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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Information System for Educators and Professionals : Journal of Information System JITK (Jurnal Ilmu Pengetahuan dan Komputer) KOPERTIP: Jurnal Ilmiah Manajemen Informatika dan Komputer Antivirus : Jurnal Ilmiah Teknik Informatika Jurnal ICT : Information Communication & Technology Tematik : Jurnal Teknologi Informasi Komunikasi Jurnal Mahasiswa Sistem Informasi (JMSI) INFORMATIKA Journal of Artificial Intelligence and Engineering Applications (JAIEA) INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Jurnal Mahasiswa Ilmu Komputer AMMA : Jurnal Pengabdian Masyarakat INTERNAL (Information System Journal) Intechno Journal : Information Technology Journal
Dian Ade Kurnia
Manajemen Informatika; STMIK IKMI Cirebon
Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Nursing Other

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Analisis Sentimen Ulasan by.U dengan Pelabelan Rating dan Leksikon Menggunakan Multinomial Naïve Bayes Fatihanursari Dikananda; Bani Nurhakim; Dian Ade Kurnia; Ahmad Rifai; Mugi Praseptiawan
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.v13i1.2996

Abstract

Perkembangan layanan telekomunikasi digital mendorong bertambahnya jumlah ulasan pengguna yang digunakan sebagai bahan informasi guna mendukung pengambilan keputusan berbasis data. Penelitian ini bertujuan menganalisis sentimen ulasan aplikasi by.U menggunakan dua metode pelabelan data, yaitu rating-based labeling dan lexicon-based labeling, menggunakan algoritma Multinomial Naïve Bayes (MNB). Metode penelitian menerapkan framework Knowledge Discovery in Databases yang meliputi tahapan selection, preprocessing, transformation, data mining, dan evaluation. Dataset penelitian diperoleh dari Google Play sebanyak 8.000 ulasan berbahasa Indonesia. Tahap prapemrosesan mencakup cleaning, case folding, normalisasi, tokenisasi, stopword removal, serta stemming. Representasi fitur dilakukan menggunakan TF-IDF, sedangkan penyeimbangan data diterapkan melalui metode SMOTE. Evaluasi model dilakukan menggunakan metrik accuracy, precision, recall, dan F1-score dengan skema 10-fold cross validation. Hasil penelitian menunjukkan bahwa pendekatan lexicon-based labeling memberikan performa yang lebih baik dibandingkan rating-based labeling. Pendekatan rating-based menghasilkan accuracy sebesar 82,59%, precision 83,79%, recall 82,59%, dan F1-score 82,43%. Sementara itu, pendekatan lexicon-based memperoleh accuracy sebesar 88,96%, precision 89,69%, recall 88,96%, serta F1-score 88,91%. Temuan tersebut menunjukkan bahwa strategi pelabelan memiliki pengaruh terhadap performa klasifikasi sentimen. Pendekatan berbasis leksikon dinilai lebih efektif karena mampu memahami konteks linguistik dan ekspresi emosional pengguna secara lebih baik dibandingkan pendekatan berbasis rating.
PERBANDINGAN MODEL LSTM DAN GRU UNTUK PREDIKSI HARGA SAHAM TELEKOMUNIKASI INDONESIA Ahmad Jamalul Noor; Dian Ade Kurnia; Yudhistira Arie Wijaya; Heliyanti Susana
Jurnal Mahasiswa Ilmu Komputer Vol. 7 No. 1 (2026): Jurnal Mahasiswa Ilmu Komputer March 2026
Publisher : Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/ilmukomputer.v7i1.10651

Abstract

Penelitian ini bertujuan untuk mengevaluasi dan membandingkan performa model Long Short-Term Memory (LSTM) dan Gated Recurrent Unit (GRU) dalam memprediksi harga saham harian pada sektor telekomunikasi Indonesia, sebuah sektor yang memiliki karakteristik volatilitas fluktuatif dan dipengaruhi oleh dinamika pasar jangka pendek. Dua emiten yang dianalisis adalah GHON dan EXCL dengan rentang data dua tahun yang diambil dari platform Investing.com. Proses penelitian mencakup tahapan preprocessing, normalisasi menggunakan MinMaxScaler, pembentukan sliding window sepanjang 30 hari, serta pembagian data secara kronologis menjadi data latih, validasi, dan uji. Optimasi hyperparameter dilakukan menggunakan KerasTuner dengan pendekatan Random Search untuk memperoleh konfigurasi terbaik bagi masing-masing model. Evaluasi performa menggunakan tiga metrik utama yakni Root Mean Square Error (RMSE), Mean Absolute Error (MAE), dan Mean Absolute Percentage Error (MAPE). Hasil eksperimen menunjukkan bahwa GRU memberikan performa yang lebih unggul pada saham EXCL yang memiliki volatilitas tinggi, ditunjukkan oleh nilai RMSE, MAE, dan MAPE yang lebih rendah dibandingkan LSTM. Sebaliknya, pada saham GHON yang lebih stabil, kedua model menghasilkan performa yang relatif sebanding. Temuan ini menegaskan bahwa efektivitas model sangat dipengaruhi oleh karakteristik data, di mana GRU lebih adaptif pada pola harga yang dinamis, sedangkan LSTM tetap kompetitif pada pola yang lebih konsisten. Secara keseluruhan, GRU dapat direkomendasikan sebagai model yang lebih efisien dan akurat untuk prediksi harga saham pada lingkungan pasar yang berfluktuasi tinggi.
Enhancing Face Authentication for Online Examination Systems Using Median Filtering and MobileNetV2 Dadang Sudrajat; Dian Ade Kurnia; Rudi Kurniawan; Othman bin Mohd; Maulana Sujarwadi; Salman Alfarizi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.7185

Abstract

Digital transformation in higher education is driving the uptake of online tests, which require academic integrity, security, and robust user experience. In the context of authentication of users, deep learning based face recognition, in particular the Convolutional Neural Network (CNN) architectures, such as MobileNetV2, combined with intermediate filter, promises to deliver a consistent performance across a wide range of devices and imaging environments. However, there are limited comprehensive studies evaluating the final integration of the median filter and MobileNetV2 in high-value test scenarios. This study contributes by proposing an effective end-to-end Face Authentication Pipeline, assessing the median impact of filtering on MobileNetV2 performance, and validating it with a prototype application. The authentic face dataset was collected using the Teachable Machine, preprocessed with cropping, resizing, and median filtering, and then augmented through rotation, shift, shear, zoom, reversal, and brightness adjustment. The MobileNetV2 model was trained with Adam in a stepwise manner, starting with 0.001 and then 0.0001 for 20 epochs in a batch size of 32, and was evaluated for accuracy, precision, recall, and F1 score. Results show that the accuracy curve has remained stable at almost 95 percent during the 20th epoch; most grades achieved 1.00 in both precis, recall and F1, with some classings showing a limited decrease due to facial similarity or expression differences. These findings confirm that MobileNetV2 median filtering can be the basis for an effective, accurate and ready to integrate face recognition in online testing applications on a wide range of devices.
The Effectiveness of Dropout Layers in LSTM Architecture for Reducing Overfitting in Sony Stock Prediction Roni Saputra; Dian Ade Kurnia; Yudhistira Arie Wijaya
Intechno Journal : Information Technology Journal Vol. 7 No. 2 (2025): December
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2025v7i2.2369

Abstract

This study investigates the effectiveness of dropout layers in reducing overfitting within Long Short-Term Memory (LSTM) neural networks for Sony stock price prediction. Financial time series forecasting presents significant challenges due to market volatility and noise, often leading to models that overfit historical data while failing to generalize to unseen market conditions. We implemented two LSTM models: one without dropout layers and another with dropout layers (rate=0.2) applied after each LSTM layer. Using historical Sony stock data from 2015-2025, we evaluated both models using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) metrics. The model with dropout demonstrated superior performance on testing data, achieving RMSE of 0.5971, MAE of 0.4411, and MAPE of 2.1502%, compared to the model without dropout which obtained RMSE of 0.7124, MAE of 0.5636, and MAPE of 2.6684%. Furthermore, the dropout model exhibited significantly reduced overfitting, with smaller performance gaps between training and testing datasets across all metrics, particularly in MAPE where the difference approached zero (0.0509%). This research provides empirical evidence that dropout regularization effectively enhances LSTM model generalization for stock prediction, offering practical value for developing more reliable financial forecasting models. Future research could explore optimal dropout rates for different market conditions and investigate combinations of dropout with other regularization techniques.
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.
PELATIHAN INTEGRASI DATABASE DAN AUTOMASI DATA MENGGUNAKAN N8N UNTUK OPTIMALISASI SISTEM INFORMASI Khaerul Anam; Dian Ade Kurnia; Muhammad Burhanudin; Johana Darmawan
AMMA : Jurnal Pengabdian Masyarakat Vol. 5 No. 5 : Juni (2026): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The community service activity with the theme "Database Integration and Data Automation Training Using n8n for Information System Optimization" was carried out as an effort to improve the digital competence of vocational high school students and educators in Cirebon Regency in facing the needs of digital transformation. The activity was carried out on April 11, 2026 through workshops, demonstrations, and direct practice using the n8n platform. This program was motivated by the fact that many schools still manage academic data manually using separate spreadsheets, resulting in inefficient data processing, potentially leading to data duplication, and complicating the monitoring and reporting process. In addition, participants' understanding of database integration and workflow automation was still relatively limited. The training material covered basic concepts of information systems, database integration, utilization of APIs and webhooks, and implementation of workflow automation using the n8n platform. Participants gained hands-on experience in building simple workflows, such as integrating Google Sheets with Telegram, synchronizing digital form data, and automating notification delivery. The activity was attended by participants from SMK Presiden Cirebon, SMKN 1 Kedawung, SMK Islamic Center Cirebon, SMK Muhammadiyah Cirebon, SMK Wahidin Cirebon City, and SMK Budi Tresna Cirebon Regency. The results of the activity showed an increase in participants' understanding and skills in applying data integration technology and information system automation. Participants successfully developed various simple automation workflows that can be implemented to support the effectiveness of academic administration in schools. The evaluation of the activity showed that the level of participant satisfaction was in the very good category. In general, this activity succeeded in making a positive contribution to increasing digital literacy, strengthening information technology competencies, and developing the use of n8n-based automation workflows in vocational education environments.
Co-Authors Abdul Aziz Ade Irma Purnamasari Ahmad Jamalul Noor Ahmad Rifai Ahmad Rifai Ikhsanudin Ananda Rafly Arif Rinaldi Dikananda Bani Nurhakim Cep Lukman Rohmat Dadang Sudrajat Dadang Sudrajat Denni Pratama Denni Pratama Dodi Solihudin Farids Al Ma’ruf Fatihanursari Dikananda Fatihanursari Dikananda Gifthera Dwilestari Hegarmanah Muhabatin Heliyanti Susana Hilman Rifa'i Iin Irfan Ali Johana Darmawan Kaslani Khaerul Anam Leli Oktaviani Martanto . Maulana Sujarwadi Mochamad Aditya Sunaryo Mubarok Mubarok Muhamad Fahrurozi Muhammad Adithya Pratama Muhammad Aji Pratama Muhammad Burhanudin Mulyawan Nenda Alfadil Seputra Odi Nurdiawan Othman bin Mohd Praseptiawan, Mugi Puji Pramudya Marta Ramadhan Ramadhan Roni Saputra Rosidin Rudi Kurniawan Ryan Hamonangan Saeful Anwar Salman Alfarizi Sulistiyana Sulistiyana Tati Suprapti Tri Anelia Ujang Supriatna Witriyani Witriyani Yudhistira Arie Wijaya