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All Journal Transmisi: Jurnal Ilmiah Teknik Elektro JurTI (JURNAL TEKNOLOGI INFORMASI) Journal of Information Systems and Informatics Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Ekonomi dan Teknik Informatika Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Journal of Informatics and Electronics Engineering Abdi Wina : Jurnal Pengabdian Kepada Masyarakat Universitas Kristen Wira Wacana Sumba Komputasi : Jurnal Ilmiah Ilmu Komputer dan Matematika Jurnal Komputer, Informasi dan Teknologi Jurnal Publikasi Ilmu Komputer dan Multimedia
Hery Oktafiandi
Satu University
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Veterinary

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The Landscape Image Classification Using Convolutional Neural Network on Intel Image Classification Datase Winarnie Winarnie; Hery Oktafiandi; Pebriyanti Panjaitan; M. Fajar Ramadhan; Yohanes Yohanes
Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Vol. 23 No. 2 (2026): Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika
Publisher : Program Studi Ilmu Komputer, Universitas Pakuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33751/komputasi.v23i2.112

Abstract

Image classification is an important area of computer vision and artificial intelligence that enables computers to automatically recognize and categorize visual information. This research aims to develop a Convolutional Neural Network (CNN)-based image classification model for recognizing six categories of natural and urban landscapes using the Intel Image Classification dataset from Kaggle. The preprocessing stage included image resizing, data augmentation, and pixel normalization to improve model generalization and reduce overfitting. The dataset was divided into 80% training data and 20% testing data. The proposed CNN architecture consists of four convolutional layers, max-pooling layers, and three fully connected dense layers with ReLU and Softmax activation functions. The novelty of this study lies in the development of a lightweight CNN architecture that achieves competitive performance without relying on pretrained models or transfer learning approaches, making it suitable for deployment on resource-constrained devices. Experimental results show that the model achieved 85.85% training accuracy and 85.47% testing accuracy. Performance evaluation using precision, recall, F1-score, and confusion matrix indicates balanced classification performance across all classes. Furthermore, the trained model was successfully converted into TensorFlow SavedModel, TensorFlow Lite, and TensorFlow.js formats to support cross-platform deployment. The findings demonstrate that the proposed CNN model is effective, efficient, and suitable for real-world landscape image classification applications.
Evaluasi Kinerja IndoBERT dan N-Gram untuk Klasifikasi Umpan Balik Pengajaran Yohanes Yohanes; Hery Oktafiandi; Febriyanti Panjaitan; M.Fajar Ramadhan; Winarnie Winarnie
Journal of Informatics and Electronics Engineering Vol. 6 No. 01 (2026): Juni 2026
Publisher : Unit Penelitian dan Pengabdian kepada Masyarakat Politeknik TEDC Bandung Jl. Pesantren Km 2 Cibabat Cimahi Utara – Cimahi 40513 Jawa Barat – Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70428/jiee.v6i01.1595

Abstract

Pendidikan adalah proses fundamental yang bertujuan untuk mencerdaskan kehidupan bangsa, dan harus dilaksanakan secara profesional oleh setiap guru. Salah satu pendekatan untuk meningkatkan kualitas pendidikan adalah dengan memperoleh umpan balik dari siswa, yang memungkinkan guru melakukan refleksi diri dan memperbaiki metode pengajarannya. Analisis sentimen membantu mengidentifikasi perasaan yang diungkapkan siswa dalam umpan balik mereka, apakah positif, netral, atau negatif. Selain itu, klasifikasi umpan balik digunakan untuk memperoleh wawasan mengenai berbagai aspek pengajaran. Penelitian ini mencapai tingkat akurasi terbaik sebesar 83,50% untuk klasifikasi umpan balik dan 88,35% untuk analisis sentimen menggunakan algoritma IndoBERT, yang sebelumnya melalui tahap preprocessing dengan metode N-Gram (N1 + N2), melampaui hasil penelitian sebelumnya. Berdasarkan model yang dikembangkan untuk menganalisis umpan balik siswa, dibuatlah sebuah aplikasi berbasis web yang dapat digunakan oleh kepala sekolah dan guru untuk menganalisis umpan balik secara lebih efisien dan efektif. Alat ini dapat menjadi sarana refleksi bersama untuk meningkatkan efektivitas pengajaran di dalam kelas.
Evaluation of Machine Learning Models for Sentiment Analysis in the South Sumatra Governor Election Using Data Balancing Techniques Febriyanti Panjaitan; Win Ce; Hery Oktafiandi; Ghanim Kanugrahan; Yudi Ramdhani; Vito Hafizh Cahaya Putra
Journal of Information System and Informatics Vol 7 No 1 (2025): March
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i1.1019

Abstract

Sentiment analysis is crucial for understanding public opinion, especially in political contexts like the 2024 South Sumatra gubernatorial election. Social media platforms such as Twitter and YouTube provide key sources of public sentiment, which can be analyzed using machine learning to classify opinions as positive, neutral, or negative. However, challenges such as data imbalance and selecting the right model to improve classification accuracy remain significant. This study compares five machine learning algorithms (SVM, Naïve Bayes, KNN, Decision Tree, and Random Forest) and examines the impact of data balancing on their performance. Data was collected via Twitter crawling (140 entries) and YouTube scraping (384 entries), and text features were extracted using CountVectorizer. The models were then evaluated on imbalanced and balanced datasets using accuracy, precision, recall, and F1-score. The Decision Tree and Random Forest models achieved the highest accuracies of 79.22% and 75.32% on imbalanced data, respectively. However, they also exhibited overfitting, as indicated by their near-perfect training performance. Naïve Bayes, on the other hand, demonstrated the lowest accuracy at 54.55% despite achieving high precision, suggesting frequent misclassification, particularly for the minority class. SVM and KNN also struggled with imbalanced data, recording accuracies of 58.44% and 63.64%, respectively. Significant improvements were observed after applying data balancing techniques. The accuracy of SVM increased to 71.43%, and KNN improved to 66.23%, indicating that these models are more stable and effective when class distributions are even. These findings highlight the substantial impact of data balancing on model performance, particularly for methods sensitive to class distribution. While tree-based models achieved high accuracy on imbalanced data, their tendency to overfit underscores the importance of balancing techniques to enhance model generalization.
Sentiment Analysis on Coretax Data Using SVM and Random Forest with SMOTE and Tomek-Link Hery Oktafiandi; Winarnie Winarnie; M. Fajar Ramadhan; Febriyanti Panjaitan
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v7i3.1279

Abstract

This study is motivated by the increasing adoption of digital tax platforms in Indonesia, particularly Coretax, which enables online tax reporting and payment. Understanding user sentiment is crucial for evaluating system effectiveness and identifying areas for improvement. However, sentiment data is often imbalanced, making it challenging to detect the sentiments of the minority class. This research evaluates the performance of Support Vector Machine (SVM) and Random Forest (RF) in classifying sentiment from Coretax related reviews collected between March and September 2025 from Twitter, YouTube, and the DJP application. Lexicon-based labeling and preprocessing were applied, followed by class balancing using Tomek-Link, SMOTE, and SMOTE-Tomek techniques. On the original data, SVM achieved an accuracy of 98.56%, while Random Forest reached 98.43%, both performing strongly on the majority class. However, minority class detection was improved through SMOTE and SMOTE-Tomek, albeit with a slight decrease in overall accuracy due to the risk of overfitting. The novelty of this study lies in its focus on Coretax 2025 data and a comparative analysis of multiple resampling techniques, providing practical insights into improving sentiment analysis performance on imbalanced digital tax data.
Co-Authors Aghus Sofwan Agung Budi Prasetijo Ardiansyah, Difta Ari Indra Indra Widyawan Ayuningtyas, Pratika Febriyanti Panjaitan Febriyanti Panjaitan Febriyanti Panjaitan Ghanim Kanugrahan Hafizh Cahaya Putra, Vito Lutfi Ashar Mauludin, Lutfi Ashar M. Fajar Ramadhan M. Fajar Ramadhan M. Fajar Ramadhan M.Fajar Ramadhan Mukti Setiono Nur, Wahid Olajuwon, Sayyid M. Raziq Pebriyanti Panjaitan Pebriyanti Panjaitan Permai, Antika Pratika Ayuningtyas Sayid M. Raziq Olajuwon Win Ce Win Ce Winarnie Winarnie Winarnie Yohanes Yohanes Yohanes Yohanes Yohanes Yohanes Yudi Ramdhani