gap by implementing the Cascade Hybrid Filtering method, utilizing the Convolutional Neural Network (CNN) as the evaluative instrument. This research presents a significant contribution by implementing the Cascade Hybrid Filtering method based on CNN. This research uses several scenarios to compare methods to produce the most accurate model. This study's findings demonstrate that the application of Cascade Hybrid Filtering, incorporating CNN and optimized with RMSProp, yields a movie recommendation system with notable performance metrics, including an MAE of 0.8643, RMSE of 0.6325, and the highest accuracy rate recorded at 86.95%. The RMSprop optimizer, facilitating a learning rate of 6.250551925273976e-06, enhances accuracy to 88.40%, showcasing a remarkable improvement of 6.00% from the baseline. These outcomes underscore the significant contribution of the paper in enhancing the precision and effectiveness of movie recommendation systems

Computational Study of Oxidation Potential Fluctuation of Ketone Molecule

This study focus on investigating the oxidation potential fluctuation of organic molecule in the solution. The organic molecule that was investigated is 3-pentanone molecule that has oxi-dation potential 0.143 V experimentally. The oxidation potential was calculated using Born-Haber cycle approximation involving the calculation of gas phase Gibbs free energy and solvation energy of reduced and the oxidized state. The reduced state represents a neutral charge molecule and the oxidized state represents a radical cation molecule. The first, molecular dynamics (MD) simulation of both state was performed for 2 ns time. Then, 400 snapshot structures of both state molecule was captured. Gas phase Gibbs free energy and solvation energy were calculated using MP2 theory with cc-pvdz basis set and the solvation effect was approached using Polarizable Continuum Model (PCM). Normal Hydrogen Electrode (NHE), that has redox potential 4.44 V experimentally, was used as reference electrode. The result shows the different of gas phase Gibbs free energy average of both state was 756.97 ± 21.598 kJ/mol, and solvation energy average of reduced and oxidized state were -18.42 kJ/mol ± 1.482 kJ/mol, and -219.02 ± 1.094 kJ/mol respectively. Then, the oxidation potential was calculated by substituting gas phase Gibbs free energy and solvation energy into Born-Haber cycle approximation. The calculation result shows the average of oxidation po-tential value is 1.396 ± 0.225 V. The deviation of oxidation potential confirms the fluctuation of oxidation potential during the simulation

Penerapan Akuntansi Zakat, Infaq, dan Shodaqoh pada Baitul Maal Hidayatullah di Balikpapan

Penelitian ini bertujuan untuk menyajikan laporan keuangan pada lembaga manajemen zakat berdasarkan PSAK No. 109, penelitian ini memproses data pada tahun 2014 di Baitul Maal Hidayatullah di Balikpapan dan membandingkannya dengan PSAK No. 109 tentang Akuntansi Zakat, Infaq, dan Shodaqoh. Penelitian ini untuk menemukan dan melihat kesesuaian (mengevaluasi) perlakuan Akuntansi Zakat, Infaq, dan Shodaqoh yang diterapkan Baitul Maal Hidayatullah Balikpapan sesuai dengan PSAK No. 109 tentang Akuntansi Zakat, Infaq dan Shodaqoh. Analisis ini menggunakan PSAK No. 109 untuk menyiapkan laporan keuangan tentang Baitul Ma'al Hidayatullah Balikpapan. Lembaga ini sebelumnya tidak memiliki laporan keuangan sesuai dengan PSAK No. 109, yang digunakan untuk laporan ini hanya sumber bulanan dan penggunaan dana. Hasil penelitian ini mengungkapkan bahwa Baitul Maal Hidayatullah Balikpapan dalam menyusun laporan keuangan harus berdasarkan PSAK No. 109 agar laporan keuangan memberikan informasi yang akurat bagi Muzakki untuk mendistribusikan dana kepada penyelenggara zakat dalam hal ini adalah BMH Balikpapan

Theoretical studies on electronic structure and properties of type I copper center in copper proteins

We present a cluster model representing type I copper (T1Cu) center of copperprotein, which corrsponds to Multicopper Oxidases, Azurin, Stelacyanin and so on. Theelectronic structure and physical properties such as molecular orbital, atomic partial charge,partial spin densities, ionization energy (IP) of reduced T1Cu, electron affinity (EA) of oxidizedT1Cu, the bond and the angle constants etc. are calculated by using two typicalDensity Functional Theory (DFT) functionals, which are B3LYP and M06, with 6-31G(d)basis set. We find the dependency of several properties such as atomic partial charge, partialspin densities, IP, and EA on the DFT functionals. We also find that the DFT functionals givea better contribution to bond constants, especially in case of the interaction between copperand the axial ligand. We calculate the maximum absorption wavelength of T1Cu center andfind relatively a good agreement with experimental data

Molecular dynamics study of free energy profile for dissociation of ligand from CA I active site

We investigate the binding/dissociation process of ligand molecule from carbonicanhydrase (CA) I carbonic anhydrase (CA) I enzyme by using all-atom moleculardynamics simulation. The force field parameters of zinc ion in the CA I active site are estimatedby quantum chemical calculations and are summarized in this paper. The free energyprofile for binding/dissociation process of ligand from CA I active site is calculated by thethermodynamic integration combined with the all-atom molecular dynamics simulation. Thebinding free energy as a function of the distance between the center of mass positions of CAI active site and the ligand molecule is estimated. The radial distribution function of theCA I-ligand complex is calculated from the trajectory of all-atom molecular dynamics (MD)simulation. We estimate the free energy surface from the radial distribution function. Wecan obtain the bond constant of the equilibrium state from the value of the free energy surface.We discuss the binding/dissociation process of ligand molecule by calculating the freeenergy profile to know the stability of the CA I-ligand complex with some thermodynamicproperties such as the binding free energy, the equilibrium state of the free energy surfaceand so on

PERANAN MULTIMEDIA PEMBELAJARAN KIMIA BERORIENTASI STRUKTUR PADA TOPIK LARUTAN PENYANGGA

Telah dilakukan penelitian mengenai peranan multimedia pembelajaran kimia berorientasi struktur untuk meningkatkan pemahaman konsep larutan penyangga siswa SMA Kelas XI. Penelitian ini dilakukan di salah satu SMA di Kabupaten Tasikmalaya dengan desain penelitian pseudo eksperimen pre-post test. Pada kelas eksperimen dilakukan pembelajaran dengan menggunakan multimedia sedangkan pada kelas kontrol pembelajaran dilakukan secara konvensional. Hasil analisis statistik menunjukkan bahwa rata-rata n-gain kelas eksperimen lebih tinggi dibandingkan dengan kelas kontrol. Selain itu jumlah siswa di kelas eksperimen yang termasuk tingkat pencapaian n-gain tinggi lebih banyak dibandingkan kelas kontrol. Hasil analisis terhadap nilai gain menunjukkan adanya perbedaan nilai gain pada kedua kelas. Hal ini menunjukkan bahwa penggunaan multimedia dalam pembelajaran menujukkan pengaruh yang signifikan terhadap pemahaman konsep larutan penyangga siswa. Selanjutnya analisis terhadap tiap level representasi menunjukkan bahwa penggunaan multimedia memberikan pengaruh yang signifikan terhadap pemahaman konsep siswa pada level mikroskopik terutama konsep larutan penyangga asam dan basa