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All Journal Journal of Education and Learning (EduLearn) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Tekinkom (Teknik Informasi dan Komputer)
Deni, Rahmad
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Humanities Computer Science & IT Education Library & Information Science Social Sciences Other

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Evaluation of school facility management: the case of a high school context in Indonesia Fitriani, Somariah; Sari, Yessy Yanita; Deni, Rahmad
Journal of Education and Learning (EduLearn) Vol 19, No 3: August 2025
Publisher : Intelektual Pustaka Media Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/edulearn.v19i3.21891

Abstract

This study employed the context, input, process, and product (CIPP) model to evaluate the facilities management of a private high school in Jakarta, Indonesia. Participants included teachers, staff, parents, students, and vice principals. Data collection methods encompassed interviews and checklist observations, with participant triangulation used for data validation and verification. Findings indicated a moderate alignment between the context and the objectives of facilities management. While the input, processes, and outputs somewhat addressed stakeholders' educational needs, the school principal effectively facilitated the teaching and learning environment through her roles as a planner, implementer, and supervisor of facilities management. Nevertheless, the school encounters several challenges, such as adapting to the digital era, securing funding, competition, the necessity for a qualified facilities manager, and the need for repairs in several facilities. By identifying the strengths and challenges in the current management practices, including the role of the principal and the impact of digital transformation, the study provides valuable insights for improving facility management. The study recommended the development of a digital facility management system to enhance accessibility for both educators and students.
Optimalisasi Pengelompokan Gangguan Kecemasan dalam Mendukung Tujuan Pembangunan Berkelanjutan Menggunakan Algoritma K-Means dan K-Medoids Aulia, Rahma; Julianti, Nadea; Putri, Siti Faradila; Efrizoni, Lusiana; Deni, Rahmad
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 12 No 2 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i2.11495

Abstract

Abstract Data clustering is a data mining technique that aims to find hidden patterns in a dataset. The dataset used in this study was taken from the Kaggle public dataset on anxiety attacks. Anxiety disorder is a mental condition characterized by excessive and prolonged feelings of anxiety. Clustering anxiety disorders facilitates finding the cause, effect, and better treatment. Therefore, this study aims to group anxiety disorders using the K-Means and K-Medoids algorithms by considering attributes such as stress level, sleep patterns, and physical activity. The performance of the model is evaluated using the Davies-Bouldin Index (DBI). The results showed that the K-Means algorithm produced the lowest DBI value in cluster ten with an accuracy value of 2.331. This shows that the K-Means algorithm is able to identify significant patterns in anxiety disorder data. This study can be a recommendation for health professionals in making more precise diagnoses, understanding the characteristics of the causes of anxiety disorders. In addition, this study also supports the achievement of the Sustainable Development Goals in an effort to improve the overall health and welfare of the community. Keywords— K-Means, K-Medoids, Anxiety Disorders, Sustainable Development Go als
ANALISIS PERBANDINGAN ALGORITMA C4.5 DAN NAIVE BAYES UNTUK MEMPREDIKSI KETERCAPAIAN TARGET PO DALAM MEMBANGUN PROJECT FTTH (FIBER TO THE HOME) Pratama, Ahmad Tara; Deni, Rahmad; Agustin, Agustin; Asnal, Hadi
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2309

Abstract

In the digital era, the demand for high-speed and stable internet has become essential to support communication and information access. Fiber to the Home (FTTH) is one of the main solutions implemented by internet service providers such as MyRepublic. A critical component in FTTH network development is the issuance of Purchase Orders (PO) to vendors, which directly impacts the achievement of sales targets. This study aims to compare the performance of the C4.5 and Naïve Bayes classification algorithms in predicting PO target achievement to assist project planning and decision-making. The research uses historical data from FTTH projects and applies data partitioning scenarios of 70:30, 80:20, and 90:10 for model training and testing. Evaluation was conducted using accuracy as the main performance metric. The results show that the Naïve Bayes algorithm achieved the highest accuracy of 85.64% with a 70:30 data split, while C4.5 obtained 83.54% accuracy with a 90:10 data split. Based on these findings, the Naïve Bayes algorithm is considered more effective and consistent in predicting PO target achievement and is recommended for implementation in similar project scenarios.
Co-Authors Agustin Agustin Aulia, Rahma Efrizoni, Lusiana Hadi Asnal, Hadi Julianti, Nadea Pratama, Ahmad Tara Putri, Siti Faradila SARI, Yessy Yanita Somariah Fitriani