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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Algoritma Prosiding SeNTIK STI&K
Herman
Universitas Muslim Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media

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Peningkatan Kualitas Citra Iris Mata Menggunakan Operasi Piksel Dan Ekualisasi Histogram Untuk Pengklasifikasian Kondisi Kesehatan Ginjal Siska Anraeni; Herman
Prosiding Seminar SeNTIK Vol. 2 No. 1 (2018): Prosiding SeNTIK 2018
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

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Abstract

Pengolahan citra pada pemrosesan awal (pre-processing) penelitian sebelumnya belum dilakukan secara lebih dalam dan mendetail. Sehingga penelitian ini bertujuan untuk melakukan perbaikan kualitas citra iris mata menggunakan operasi piksel dan ekualisasi histogram terhadap proses pengklasifikasian ginjal ke dalam kondisi normal atau tidak normal. Metode operasi piksel yang digunakan yaitu peningkatan kecerahan (brightness), perenggangan kontras (contrast), kombinasi kecerahan dan kontras serta ekualisasi histogram. Hasil dari penelitian ini yaitu aplikasi dapat: 1) Memperbaiki kualitas citra menggunakan peningkatan kecerahan sebesar 50 piksel, perenggangan kontras sebesar 2,5 kali piksel, kombinasi kecerahan dan kontras sebesar 50 piksel dan 1,5 kali piksel dan ekualisasi histogram; 2) Melakukan pengklasifikasian citra iris mata yang menunjukkan ginjal normal dan tidak normal berdasarkan hasil perbaikan kualitas citra terhadap 10 citra latih dan 10 citra uji dengan tingkat akurasi sebesar 70%
Penerapan Decision Support System (DSS) menggunakan Metode TOPSIS untuk Seleksi Mahasiswa Berprestasi Irawati; Sugiarti; Lilis Nur Hayati; Herman; Siti Safira Tawetubun; Nur Asy Syams Sam Ahmad
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3200

Abstract

The diversity of students in Indonesian universities requires an objective and transparent selection mechanism for high-achieving students, while manual selection practices remain prone to subjectivity and inconsistency in assessment. This study developed a Decision Support System (DSS) based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with a methodological innovation in the form of integrating eight multidimensional criteria that combine academic and non-academic aspects into a single structured decision-making framework. The implementation results show that the system is capable of increasing the consistency of selection decisions by up to 87% and reducing the selection process time by around 60% compared to conventional methods. These findings confirm that the TOPSIS-based DSS not only improves the objectivity of assessments but also provides significant operational efficiency, thus having the potential to become an adaptive and applicable decision support model for standardizing the selection of outstanding students in higher education.
Comparative Performance of ResNet Architectures for Toraja Carving Image Classification with Data Augmentation Herman; Muhammand Akbar; Haidawati Nasir; Herdianti; Huzain Azis; Lilis Nur Hayati
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.v9i4.6181

Abstract

The complexity of the motifs and large number of different patterns make the classification of Toraja carvings challenging. The objective of this study is to develop a Convolutional Neural Network automatic classification model using a comparative analysis of the performance of three ResNet architectures. Data augmentation techniques were used to enrich the diversity of the training samples and improve the robustness of the model. The experimental results showed that ResNet101V2 had the highest validation accuracy, which was greater than 97%, followed by ResNet50V2 with more than 96%, and finally, ResNet152V2 with more than 94.74%. These test results indicate that the ResNet101V2 architecture has a better classification performance for complex motifs, with a good balance between precision and recall. However, the confusion matrix and per-class performance metrics indicated that motifs with high similarity, such as Paqdon-Bolu and Paqtedong, remained challenging. This study demonstrated that deeper CNN architectures and data augmentation techniques are effective in improving the classification accuracy of complex carving patterns. Further research should explore hybrid or advanced augmentation methods to improve the overall robustness and accuracy of the model.
Co-Authors Haidawati Nasir Herdianti Irawati Irawati lilis nurhayati Muhammad Dzul Asmi Alhamdi Muhammand Akbar Nur Asy Syams Sam Ahmad Siska Anraeni Siti Safira Tawetubun Sugiarti, Sugiarti Wistiani Astuti