Maintaining consistency in client-server database systems with client-side caching

PhD ThesisCaching has been used in client-server database systems to improve the performance of applications. Much of the current work has concentrated on caching techniques at the server side, since the underlying assumption has been that clients are “thin” with application level processing taking place mainly at the server side. There are also a new class of “thick client” applications where clients need to access the database at the server but also perform substantial amount of processing at the client side; here client-side caching is needed to provide good performance for applications. This thesis presents a transactional cache consistency scheme suitable for systems with client-side caching. The scheme is based on the optimistic approach to concurrency control. The scheme provides serializability for committed transactions. This is in contrast to many modern systems that only provide the snapshot isolation property which is weaker than serializability. A novel feature is that the processing load for validating transactions at commit time is shared between clients and the database server, thereby reducing the load at the server. Read-only transactions can be validated at the client-side, without communicating with the server. Another feature is that the scheme permits disconnected operation, allowing clients with cached objects to work offline. The performance of the scheme is evaluated using simulation experiments. The experiments demonstrate that for mostly read only transaction load – for which caching is most effective - the scheme outperforms the existing concurrency control scheme with client-side caching considered to be the best, and matches the performance of the widely used scheme that only provides snapshot isolation. The results also show that the scheme in a disconnected environment provides reasonable performance.Directorate General of Higher Education, Ministry of National Education, Indonesia

Bloom Filter Implementation in Cache with Low Level of False Positive

Searching techniques significantly determine the speed of getting the information or objects. Finding an object in a set is related to membership checking. In the case of massive data, it needs an appropriate technique to search an object accurately and faster. This research implements searching methods, namely Bloom Filter and Sequential Search algorithms, to find objects in a set of data. It aims to improve our system getting a proper item. Due to the possibility of False-Positive existence as a result of Bloom filter technique, there is a potentially inaccurate representation to object sought. Some parameters are influencing False-Positive, namely the number of objects, available bits, and the number of mapped-bit. A Combination of those parameters could decrease the level of False-Positive and improve their accuracy and faster accessibility. In this research, we use three data object variations with the biggest object size of  2000000. Cached objects used in our experiments is between 2 – 20% of variation from the generated objects. The best results with the lowest False-Positive is a combination of bit = 8, mapped bit = 7, and 6% of cache size from 2000000 generated objects

Influences of Buffer Size and Eb/No on Very Small Aperture Terminal (VSAT) Communictions

In data communication of the signal transmitted from the transmitter (Tx) to receiver (Rx) stations is very influential. Buffer and Eb/No are two parameters that influence the quality of signal. This research measures those parameters and the relationship among them. This research employs data collected on the Link STM-1 side in Makassar and Timika operated by PT. Telkom Metra Bogor. The period of data is carried out for 56 days taken by using Simple Management Network Protocol (SNMP). To analyze the relationship among those two parameters, we use product moment correlation (PMC) method. The result correlation of the data buffer and Eb/No with a level of real is 0.05 and then buffer set in modem CDM 700 is 50% with threshold Eb/No 12.1 dB and the modulations used 64-QAM. That resulted correlation of side in Makassar is 0.648 and the p-value is 0.000. Correlation of side in Timika is 0.722 and the p-value is 0.000. These results suggest that the two parameters are correlated strong and significant.

Pattern Generation for Three Dimensional Cutting Stock Problem

We consider the problem of three-dimensional cutting of a large block that is to be cut into some small block pieces, each with a specific size and request. Pattern generation is an algorithm that has been used to determine cutting patterns in one-dimensional and two-dimensional problems. The purpose of this study is to modify the pattern generation algorithm so that it can be used in three-dimensional problems, and can determine the cutting pattern with the minimum possible cutting residue. The large block will be cut based on the length, width, and height. The rest of the cuts will be cut back if possible to minimize the rest. For three-dimensional problems, we consider the variant in which orthogonal rotation is allowed. By allowing the remainder of the initial cut to be rotated, the dimensions will have six permutations. The result of the calculation using the pattern generation algorithm for three-dimensional problems is that all possible cutting patterns are obtained but there are repetitive patterns because they suggest the same number of cuts.

Formulation of Sudoku Puzzle Using Binary Integer Linear Programming and Its Implementation in Julia, Python, and Minizinc

Sudoku is a number puzzle game popular among people with various difficulty levels (easy, medium, hard, and extremely hard). Sudoku can be modeled as a linear programming problem in mathematics, particularly binary integer linear programming (BILP). Completing Sudoku using BILP is quite tricky because it requires many iterations. Therefore, this study aims to analyze the Sudoku problem using the BILP formulation and implement the problem using Julia, Python, and MiniZinc. Out of 15 cases for each difficulty level, Julia performs better than Python and MiniZinc based on computation time. Moreover, Sudoku with easy difficulty levels is solved with a longer computation time than the other three difficulty levels. The computation time for solving BILP is getting faster as the difficulty level of the Sudoku problem increases. This is because Sudoku problems with easy difficulty levels have more known values as clues and generate more constraints than other difficulty levels

PREDIKSI MASA STUDI MAHASISWA MATEMATIKA IPB BERDASARKAN INDEKS PRESTASI KUMULATIF MENGGUNAKAN JARINGAN SYARAF TIRUAN

Akreditasi sebuah program studi sangat dipengaruhi oleh masa studi dan Indeks Prestasi Kumulatif (IPK) lulusannya. Beberapa penelitian menunjukkan adanya keterkaitan antara kelulusan dengan IPK mahasiswa. Namun, model prediksi lama masa studi berdasarkan IPK masih sedikit. Oleh karena itu, penelitian ini bertujuan untuk memprediksi masa studi mahasiswa berdasarkan IPK menggunakan model jaringan syaraf tiruan (JST) berbasis backpropagation. Beberapa fungsi pelatihan diterapkan, meliputi gradient descent, Nesterov accelerated gradient descent, Adaptive moment estimation (Adam), dan Nesterov Adam (Nadam). Data yang digunakan dalam penelitian ini adalah data masa studi dan IPK semester 1-6 mahasiswa S1 Matematika IPB. Hasil penelitian menunjukkan bahwa model JST terbaik dihasilkan oleh jaringan dengan jumlah input node 6 yang dinormalisasi dengan batch normalization (BatchNorm), hidden node 10 dan output node 1. Parameter jaringan terbaik diperoleh dari percobaan menggunakan fungsi pelatihan gradient descent dan laju pembelajaran 0.5 dengan MAE sebesar 1.887 pada data testing. Fungsi pelatihan gradient descent memperlihatkan adanya penurunan nilai MAE ketika nilai laju pembelajaran meningkat. Sementara itu, pada fungsi pelatihan lainnya, terdapat tren bahwa semakin kecil nilai laju pembelajaran maka semakin kecil pula nilai MAE yang dihasilkan. Berdasarkan model JST terpilih, nilai IPK yang paling berpengaruh pada masa studi mahasiswa matematika IPB adalah nilai IPK pada semester 3, yaitu masa mahasiswa matematika IPB pertama kali menerima mata kuliah mayor dari Departemen Matematika secara keseluruhan. Kepentingan dari fitur ini sangat tinggi, mencapai 75.62%. Model JST terpilih menghasilkan MAPE sebesar 3.8% dan RMSPE sebesar 4.9% pada data testing

PENERAPAN MODEL SEIRU PADA KASUS COVID-19 DI JAKARTA

Sejak awal penyebaran COVID-19, telah diambil langkah-langkah pembatasan aktivitas publik untuk meredakan laju penularan, termasuk di Provinsi DKI Jakarta yang menerapkan Pembatasan Sosial Berskala Besar (PSBB). Dalam upaya menganalisis dampak kebijakan tersebut, digunakan model epidemiologi SEIRU, yang mempertimbangkan periode laten dan efek pembatasan aktivitas publik. Penelitian ini mengimplementasikan model SEIRU pada kasus COVID-19 di Jakarta, mengevaluasi parameter yang paling sesuai untuk merepresentasikan dinamika kasus, serta mengidentifikasi dampak dari penerapan PSBB terhadap kesesuaian model. Bahasa pemrograman Julia digunakan untuk mengimplementasikannya. Dari penelitian ini ditunjukkan bahwa model SEIRU cocok untuk menggambarkan perkembangan kasus COVID-19 hingga berakhirnya PSBB pertama, tetapi kurang sesuai untuk masa perpanjangan PSBB. Analisis juga mengindikasikan bahwa penerapan PSBB dapat mengurangi jumlah kasus terlapor hingga 41%, dengan rata-rata waktu individu yang terinfeksi namun tidak menunjukkan gejala adalah 7 hari, dan durasi rata-rata periode laten adalah 6 jam

PERFORMANCE COMPARISON OF GRADIENT-BASED CONVOLUTIONAL NEURAL NETWORK OPTIMIZERS FOR FACIAL EXPRESSION RECOGNITION

A convolutional neural network (CNN) is one of the machine learning models that achieve excellent success in recognizing human facial expressions. Technological developments have given birth to many optimizers that can be used to train the CNN model. Therefore, this study focuses on implementing and comparing 14 gradient-based CNN optimizers to classify facial expressions in two datasets, namely the Advanced Computing Class 2022 (ACC22) and Extended Cohn-Kanade (CK+) datasets. The 14 optimizers are classical gradient descent, traditional momentum, Nesterov momentum, AdaGrad, AdaDelta, RMSProp, Adam, Radam, AdaMax, AMSGrad, Nadam, AdamW, OAdam, and AdaBelief. This study also provides a review of the mathematical formulas of each optimizer. Using the best default parameters of each optimizer, the CNN model is trained using the training data to minimize the cross-entropy value up to 100 epochs. The trained CNN model is measured for its accuracy performance using training and testing data. The results show that the Adam, Nadam, and AdamW optimizers provide the best performance in model training and testing in terms of minimizing cross-entropy and accuracy of the trained model. The three models produce a cross-entropy of around 0.1 at the 100th epoch with an accuracy of more than 90% on both training and testing data. Furthermore, the Adam optimizer provides the best accuracy on the testing data for the ACC22 and CK+ datasets, which are 100% and 98.64%, respectively. Therefore, the Adam optimizer is the most appropriate optimizer to be used to train the CNN model in the case of facial expression recognition

Heterogeneous Correlation Map Between Estimated ENSO And IOD From ERA5 And Hotspot In Indonesia

El Nino-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) can reduce the amount of rainfall in Indonesia. The previous study found that ENSO and IOD derived from the OISST dataset have an association with hotspots in Indonesia, especially in southern Sumatra dan Kalimantan. But the correlation results are still too small, and the correlation strength between regions has not been analyzed. Therefore, this study quantifies the association of the estimated ENSO and IOD derived from the ERA5 dataset on hotspots in Indonesia based on a Heterogeneous Correlation Map (HCM) and analyzes the correlation strength between regions in Indonesia. We use a singular value decomposition method to quantify this HCM. Besides OISST, ERA5 is an estimation data often used for weather forecast analysis. Therefore, this study quantifies the association of the estimated ENSO and IOD derived from the ERA5 dataset on hotspots in Indonesia based on a Heterogeneous Correlation Map (HCM) and analyzes the correlation strength between regions in Indonesia. Based on variance explained and correlation strength, the hotspot in Indonesia is more sensitive to ENSO and IOD derived from ERA5 than OISST. Consequently, the ERA5 data more useful to statistical analysis that requiring a substantial correlation