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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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Digitalisasi Wisata Desa: Pelatihan E-Ticket Untuk Kemudahan Kunjungan Di Desa Cisantana Dian Ade Kurnia; Kaslani; Cep Lukman Rohmat
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 09 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

Local tourism development is an important effort to support economic growth and sustainability at the village level. Cisantana Village, with its abundant natural and cultural riches, has great potential to develop the tourism sector. However, to achieve its potential, efficient and transparent management is needed. One of the proposed solutions is through the use of a tourist e-ticket application. This situation analysis will describe the location of partners, cases that have occurred, social and cultural aspects, special problems faced, service methods or approaches used, as well as the implications of the results of service to the community . Cisantana Village is located in a rural area that has extraordinary natural and cultural tourism potential. Tourists often come to this village to enjoy the natural beauty, interact with local residents, and experience the rich traditional culture. However, in recent years, increasing tourist interest has also led to complex management problems. Cases that have occurred are a surge in tourist visits that are not well organized, ticket sales that are difficult to monitor, and difficulties in understanding the magnitude of the impact of tourism on the community. As a result of this community service, we also held a successful marketing campaign to promote the use of e-ticketing to tourists. Through social media, village websites, and physical promotional materials, the benefits of e-ticketing are clearly communicated. Travelers are provided with information on how e-ticketing can save them time, avoid long queues, and provide a more comfortable visiting experience. The results of this community service are very satisfying, with a significant increase in tourist visits and positive feedback from users. The digitalization of village tourism through e-ticketing training has opened new doors to ease and comfort in enjoying the charm of Cisantana Village.
Rancang Sistem Presensi Online dengan Metode Gamifikasi dan Online Collaborative Learning Dodi Solihudin; Iin Iin; Dian Ade Kurnia
INTERNAL (Information System Journal) Vol. 5 No. 2 (2022)
Publisher : Masoem University

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Abstract

Online lectures are still being held while Covid-19 pandemic is going to end. Suitable system is needed to ensure the quality of online learning. We must combine the teleconference with the Collaborative Learning System to maintenance the interactions between students and their lecturer. The research is to build a system that can implement the Collaborative Learning System in online lectures. The methods used are gamification and Extreme Programming. The results of the research is the Online Presence application with chat system features, real-time polling system, real-time question-answer, and real-time leaderboard. The application can be accessed athttps://ikmiapp.web.id/presline.
SMART ATTENDANCE TRACKING SYSTEM EMPLOYING DEEP LEARNING FOR FACE ANTI-SPOOFING PROTECTION Bani Nurhakim; Ahmad Rifai; Dian Ade Kurnia; Dadang Sudrajat; Ujang Supriatna
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.5992

Abstract

Conventional attendance systems face challenges in accuracy and efficiency, often vulnerable to spoofing and data manipulation. This study addresses these issues by developing a smart attendance system integrating Deep Learning-based facial recognition with anti-spoofing technology. The system ensures secure and reliable attendance authentication while automating and enhancing management processes. Utilizing a convolutional neural network (CNN) architecture, the system processes raw facial images directly without additional feature extraction, improving accuracy and efficiency. A novel training strategy, termed 50 Random Samples-30 Sub-epochs Count-1 Epoch, is introduced to optimize the training process. This strategy involves random sampling during each forward pass and grouping 30 passes as one epoch, enabling the use of complex CNN architectures and automatic dataset expansion. The system achieves 98.90% accuracy in identifying genuine attendance, maintaining a confidence level above 80%, significantly reducing spoofing risks and errors. This innovative solution has significant implications, particularly for educational institutions. It automates attendance tracking, minimizes manual effort, reduces errors, and supports disciplinary enforcement through accurate data. Moreover, its scalability allows for application across various environments, offering benefits to a wide range of institutions. By enhancing data accuracy and operational efficiency, this system sets a foundation for smarter, more reliable attendance management, strengthening administrative practices in education and beyond.
ADAPTIVE CLASS WEIGHTING DAN AUGMENTATION UNTUK KLASIFIKASI BATIK KERATON Witriyani Witriyani; Dian Ade Kurnia; Yudhistira Arie Wijaya; Mulyawan Mulyawan; Irfan Ali
Informatika: Jurnal Teknik Informatika dan Multimedia Vol. 6 No. 1 (2026): MEI : JURNAL INFORMATIKA DAN MULTIMEDIA
Publisher : LPPM Politeknik Pratama Kendal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/informatika.v6i1.1516

Abstract

This study aims to improve the performance of Batik Keraton motif classification on an imbalanced dataset through the integration of adaptive class weighting and data augmentation within a transfer learning framework. The dataset consists of 1,799 images across four classes (Kawung, Mega Mendung, Parang, Truntum), preprocessed to 224×224 pixels and split stratifiedly into training, validation, and test sets (80/10/10). Three transfer learning architectures—ResNet50V2, VGG16, and EfficientNetB0—were evaluated with adaptive class weighting and geometric augmentation to enhance minority-class representation. The results indicate that ResNet50V2 with pretrained weights achieved the best performance, reaching a test accuracy of 92.78%, macro precision of 93.13%, macro recall of 92.79%, and a macro F1-score of 92.83%. Adaptive class weighting improved sensitivity toward minority classes, while augmentation contributed to model stability and generalization. These findings demonstrate that combining adaptive weighting and augmentation effectively enhances Batik Keraton motif classification under imbalanced data conditions.  
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.
Penerapan Model LSTM Univariat dengan Walk-Forward Validation untuk Estimasi Harga Saham Nokia Ahmad Rifai; Roni Saputra; Dian Ade Kurnia; Fatihanursari Dikananda
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.v13i1.3000

Abstract

Prediksi harga saham merupakan permasalahan yang kompleks karena karakteristik data deret waktu finansial yang bersifat non-linear, volatil, dan dinamis. Meskipun algoritma Long Short-Term Memory (LSTM) terbukti efektif dalam menangkap pola temporal, banyak penelitian sebelumnya menggunakan pendekatan multivariat yang melibatkan variabel dengan korelasi sangat tinggi sehingga berpotensi menimbulkan redundansi informasi dan meningkatkan kompleksitas model. Penelitian ini mengusulkan model LSTM univariat untuk memprediksi harga saham Nokia Corporation (NOK) dengan menggunakan harga penutupan sebagai variabel masukan tunggal. Data historis harian periode 1 Oktober 2015 hingga 24 Oktober 2025 sebanyak 2.532 observasi diperoleh dari Yahoo Finance. Sebelum proses pemodelan, dilakukan analisis korelasi terhadap variabel Open, High, Low, Close, dan Volume. Hasil analisis menunjukkan bahwa variabel harga memiliki korelasi yang sangat tinggi (r > 0,99), sedangkan variabel Volume memiliki korelasi yang sangat rendah terhadap variabel harga (−0,052 ≤ r ≤ −0,043). Berdasarkan hasil tersebut, harga penutupan dipilih sebagai fitur utama dalam pemodelan. Untuk mengevaluasi performa model pada kondisi prediksi yang realistis, diterapkan metode Walk-Forward Validation (WFV) sebanyak 30 iterasi. Hasil pengujian menunjukkan bahwa model memperoleh nilai MSE sebesar 0,0260, RMSE sebesar 0,1613, MAE sebesar 0,1086, MAPE sebesar 2,75%, dan koefisien determinasi (R²) sebesar 0,9446. Hasil tersebut menunjukkan bahwa model mampu menjelaskan 94,46% variasi harga saham dengan tingkat kesalahan prediksi yang rendah. Penelitian ini menyimpulkan bahwa model LSTM univariat yang didukung oleh proses seleksi fitur yang sistematis dan validasi temporal yang robust mampu menghasilkan prediksi harga saham yang andal dengan kompleksitas yang lebih rendah dibandingkan pendekatan multivariat konvensional.
PENINGKATAN AKURASI KLASIFIKASI KEMATANGAN KELAPA SAWIT BERBASIS CITRA DENGAN ENSEMBLE DEEP LEARNING TEROPTIMASI DIMENSI RASIO Ahmad Rifai Ikhsanudin; Dian Ade Kurnia; Yudhistira Arie Wijaya; Dodi Solihudin; Tati Suprapti
Jurnal Mahasiswa Sistem Informasi (JMSI) Vol. 7 No. 2 (2026): Jurnal Mahasiswa Sistem Informasi (JMSI)
Publisher : Program Studi DIII Sistem Informasi - Universitas Muhammadiyah Metro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24127/jmsi.v7i2.11181

Abstract

Penentuan tingkat kematangan buah kelapa sawit secara manual sering menimbulkan subjektivitas dan menurunkan efisiensi. Penelitian ini mengembangkan metode klasifikasi berbasis citra menggunakan ensemble averaging pada tiga arsitektur MobileNetV2 dengan ukuran input berbeda (224×224, 224×300, dan 300×300) untuk mengurangi varians prediksi akibat variasi dimensi dan rasio aspek citra. Dataset yang digunakan berasal dari Kaggle berjumlah 1.380 citra, dengan pembagian 80% data latih dan 20% data validasi. Proses pengolahan mencakup rescaling, aspect-ratio-aware resizing, augmentasi, serta pelatihan menggunakan transfer learning dengan optimizer Adam dan early stopping. Hasil menunjukkan bahwa model berukuran 300×300 memberikan performa terbaik dengan akurasi 95,22% dan F1-score 0,9523. Ensemble averaging menghasilkan akurasi 94,71% dan F1-score 0,9475, yang meskipun sedikit lebih rendah dari model terbaik, memberikan stabilitas prediksi yang lebih baik dibanding model individual. Temuan ini menunjukkan bahwa resolusi input yang lebih tinggi meningkatkan kualitas ekstraksi fitur, sementara ensemble averaging tetap efektif dalam mereduksi varians dan meningkatkan ketahanan sistem klasifikasi di kondisi lapangan.
NAÏVE BAYES SENTIMENT ACCURACY WITH CHI-SQUARE AND INFORMATION GAIN Muhamad Fahrurozi; Dian Ade Kurnia; Yudhistira Arie Wijaya; Puji Pramudya Marta; Khaerul Anam
Antivirus : Jurnal Ilmiah Teknik Informatika Vol 20 No 1 (2026): Mei 2026
Publisher : Universitas Islam Balitar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35457/1nmksc45

Abstract

This study aims to evaluate the effectiveness of the Chi-Square and Information Gain feature selection techniques in improving the accuracy of sentiment classification on application reviews using the Naive Bayes algorithm. The main issue addressed in this research is the high dimensionality of features in Indonesian-language text data, which may potentially affect model performance. To address this, the study applies a text preprocessing pipeline consisting of sentiment labeling, normalization, and feature extraction using TF-IDF with an initial 5000 features. The dataset contains 225,043 Gojek application reviews, which were reduced to 220,860 valid entries after cleaning, and subsequently divided into training and testing sets using a stratified split. Experimental results show that Chi-Square achieved the highest accuracy of 0.877864 with 3000 features, while Information Gain reached an accuracy of 0.877751 with 4000 features. Both values are slightly lower than the model without feature selection, which achieved an accuracy of 0.878045. These findings indicate that feature reduction does not improve the performance of Naive Bayes, as the algorithm performs more effectively when retaining a broader distribution of words and more complete contextual representations. In conclusion, feature selection for probabilistic models should be applied cautiously, especially on Indonesian-language review data that are informal and exhibit high variation in expression.
Perbandingan Kinerja VGG 16 dan ResNet untuk Pengenalan Ekspresi Wajah Mahasiswa Berbasis CNN pada Smart Learning Environment Dian Ade Kurnia; Fatihanursari Dikananda; Saeful Anwar; Dadang Sudrajat; Abdul Aziz
TEMATIK Vol. 12 No. 2 (2025): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Desember 2025
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v12i2.2590

Abstract

Perkembangan teknologi kecerdasan buatan (AI) dan visi komputer telah membuka peluang besar dalam penerapan pengenalan ekspresi wajah pada berbagai bidang. Dalam konteks pendidikan tinggi, keterlibatan mahasiswa selama proses belajar menjadi faktor penting yang masih sulit diukur secara objektif menggunakan metode konvensional. Namun pada kenyataannya, penelitian sebelumnya masih jarang menguji performa arsitektur CNN populer secara langsung di lingkungan pembelajaran nyata dengan kondisi pencahayaan dan pose yang beragam. Penelitian ini berkontribusi dengan membandingkan kinerja dua arsitektur deep learning, yaitu VGG-16 dan ResNet, dalam klasifikasi ekspresi wajah mahasiswa pada Smart Learning Environment. Penelitian dilakukan dengan pendekatan eksperimen kuantitatif melalui lima tahapan, yaitu pengumpulan data wajah mahasiswa di kelas, preprocessing berupa cropping, resizing, dan augmentasi, pengembangan model CNN, pelatihan menggunakan data split 80% training dan 20% validasi, serta evaluasi dengan metrik akurasi, presisi, recall, dan F1-score. Hasil eksperimen menunjukkan bahwa VGG-16 unggul dalam mengenali ekspresi suka dengan nilai F1-score tertinggi sebesar 85%, sedangkan ResNet relatif lebih baik pada ekspresi bosan dengan F1-score 73,2%. Sementara itu, keduanya sama-sama lemah dalam mengenali ekspresi tidak suka. Temuan ini mengimplikasikan bahwa VGG-16 lebih sesuai digunakan untuk mendukung analisis keterlibatan mahasiswa secara real-time dalam Smart Learning Environment berbasis AI.
Deteksi Serangan SQL Injection Berulang pada Log Apache menggunakan Semantic Embedding dan Machine Learning Rosidin; Dian Ade Kurnia; Dadang Sudrajat
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.v13i1.2909

Abstract

SQL Injection (SQLi) attacks are a major threat to web application security, with attackers frequently attempting to modify query patterns repeatedly to evade detection. This research aims to develop a method for detecting repeated SQL Injection attacks in Apache logs using a semantic embedding approach. The method employed includes preprocessing log data using Natural Language Processing (NLP) techniques, generating vector representations through semantic embedding, and classifying using machine learning algorithms. The novelty of this research lies in the application of semantic embedding on Apache log queries to identify recurring and polymorphic SQL Injection attacks based on contextual similarity rather than exact syntactic matching. Experimental results show that the Support Vector Machine (SVM) model achieved the best performance with an accuracy of 94.5%, precision of 92.0%, recall of 91.0%, and F1-score of 91.5%, outperforming conventional rule-based detection methods.. The results show that the proposed approach is capable of recognizing semantic similarities between queries despite syntactic differences, thereby improving detection accuracy and reducing false positive rates compared to rule-based methods. Therefore, this method is effective for large-scale log analysis and is capable of detecting repeated and polymorphic SQL Injection attack pattern in web application 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