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All Journal JurTI (JURNAL TEKNOLOGI INFORMASI) Journal of Information Systems and Informatics Infotek : Jurnal Informatika dan Teknologi Jurnal Ekonomi dan Teknik Informatika 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 Journal of Informatics and Vocational Education
Winarnie
Universitas Satu
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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Public Health Social Sciences Veterinary

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Machine Learning Classification of SCD, CHF, and NSR Using 15-Minute ECG-Derived HRV Features Febriyanti Panjaitan; Win Ce; M. Fajar Ramadhan; Winarnie; Hery Oktafiandi
Journal of Information System and Informatics Vol 8 No 2 (2026): April
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i2.1557

Abstract

Heart disease remains one of the leading causes of mortality worldwide, making early detection essential for effective intervention. Heart Rate Variability (HRV) is widely used as a non-invasive marker for assessing cardiac conditions, and machine learning has shown potential in classifying heart diseases such as Sudden Cardiac Death (SCD) and Congestive Heart Failure (CHF). This study evaluates the performance of Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbors (KNN) using 15-minute ECG signals comprising three 5-minute segments. The dataset consists of 53 subjects, generating 159 segments, including SCD, CHF, and Normal Sinus Rhythm (NSR). To prevent data leakage, a subject-wise split (80:20) is applied for training and testing. Two evaluation scenarios are considered: per-segment classification and combined 15-minute classification. Results indicate that SVM and DT achieve consistently high, stable performance with near-perfect accuracy, precision, recall, and F1-score, whereas KNN shows lower, more variable performance, particularly in segment-based analysis. The combined 15-minute approach provides more stable results, suggesting improved HRV representation and class separability. Although the results are promising, further validation with larger, more diverse datasets is required to ensure robustness and generalizability. This study highlights the potential of HRV-based machine learning while emphasizing the importance of appropriate temporal representation and rigorous evaluation design.
Sentiment Analysis and Topic Modeling of Ruangguru Application User Reviews Using IndoBERT and BERTopic on a Kaggle Dataset Muhammad Fajar Ramadhan; Febrianti Panjaitan; Winarnie Panjaitan; Hery Oktafiandy Panjaitan; Yohanes Panjaitan
Journal of Informatics and Vocational Education Vol. 9 No. 3 (2026): November 2026
Publisher : Informatics Education Department, Faculty of Teacher Training and Education, Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/joive.v9i3.3820

Abstract

User reviews provide valuable information for evaluating digital learning platforms because they contain direct expressions of user satisfaction, complaints, and expectations. This study analyzed user reviews of the Ruangguru application using a Kaggle dataset by combining IndoBERT for sentiment classification and BERTopic for topic modeling. The study aimed to classify user sentiment, compare IndoBERT with a TF-IDF and Logistic Regression baseline, identify dominant discussion topics, and examine whether the model performance difference was statistically significant. The dataset originally contained 100,000 reviews, and 76,699 valid reviews were used after data cleaning and preprocessing. Sentiment labels were generated from rating values and grouped into negative, neutral, and positive classes. IndoBERT achieved an accuracy of 0.8920 and an F1 macro score of 0.6347, outperforming the baseline model with an accuracy of 0.8092 and an F1 macro score of 0.5666. McNemar’s test confirmed that the performance difference was statistically significant (chi-square = 594.30, p < 0.001). BERTopic generated 41 topic groups, including one outlier group. Positive sentiment dominated most topics, particularly those related to learning support, material comprehension, and video-based learning. Negative sentiment was concentrated in topics related to payment, application updates, advertisements, and account issues. These findings show that integrating IndoBERT and BERTopic provides a comprehensive understanding of user perceptions toward educational applications.
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.
Co-Authors Anggit Dwi Hartanto Ayuningtyas, Pratika Febrianti Panjaitan Febriyanti Panjaitan Febriyanti Panjaitan Hery Oktafiandi Hery Oktafiandy Panjaitan Juwahir, Amat Khairu Nisa, Nisrina Kusrini M. Fajar Ramadhan M. Fajar Ramadhan M. Fajar Ramadhan M.Fajar Ramadhan Melani, Lestari Eka Muhammad Fajar Ramadhan Mulyowati, Gandes Pebriyanti Panjaitan Pebriyanti Panjaitan Putri, Farah Tisya Anindya Rahayu, Kurnia Puji Ramadhan, M. Fajar Sayid M. Raziq Olajuwon Win Ce Yohanes Yohanes Yohanes Panjaitan Yohanes Yohanes Yohanes Yohanes