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All Journal TEKNIK INFORMATIKA Jurnal Sains dan Teknologi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat JITK (Jurnal Ilmu Pengetahuan dan Komputer) Martabe : Jurnal Pengabdian Kepada Masyarakat KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) JURIKOM (Jurnal Riset Komputer) JURTEKSI Building of Informatics, Technology and Science Jurnal Teknologi Dan Sistem Informasi Bisnis JATI (Jurnal Mahasiswa Teknik Informatika) Computer Science and Information Technologies TIN: TERAPAN INFORMATIKA NUSANTARA Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Journal of Applied Data Sciences BEES: Bulletin of Electrical and Electronics Engineering JOMLAI: Journal of Machine Learning and Artificial Intelligence Journal of Informatics, Electrical and Electronics Engineering Dike Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Hartama, Dedy
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OPTIMIZATION OF EFFICIENTNET-B0 ARCHITECTURE TO IMPROVE THE ACCURACY OF GLAUCOMA DISEASE CLASSIFICATION Akbari, Imam; Hartama, Dedy; Wanto, Anjar
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7140

Abstract

Glaucoma is a chronic eye disease that can potentially cause permanent blindness if not detected early. This study aims to improve the generalization capability and reliability of glaucoma classification by optimizing the EfficientNetB0 architecture based on a Convolutional Neural Network (CNN). Optimization was carried out by applying double dropout (0.4 and 0.3) and adding a Dense layer with 128 ReLU-activated neurons to reduce overfitting and strengthen non-linear feature representation. The dataset used consists of 1,450 fundus images (899 glaucoma and 551 normal) obtained from IEEE DataPort. Model performance evaluation was performed using accuracy, precision, recall (sensitivity), specificity, F1 score, and Area Under the Curve (AUC) metrics, complemented by confusion matrix analysis to assess overall classification performance. The results showed that the optimized EfficientNetB0 model consistently outperformed the baseline comparison model with the highest accuracy, precision, recall (sensitivity), specificity, F1 score, and AUC values ​​of 95%. Based on the system performance results obtained, the Proposed model can be used as an aid for medical personnel in classifying glaucoma conditions so that they can provide appropriate medical treatment and reduce the risk of permanent blindness due to glaucoma.
Algoritma K-Means Menggunakan Metode Elbow Untuk Mengelompokkan Kinerja Performance Index Berdasarkan Dataset Absensi Pegawai Sabila, Bunga; Sari, Juwita Permata; Kholis, Mu’Ammar Nur; Harefa, Darman Sahputra; Hartama, Dedy
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2119

Abstract

Attendance is quantitative data that reflects the level of employee discipline and has the potential to be used as a basis for performance evaluation. However, in many agencies, attendance data has not been optimally utilized in making decisions related to employee management. This study aims to classify employee performance based on attendance data using the K-Means Clustering algorithm with the Elbow Method approach. The data analyzed consisted of 57 employees with attributes of the number of tardiness, absence, and blank absences. The research process starts from data collection, pre-processing, to clustering process. Determination of the optimal number of clusters is done with the Elbow Method using Python, which shows the best K value is four. Clustering was done using RapidMiner software, by applying a visual-based workflow to group employees based on similarity in attendance patterns. The final results show the formation of four clusters with different Key Performance Index (KPI) characteristics: excellent, good, fair, and poor. These clusters reflect the level of employee discipline that can be used as a basis for objective managerial evaluation and decision-making. This research shows that the application of data mining-based clustering methods can be a tool in analyzing employee performance in an organizational environment.
Co-Authors , Rahmad Sembiring Abdillah, Mhd Khoiri Abdullah Ahmad Achmad Noerkhaerin Putra Agus Perdana Windarto Akbari, Imam Amalya, Nanda Ayunda, Yuli Septya Azhari, Ridhan B. Tambunan, Holpan Torang Damanik, Abdi Rahim Damanik, Bahrudi Efendi Dewi, Rafiqa Eko Purwanto Fitri Anggraini, Fitri Hamdani Hamdani Harefa, Darman Sahputra Herman Mawengkang Indra Gunawan Irfan Sudahri Damanik Jaya Tata Hardinata Kholis, Mu’Ammar Nur Lili Tanti MARIA BINTANG Mochamad Wahyudi Mochammad Imron Awalludin Muhammad Raqib Syahkur Muhammad Ridwan Lubis Muhammad Zarlis, Muhammad Nasution, Della Fatricia Nasution, Rizki Alfadillah Oktaviani, Selli P, Dini Rizky Sitorus Pangestu, Prayoga Parlina, Iin Poningsih Poningsih, Poningsih Poningsih, P Putra, Jaka Putri, Adelia Rafai, Muhammad Riski, Ginanti Sabila, Bunga Safii Safii Safii, M. Saragih, Irfan Christian Sari, Hanifah Urbach Sari, Juwita Permata Siahaan, Nove Viktor Boyke Solikhun Solikhun, Solikhun Suhada, S Suhendro, Dedi Sumarno . Syaputri, Vera Trianda, Dimas Wanayumini Wanto, Anjar Widodo Saputra Widyasuti, Meilin Winanjaya, Riki