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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Teknologi Informasi dan Ilmu Komputer Journal of ICT Research and Applications Seminar Nasional Informatika (SEMNASIF) Jurnal Teknologi dan Sistem Komputer Knowledge Engineering and Data Science JIKO (Jurnal Informatika dan Komputer) Jurnal TAM (Technology Acceptance Model) JURNAL INSTEK (Informatika Sains dan Teknologi) ILKOM Jurnal Ilmiah IJID (International Journal on Informatics for Development) JURIKOM (Jurnal Riset Komputer) ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal E-Komtek JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Applied Data Sciences Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Indonesia : Manajemen Informatika dan Komunikasi Journal of Informatics and Communication Technology (JICT) Konstelasi: Konvergensi Teknologi dan Sistem Informasi Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Scientific Research, Education, and Technology SmartComp Journal of Technology Informatics and Engineering Jurnal Indonesia : Manajemen Informatika dan Komunikasi The Indonesian Journal of Computer Science Journal of Informatics and Communication Technology (JICT) Jurnal TAM (Technology Acceptance Model) Jurnal Abdi Rakyat Journal of Engineering, Electrical and Informatics
Enny Itje Sela
Master Program Of Information Technology, Universitas Teknologi Yogyakarta, Jl. Siliwangi (Ringroad Utara), Jombor, Sleman, D.I. Yogyakarta 55285|Universitas Teknologi Yogyakarta
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Decision Tree C4.5 Performance Improvement using Synthetic Minority Oversampling Technique (SMOTE) and K-Nearest Neighbor for Debtor Eligibility Evaluation Priyanto, Edi; Sela, Enny Itje; Latumakulita, Luther Alexander; Islam, Noourul
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1676.373-381

Abstract

Nowadays, information technology especially machine learning has been used to evaluate the feasibility of debtors. One of the challenges in this classification model is the occurrence of imbalanced datasets, especially in the German Credit Dataset. Another challenge is developing an optimal model for evaluating debtor eligibility. Based on these challenges, this study aims to develop an optimal model for evaluating debtor eligibility on the German Credit Dataset, using the decision trees, k-Nearest Neighbor (k-NN) and Synthetic Minority Oversampling Technique (SMOTE). SMOTE and k-NN is used to overcome challenges regarding imbalanced datasets. While the decision tree are applied to produce a debtor classification model. In general, the steps taken are preparing datasets, pre-processing data, dividing datasets, oversampling with SMOTE, and classification models using decision trees, and testing. Model performance evaluation is represented by accuracy values obtained from the confusion matrix and area under curve (AUC) values generated by the Receiver Operating Characteristic (ROC). Based on the tests that have been carried out, the best accuracy value in the test is obtained at 73.00% and the AUC value is 0.708, in parameters k = 3 and Max-Depth = 25. Based on the analysis produced, the proposed model can improve performance compared to if the dataset is not applied SMOTE.
MODEL DETEKSI DDOS BERBASIS MACHINE LEARNING YANG EFISIEN, INTERPRETABLE, DAN SIAP IMPLEMENTASI OPERASIONAL Andri Yudha Pratama; Khalifatur Rauf; Enny Itje Sela
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 11 No 1 (2026): APRIL
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v11i1.65922

Abstract

Serangan Distributed Denial of Service (DDoS) menjadi ancaman serius bagi kontinuitas bisnis digital, sehingga membutuhkan sistem deteksi yang akurat, responsif, dan dapat diinterpretasikan. Sebagian besar penelitian terdahulu berfokus pada maksimalisasi akurasi melalui model kompleks, namun kerap mengabaikan efisiensi komputasi dan actionability yang esensial bagi implementasi real-time. Penelitian ini mengevaluasi sembilan skenario deteksi pada dataset CIC-DDoS2019 melalui kombinasi metode seleksi fitur (Pearson, ANOVA, RFE) dan algoritma machine learning (Decision Tree, Random Forest, Logistic Regression). Hasilnya mengungkapkan adanya trade-off signifikan antara kompleksitas model dan latensi deteksi. Penelitian ini mengidentifikasi Skenario E4 (RFE + Decision Tree) sebagai model terbaik berdasarkan trade-off akurasi, latensi, dan memori, dengan recall serangan 0,9999, latensi 900 µs (sekitar 38 kali lebih cepat dari Random Forest), dan efisiensi memori 5.760 Byte. Kontribusi utama penelitian ini mencakup evaluasi multi-objektif yang mengintegrasikan akurasi, latensi, memori, interpretabilitas, dan robustness; pemetaan fitur SHAP ke dalam matriks mitigasi Defense-in-Depth; serta bukti empiris trade-off antara efisiensi operasional dan ketahanan model terhadap serangan adaptif. Analisis SHAP menunjukkan keputusan model didasarkan pada fitur identitas, anomali TCP flag, dan pola idle time. Namun, uji robustness mengindikasikan kerentanan terhadap manipulasi input, menegaskan perlunya strategi mitigasi tambahan dalam kerangka Defense-in-Depth agar model tidak hanya unggul secara statistik, tetapi juga operasional dan adaptif terhadap ancaman cerdas.
Pattern Recognition of Puta Dino Fabric Using Web-Based Convolutional Neural Network Method Luther Alexander Latumakulita; Silviani Esther Rumagit; Hence Beedwel Lumentut; Frangky Jessy Paat; Jaidun Ramadhan Kaplale; Enny Itje Sela
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1103

Abstract

This study aims to develop an intelligent system capable of recognizing traditional woven motifs of Puta Dino, a culturally significant textile from Tidore Island. These motifs are visually complex, poorly documented, and hard for the public to distinguish, highlighting the need for a digital tool to support cultural preservation and accurate identification. This research is the first to build a structured Puta Dino motif database and provide an integrated model designed for real-world use. The approach captured primary images of eight validated motifs and applied systematic preprocessing, including normalization and data augmentation, to enhance variability and strengthen the dataset. A lightweight deep learning model predicated on a convolutional neural network was designed to achieve a compromise between accuracy and computational efficiency. The system was evaluated through cross-validation and independent test data, as well as multiple real-world trials utilizing a web interface. These trials involved different image capture scenarios, including from a distance, moderate distance, close and angled views, and when the fabric surface was folded. The model architecture and system interface with the system are illustrated in the relevant figures, and the tables provide performance data on the system’s training, accuracy in motif classification, and achieved results in real-world conditions. The system demonstrated excellent classification accuracy in controlled test conditions. It showed real-world competency, accurately classifying most motifs in various conditions. The data also point to specific issues with motif recognition in extreme distortion cases, which reflect the typical issues of laboratory-to-field model deployment. The outcomes clearly demonstrate both the possibilities and the limitations of the currently available recognition of culturally significant textiles. The study concludes by exploring the possibilities of expanding the dataset and increasing the depth of learning through more sophisticated techniques, as well as enhancing accessibility to promote sustained community and cultural engagement.
Co-Authors Adidarma, Muhamad Bahru Agus Ardiyanto Agus Ardiyanto Agus Harjoko Agus Harjoko AKBAR MAULANA Akbar, Riolandi Algadrie, Saniah Evatri Alhafiz, Amirrul Dafa Andri Yudha Pratama Anggara, Afwan Anindita Septiarini, Anindita Apriyaldi Lukman Ari Hendrawan Arif Amrulloh Asep Zainal Alfarizi Briyan Efflin Syahputra Edi Priyanto Fatah Nur Saifullah Fathul Am Fathul Am Ferryma Arba Apriansyah Frangky Jessy Paat Ganda Sinarna Hence Beedwel Lumentut Hendrawan, Ari Idham Kholed Rachmawan Islam, Noourul Jaidun Ramadhan Kaplale Julian Ega Prabowo Khalifatur Rauf Luther Latumakulita M Ihsan Munakhir Mudjosemedi Nathaniela Aptanta Parama Noviania, Reski Nugroho, Faishal Tirto Nur Wening, Nur Olwin Kirab Novaldy Prawirdani, Adil Rachmawan, Idham Kholed Retantyo Wardoyo Rianto Rianto Rianto Rianto Risky Devandra Hartana Saucha Diwandari Senoaji, Muhammad Senoaji Wibowo Silviani Esther Rumagit Sri Hartati Steven Ray Sentinuwo Syahrul Gunawan Ramdhani Syamsu Windarti Totok Suprawoto Triwibowo, Muhammad Ilham Tundo, Tundo Utami, Wahyu Sri