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All Journal METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Tematik : Jurnal Teknologi Informasi Komunikasi J-AKSI : JURNAL AKUNTANSI DAN SISTEM INFORMASI Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Sistem Informasi dan Teknologi (SINTEK) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi AMMA : Jurnal Pengabdian Masyarakat
Denni Pratama
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Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing Social Sciences

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Peningkatan Kompetensi Guru Ciayumajakuning Melalui Bimbingan Teknis Teknologi AI Gemini Dadang Sudrajat; Denni Pratama; Fatkhan Mubarok; Muhammad Zamil Farhan
AMMA : Jurnal Pengabdian Masyarakat Vol. 3 No. 2 : Maret (2024): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

The advancement of Artificial Intelligence (AI) technology has created significant transformations across various sectors, including education. However, the adoption of AI in the educational sector—particularly in the Ciayumajakuning region (Cirebon, Indramayu, Majalengka, and Kuningan)—still faces several challenges, such as low digital literacy among teachers, limited access to technological training, and a lack of understanding regarding the practical application of AI in learning and school administration contexts. The Gemini AI Technical Training Program is part of a Community Service initiative aimed at enhancing teachers' capacity to use AI technology in a practical and ethical manner within educational environments.
Peningkatan Kompetensi Akademik Dosen Melalui Pelatihan Systematic Literature Review (SLR) Dadang Sudrajat; Denni Pratama; Luthfi Adianto; Luthfiyyah Iffah Adella
AMMA : Jurnal Pengabdian Masyarakat Vol. 2 No. 2 (2023): AMMA : Jurnal Pengabdian Masyarakat (INPRESS)
Publisher : CV. Multi Kreasi Media

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Abstract

The quality of academic research is significantly influenced by the ability of lecturers to conduct systematic and comprehensive literature reviews. This Community Partnership Program aims to enhance the competence of Kopertip Indonesia lecturers in conducting Systematic Literature Reviews (SLR) as an essential part of the academic research process. This training is designed to provide an in-depth understanding of SLR methodology, including formulating appropriate research questions, effective literature search strategies, study inclusion and exclusion criteria, synthesis and analysis of literature data, and the preparation of high-quality SLR reports. It is expected that, through this training, Kopertip Indonesia lecturers can improve their ability to produce more relevant, comprehensive research that significantly contributes to the advancement of knowledge.
Pengembangan Sistem Informasi Learning Analytics untuk Monitoring dan Evaluasi Kompetensi Digital Pelaku UMKM pada Platform SkillUP Denni Pratama; Dian Ade Kurnia; Saeful Anwar
TEMATIK Vol. 13 No. 1 (2026): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2026
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v13i1.2994

Abstract

Transformasi digital telah mendorong kebutuhan peningkatan kompetensi digital bagi pelaku Usaha Mikro, Kecil, dan Menengah (UMKM) di Indonesia. Platform microlearning SkillUP telah berhasil dikembangkan dan diterima pengguna berdasarkan Technology Acceptance Model (TAM), namun masih belum dilengkapi mekanisme monitoring dan evaluasi kompetensi digital secara komprehensif. Penelitian ini bertujuan mengembangkan Sistem Informasi Learning Analytics (SILA) untuk monitoring dan evaluasi kompetensi digital pelaku UMKM pada platform SkillUP menggunakan metode Design Science Research (DSR). Sistem yang dikembangkan mengintegrasikan data aktivitas pembelajaran ke dalam tiga lapisan pengumpulan data, mesin analitik, dan dashboard. Novelty penelitian mencakup Digital Competency Monitoring Framework dan Digital Competency Progress Index (DCPI) yang dihitung dari Learning Engagement Score (LES), Learning Completion Rate (LCR), Quiz Achievement Score (QAS), dan Competency Achievement Score (CAS). Evaluasi sistem menggunakan standar ISO/IEC 25010 dengan fokus pada Functional Suitability, Usability, dan Performance Efficiency. Hasil evaluasi menunjukkan nilai Functional Suitability sebesar 1,00 (sangat baik), Usability sebesar 87,5 (excellent berdasarkan SUS), dan rata-rata response time 1,87 detik. Sistem ini berkontribusi dalam menyediakan data berbasis bukti untuk pengambilan keputusan strategis terkait pengembangan kompetensi digital UMKM di Indonesia.
Optimalisasi Convolutional Neural Network Kontra VGG16 Klasifikasi Citra Daun Sawi Rio Febriyan; Ade irma Purnamasari; Denni Pratama; Puji Pramudya Marta; Yudhistira Arie Wijaya
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 6 No 1 (2026): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol6No1.pp30-34

Abstract

Manual detection of pests on mustard greens (caisim) is a major constraint in reducing harvest productivity, as manual methods are inefficient, time-consuming, and require specialized expertise. Furthermore, deep learning models often suffer from overfitting when applied to limited agricultural datasets. This study aimed to develop and compare the effectiveness of a Convolutional Neural Network (CNN) from scratch model versus the VGG16 transfer learning architecture for automatic classification of healthy and pest-affected mustard leaf images. A dataset of 1,000 images was used for training and testing across four experimental scenarios (A to D), with Percobaan C being the optimized CNN from scratch model (using data augmentation) and Percobaan D using VGG16. The results showed that the VGG16 transfer learning model achieved the highest test accuracy of 95.0% (F1-score: 0.95), while the optimized CNN from scratch model achieved 92.0% (F1-score: 0.92). Therefore, transfer learning with VGG16 is the most effective and optimal approach, demonstrating superior performance and efficiency by achieving high accuracy without complex data augmentation.
OPTIMASI KLASTERISASI PENERIMAAN PAJAK BUMI DAN BANGUNAN MENGGUNAKAN ALGORITMA K-MEDOIDS Febri Abdi Annur Dhuha; Ade Irma Purnamasari; Denni Pratama; Edi Tohidi; Edi Wahyudin
JURNAL AKUNTANSI DAN SISTEM INFORMASI Vol 7 No 1 (2026): Edisi Februari 2026
Publisher : Program Studi Akuntansi Fakultas Ekonomika dan Bisnis Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/j-aksi.v7i1.16659

Abstract

Pajak Bumi dan Bangunan (PBB) merupakan komponen strategis dalam Pendapatan Asli Daerah (PAD) yang berperan penting dalam mendukung penyelenggaraan pembangunan dan pelayanan publik. Namun, heterogenitas data dan variasi karakteristik objek pajak menyebabkan pemerintah daerah mengalami kesulitan dalam memetakan potensi fiskal dan tingkat kepatuhan wajib pajak secara akurat. Penelitian ini bertujuan menganalisis efektivitas algoritma K-Medoids dalam mengelompokkan wajib pajak di Kecamatan Tanjung berdasarkan atribut numerik, yaitu luas tanah, luas bangunan, NJOP tanah, NJOP bangunan, dan nilai PBB tahun berjalan. Metode penelitian meliputi tahapan pengumpulan data, pra-pemrosesan, transformasi logaritmik, normalisasi, implementasi algoritma K-Medoids, serta evaluasi hasil klaster menggunakan metrik Silhouette Coefficient dan Davies–Bouldin Index. Proses komputasi dilakukan menggunakan Python dengan pustaka pyClustering dan scikit-learn. Hasil penelitian menunjukkan terbentuknya empat klaster wajib pajak dengan karakteristik berbeda: klaster aset besar berkontribusi rendah, klaster premium berkontribusi tinggi, klaster ekonomi rendah dengan pola pembayaran tidak stabil, dan klaster ekonomi menengah dengan kepatuhan cukup baik. Evaluasi kualitas model menghasilkan Silhouette Coefficient sebesar 0,4204 dan Davies–Bouldin Index sebesar 0,7893, yang menunjukkan struktur klaster cukup baik dan stabil. Temuan ini memberikan kontribusi empiris dalam mendukung optimalisasi pengelolaan PBB berbasis analitik, serta dapat digunakan sebagai dasar penyusunan strategi penagihan berbasis prioritas dan formulasi kebijakan fiskal yang lebih tepat sasaran.
Evaluasi Pengaruh Kualitas Data Terhadap Performa Model Machine Learning Menggunakan Pendekatan Data-Centric AI Bisma Mahendra; Martanto; Denni Pratama; Ahmad Faqih; Rudi Kurniawan
Jurnal Sistem Informasi dan Teknologi Vol 6 No 1 (2026): Jurnal Sistem Informasi dan Teknologi (SINTEK)
Publisher : LPPM STMIK KUWERA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56995/sintek.v6i1.211

Abstract

Penelitian ini mengevaluasi pengaruh kualitas data terhadap performa model machine learning menggunakan pendekatan Data-Centric Artificial Intelligence (DCAI). Eksperimen dilakukan pada Titanic Dataset dengan membandingkan Random Forest dan Support Vector Machine (SVM) dalam tiga skenario penanganan missing values, yaitu Drop Missing, Mean Imputation, dan No Imputation. Kinerja model dievaluasi menggunakan metrik Accuracy, F1 Score, dan Area Under Curve (AUC). Hasil menunjukkan bahwa intervensi kualitas data memberikan dampak signifikan terhadap performa model. Random Forest mencapai performa terbaik pada skenario Drop Missing dengan Accuracy 0.813, F1-Score 0.758, dan AUC 0.859, sedangkan SVM memperoleh Accuracy tertinggi sebesar 0.822 pada skenario Mean Imputation. Uji statistik Paired t-Test menunjukkan tidak terdapat perbedaan performa yang signifikan secara statistik antara kedua model (p-value > 0.05). Temuan ini menegaskan bahwa peningkatan kualitas data lebih berpengaruh terhadap kinerja model dibandingkan pemilihan algoritma, sehingga mendukung paradigma Data-Centric AI.
ALGORITMA RANDOM FOREST UNTUK PREDIKSI STATUS PINJAMAN BERDASARKAN SKOR KREDIT Hadit Attaufiqqurrohman; Ade Irma Purnamasari; Denni Pratama; Nining Rahaningsih; Willy Prihartono
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 1 (2026): Volume 12 Nomor 1 Tahun 2026
Publisher : Universitas Methodist Indonesia

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Abstract

The rapid development of financial technology has encouraged financial institutions to adopt data-driven credit scoring systems in order to minimize the risk of default. However, many loan eligibility prediction models still face challenges such as data imbalance (class imbalance) and the limited capability of traditional models to capture non-linear relationships among variables. This study aims to develop a loan status prediction model using the Random Forest algorithm combined with the Synthetic Minority Oversampling Technique (SMOTE) and One-Hot Encoding (OHE) to improve model accuracy and generalization capability. The data used in this study are secondary data obtained from the public Kaggle platform, consisting of 45,000 records with 14 demographic and financial attributes. The research method employs a supervised learning approach with several stages, including data acquisition and preprocessing (data cleaning, normalization, encoding, and data balancing), Random Forest model training, and performance evaluation using accuracy, precision, recall, F1-score, and AUC metrics. The results show that the combination of Random Forest, SMOTE, and OHE achieves high predictive performance, with an accuracy of 94.8%, precision of 95.6%, recall of 93.7%, F1-score of 94.6%, and an AUC value of 0.972. The most influential variables in loan status prediction are credit_score, person_income, and loan_amnt. This approach is proven to be effective in addressing data imbalance issues and improving classification accuracy in identifying creditworthy and non-creditworthy borrowers.
Optimization of Convolutional Neural Networks Using Resizing Techniques for Banana Leaf Disease Classification Aldiyansyah Kurniawan; Ade Irma Purnamasari; Denni Pratama; Edi Tohidi; Edi Wahyudin
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1876

Abstract

Early and accurate identification of banana leaf diseases is essential for supporting digital agriculture, as visual symptoms often require rapid and reliable analysis. This study investigates the impact of three image resizing techniques squashing, letterboxing, and random resized crop on the performance of the MobileNetV2 architecture in classifying four categories of banana leaf images using the Banana Leaf Disease Dataset v4 consisting of 4,675 samples. The experiments were conducted using a transfer learning approach with an 80:10:10 data split, standardized normalization, and data augmentation. The results show that all resizing techniques achieved test accuracies above 92%. Squashing produced the highest accuracy and fastest training time, letterboxing demonstrated the most stable performance with the lowest validation loss, and random resized crop improved generalization to variations in object position. These findings confirm that resizing strategies significantly influence the stability and effectiveness of CNN models. Overall, MobileNetV2 proves capable of delivering accurate and efficient classification of banana leaf diseases when supported by an appropriate preprocessing pipeline. This study provides empirical evidence for developing image-based plant disease diagnosis systems within smart agriculture.
Analysis of the Effectiveness of Manual Deployment and CI/CD Github Actions in the Braisee Application Nenda Alfadil Seputra; Odi Nurdiawan; Arif Rinaldi Dikananda; Denni Pratama; Dian Ade Kurnia
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1916

Abstract

In the modern cloud-based software development ecosystem, the speed and reliability of the deployment process are critical elements. This study aims to evaluate the effectiveness of implementing Continuous Integration/Continuous Deployment (CI/CD) using GitHub Actions compared to manual methods for the machine learning API of the Braisee application hosted on Google Cloud Run. Using a quantitative approach with a comparative experimental design across ten testing iterations, this research measures deployment time efficiency, error rates, and system stability. The experimental results show a significant performance disparity, where the automated method based on GitHub Actions is considerably more efficient, with an average total duration of 111–167 seconds, reducing operational time by 40–60% compared to the manual method, which requires 297–364 seconds. In terms of reliability, the automated method achieves a 100% success rate with high consistency, whereas the manual method demonstrates substantial vulnerability to human errors such as mistyped project IDs and inconsistent image tagging. It is concluded that implementing CI/CD through GitHub Actions is a superior solution that improves time efficiency and ensures the stability of cloud-based applications compared to manual procedures.
Comparative Analysis of Serverless Container Service Performance Between Google Cloud Run and AWS App Runner in Cross-Cloud Architecture Muhammad Adithya Pratama; Odi Nurdiawan; Arif Rinaldi Dikananda; Denni Pratama; Dian Ade Kurnia
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1919

Abstract

Research on the performance of serverless container services is becoming increasingly important as the need for modern distributed and cross-cloud architectures grows. This study analyzes the performance of two leading serverless services, Google Cloud Run and AWS App Runner, in a cross-cloud architecture scenario. Testing was conducted using identical parameters, including container configuration, region, memory, vCPU, and concurrency. Performance testing included p95 latency, throughput, and error rate metrics using loads of up to 1000 virtual users. The results showed that Google Cloud Run provided more stable performance with p95 latency of 47–71 ms, throughput of 436–438 RPS, and 0% error rate. In contrast, AWS App Runner showed p95 latency of 490–651 ms with throughput variation of 388–410 RPS and an error rate of 2–4.41%. The difference in performance was due to autoscaling mechanisms, cross-cloud communication overhead, and resource contention. This study provides empirical evidence for selecting the optimal serverless service for distributed architectures.
Co-Authors Ade Irma Purnamasari Ahmad Faqih Aldiyansyah Kurniawan Arif Rinaldi Dikananda Bisma Mahendra Dadang Sudrajat Dian Ade Kurnia Edi Tohidi Edi Wahyudin Fatkhan Mubarok Febri Abdi Annur Dhuha Hadit Attaufiqqurrohman Indra Wiguna Marthanu Kaslani Luthfi Adianto Luthfiyyah Iffah Adella Martanto . Mochammad Rifqi Aqila Muhammad Adithya Pratama Muhammad Haidar Hammam Muhammad Zamil Farhan Nenda Alfadil Seputra Nining Rahaningsih Odi Nurdiawan Puji Pramudya Marta Raditya Danar Dana Revan Arrayyan Oktevanio Rio Febriyan Rudi Kurniawan Saeful Anwar Willy Prihartono Yudhistira Arie Wijaya