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All Journal Sistemasi: Jurnal Sistem Informasi Techno Nusa Mandiri : Journal of Computing and Information Technology Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Pengabdian Masyarakat Tapis Berseri Nuansa Informatika Jurnal Bhakti Karya dan Inovatif Jurnal Accounting Information System (AIMS)
Acep Hendra
Universitas Informatika dan Bisnis Indonesia
Humanities Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Social Sciences Other

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Teknik Pengembangan Rencana Tugas Kelas berbasis Kecerdasan Generatif sebagai Solusi Untuk Menangani Kecurangan dengan Bantuan AI Acep Hendra; Supeno, Handoko
TEMATIK Vol. 11 No. 1 (2024): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2024
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v11i1.1841

Abstract

Pendidikan merupakan hal yang vital bagi perkembangan sebuah bangsa dan negara. Sayangnya teknik dan proses belajar mengajar terutama strategi pemberian tugas ajar yang ada cenderung tertinggal dengan kemajuan teknologi yang semakin pesat, hal ini menyebabkan minimnya pencegahan kecurangan yang saat ini semakin dimudahkan dengan keberadaan kecerdasan buatan terutama kecerdasan generatif dengan kemampuan yang luar biasa untuk menciptakan jawaban tugas secara instan. Jika terus dibiarkan maka hal ini dapat menurunkan kualitas pendidikan di Indonesia. Walaupun kajian mengenai pemanfaatan kecerdasan generatif telah banyak dilakukan namun masih belum ada metodologi praktis yang membimbing pengajar untuk dapat merencanakan tugas siswa berbasis kecerdasan generatif tersebut. Penelitian ini mengusulkan metodologi untuk pengembangan rencana tugas kelas yang berbasis kecerdasan generatif yang kemudian kami namakan sebagai Generative AI-based Classroom Task Planning(GACTP). Kata kunci: Kecerdasan generatif, proses belajar mengajar, kecurangan dengan bantuan AI
Pemberdayaan Siswa melalui Pendidikan Kewirausahaan dan Penggunaan Google Analytics di SMAN 16 Kota Bandung Budiman Budiman; Acep Hendra; Dirham Triyadi; Yoga Rizki Rahmawan
Jurnal Bhakti Karya dan Inovatif Vol 4 No 2 (2024): Jurnal Bhakti Karya dan Inovatif
Publisher : LPPM Universitas Informatika dan Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37278/bhaktikaryadaninovatif.v4i2.875

Abstract

Kegiatan pengembangan diri di SMAN 16 Bandung dan inovasi kurikulum double track memiliki dampak signifikan dalam konteks pendidikan dan persiapan siswa. Survei angket 2022/2023 menunjukkan 28% siswa kelas XII tidak berencana melanjutkan ke perguruan tinggi, mendorong sekolah untuk memperkenalkan kurikulum double track. Program ini menyediakan keterampilan kewirausahaan bagi siswa yang tidak melanjutkan ke perguruan tinggi, bekerja sama dengan UNIBI untuk memberikan pelatihan kewirausahaan. Kegiatan pengembangan diri juga mempersiapkan siswa untuk menjadi anggota produktif dalam masyarakat dengan program yang relevan dengan kebutuhan pasar kerja. Dalam bisnis rintisan, strategi pemasaran digital melalui Google Analytics membantu meningkatkan kesadaran merek dan menjangkau target pasar lebih efektif. Analisis data digital memberikan wawasan tentang perilaku konsumen dan efektivitas kampanye pemasaran, memungkinkan penyesuaian strategi pemasaran. Dengan pendekatan holistik ini, SMAN 16 Bandung tidak hanya menyediakan pendidikan formal tetapi juga membentuk karakter dan kesiapan siswa untuk menghadapi tantangan dunia nyata, memastikan manfaat berkelanjutan bagi siswa dan masyarakat.
OPTIMIZED FACEBOOK PROPHET FOR MPOX FORECASTING: ENHANCING PREDICTIVE ACCURACY WITH HYPERPARAMETER TUNING Alamsyah, Nur; Restreva Danestiara, Venia; Budiman, Budiman; Nursyanti, Reni; Setiana, Elia; Hendra, Acep
Jurnal Techno Nusa Mandiri Vol. 22 No. 1 (2025): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v22i1.6507

Abstract

MPOX (Monkeypox) has become a significant global health concern, requiring accurate forecasting for effective outbreak management. This study improves MPOX case prediction using Facebook Prophet with hyperparameter optimization. The dataset consists of global MPOX case records collected over time. Data preprocessing includes missing value imputation, normalization, and aggregation. Facebook Prophet is applied to forecast case trends, with model performance evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). A baseline Prophet model is first trained using default parameters. The model is then optimized by fine-tuning seasonality mode, changepoint prior scale, and growth model. The results show that hyperparameter tuning significantly enhances forecasting accuracy. The optimized model reduces MSE from 541,844.77 to 320,953.34 and RMSE from 736.10 to 566.53, demonstrating improved precision. The model also captures trend shifts and seasonal fluctuations more effectively. In conclusion, this study confirms that tuning Facebook Prophet improves epidemic forecasting, making it a reliable tool for MPOX monitoring. Future research should integrate external factors, such as vaccination rates and mobility data, to further refine predictions.
Strategi Digital untuk Agripreneur 4.0: Meningkatkan Pemasaran, Penjualan, dan Branding dalam Agribisnis Hendra, Acep; Habibi, Chairul; Ramadan, Diki; Mikala, Azka Khafifan
Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) Vol. 4 No. 1 (2025): Jurnal Pengabdian Masyarakat Tapis Berseri (JPMTB) (Edisi April)
Publisher : Pusat Studi Teknologi Informasi Fakultas Ilmu Komputer Universitas Bandar Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36448/jpmtb.v4i1.142

Abstract

The development of digital technology presents both challenges and significant opportunities for the agribusiness sector in Indonesia, especially for farmers and small-scale agribusiness entrepreneurs. This community service initiative aims to provide understanding and skills related to digital strategies in marketing, sales, and branding to strengthen the competitiveness of agribusiness products. Through training and mentoring at SMKN PP Lembang, participants are empowered to leverage digital technology, such as social media marketing, SEO optimization, and the use of e-commerce platforms to expand their market reach and increase agribusiness sales. The results of this activity show an increased understanding among participants of the importance of digitalization in agribusiness and the implementation of effective marketing strategies to introduce products to a broader consumer base. However, key challenges faced include limited infrastructure and low digital literacy among some farmers and agribusiness actors. Therefore, ongoing support from the government and private sectors in terms of infrastructure and training is essential to support the digital transformation of the agribusiness sector.
Emoji-Based Sentiment Classification Using Ensemble Learning with Cross-Validation: A Lightweight Approach for Social Media Analysis: Klasifikasi Sentimen Berbasis Emoji Menggunakan Ensemble Learning dengan Validasi Silang: Pendekatan Ringan untuk Analisis Media Sosial Alamsyah, Nur; Bayu Wibisono, Gunthur; Parama Yoga, Titan; Budiman; Hendra, Acep
NUANSA INFORMATIKA Vol. 19 No. 2 (2025): Nuansa Informatika 19.2 Juli 2025
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v19i2.396

Abstract

The increasing use of emojis in online communication reflects emotional expression that is often more immediate and intuitive than text. This study proposes a lightweight sentiment classification approach that utilizes only emoji features extracted from social media posts, without relying on textual content. The importance of this research lies in its relevance to short-form digital content, where textual sentiment cues are minimal or absent. To address the classification problem, we implement and compare multiple machine learning models including Random Forest (RF), Support Vector Machine, and an ensemble Voting Classifier combining both. Emoji tokens were vectorized using character-level count vectorization, and performance was evaluated using 5-fold cross-validation to ensure robustness and generalizability. Results show that the ensemble model achieved the highest average accuracy of 93.6%, outperforming the individual classifiers. These findings confirm that emojis alone can serve as reliable indicators of sentiment and support the deployment of fast, interpretable, and scalable models for social media sentiment analysis.
A Bidirectional GRU Approach with Hyperparameter Optimization for Sentiment Classification in Game Reviews : Pendekatan GRU Dua Arah dengan Optimasi Hiperparameter untuk Klasifikasi Sentimen dalam Ulasan Game Alamsyah, Nur; Titan Parama Yoga; Budiman; Imannudin Akbar; Hendra, Acep; Januantara Prima, Alif
NUANSA INFORMATIKA Vol. 19 No. 2 (2025): Nuansa Informatika 19.2 Juli 2025
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v19i2.399

Abstract

Sentiment analysis plays a vital role in understanding user perspectives, especially in domains such as game reviews where user feedback influences product perception and engagement. This study presents a comparative approach using Gated Recurrent Unit (GRU), hyperparameter-tuned GRU, and Bidirectional GRU models to classify sentiments in a dataset of game reviews. The experiment begins with standard preprocessing and tokenization steps, followed by vectorization and supervised training. Hyperparameter optimization is conducted using Keras Tuner to identify the most effective configuration of embedding dimensions, GRU units, dropout rates, and learning rates. The best model, a Bidirectional GRU with tuned parameters, achieves a validation accuracy of 85.37% and shows superior performance across key metrics such as precision, recall, and F1-score. Despite the relatively small and imbalanced dataset, the Bidirectional GRU model demonstrates robust generalization. This study also highlights future directions, including class balancing techniques and the integration of pretrained word embeddings to further improve model performance.
OPTIMIZED DEEP AUTOENCODER WITH L1 REGULARIZATION AND DROPOUT FOR ANOMALY DETECTION IN 6G NETWORK SLICING Jennifer Kaunang, Valencia Claudia; Alamsyah, Nur; Parama Yoga, Titan; Hendra, Acep; Budiman, Budiman
Jurnal Techno Nusa Mandiri Vol. 20 No. 2 (2025): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v20i2.6912

Abstract

The increasing complexity of 6G network slicing introduces new challenges in identifying abnormal behavior within highly virtualized and dynamic network infrastructures. This study aims to address the anomaly detection problem in 6G slicing environments by comparing the performance of three models: a supervised random forest classifier, a basic unsupervised autoencoder, and an optimized deep autoencoder enhanced with L1 regularization and dropout techniques. The optimized autoencoder is trained to reconstruct normal data patterns, with anomaly detection performed using a threshold- based reconstruction error approach. Reconstruction errors are evaluated across different percentile thresholds to determine the optimal boundary for classifying abnormal behavior. All models are tested on a publicly available 6G Network Slicing Security dataset. Results show that the optimized autoencoder outperforms both the baseline autoencoder and the random forest in terms of anomaly sensitivity. Specifically, the optimized model achieves an F1- score of 0.1782, a recall of 0.2095, and an accuracy of 0.714. These results indicate that introducing regularization and dropout significantly improves the ability of autoencoders to generalize and isolate anomalies, even in highly imbalanced datasets. This approach provides a lightweight and effective solution for unsupervised anomaly detection in next- generation network environments.
Analisis Sentimen Aplikasi BYOND by BSI di Google Play Store Menggunakan Metode SVM Akbar, Imannudin; Sinaga, Arnold Ropen Sinaga; Yoga, Titan Parama; Hendra, Acep; Setiana, Elia Setiana
Jurnal Accounting Information System (AIMS) Vol. 8 No. 2 (2025)
Publisher : Ma'soem University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32627/aims.v8i2.1583

Abstract

The BYOND by BSI application has received various user reviews on the Google Play Store, reflecting user perceptions and satisfaction. Sentiment analysis is needed to understand these opinion patterns and support service quality improvement. This study aims to analyze the sentiment of BYOND by BSI user reviews by applying the Support Vector Machine (SVM) method. Review data were collected from the Google Play Store and processed through text preprocessing stages followed by SVM classification modeling. The results show a classification accuracy of 87%, with strong performance in the Positive class (F1-score 0.91) and Negative class (F1-score 0.88), but SVM failed to detect the Neutral class due to data imbalance, where the Neutral class accounted for only 5.85% of the total samples. In conclusion, these findings highlight the importance of handling class imbalance through approaches such as resampling, ensemble algorithms, or class-weight optimization in SVM to improve the accuracy of Neutral sentiment detection.
A Fall Risk Detection Model for Infants While Sleeping based on Convolutional Neural Networks Hendra, Acep; Supeno, Handoko
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4644

Abstract

Falling from a bed is a common problem among infants, often leading to serious injuries such as head trauma, fractures, and even long-term neurological damage. According to data from the World Health Organization (WHO), falls are a leading cause of unintentional injuries among children, especially infants. To prevent these incidents, an effective early detection system is needed. Traditional approaches, such as motion sensors and surveillance cameras, have been employed to monitor infant movements and detect fall risks. However, sensor-based systems face limitations in terms of accuracy and coverage area. As an alternative, computer vision techniques have shown rapid advancements in recent years, with Convolutional Neural Networks (CNNs) proving to be highly effective in recognizing visual patterns, including human motion and posture detection. In this study, we propose a CNN-based model to detect the risk of infants falling from a bed while sleeping. The CNN architecture is designed to accurately recognize movements indicative of fall risks, such as approaching the edge of the bed or sudden changes in posture. Our contributions include (1) the design of a CNN architecture that supports effective and efficient training for fall risk detection, and (2) the creation of a dataset to classify infants as either safe or at risk of falling. Experimental results demonstrate that our proposed system achieves high accuracy in detecting potential fall risks, providing a reliable solution for infant safety monitoring.
Co-Authors Akbar, Imannudin Bayu Wibisono, Gunthur Budiman Budiman Budiman Dirham Triyadi Habibi, Chairul Januantara Prima, Alif Jennifer Kaunang, Valencia Claudia Mikala, Azka Khafifan Nur Alamsyah PARAMA YOGA, TITAN Ramadan, Diki Reni Nursyanti Setiana, Elia Setiana, Elia Setiana Sinaga, Arnold Ropen Sinaga Supeno, Handoko Titan Parama Yoga Venia Restreva Danestiara Yoga Rizki Rahmawan