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All Journal Journal of Information Systems and Informatics Journal of Artificial Intelligence and Digital Business Rekayasa Mekanika: Jurnal Ilmiah Teknik Mesin Bakti Insani: Jurnal Pengabdian dan Pemberdayaan
Ramadhan, M. Fajar
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Religion Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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“EFFICIENCY ANALYSIS OF 3 DIFFERENT FUEL TOWARDS GASOLINE FUEL MOTORS ON EME 35 ENGINES IN LAPAN SURVELLANCE UAV-02 (LSU-02)” Ramadhan, M. Fajar; Bismantolo, Putra; Suryadi, Dedi; Utama, Agus Bayu
Rekayasa Mekanika: Jurnal Ilmiah Teknik Mesin Vol. 8 No. 2 (2024): Oktober 2024
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/rekayasamekanika.v8i2.38341

Abstract

This study aims to analyze the efficiency of three different types of fuel on the EME 35 engine on the LSU 02 aircraft. The fuel used is Pertalite, Pertamax, and Pertamax turbo. The method used in this study was an experiment using the LSU 02 aircraft as the research object. The data is taken from measurements of fuel consumption and engine performance on each type of fuel. The analysis is carried out by comparing the efficiency and performance of the engine on each type of fuel used. The results showed that the efficiency and performance of the engine on Pertamax fuel was better than that of Pertamax turbo and Pertalite. Fuel consumption on Pertamax is more efficient than Pertalite and Pertamax turbo, so that Pertamax is more efficient in fuel use. In addition, engines on Pertamax fuel have better performance with greater power and are more stable compared to Pertalite and Pertamax turbo. Therefore, it is recommended to use Pertamax fuel in the EME 35 engine on the LSU 02 aircraft to achieve better efficiency and performance.
Klasifikasi Topik dan Sentimen Judul Berita dengan Augmentasi dan TF-IDF Ramadhan, M. Fajar
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 2 (2025): Mei - Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i2.1692

Abstract

Media berita daring menjadi sumber utama informasi masyarakat Indonesia, namun tantangan muncul dalam memahami dan menganalisis konten berita yang singkat dan bersifat implisit serta emosional. Penelitian ini bertujuan mengembangkan sistem klasifikasi ganda untuk judul berita berbahasa Indonesia, dengan mengidentifikasi topik dan sentimen secara bersamaan menggunakan metode supervised learning. Pendekatan yang digunakan meliputi pengumpulan data melalui web scraping dari portal berita, proses labeling manual, preprocessing teks, augmentasi data untuk mengatasi ketidakseimbangan kelas, serta tuning parameter TF-IDF. Sampel penelitian terdiri dari 300 judul berita yang kemudian diolah dan diuji menggunakan algoritma Support Vector Machine (SVM), Naive Bayes, dan Logistic Regression. Analisis data dilakukan dengan metrik akurasi, F1-score, dan confusion matrix. Hasil menunjukkan bahwa model SVM dengan tuning parameter TF-IDF mencapai akurasi tertinggi pada klasifikasi topik, sementara Naive Bayes unggul dalam klasifikasi sentimen setelah augmentasi data, dengan akurasi hingga 94,5%. Kesimpulan, pendekatan ini efektif dalam meningkatkan performa sistem klasifikasi berita singkat dan dapat dikembangkan untuk aplikasi monitoring media digital.
Sentiment Analysis on Coretax Data Using SVM and Random Forest with SMOTE and Tomek-Link Oktafiandi, Hery; Winarnie, Winarnie; Ramadhan, M. Fajar; Panjaitan, Febriyanti
Journal of Information System and Informatics Vol 7 No 3 (2025): September
Publisher : Universitas Bina Darma

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.
Anti Bullying: Mengatasi Bullying dalam Pendidikan Karakter di SDN 1 Panunggalan Safira, Nur; Sari, Ayu Indah; Ifadah , Nely; Assirojil Munir, M. Ainul Yaqin; Ramadhan, M. Fajar; Rosyidi, Bahrul ‘Alam
Bakti Insani: Jurnal Pengabdian dan Pemberdayaan Vol. 1 No. 1 (2025): Bakti Insani: Jurnal Pengabdian dan Pemberdayaan
Publisher : Yayasan Mutiara Hati Moeslem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64131/baktiinsani.v1i1.15

Abstract

Bullying merupakan masalah serius di lingkungan pendidikan yang berdampak negatif pada kesejahteraan emosional dan psikologis siswa. Di Indonesia, fenomena ini mencakup berbagai bentuk seperti bullying fisik, verbal, sosial, dan cyberbullying yang dapat mengakibatkan stres, penurunan harga diri, dan gangguan kesehatan mental jangka panjang. Pendidikan karakter telah diidentifikasi sebagai pendekatan efektif dalam mengatasi bullying dengan menanamkan nilai-nilai moral, empati, penghargaan terhadap perbedaan, dan pengendalian diri. Penelitian ini bertujuan untuk mengeksplorasi bagaimana pendidikan karakter dapat berperan dalam mencegah dan menangani bullying di sekolah. Melalui seminar bertema "Anti Bullying: Mengatasi Bullying dalam Pendidikan Karakter," diharapkan dapat meningkatkan pemahaman dan kesadaran di kalangan orang tua, guru, dan siswa mengenai pentingnya pendidikan karakter dalam menciptakan lingkungan sekolah yang aman dan bebas dari bullying. Hasil dari seminar ini menunjukkan bahwa keterlibatan aktif orang tua dalam pendidikan karakter di rumah berkontribusi signifikan terhadap pengembangan sikap anti-bullying pada anak-anak. Selain itu, dukungan dari pihak sekolah dalam menerapkan kebijakan anti-bullying dan pelatihan bagi guru sangat penting untuk menciptakan suasana belajar yang kondusif. Dengan pemahaman yang lebih baik mengenai pendidikan karakter, diharapkan semua pihak yang terlibat dapat berkontribusi dalam mencegah bullying dan membangun lingkungan sekolah yang positif
Machine Learning Classification of SCD, CHF, and NSR Using 15-Minute ECG-Derived HRV Features Panjaitan, Febriyanti; Ce, Win; Ramadhan, M. Fajar; Winarnie; Oktafiandi, Hery
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.
Co-Authors Assirojil Munir, M. Ainul Yaqin Bismantolo, Putra Ce, Win Dedi Suryadi Febriyanti Panjaitan Hery Oktafiandi Ifadah , Nely Rosyidi, Bahrul ‘Alam Safira, Nur Sari, Ayu Indah Utama, Agus Bayu Winarnie Winarnie