Developing an Enhanced Algorithms to Solve Mixed Integer Non-Linear Programming Problems Based on a Feasible Neighborhood Search Strategy

Engineering optimization problems often involve nonlinear objective functions, which can capture complex relationships and dependencies between variables. This study focuses on a unique nonlinear mathematics programming problem characterized by a subset of variables that can only take discrete values and are linearly separable from the continuous variables. The combination of integer variables and non-linearities makes this problem much more complex than traditional nonlinear programming problems with only continuous variables. Furthermore, the presence of integer variables can result in a combinatorial explosion of potential solutions, significantly enlarging the search space and making it challenging to explore effectively. This issue becomes especially challenging for larger problems, leading to long computation times or even infeasibility. To address these challenges, we propose a method that employs the "active constraint" approach in conjunction with the release of nonbasic variables from their boundaries. This technique compels suitable non-integer fundamental variables to migrate to their neighboring integer positions. Additionally, we have researched selection criteria for choosing a nonbasic variable to use in the integerizing technique. Through implementation and testing on various problems, these techniques have proven to be successful

Rancangan Sistem Informasi Akademik (SIAKAD) Program Pascasarjana Universitas Negeri Jakarta (UNJ)

Abstract— Universities are one of the agents of change that undertake the task of developing human resource tasks through formal education process. Graduate Program Jakarta State University is a leading educational institution in Jakarta. But there are still less information systems that are still manual, as in academic management is generally more of a written method such as student input value, documentation absent, assignment to students. Most of the students already know and utilize information technology in communication tools, but in reality educational institutions do not have information systems that are not in line with the development of students' knowledge of the technology they use. With these shortcomings, the researchers provide solutions that will be useful for the Graduate Program of State University of Jakarta for its development, namely by website-based academic information system using PHP programming language. With this website will facilitate the institution in providing accurate and fast information for all users both students, lecturers and the general public. Intisari - Perguruan tinggi merupakan salah satu agen perubahan yang mengemban tugas mengembangkan tugas sumber daya manusia melalui proses pendidikan formal. Program Pascasarjana Universitas Negeri Jakarta merupakan lembaga pendidikan yang terkemuka di Jakarta. Tetapi masih ada yang kurang yaitu sistem informasi yang masih manual, seperti dalam pengelolaan akademik pada umumnya lebih bersifat metode tertulis misalnya input nilai mahasiswa, pendokumentasian absen, pemberian tugas kepada mahasiswa. Sebagian besar mahasiswa sudah mengenal dan memanfaatkan teknologi informasi pada alat komunikasinya, tetapi pada kenyataannya institusi pendidikan belum memiliki sistem informasi yang tidak sejalan dengan perkembangan pengetahuan mahasiswa akan teknologi yang mereka pakai. Dengan kekurangan ini maka peneliti memberikan solusi yang akan berguna bagi Program Pascasarjana Universitas Negeri Jakarta untuk perkembangannya, yaitu dengan sistem infomasi akademik berbasis website dengan menggunakan bahasa pemrograman PHP. Dengan adanya website ini akan memudahkan pihak institusi dalam memberikan informasi yang akurat dan cepat untuk semua user baik mahasiswa, dosen dan masyarakat umumnya Kata Kunci: Informasi, Pemrograman, Websit

Application of Neural Network Variations for Determining the Best Architecture for Data Prediction

Abstract This study focuses on the application and comparison of the epoch, time, performance/MSE training, and performance/MSE testing of variations of the Backpropagation algorithm. The main problem in this study is that the Backpropagation algorithm tends to be slow to reach convergence in obtaining optimum accuracy, requires extensive training data, and the optimization used is less efficient and has performance/MSE which can still be improved to produce better performance/MSE in this research—data prediction process. Determination of the best model for data prediction is seen from the performance/MSE test. This data prediction uses five variations of the Backpropagation algorithm: standard Backpropagation, Resistant Backpropagation, Conjugate Gradient, Fletcher Reeves, and Powell Beale. The research stage begins with processing the avocado production dataset in Indonesia by province from 2016 to 2021. The dataset is first normalized to a value between 0 to 1. The test in this study was carried out using Matlab 2011a. The dataset is divided into two, namely training data and test data. This research's benefit is producing the best model of the Backpropagation algorithm in predicting data with five methods in the Backpropagation algorithm. The test results show that the Resilient Backpropagation method is the best model with a test performance of 0.00543829, training epochs of 1000, training time of 12 seconds, and training performance of 0.00012667.  Abstract This study focuses on the application and comparison of the epoch, time, performance/MSE training, and performance/MSE testing of variations of the Backpropagation algorithm. The main problem in this study is that the Backpropagation algorithm tends to be slow to reach convergence in obtaining optimum accuracy, requires extensive training data, and the optimization used is less efficient and has performance/MSE which can still be improved to produce better performance/MSE in this research—data prediction process. Determination of the best model for data prediction is seen from the performance/MSE test. This data prediction uses five variations of the Backpropagation algorithm: standard Backpropagation, Resistant Backpropagation, Conjugate Gradient, Fletcher Reeves, and Powell Beale. The research stage begins with processing the avocado production dataset in Indonesia by province from 2016 to 2021. The dataset is first normalized to a value between 0 to 1. The test in this study was carried out using Matlab 2011a. The dataset is divided into two, namely training data and test data. This research's benefit is producing the best model of the Backpropagation algorithm in predicting data with five methods in the Backpropagation algorithm. The test results show that the Resilient Backpropagation method is the best model with a test performance of 0.00543829, training epochs of 1000, training time of 12 seconds, and training performance of 0.00012667. &nbsp

Komparasi K-Means Clustering dan K-Medoids Clustering dalam Mengelompokkan Produksi Susu Segar di Indonesia Berdasarkan Nilai DBI

The purpose of this study was to find the optimal grouping from the comparison of the two methods in grouping fresh milk production using the K-Means algorithm and the K-Medoids algorithm. To find optimal grouping, the authors compare the grouping results by looking for the smallest DBI (Davies Bouldin Index) value. The data used in this study is data on fresh milk production in Indonesia which is sourced from the Indonesian Central Bureau of Statistics for 2018-2020. Evaluation of the DBI value for the K-Means Clustering algorithm is 0.094 and the DBI value for K-Medoids Clustering is 0.072. Therefore, grouping fresh milk production using the K-Medoids algorithm has better results than using the K-Means Clustering algorithm, because the K-Medoids Clustering algorithm has a smaller DBI value of 0.072. The benefit of this study is to obtain optimal clusters in classifying fresh milk in Indonesia to provide information to the government in increasing fresh production in Indonesia in the future.Tujuan penelitian ini adalah untuk mencari pengelompokan yang optimal dari perbandingan dua metode dalam pengelompokkan produksi susu segar menggunakan algoritma K-Means dan algoritma K-Medoids. Untuk mencari pengelompokkan yang otimal penulis membandingkan hasil pengelompokkan dengan mencari nilai DBI(Davies Bouldin Index) terkecil. Data yang digunakan pada penelitian ini adalah data produksi susu segar di Indonesia yang bersumber dari Badan Pusat  Statistik Indonesia Tahun 2018-2020. Evaluasi nilai DBI untuk algoritma K-Means Clustering adalah 0,094 dan nilai DBI untuk K-Medoids Clustering adalah 0,072. Oleh karena itu, pengelompokkan produksi susu segar menggunakan algoritama  K-Medoids memiliki hasil yang lebih baik daripada menggunakan algoritma K-Means Clustering, karena algoritma K-Medoids Clustering memiliki nilai DBI lebih kecil yaitu 0,072. Manfaat dari penelitian ini adalah untuk mendapatkan cluster yang optimal dalam mengelompokkan susu segar di Indonesia untuk memberikan informasi kepada pemerintah dalam meningkatkan produksi segar di Indonesia pada masa yang akan datang