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All Journal Jurnal Informasi Sains dan Teknologi (ISAINTEK) Nuansa Informatika Journal of Embedded Systems, Security and Intelligent Systems Proceeding of Community Service and Engagement
Utami Kusuma Dewi
Universitas Telkom
Computer Science & IT Other

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Desain, Implementasi Dan Evaluasi Sistem Informasi Penggajian Di Universitas Berbasis Web Villy Satria Praditha; Ario Harry Prayogo; Anranur Uwaisy Marchiningrum; Feddy Dea Reskyadita; Nurjayanti Nurjayanti; Utami Kusuma Dewi; Ryan Lingga Wicaksono
Jurnal Informasi, Sains dan Teknologi Vol. 7 No. 1 (2024): Juni: Jurnal Informasi Sains dan Teknologi
Publisher : Politeknik Negeri FakFak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/isaintek.v7i1.183

Abstract

The university's payroll process includes information about various employees, including lecturers and administrative employees with varying details such as personal data, position and salary information. This research is focused on rebuilding the payroll application due to several shortcomings or challenges associated with the existing application. This research presents a detailed investigation into the design, implementation, and evaluation of a payroll application tailored for the university environment. This research covers main aspects such as use case analysis, relational database schema, application display and testing. Use case analysis begins with identifying and understanding the various scenarios and requirements specific to a university payroll system. Through thorough exploration of user roles, system functionality. Relational database schemas are designed to efficiently store and manage complex payroll data, ensuring accurate and efficient processing. Compatibility testing plays an important role in ensuring the integration of payroll applications with existing systems. This research performs black-box testing to test application functionality and application compatibility testing with various hardware configurations, operating systems, and web browsers to ensure optimal performance and accessibility. The results of this research contribute to the advancement of payroll management and provide guidance for the development of deep payroll applications in universities.
Performance and Efficiency Testing Analysis of Database Systems in Academic Information Systems: Analisis Pengujian Kinerja dan Efisiensi Sistem Basis Data dalam Sistem Informasi Akademik Utami Kusuma Dewi; Ryan Lingga Wicaksono; Mahendra Dwifebri Purbolaksono; Villy Satria
NUANSA INFORMATIKA Vol. 19 No. 2 (2025): Nuansa Informatika 19.2 Juli 2025
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v19i2.438

Abstract

This study examines the performance and efficiency of database systems within academic information systems, acknowledging the increasing demand for responsiveness and reliability in managing complex academic data. As educational institutions increasingly rely on digital systems, performance testing becomes essential to ensure that these systems continue to support the learning environment effectively. Guided by the ISO/IEC 25010 standard, the research focuses on evaluating three key aspects of performance efficiency: time behavior, resource utilization, and capacity. Using JMeter, a range of user load scenarios were simulated, and the results were examined through Control Quality Charts and Nelson Rules to detect underlying issues affecting system performance. The findings reveal that 82.5% of queries demonstrated good time behavior, and 80% performed well in resource usage. However, half of the tests related to capacity highlighted the need for further improvements. Some queries experienced delays and consumed excessive CPU and memory resources, indicating areas where optimization is required. These insights highlight the importance of refining queries and managing resources more effectively to ensure a seamless user experience. Future research should consider automated optimization, machine learning-based performance prediction, and system scalability, especially in more dynamic and distributed academic environments.
Pengenalan AI untuk Pengembangan Pembelajaran Villy Satria Praditha; Ryan Lingga Wicaksono; Utami Kusuma Dewi; Resha Akbar; Elvira Lailatuth Thohiroh
The Proceeding of Community Service and Engagement (COSECANT) Seminar Vol. 5 No. 2 (2025): Prosiding COSECANT : Community Service and Engagement Seminar
Publisher : Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/cosecant.v5i2.10274

Abstract

Kegiatan pengabdian masyarakat ini bertujuan untuk memperkenalkan konsep dasar artificial intelligence (AI) kepada guru dan siswa di SMP PGRI 7 Bandung, serta meningkatkan literasi digital melalui pemanfaatan tools AI dalam proses pembelajaran. Kegiatan ini dilakukan dalam bentuk pelatihan yang mencakup pengenalan berbagai tools AI seperti chatbot, content generator, dan image generator, yang dapat membantu dalam merangkum materi, menyusun latihan soal, serta membuat media pembelajaran. Metodologi yang digunakan terdiri dari identifikasi kebutuhan, pengembangan modul pelatihan, sosialisasi dan pelatihan intensif, pendampingan implementasi, serta evaluasi dan monitoring. Hasil kegiatan menunjukkan tingkat kepuasan yang tinggi di kalangan peserta, dengan mayoritas responden menyatakan bahwa materi yang disampaikan relevan dan mudah dipahami, serta waktu pelaksanaan kegiatan sesuai dengan jadwal yang telah ditentukan. Pelatihan ini juga memperoleh apresiasi positif dari peserta, yang mengungkapkan harapan agar kegiatan serupa dapat dilanjutkan di masa mendatang. Melalui kegiatan ini, SMP PGRI 7 Bandung diharapkan dapat meningkatkan kemampuan guru dan siswa dalam memanfaatkan teknologi digital, khususnya AI, untuk mendukung pembelajaran yang lebih efektif dan adaptif di era digital
A Comparative Study of Decision Tree and Gradient Boosting Tree Algorithms for Predicting College Enrollment Decisions of High School Students Rezty Amalia Aras; Utami Kusuma Dewi; Yabes Dwi Nugroho
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 1 (2026): March 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i1.10611

Abstract

The declining interest of high school students in pursuing higher education has become a major concern in Indonesia's education sector. This study aims to develop a data-driven predictive model to assist schools in identifying students’ decisions regarding further education. The study compares two popular classification algorithms, Decision Tree and Gradient Boosted Tree, using a dataset of 300 high school students comprising 10 attributes such as school accreditation, parental income, interest level, and residential status. The research method involves data preprocessing, model training, and performance evaluation using a confusion matrix to measure accuracy, precision, and recall. The results show that the Decision Tree algorithm achieved an accuracy of 76.67%, with a precision of 78.57% and a recall of 73.33% for the "college" class. Meanwhile, the Gradient Boosted Tree produced an accuracy of 73.33%, with a strength in recall for the "not attending college" class at 80%, but was less optimal in detecting students who pursued higher education. It can be concluded that the Decision Tree outperforms in terms of accuracy and interpretability, making it more suitable for use in school environments as a decision-support tool for early intervention, scholarship programs, and career counseling.
Co-Authors Anranur Uwaisy Marchiningrum Aras, Rezty Amalia Ario Harry Prayogo Feddy Dea Reskyadita Lailatuth Thohiroh, Elvira Mahendra Dwifebri Purbolaksono Mega Fatimah Rosana Resha Akbar Ryan Lingga Wicaksono Villy Satria Villy Satria Praditha Yabes Dwi Nugroho