http://localhost:8080//handle/123456789/583 - Fri, 17 Apr 2026 06:33:13 GMT 2026-04-17T06:33:13Z http://localhost:8080//handle/123456789/694 Optimasi Kinerja Tim Pengembangan Perangkat Lunak melalui Kepemimpinan Tanpa Keahlian Koding Sari, Indah Clara; Taufiqurrahman Penelitian ini bertujuan untuk menganalisis dampak kepemimpinan terhadap kinerja tim pengembangan perangkat lunak, terutama bagi individu yang tidak memiliki pengetahuan pemrograman yang luas. Kelompok eksperimen yang menerima pelatihan kepemimpinan memiliki kinerja yang lebih baik daripada kelompok kontrol yang tidak menerima pelatihan kepemimpinan. Kelompok eksperimen memiliki tingkat kepuasan kerja yang lebih tinggi, tingkat komitmen yang lebih tinggi, dan tingkat produktivitas yang lebih tinggi. Kepemimpinan yang efektif dapat meningkatkan kinerja tim pengembangan perangkat lunak, bahkan bagi individu yang tidak memiliki pengetahuan pemrograman yang luas. Kepemimpinan yang efektif dapat memotivasi tim, mengelola konflik, dan membuat keputusan strategi yang tepat, yang semuanya dapat berkontribusi pada kesuksesan proyek. Mon, 01 Jan 2024 00:00:00 GMT http://localhost:8080//handle/123456789/694 2024-01-01T00:00:00Z http://localhost:8080//handle/123456789/641 Analysis of Dimensional Reduction Effect on K-Nearest Neighbor Classification Method Taufiqurrahman; Nababan, Erna Budhiarti; Efendi, Syahril Classification algorithms mostly become problematic on data with high dimensions, resulting in a decrease in classification accuracy. One method that allows classification algorithms to work faster and more effectively and improve the accuracy and performance of a classification algorithm is by dimensional reduction. In the process of classifying data with the K-Nearest Neighbor algorithm, it is possible to have features that do not have a matching value in classifying, so dimension reduction is required. In this study, the dimension reduction method used is Linear Discriminant Analysis and Principal Component Analysis and classification process using KNN, then analyzed its performance using Matrix Confusion. The datasets used in this study are Arrhythmia, ISOLET, and CNAE-9 obtained from UCI Machine Learning Repository. Based on the results, the performance of classifiers with LDA is better than with PCA on datasets with more than 100 attributes. Arrhythmia datasets can improve performance on K-NN K=3 and K=5. The best performance is obtained by LDA+K-NN K=3 which produces an accuracy value of 98.53%, the lowest performance found in K-NN without reduction with K=3. ISOLET datasets, the best performance results are also obtained by data that has been reduced with LDA, but the best performance is obtained when the classification of K-NN with K=5 and the lowest performance is found in PCA+ K-NN with a value of K=3. As for the best performance, dataset CNAE-9 is also achieved by LDA+K-NN, while the lowest performance is PCA+K-NN with the value of K=3. Fri, 01 Oct 2021 00:00:00 GMT http://localhost:8080//handle/123456789/641 2021-10-01T00:00:00Z http://localhost:8080//handle/123456789/640 Analysis of Model-Free Reinforcement Learning Algorithm for Target Tracking Fikry, Muhammad; Adek, Rizal Tjut; Hartanto, Subhan; Taufiqurrahman; Rinawati, Dyah Ika Target tracking is a process that can find points in different domains. In tracking, some places contain prizes (positive or negative values) that the agent does not know at first. Therefore, the agent, which is a system, must learn to get the maximum value with various learning rates. Reinforcement learning is a machine learning technique in which agents learn through interaction with the environment using reward functions and probabilistic dynamics to allow agents to explore and learn about the environment through various iterations. Thus, for each action taken, the agent receives a reward from the environment, which determines positive or negative behavior. The agent's goal is to maximize the total reward received during the interaction. In this case, the agent will study three different modules, namely sidewalk, obstacle, and product, using the Q-learning algorithm. Each module will be training with various learning rates and rewards. Q-learning can work effectively with the highest final reward at a learning rate of 0.8 for 500 rounds with an epsilon of 0.9. Fri, 01 Apr 2022 00:00:00 GMT http://localhost:8080//handle/123456789/640 2022-04-01T00:00:00Z http://localhost:8080//handle/123456789/639 The Design of Electronic Monitoring Process Model and Evaluation of Development in the Government (Case Study: Pakpak Bharat District) Ramli, Muslim; Suwilo, Saib; Situmorang, Zakarias Information technology leads the public to be involved in the planning, implementation and oversight process of public policy. Many regions in Indonesia have not yet applied the concept. E-government with the application of electronic monitoring and development model, addressing the issue of information disclosure. The method is DSRM. The public can see directly every department and agency under the district government. The information displayed in the form of graphs that sort according to the achievements of each institution. Sat, 01 Sep 2018 00:00:00 GMT http://localhost:8080//handle/123456789/639 2018-09-01T00:00:00Z