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All Journal TEKNIK INFORMATIKA Tech-E Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL SCIENTIFIC OF MANDALIKA (JSM) Jurnal Teknologi Informasi Cyberku
M. Arief Soeleman
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Other

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KLASTERISASI GENRE CERPEN KOMPAS MENGGUNAKAN AGGLOMERATIVE HIERARCHICAL CLUSTERING- SINGLE LINKAGE Zenal Arifin; Stefanus Santosa; M. Arief Soeleman
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 13 No 2 (2017): Jurnal Teknologi Informasi CyberKU Vol. 13, no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (356.675 KB)

Abstract

Teks merupakan sarana interaksi dalam semua media komunikasi tulisan. Oleh karena peningkatan ukuran dan jenisnya yang sangat cepat, maka proses analisis data teks menjadi sesuatu yang bermakna sangatlah penting. Penggalian teks telah menjadi teknologi yang penting terutama dalam pengolahan dokumen cerpen. Pembaca cerpen saat ini kesulitan untuk memperoleh cerpen yang diinginkan jika cerpen tersebut tidak terkelompok dengan baik. Jika pengelompokan dilakukan secara manual membutuhkan waktu yang sangat lama. Oleh sebab itu, clustering menjadi solusi untuk mengatasi masalah tersebut. Clustering cerpen berfungsi untuk mengelompokkan dokumen cerpen berdasarkan tingkat kemiripan dari dokumen cerpen tersebut. Penelitian ini mengusulkan suatu model klasterisasi berbasis metode Hierarchical Clustering, khususnya Single Linkage Clustering. Metode Hierarchical Aggomerative Clustering terbukti memiliki performansi yang lebih baik daripada pendekatan penelitian sebelumnya yang menggunakan k-Means. Dari 127 dataset cerpen yang telah diujicobakan didapatkan nilai akurasi dari metode Agglomerative Hierarchical Clustering Single Linkage 47,2441 %, sedangkan metode k-Means hanya 37,7953 %.
Latent Semantic Analysis (LSA) Dengan Metode Support Vector Machine (SVM) dan Algoritma Naïve Bayes Pada Identifikasi Berita Palsu Dito, Aliffia Putri; Pulung Nurtantio Andono; M. Arief Soeleman
Journal Scientific of Mandalika (JSM) e-ISSN 2745-5955 | p-ISSN 2809-0543 Vol. 6 No. 10 (2025)
Publisher : Institut Penelitian dan Pengembangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/10.36312/vol6iss10pp3837-3850

Abstract

Berita palsu atau disebut hoax banyak beredar di masyarakat. Penyebaran berita palsu dapat mudah menyerap masyarakat terlebih melalui media sosial. informasi yang tersebar melalui platform media sosial sangat mudah terserap bagi masyarakat. Para pengguna media sosial biasanya menjadi pembuat konten dengan jumlah penyebaran informasi yang cukup luas, dan memungkinkan adanya misinformasi yang tidak dapat diabaikan. Kredibilitas dari sumber informasi tersebut juga sangat penting untuk menghindari resiko mengkonsumsi berita palsu. Menurut data statistik yang diterbitkan oleh Stanford University academics, sebanyak 72,3 persen berita palsu berasal dari outlet berita sosial dan platform media sosial online. Identifikasi dalam berita palsu tersebut semakin meningkat penggunaannya namun pemeriksaan fakta dalam banyak kasus cukup sulit, memakan waktu dan memerlukan biaya yang besar. Penelitian ini dilakukan dengan menggunakan latent semantic analysis dengan metode support vector machine dan algoritma naïve bayes dalam identifikasi berita palsu. Hasil pengujian ini menghasilkan nilai akurasi sebesar 82,28% dengan metode support vector machine dan 81,39% pada algoritma naïve bayes.
Optimasi Hyperparameter Convolutional Neural Network untuk Klasifikasi Jenis Penyakit Daun Jagung menggunakan CLAHE Al-Fatih, Gilang Fajar; Pulung Nurtantio Andono; M. Arief Soeleman
Tech-E Vol. 9 No. 1 (2025): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v9i1.3541

Abstract

Corn plays an important role as one of the main food sources in Indonesia and around the world. Diseases in corn plants are often visible through their leaves. However, problems arise when farmers have difficulty detecting diseases that attack corn plants, making it difficult to take appropriate action to control them. Diseases in corn plants can lead to reduced photosynthesis, disrupt agricultural productivity, and cause financial losses for farmers. Therefore, a digital approach that can detect various types of diseases in corn plants is highly needed. In recent years, the emergence of machine learning algorithms has provided support systems for classifying corn leaf diseases. This research aims to classify types of corn leaf diseases using the Optimization of Convolutional Neural Network (CNN) Method for Classifying Types of Corn Leaf Diseases Using Contrast Limited Adaptive Histogram Equalization (CLAHE). The research stages include data collection, image enhancement with CLAHE, data augmentation, data preprocessing, classification, and evaluation. The Optimization of the CNN Method for Classifying Types of Corn Leaf Diseases Using CLAHE resulted in an accuracy of 94%, indicating that this experiment is capable of classifying corn leaf diseases effectively.
Comparative Analysis of ResNet-Based Wagner-Scale Classification for Imbalanced DFU Data Ramadhan, Aditya Wahyu; Pulung Nurtantio Andono; M. Arief Soeleman
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.7016

Abstract

Diabetic Foot Ulcers (DFU) are a serious complication of diabetes mellitus and carry a high risk of lower extremity amputation if not treated in a timely manner. The conventional classification process, which relies on visual inspection by clinicians, tends to be subjective and inconsistent. Therefore, this study proposes a multiclass classification model for DFU based on the Wagner Scale (Grades 0–5) using the ResNet-50 architecture with a transfer learning approach as the core machine learning method. The dataset used in this study consists of 1,415 clinical wound images that were annotated and verified by medical professionals. The dataset is highly imbalanced, with 543 images in Grade 0, 110 in Grade 1, 252 in Grade 2, 145 in Grade 3, 293 in Grade 4, and only 72 images in Grade 5. To address this imbalance, random oversampling (ROS) was applied, in addition to standard preprocessing techniques such as normalization and data augmentation to increase training data diversity.Experimental results demonstrate that the proposed model achieves high classification performance based on accuracy, precision, recall, and F1-score. Specifically, the model obtained a precision of 0.96, recall of 0.95, and F1-score of 0.95, indicating consistent and robust classification performance across all Wagner grades. The best configuration (ResNet-50 + ROS) successfully improved the classification performance across minority grades (e.g., Grade 1 and Grade 5). Moreover, the model consistently identifies minority classes and does not exhibit signs of overfitting. Model optimization using the Adam optimizer and data balancing strategies significantly improves the generalization capability of the classifier. These findings indicate that the proposed model is not only effective for automatic DFU classification, but also has great potential to support objective clinical decision making and accelerate diagnosis, particularly in healthcare facilities with limited resources.
Integration of YOLOv8 and ResNet-50 to Improve Road Damage Detection Performance Rendi Andrea Pramana; M. Arief Soeleman
JURNAL TEKNIK INFORMATIKA Vol. 19 No. 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v19i1.46941

Abstract

Automatic road damage detection is an important solution for more effective and efficient transportation infrastructure maintenance. This study proposes the implementation of the You Only Look Once version 8 (YOLOv8) method with ResNet50 as a backbone to improve feature extraction capabilities in detecting various types of road damage. The model was trained using a road damage image dataset that has gone through preprocessing and data augmentation stages to enrich image variations. Test results show that the proposed model is able to achieve excellent performance, with an accuracy value of 95.2%, a precision of 0.979, a recall of 0.968, and an F1-score of 0.974. This achievement proves that the integration of YOLOv8 with ResNet50 as a backbone can improve the reliability of the road damage detection system compared to the original model. With this performance, this method has the potential to be applied in a real-time road monitoring system to support more optimal transportation infrastructure maintenance planning.
Co-Authors Al-Fatih, Gilang Fajar Dito, Aliffia Putri Pulung Nurtantio Andono Ramadhan, Aditya Wahyu Rendi Andrea Pramana Stefanus Santosa Zenal Arifin