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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SENSITEK JTIK (Jurnal Teknik Informatika Kaputama) U-NET Jurnal Teknik Informatika Prioritas : Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Pada Masyarakat METHABDI Majalah Ilmiah METHODA TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Jurnal Multidisipliner Kapalamada Jurnal Pemberdayaan Kepada Masyarakat
Nita Syahputri
Universitas Potensi Utama
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Physics Public Health Social Sciences Other

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ANALYSIS OF TEACHER PERFORMANCE ASSESSMENT USING THE 360 DEGREE FEEDBACK METHOD IN VOCATIONAL HIGH SCHOOLS Elida Tuti Siregar; Nita Syahputri; Nurhayati Nurhayati; Erwin Ginting
Jurnal Multidisipliner Kapalamada Vol. 4 No. 04 (2025): JURNAL MULTIDISIPLINER KAPALAMADA
Publisher : Pusat Studi Ekonomi, Publikasi Ilmiah dan Pengembangan SDM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62668/kapalamada.v4i04.1872

Abstract

Teacher performance assessments in vocational education units need to be designed objectively, comprehensively, and involve various sources of assessors so that the portrait of teacher work quality can be mapped more reliably. This study aims to analyze the application of the 360-degree feedback method in the performance assessment process of productive TKJ and RPL teachers at SMK PAB 8 Sampali. The study used a mixed method approach with data collection through Likert-scale questionnaires, interviews, and learning document reviews. The research subjects were 20 productive teachers. Data analysis was carried out by converting quantitative scores and strengthening qualitative narratives through source triangulation. The results showed that the application of 360-degree feedback provided a more balanced assessment picture through the involvement of the principal, colleagues, students, and teacher self-evaluation. These findings reinforce the urgency of using multi-source-based assessments to improve the quality of the learning process in competency-based vocational schools
Analysis of the Impact of Backpropagation Hyperparameter Optimization on Heart Disease Prediction Models Nita Syahputri; Putrama Alkhairi; Enok Tuti Alawiah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6473

Abstract

Heart disease is a major global health issue, highlighting the need for early and accurate prediction to reduce complications and improve patient outcomes. The Backpropagation Neural Network (BPNN) is a widely used method for heart disease prediction, but its performance relies heavily on proper hyperparameter selection, including neuron count, activation function, optimizer, and batch size. This study analyzed the impact of hyperparameter optimization on BPNN performance. A standard BPNN model was compared with an optimized version, where key hyperparameters were fine-tuned to enhance predictive accuracy and stability. Both models were trained and tested on the same dataset, and their performance was evaluated using Accuracy, Precision, Recall, Mean Squared Error (MSE), and Mean Absolute Error (MAE). The results show that the optimized model achieves a slightly better accuracy (99.11% vs. 99.09%) and lower error rates (MSE and MAE of 0.0089 vs. 0.0091). It also demonstrates higher precision, reflecting an improved capability in correctly identifying heart disease cases. Although the performance gap was small, the optimized model showed a more balanced and consistent outcome. These findings highlight the importance of hyperparameter tuning for improving neural network models for medical prediction. This study contributes to the development of more accurate and reliable AI tools for the early diagnosis of heart disease. Future studies may apply advanced optimization techniques, such as Bayesian Optimization or Genetic Algorithms, and use larger and more diverse datasets to enhance model generalization.
Co-Authors Ananda Pratama Anggia Nikita Hutabarat Ceriawati Gulo Dedek Indra Gunawan Hutasuhut Efani Desi Elida Tuti Siregar Enok Tuti Alawiah Erwin Ginting Jamaluddin Khairani Puspita M. Haidil Umam M. Safii Muhammad Taufiq Rustam Nurhayati Nurhayati Ommi Alfina Putrama Alkhairi Siti Aliyah Siti Fatimah Sihotang Ulfah Indriani Wirhan Fahrozi Wiwik Malina Nst