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All Journal JISKa (Jurnal Informatika Sunan Kalijaga) ILKOMNIKA: Journal of Computer Science and Applied Informatics Tematik : Jurnal Teknologi Informasi Komunikasi
Suhartono Suhartono
Universitas Islam Negeri Maulana Malik Ibrahim
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Library & Information Science

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Pencarian Model Proses dalam Workflow Repository Berbasis Graph Database Menggunakan BPMN-Q Galang Luhur Pekerti; Muhammad Ainul Yaqin; Suhartono Suhartono
ILKOMNIKA: Journal of Computer Science and Applied Informatics Vol 3 No 1 (2021): Volume 3, Nomor 1, April 2021
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v3i1.209

Abstract

Workflow repositroy menyimpan banyak workflow. Workflow yang disimpan akan dicari sesuai kebutuhan. Sebagian besar pencarian alur kerja berbasis teks. Penelitian ini menggunakan BPMN-Q untuk mencari BPMN pada workflow repository berbasis database grafik (NEO4J). BPMN yang disimpan dalam bentuk file XPDL agar dapat diparsing. Selanjutnya XPDL akan diparsing untuk disimpan dalam graph database menggunakan neo4j sebagai DBMS. Kemudian BPMN-Q dibuat menggunakan microsoft visio 2007. Lalu akan diparsing untuk diterjemahkan ke bahasa cypher. Selanjutnya cypher memproses dalam database NEO4J. Dan menghasilkan BPMN yang sesuai dalam bentuk daftar. BPMN dan BPMN-Q yang diekstrak harus disimpan dalam bentuk xpdl. Penelitian ini memberikan skema database yang sesuai untuk penyimpanan workflow dalam bentuk grafik. Arsitektur ini juga menyimpan definisi alur kerja, seperti judul, penulis, dan lainnya. Performa penggunaan BPMN-Q pada workflow repository berbasis graph database adalah cukup baik dengan nilai Precision dari pengujian adalah 82.74%.
Pengaruh Orientasi Citra MRI pada Klasifikasi Tumor Otak Berbasis GLCM dan SVM Yoza Setya Febriyanti; Okta Qomaruddin Aziz; Suhartono Suhartono
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 2 (2026): May 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.5935

Abstract

Brain tumors are a global health problem, ranking 12th as a cause of death. MRI is used in the diagnosis of brain tumors because of its ability to display soft tissue structures in detail, but manual interpretation of MRI images by radiologists is still subjective. Therefore, a more objective computer-based classification approach is needed. One factor that could potentially affect classification performance is the difference in MRI image orientation, namely axial, sagittal, and coronal. This study aims to analyze the effect of MRI image orientation on GLCM and SVM-based brain tumor classification. The preprocessing stage includes cropping, noise reduction, and resizing. Feature extraction was performed using GLCM with distance d = 1 at angles of 0°, 45°, 90°, and 135° with contrast, correlation, energy, and homogeneity features. Classification was performed using SVM with Linear, Polynomial, RBF, and Sigmoid kernels. The test results show that the axial orientation produces the highest accuracy of 78% with the Linear kernel, the sagittal orientation achieves an accuracy of 83% with the Polynomial kernel, and the coronal orientation provides the highest accuracy of 86% with the RBF kernel. These findings indicate that the orientation of MRI images affects the performance of texture-based brain tumor classification.
Essay Score Prediction Based on Combined Question and Answer Data Using FastText and LSTM Algorithm Sefti Agustini; M. Ainul Yaqin; Suhartono Suhartono
TEMATIK Vol. 13 No. 1 (2026): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2026
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v13i1.2974

Abstract

Automated Essay Scoring is one of the challenges in the field of educational technology, particularly in subjects requiring language assessment, such as the English language. Manual assessment performed by teachers is time-consuming, subjective, and has the potential for inconsistency between assessors, which can give rise to unfairness in scoring. In order to overcome this issue, this study proposes an Automated Essay Scoring (AES) approach by combining FastText word embeddings and the Long Short-Term Memory (LSTM) algorithm to predict the scores of student essays. The innovation of this research lies in combining question and answer data into a single input variable, where the student's answer is positioned between the special tokens <startanswer> (prefix) and <endanswer> (suffix), allowing the model to learn the context of the question and answer simultaneously. The data 500 data points (100 students x 5 essay questions). The LSTM model was then trained with a combination of hyperparameters, namely the number of units in the hidden layer, learning rate, batch size, and dropout rate. Model performance was measured using Mean Absolute Percentage Error. Based on the experimental results, the Bi-LSTM model with the best hyperparameter settings achieved a MAPE value of 7.84%, which is better than the unidirectional LSTM model at 9.12%. This study has proven that the combination of FastText and Bi-LSTM is an effective approach for essay score prediction in the context of language learning for junior high school students
Co-Authors Galang Luhur Pekerti Ida Ayu Putu Sri Widnyani M. Ainul Yaqin Okta Qomaruddin Aziz Sefti Agustini Yoza Setya Febriyanti