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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Data and Science 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 Mathematics

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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.
A Comperative Study on Efficacy of CNN VGG-16, DenseNet121, ResNet50V2, And EfficientNetB0 in Toraja Carving Classification Herman; An'nisa Pratama Putri; Megat Norulazmi Megat Mohamed Noor; Herdianti Darwis; Lilis Nur Hayati; Irawati; Ihwana As’ad
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.220

Abstract

Introduction: Passura', or Toraja carvings, are an essential element of the cultural heritage of the Toraja people in Indonesia. These carvings feature complex motifs rooted in nature, folklore, and spiritual symbolism. This study aims to evaluate the efficacy of four Convolutional Neural Network (CNN) architectures—VGG-16, DenseNet121, ResNet50V2, and EfficientNetB0—in classifying seven traditional Toraja carving motifs. Methods: A dataset of 700 images was collected and categorized into seven motif classes. The dataset was split into 80% for training and 20% for validation. Each CNN model was trained for 25 epochs with standard pre-processing, including resizing to 224×224 and normalization. Performance evaluation was conducted based on validation accuracy and confusion matrix analysis to assess classification precision and model overfitting. Results: EfficientNetB0 achieved the highest validation accuracy of 98%, although signs of overfitting were observed. ResNet50V2 followed closely with a validation accuracy of 95.33% and demonstrated the most balanced classification results across all motif categories. VGG-16 and DenseNet121 achieved 94.67% and 81.82%, respectively. Confusion matrix analysis confirmed the robustness of ResNet50V2 in correctly identifying complex patterns. Conclusions: The findings indicate that ResNet50V2 provides a reliable balance between accuracy and generalizability for classifying Toraja carvings, making it suitable for digital preservation of cultural heritage. EfficientNetB0, while achieving higher accuracy, may require additional regularization to avoid overfitting. This study contributes to the development of AI-driven cultural documentation and suggests future research with larger and more diverse datasets to improve model robustness
Co-Authors An'nisa Pratama Putri Haidawati Nasir Herdianti Ihwana As’ad Irawati Irawati lilis nurhayati Megat Norulazmi Megat Mohamed Noor Muhammad Dzul Asmi Alhamdi Muhammand Akbar Nur Asy Syams Sam Ahmad Siska Anraeni Siti Safira Tawetubun Sugiarti, Sugiarti Wistiani Astuti