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DESIGN AND DEVELOPMENT of FINANCIAL FLOW MOSQUE INFORMATION SYSTEM (SIKEMAS) USING CLIENT SERVER-BASED OBJECT ORIENTED Diana Effendi; Rani Puspita Dhaniawaty; Mia Fitriawati; Muhammad Yasir Mumtaz2
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 9, No 3 (2023): Juni 2023
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v9i3.2225

Abstract

Abstract: DKM has objectives such as overseeing the security and orderliness of the mosque as a whole, and managing the financial flows that exist within the mosque. Financial flow is a means of cash flow in a period related to the responsibility of company or agency management in managing cash both from operational, funding and investment activities. In managing current financial flows, DKM Masjid Al Ashlah Kopo Bandung still uses conventional manual recording methods. Such a system raises problems, including documentation of donors, calculations of zakat to be paid by congregations that are not in accordance with procedures. This makes the management and recording of cash flows less effective and efficient and also makes the management of the Bendahara DKM limited. SiKeMas is a system that has the goal of providing convenience in facilitating various DKM needs in managing financial flows, as well as informing DKM about the management of financial flow data. The research method used is the Object Oriented approach with various tools the design uses the UML. The system development is carried out using the prototype method. The results of this research are an application based on Desktop Client-Server with JAVA NetBeans IDE 7 and MySQL DBMS.            Keywords: DKM; Financial Flow; SiKeMas;  Abstrak: DKM memiliki tujuan antara lain mengawasi keamanan dan ketertiban masjid secara keseluruhan, serta mengelola aliran keuangan yang ada di dalam masjid. Arus keuangan merupakan sarana arus kas dalam suatu periode yang berkaitan dengan tanggung jawab manajemen perusahaan atau instansi dalam mengelola kas baik yang berasal dari kegiatan operasional, pendanaan maupun investasi. Dalam mengelola aliran keuangan saat ini, DKM Masjid Al Ashlah Kopo Bandung masih menggunakan metode pencatatan manual yang konvensional. Sistem seperti itu menimbulkan masalah, antara lain dokumentasi donatur, perhitungan zakat yang harus dibayarkan jamaah tidak sesuai prosedur. Hal ini membuat pengelolaan dan pencatatan arus kas menjadi kurang efektif dan efisien serta membuat pengelolaan DKM Bendahara menjadi terbatas. SiKeMas merupakan sistem yang bertujuan untuk memberikan kemudahan dalam memfasilitasi berbagai kebutuhan DKM dalam mengelola aliran keuangan, serta menginformasikan kepada DKM tentang pengelolaan data aliran keuangan. Metode penelitian yang digunakan adalah pendekatan Object Oriented dengan berbagai tools perancangan menggunakan UML. Pengembangan sistem dilakukan dengan menggunakan metode prototype. Hasil dari penelitian ini adalah sebuah aplikasi berbasis Desktop Client-Server dengan JAVA NetBeans IDE 7 dan MySQL DBMS. Kata kunci: Arus Keuangan; DKM; SiKeMas;
Epistemologi Artificial Intelligence: Kebenaran, Validitas, dan Otoritas Algoritmik Sri Nurhayati; Diana Effendi; Agus Nursikuwagus; Usep Mohamad Ishaq; Andrias Darmayadi
AL-MIKRAJ Jurnal Studi Islam dan Humaniora (E-ISSN 2745-4584) Vol. 6 No. 1: Al-Mikraj, Jurnal Studi Islam dan Humaniora
Publisher : Pascasarjana Institut Agama Islam Sunan Giri Ponorogo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37680/almikraj.v6i1.8530

Abstract

The development of artificial intelligence (AI) has brought fundamental changes in the way knowledge is produced, validated, and accepted in various sectors of life. Algorithmic models, especially deep learning, generate predictions and recommendations that are often treated as operational truths even though the inference process is not fully explainable. This study analyzes how AI changes the understanding of truth, validity, and epistemic authority from the perspective of the philosophy of science, and links it to the ontological and axiological dimensions in modern knowledge production. A qualitative approach based on philosophical analysis is used to integrate the thoughts of Popper, Kuhn, Lakatos, Van Fraassen, and Floridi. The results show that AI shifts knowledge from rational justification to performative and statistical validity, and challenges the position of humans as the primary epistemic agents. This study asserts that the epistemic transformation triggered by AI requires ontological and axiological reflection so that the development of knowledge remains in line with humanitarian principles and ethical responsibility
Hyperparameter Optimization of Random Forest for Multiclass Classification of Student Academic Performance Using Multidimensional Factors Sri Nurhayati; Diana Effendi; Bobi Kurniawan Soegoto; Adam Mukharil Bachtiar; Hanhan Maulana; Ednawati Rainarli
Komputika : Jurnal Sistem Komputer Vol. 15 No. 1 (2026): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v15i1.18885

Abstract

Classification for academic performances among students in a multi-class scenario is a challenging task due to its dependencies on multiple factors and characteristics, particularly in the medium academic performance category. This scenario makes it a problem for some models with their conventional settings in terms of their ability to optimally distinguish categories of academic performances while being used in classification tasks, thus leading to the need for optimization techniques in enhancing their performances. This research paper will design an optimization strategy for improving the performances of the Random Forest algorithm in a multi-class academic performance classification among students. This will help in enhancing decision-making systems in education. The research method used is a machine learning approach with a Random Forest algorithm optimized through hyperparameter tuning using RandomizedSearchCV. This study utilizes secondary student data obtained from the Kaggle public repository, consisting of 6,607 data points with 20 determining factors covering academic, behavioral, social, environmental, and health aspects. The results showed that Random Forest hyperparameter optimization was able to improve model performance from a baseline accuracy of 79.56% to 81.08% on the validation data, and achieved an accuracy of 81.69% on the test data. In addition, there was an improvement in performance in the Medium category classification, as indicated by an increase in the F1-score value from 0.69 to 0.72. Therefore, the optimization of Random Forest proved to be good in enhancing the performance and stability of multiclass classification of student academic performance.