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All Journal InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Teknovasi : Jurnal Teknik dan Inovasi Mesin Otomotif, Komputer, Industri dan Elektronika Zero : Jurnal Sains, Matematika, dan Terapan ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA JOURNAL OF SCIENCE AND SOCIAL RESEARCH JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Ensiklopedia Education Review Jurnal Mantik Journal of Applied Engineering and Technological Science (JAETS) Jatilima : Jurnal Multimedia Dan Teknologi Informasi Journal of Computer System and Informatics (JoSYC) INFOKUM Brahmana : Jurnal Penerapan Kecerdasan Buatan Jurnal Sains Teknologi dan Sistem Informasi Jurnal Info Sains : Informatika dan Sains International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) Jurnal Minfo Polgan (JMP) pendidikan, science, teknologi, dan ekonomi Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Journal of Artificial Intelligence and Digital Business Indonesian Journal of Education And Computer Science Journal of Technology and Computer (JOTECHCOM) Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK) International Journal of Industrial Innovation and Mechanical Engineering Jurnal Bisantara Informatika Proceedings of The International Conference on Computer Science, Engineering, Social Sciences, and Multidisciplinary Studies Jurnal Pengabdian Kepada Masyarakat Teknologi Informasi dan Komunikasi
Muhammad Syahputra Novelan
Universitas Pembangunan Pancabudi
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences

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Journal : Journal of Technology and Computer (JOTECHCOM)

 1 
Comparative Machine Learning Analysis for Sentiment Classification of Sumatra Disaster 2025 Alfarizi, Nauval; Lydia, Prima; Novelan, Muhammad Syahputra; Putra, Adi; Sinurat, Satria
Journal of Technology and Computer Vol. 3 No. 1 (2026): February 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

Indonesia is highly vulnerable to natural disasters due to its geological position, resulting in extensive disaster-related news coverage that shapes public sentiment. This study presents a comparative machine learning analysis for sentiment classification of online news related to natural disasters in Sumatra during December 2025. The dataset was collected through web scraping from two major Indonesian news portals, like CNN Indonesia and Detik, and categorized into three sentiment classes: negative, neutral, and positive. Sentiment classification was conducted using Naive Bayes, Support Vector Machine (SVM), and k-Nearest Neighbors (KNN) algorithms. The results demonstrate that Naive Bayes achieved accuracy values of 0.57 on the CNN Indonesia dataset and 0.61 on the Detik dataset. However, its performance was highly biased toward the dominant negative class, as indicated by low macro-average F1-scores of (0.24) and (0.39). In contrast, SVM showed the most balanced performance by reducing class bias, achieving accuracies of (0.68) and (0.67) with macro-average F1-scores of (0.51) and (0.59), respectively. KNN demonstrated moderate performance, with accuracy values of 0.60 and 0.59, but remained less effective than SVM in handling minority sentiment classes.
Comparison of Random Forest and Naïve Bayes Classifier Methods for Monkeypox Classification Aprilia, Katharina Tyas; Sitorus, Irwansyah Putera; Ridha, Muhammad Rasyid; Novelan, Muhammad Syahputra
Journal of Technology and Computer Vol. 3 No. 1 (2026): February 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Monkey Pox is a disease caused by a virus with the genus orthopoxvirus that can infect humans. The initial symptoms of this disease are the appearance of lumps due to swollen lymph nodes, muscle pain, fever, feeling tired and weak. Although similar to Chickenpox, Monkey Pox is clinically difficult to distinguish from other smallpox diseases. This study aims to classify Monkey Pox disease using the "Monkey-Pox PATIENTS Dataset". Classification of Monkey Pox disease is done using Random Forest and Naïve Bayes methods. Random Forest produces higher accuracy than Naïve Bayes in classifying Monkey Pox disease, which is 69.24% with a k-fold value of 5 and the number of trees 64 using an unbalanced dataset with 6 attributes. While Naïve Bayes produces an accuracy of 68.56% using a dataset without balancing with 8 attributes (k-fold=5, kernel=Gaussian) and 9 attributes (k-fold=3 and 10, kernel=Gaussian).
Predictive Analysis of Flood Risk Factors Based on a Machine Learning Approach: Comparative Study of SVM and XGBoost Algorithms Darma, Surya; Al Fayed, Ahmad Jihad; P Pardede, Surya Maruli; Aqsha, Muhammad Hizbul; Novelan, Muhammad Syahputra
Journal of Technology and Computer Vol. 3 No. 1 (2026): February 2026 - Journal of Technology and Computer
Publisher : PT. Technology Laboratories Indonesia (TechnoLabs)

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Abstract

Flood events in Indonesia continue to increase in frequency and impact due to high rainfall variability, land-use change, and complex hydrological conditions. Accurate predictive modeling is therefore essential to support flood risk assessment and mitigation planning. This study evaluates the predictive performance of two supervised machine learning algorithms, Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost), for flood risk classification. The analysis is conducted using a publicly available dataset comprising 500 samples that represent multiple environmental and spatial factors related to flood occurrence. Data preprocessing includes cleaning, normalization, and feature consistency adjustment prior to model implementation. Both algorithms are trained and tested using the same dataset configuration to ensure objective comparison. Model performance is assessed using accuracy, precision, recall, and F1-score metrics. Experimental results indicate that XGBoost achieves higher accuracy and precision, demonstrating stronger capability in reducing false-positive predictions, while SVM shows relatively higher recall, reflecting better sensitivity in identifying flood-prone cases. Overall, XGBoost provides more reliable predictive performance for flood risk modeling on the dataset used. The findings confirm the effectiveness of machine learning-based approaches for flood risk prediction and highlight the importance of algorithm selection in disaster risk analysis.
Co-Authors ', Khairunnisa , Arpan Adi Putra Adli Abdillah Nababan Adli Abdillah Nababan Afif Yasri Al Fayed, Ahmad Jihad Aldy Agustian Alfarizi, Nauval Amin, Muhammad Aminuddin Indra Permana Andri Saputra Andysah Putera Utama Siahaan Antoni, Robin Anugrah, Maisya Fitri Aprilia, Katharina Tyas Aqsha, Muhammad Hizbul Ardiansyah ARDIANSYAH ARDIANSYAH Aria Dhanu Tirta Arpan Aurelia, Cindy Aisha Ayumi Kartika Sari Ayumi Kartika Sari Bayu Angga Wijaya Daniel Panjaitan Darmeli Nasution Datin, Maha Valne Defri Abdul Majid Nasution Dian Kurnia Fachri, Barany Fajri Razak Fathia, Aulia Ukhti Febby Sittah Gunawan Fitri Anugrah, Maisya Gunawan, Andri Harahap, Nur Azizah Hardinata, Rio Septian Harefa, Ade May Luky Haryadi, Patrialman Heri Eko Rahmadi Putra Ibnu Gunawan Ilka Zufria IQBAL , MUHAMMAD Irhami, Zahara Reva Islam, Muhammad Remanul Jacky Lius Juliyandri Saragih Khumairoh, Annisa KIKI WULANDARI Lewika Tampubolon Limbong, Yohannes France Lubis, Syaiful Rahman Lydia, Prima Mestika, Dani Mufida Padilla, Eva Muhammad Iqbal Muhammad Rasyid Ridha Muhammad Rizki Muhammad Wahyudi Muhammad Zen, Muhammad Muhardi Saputra Nasution, Abdul Muin Nasution, Indra Norita Tampubolon P Pardede, Surya Maruli Padilla, Eva Mufida Penggabean Siahaan Perianus lombu Prayogi, Dhimas Putra, Purwa Hasan Putri, Ranti Eka Rahmat Idhami Raja Nasrul Fuad Rambe, Siska Mayasari Ramlan Marbun Rido Favorit Saronitehe Waruwu Rio Septian Hardinata Rizal, Chairul Rizko, M. Azhari Rizky Putro Nugroho Dwi Cahyo Robet Silaban Safii, Aidul Safi’i, Aidul Sari Harahap, Nurlina Sella Monika Br Tarigan Selvida, Desilia Septiansyah, Yudha Setiawan, Ahmad Deni Setiawan, Albin Simanullang, Rahma Yuni Sinurat, Satria Siregar, Andree Rizky Yuliansyah Sitepu, Andri Ismail Sitepu, Nabila Putri Br Siti Aisyah Sitorus , Zulham Sitorus, Irwansyah Putera Solly Aryza Suhendar - Surya Darma Suteja, Ade Guna Sutiono, Sulis Syafitri, Febry Dwi Syahputra, Zulfahmi Syahputri, Maulisa Syahri, Rahma Taufa Fadly Uc Mariance Utari Utari Wanny, Puspita Wijaya, Rian Farta Wiwik Handayani Zailani Sinabariba Zulfahmi Syahputra Zulfahmi Syahputra Zulfahmi Syahputra