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All Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Humuntal Rumapea
Methodist University Of Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Penerapan Metode Support Vector Machine untuk Pengenalan Pola Aksara Batak Toba Panjaitan, Efdi Sarjono; Rumapea, Humuntal; Jaya, Indra Kelana
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 2(SEMNASTIK) (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akunt
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No2(SEMNASTIK).pp49-55

Abstract

The usage of the Batak Toba script has declined, and its complex forms pose challenges in pattern recognition. This study employs the Support Vector Machine (SVM) method to classify Batak Toba script patterns, utilizing a Histogram of Oriented Gradients (HOG) as a feature extraction technique. The data used comes from various sources, totaling 285 script images. After preprocessing, SVM was applied to separate characters into two main classes, which were further subdivided into subclasses until final classification was achieved. The results show that the combination of HOG and SVM can classify Batak Toba script characters with an accuracy of 89,47%. This research makes a significant contribution to the preservation of the Batak Toba script and has broader potential applications in pattern recognition and image classification.
PEMANFAATAN GOOGLE EARTH ENGINE DAN ALGORITMA RANDOM FOREST UNTUK PEMETAAN LAHAN PERKEBUNAN JERUK Dian Agaventa, Chrissandro; Rumapea, Humuntal; Indra Kelana Jaya
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 11 No. 2 (2025): Volume 11 Nomor 2 Tahun 2025
Publisher : Universitas Methodist Indonesia

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Abstract

This study employed Google Earth Engine (GEE) and the Random Forest algorithm to map citrus plantations in Silimakuta District, Simalungun Regency, North Sumatra. As a major citrus production center—reaching 840,000 quintals in 2023—the region faces challenges in producing accurate and efficient maps of plantation distribution. By processing Sentinel-2 and Sentinel-1 satellite imagery in GEE, this study provides a more detailed and reliable mapping solution. The Random Forest model achieved a land-cover classification accuracy of 97% and a Kappa coefficient of 96.3%, demonstrating the method’s effectiveness for land mapping. This approach can overcome existing limitations in land data and deliver visual information useful for increasing citrus plantation productivity in the region. Therefore, the combined use of Google Earth Engine and the Random Forest algorithm shows strong potential to support more optimal and sustainable land management.
Implementasi Deep Learning untuk Deteksi Dini Bencana Cuaca Ekstrem Berbasis Analisis Citra Awan Rumapea, Humuntal
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 2 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No2.pp313-318

Abstract

This study aims to implement Deep Learning methods for early detection of extreme weather disasters based on satellite cloud image analysis. The dataset consists of multi-spectral imagery obtained from the Himawari-8 satellite, covering various atmospheric conditions. The proposed approach employs two main models: Convolutional Neural Network as the baseline model and Vision Transformer as the comparative model. The research methodology includes data preprocessing, model training, evaluation using accuracy, precision, recall, and F1-score metrics, and model interpretation using Explainable AI techniques. The results indicate that the Vision Transformer outperforms the CNN model, achieving an accuracy of over 92%. Furthermore, Grad-CAM visualization demonstrates that the model effectively identifies cloud regions associated with extreme weather phenomena. This study contributes to the development of an accurate and interpretable cloud-based early warning system, with potential applications in disaster mitigation, particularly in regions prone to extreme weather such as Indonesia.
Evaluasi Kinerja CNN dan Vision Transformer pada Klasifikasi Citra Resolusi Tinggi Berbasis Deep Learning Rumapea, Humuntal
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No2.pp372-379

Abstract

This study aims to evaluate and compare the performance of Convolutional Neural Networks (CNN) and Vision Transformers (ViT) in high-resolution image classification based on deep learning. The dataset consists of high-resolution images that undergo preprocessing and data augmentation, and is divided into training, validation, and testing sets. The CNN models used include ResNet50 and EfficientNet as baselines, while Vision Transformer is employed as a comparative model utilizing a self-attention mechanism. Performance evaluation is conducted using metrics such as accuracy, precision, recall, F1-score, as well as training and inference time. The results indicate that Vision Transformer achieves superior classification performance compared to CNN, with an accuracy of up to 93.85%. However, CNN demonstrates better computational efficiency with lower training and inference time. Furthermore, increasing image resolution improves the performance of both models, albeit at the cost of higher computational complexity, particularly for Vision Transformer. This study highlights a trade-off between accuracy and efficiency, suggesting that model selection should be aligned with specific application requirements.
LEXICON BASED ANALISIS DAN RANDOM FOREST TERHADAP ISU POLITIK DINASTI INDONESIA PADA APLIKASI X Rumapea, Humuntal; Krisna Diva; Simanullang, Harlen Gilbert
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 1 (2026): Volume 12 Nomor 1 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i1.4700

Abstract

Dynastic politics in Indonesia remains a widely discussed issue, eliciting diverse public opinions ranging from support as a political right to criticism of democratic quality, with social media, particularly the X platform, serving as an important venue for public sentiment analysis. This study employs a combination of the Lexicon Based method using the InSet Lexicon and the Random Forest algorithm to analyze public sentiment on dynastic politics. The dataset consists of 1,593 tweets collected from August 1 to December 24, 2024, which underwent text preprocessing, labeling into three sentiment categories: positive, negative, and neutral, and word weighting using TF-IDF. The methodology includes splitting the data into training and testing sets with an 80:20 ratio, applying undersampling on the training data to balance class distribution, and training a Random Forest model with 100 decision trees and a maximum depth of 5 per tree, based on the entropy criterion. Evaluation results show that the model successfully classifies public sentiment with an accuracy of 89%, precision of 82%, recall of 81%, and f1-score of 81%.
Co-Authors Dian Agaventa, Chrissandro Hasibuan, Doli Indra Kelana Jaya Krisna Diva Panjaitan, Efdi Sarjono Rajagukguk, Edward Siboro, Indrayana Simanullang, Harlen Gilbert Yunita, Ana