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All Journal Jurnal Penelitian Pos dan Informatika InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Dinamisia: Jurnal Pengabdian Kepada Masyarakat International Journal of New Media Technology JURTEKSI KOMPUTIKA - Jurnal Sistem Komputer KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Jurnal Teknologi dan Informasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Tata Kelola dan Kerangka Kerja Teknologi Informasi Al-MIKRAJ: Jurnal studi Islam dan Humaniora
Diana Effendi
Universitas Komputer Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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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.
Pengembangan Model Tata Kelola Explainable AI pada Sistem Peringatan Dini Stunting : Pendekatan Socio-Technical dan Technology Acceptance Diana Effendi; Sri Nurhayati; Agus Nursikuwagus; Yeffry Handoko Putra; Rio Yunanto
Jurnal Tata Kelola dan Kerangka Kerja Teknologi Informasi Vol. 12 No. 2 (2026): Agustus 2026
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/jtk3ti.v12i2.19959

Abstract

This study develops a governance model for Explainable Artificial Intelligence (XAI) in a stunting early warning system by integrating a socio-technical approach and the Technology Acceptance Model (TAM). The main issues examined are the low transparency of AI systems, the lack of structure in health AI governance, and the need to build user trust before predictive systems are used in public health services. The research method employed a mixed-methods approach, consisting of a quantitative survey of 100 respondents and semi-structured interviews with 10 informants from the Health Department, Community Health Centers (Puskesmas), the Communication and Information Department (Diskominfo), midwives, and Posyandu cadres. Quantitative data were analyzed using multiple linear regression, while qualitative data were used to strengthen the socio-technical interpretation. The results indicate that XAI and governance have a positive influence on trust. Furthermore, trust and perceived usefulness have a positive influence on behavioral intention. The resulting model identifies transparency, accountability, security, compliance, periodic validation, audits, and feedback as governance mechanisms that link the technical quality of AI with user acceptance. The contribution of this research is a conceptual model of XAI governance that can serve as the basis for developing a transparent, accountable, and user-accepted early warning system for stunting. Keywords – Behavioral Intention; Explainable AI; Socio-Technical; Stunting; Governance.  
Symantic Literatur Review : Artificial Intelligence dalam Telemedicine dan Remote Patient Monitoring Diana Effendi; Sri Nurhayati; Zainal Arifin Hasibuan; Bobi Kurniawan S.; Sri Supatmi
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 15 No 1 (2026): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v15i1.18702

Abstract

The development of telemedicine and Remote Patient Monitoring (RPM) is increasing along with the need for efficient, adaptive, and data-driven remote healthcare services. Artificial Intelligence (AI) plays a crucial role in strengthening these systems through predictive analysis, medical classification, and real-time patient monitoring. However, research on AI integration in telemedicine and RPM remains scattered and exhibits wide methodological variation, necessitating a systematic review to understand the consistency of findings and the direction of research development. This study conducted a Systematic Literature Review (SLR) following the PRISMA 2020 protocol, analyzing 128 publications from 2020–2025 obtained from Scopus, PubMed, IEEE Xplore, and Google Scholar. This study combined SLR synthesis with bibliometric mapping (co-occurrence and thematic mapping) to highlight the evolution of themes and topical interrelationships more explicitly. Bibliometric analysis results show an increase in the number of publications from 12 articles in 2020 to 45 articles in 2024, a nearly fourfold increase, before stabilizing in 2025. Co-occurrence and thematic mapping findings reveal four main themes: telemedicine–AI, computational methods based on machine learning and deep learning, physiological monitoring, and human factors in clinical evaluation. The study also identifies several challenges, including data security, signal quality, model transparency, and healthcare worker readiness. Theoretically, the findings emphasize that AI integration in telemedicine–RPM needs to be understood as a socio-technical issue that demands human-centered evaluation. Policy-wise, strengthening data governance and clinical validation standards is necessary for more accountable and secure implementation. This study concludes that AI plays a central role in the development of telemedicine and RPM, but further studies are needed on service personalization, multimodal data integration, and large-scale clinical validation.
Co-Authors Adam Mukharil Bachtiar Agus Nursikuwagus Andrias Darmayadi Bella Hardiyana Berry Juanda Prawira Bobi Kurniawan S. Bobi Kurniawan Soegoto Ednawati Rainarli I Made Wiratama Maulana, Hanhan Mia Fitriawati Muhammad Yasir Mumtaz2 noviansyah, beri Noviansyah, Berri Rani Puspita Dhaniawaty Rio Yunanto Sri Ela Dinasti Sri Nurhayati Sri Nurhayati Sri Supatmi Usep Mohamad Ishaq Vany Varha Vauzia Yeffry Handoko Putra Zainal Arifin Hasibuan