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All Journal Populer: Jurnal Penelitian Mahasiswa Technema: Journal of Intelligent Engineering and Computing
Sabila Wahyu Pratama
Universitas Islam Sultan Agung
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

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Development of a Web-Based Campus Facility Complaint Information System Prototype Using Rapid Application Development Muh. Akhyarul Insan; Fajar Octanouva; Nur Vitriati; Sabila Wahyu Pratama; Aditya Sendy Ardiansyah
Technema: Journal of Intelligent Engineering and Computing Vol. 1 No. 1 (2026): March: Technema: Journal of Intelligent Engineering and Computing
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

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Abstract

Campus facility complaint management requires a structured mechanism for recording, monitoring, and tracing reported problems to support transparent service processes. This study developed a web-based Campus Facility Complaint Information System (SIPFAS) prototype using the Rapid Application Development (RAD) method. The development process comprised requirement planning, user design, construction, and cutover and verification stages, with the prototype designed to support service information access, authentication, complaint submission, status monitoring, and complaint-detail retrieval. Evaluation focused on the public page, login interface, user dashboard, and complaint-detail page containing the status history of complaint PGD-002. Interface consistency verification showed that the dashboard summary was arithmetically consistent, with 3 complaints in Waiting status, 4 in In Process status, and 5 in Completed status, resulting in a total of 12 complaints. The PGD-002 identity was also traceable across the dashboard and complaint-detail interfaces. However, two inconsistencies were identified, namely a discrepancy between the complaint date of 19 August 2026 and the timeline submission timestamp of 20 August 2026 at 09:14, and a mismatch between the administrator-verification timestamp and its visual state. The prototype establishes a structured complaint-monitoring workflow, although data consistency, functional testing, authorization, backend persistence, and usability evaluation require further verification before full implementation.
Implementasi Prediksi Stunting Menggunakan Algoritma Xgboost dan Smote Berdasarkan Data BKKBN Sabila Wahyu Pratama; Ghufron Ghufron
Populer: Jurnal Penelitian Mahasiswa Vol. 5 No. 3 (2026): September : Populer: Jurnal Penelitian Mahasiswa
Publisher : Universitas Maritim AMNI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58192/populer.v5i3.5468

Abstract

The management and intervention of stunting cases in toddlers still face major challenges due to the high class imbalance in risk assessment data, where the population of normal toddlers significantly dominates the at-risk class. This condition often causes machine learning classification models to suffer from prediction bias and reduced accuracy. This study aims to build a Machine Learning classification model based on the XGBoost algorithm combined with the Synthetic Minority Over-sampling Technique (SMOTE) to predict stunting risk and analyze its primary key determinants (Feature Importance) using Stunting Risk Family (KRS) data from BKKBN Central Java. The dataset comprises 7,605 family data records with evaluation performed on 2,771 pure test data points. The evaluation results demonstrate that the combination of XGBoost and SMOTE achieved an Accuracy of 88.20% and a Precision of 99.91%. Furthermore, Gain analysis confirms that Gender (score 0.3232) and Sanitation/Latrine Facilities (score 0.2182) are the two most dominant risk factors for stunting. These findings demonstrate the potential of leveraging artificial intelligence systems to support efficient decision-making for healthcare workers within a smart governance framework.
Co-Authors Aditya Sendy Ardiansyah Fajar Octanouva Ghufron Ghufron Muh. Akhyarul Insan Nur Vitriati