cover
Contact Name
Meri Mayang Sari
Contact Email
cerita@raharja.info
Phone
+6281211103128
Journal Mail Official
cerita@raharja.info
Editorial Address
Jl. Jenderal Sudirman No. 40 Modern Cikokol Tangerang - Banten 15117 Indonesia
Location
Kota tangerang,
Banten
INDONESIA
Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics
Published by UNIVERSITAS RAHARJA
ISSN : 24611417     EISSN : 26552574     DOI : 10.33050/cerita
Journal CERITA: Creative Education Of Research in Information Technology And Artificial Informatics adalah jurnal ilmiah nasional yang diterbitkan oleh Universitas Raharja Tangerang guna mempublikasikan ringkasan hasil penelitian civitas akademika pada bidang informatika dan komputer.
Articles 220 Documents
Penerapan PSO-SVM Untuk Deteksi Serangan Web Dengan Pendekatan Hybrid Anomaly-Signature Based Pratama, Novandi Kevin; Junaidi, Achmad; Nurlaili, Afina Lina
Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Vol 11 No 1 (2025): Journal CERITA : Creative Education of Research in Information Technology and Ar
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/cerita.v11i1.3497

Abstract

The security of web applications is becoming increasingly crucial with the growing use of web platforms in education and business, especially due to the management of sensitive data. Attacks such as SQL Injection often pose serious threats to data integrity by exploiting weaknesses in input validation. Signature-based approaches are employed to detect known attacks, but they are often ineffective against new threats. On the other hand, anomaly-based approaches using Machine Learning can identify anomalous patterns but are typically slow for real-time detection. This study implements PSO-SVM (Particle Swarm Optimization-Support Vector Machine) to enhance the detection of attacks on web applications by combining anomaly and signature-based approaches. PSO is utilized to optimize SVM parameters, aiming to improve the accuracy of detecting new attacks and reduce the number of undetected threats. Evaluation through testing scenarios demonstrated an accuracy improvement of up to 99.3%, confirming that this hybrid approach is effective in enhancing the security of web applications.
Penerapan Simple Additive Weighting Sebagai Sistem Pendukung Keputusan Pemilihan dan Penentuan Siswa Terbaik Gulo, Nitema; Purba, Eduard Hotman; Supriyanti, Dedeh; Rakhmansyah, Mohamad
Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Vol 11 No 1 (2025): Journal CERITA : Creative Education of Research in Information Technology and Ar
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/cerita.v11i1.3679

Abstract

The purpose of this study is to design and create a decision support system for SMA Negeri 1 Cilegon, also known as SMANCIL. The use of the Simple Additive Weighting method to achieve the objectives of the study. So far, certain methods have not been used to select the best students, so that the decisions made are considered non-objective and not on target. Therefore, a decision support system is expected to help in making decisions about the best students. This study will investigate the case of finding the best alternative based on predetermined criteria. To calculate the weighted sum of the performance ratings for each alternative in all attributes, the SAW method is needed. By using the SAW method, several criteria are calculated to provide recommendations for the desired students. The ranking results show V6 = 0.97 and V28 = 1.00. Based on these results, it can be concluded that of all the specified criteria, V28 is the best student choice. This study is expected to produce a design for selecting the best students that will help teachers choose the best students according to their wishes.
Rancang Bangun Aplikasi Display Multimedia Dengan Fitur Kreasi Konten Interaktif Berbasis Augmented Reality Roedavan, Rickman; Leman, Abdullah Pirus
Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Vol 11 No 1 (2025): Journal CERITA : Creative Education of Research in Information Technology and Ar
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/cerita.v11i1.3688

Abstract

Bandung Techno Park (BTP) is one of Telkom University's innovation showcases frequently visited by students from both Indonesia and other countries such as Malaysia and Korea. However, BTP faces challenges in providing multimedia displays that effectively attract attention and offer interactive experiences for visitors. To address this issue, this study develops ARMazing, an Augmented Reality (AR)-based multimedia application designed to display various 3D animals in both realistic and cartoon forms. The research methodology includes literature review, system design based on the Multimedia Development Life Cycle (MDLC) model, and user testing and evaluation through a survey of 43 BTP visitors. The application features a creation mode, allowing users to select virtual animals, take photos, and record videos with them. Survey results indicate a positive response, with 83.2% of respondents appreciating the visual design, 81.4% valuing the educational content on animal species, and 90.7% enjoying the content creation feature. Based on the findings, ARMazing has proven to be an effective multimedia display medium that enhances attraction and interactivity at Bandung Techno Park while providing visitors with a more engaging educational experience.
Klasifikasi Citra Pada Wayang Kulit Menggunakan Convolutional Neural Network Nurhasanah, Wulandari; Witanti, Wina; Ashaury, Herdi
Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Vol 11 No 1 (2025): Journal CERITA : Creative Education of Research in Information Technology and Ar
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/cerita.v11i1.3700

Abstract

This research aims to develop a Convolutional Neural Network (CNN)-based shadow puppet image classification system by utilizing the ResNet-18 architecture, which is known to be efficient in handling image data and has a high level of accuracy. The system is designed to classify the Punakawan characters in shadow puppets, namely Bagong, Gareng, Petruk, and Semar, which are part of Indonesia's cultural heritage. In addition, this study also compares the performance of ResNet-18 with two other architectures, namely MobileNetV2 and DenseNet121. The dataset used consists of 2,148 images, which were obtained through live shooting and online searches. The images were processed using augmentation techniques and divided in a ratio of 70:15:15 for training, validation, and testing. The model was trained using optimal hyperparameters, such as learning rate 0.001 and batch size 32, to evaluate the performance of the three architectures. The evaluation results showed that the ResNet-18 architecture, as the main focus of the research, achieved an overall accuracy of 93.90%, with precision, recall, and F1-score of 94% each. In comparison, MobileNetV2 produced the highest validation accuracy of 96%, with better performance in generalization, while DenseNet121 produced a validation accuracy of 95%. This result confirms that although MobileNetV2 has the best performance in shadow puppet image classification, ResNet-18 still shows excellent results with simpler complexity, so it can be an efficient solution for the implementation of Punakawan shadow puppet classification system.
Prediksi Transaksi Minat Pembelian Online Menggunakan Kombinasi CNN Conv1D dan BiLSTM Herawati, Maimi; Kusrini, Kusrini
Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Vol 11 No 1 (2025): Journal CERITA : Creative Education of Research in Information Technology and Ar
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/cerita.v11i1.3702

Abstract

The rapid development of information technology has transformed consumer shopping behavior, particularly through e-commerce platforms. Online shopping has become a primary trend due to its convenience and the growing penetration of the internet. Understanding online purchase intention is therefore crucial for businesses in devising effective marketing strategies. Purchase intention is influenced by factors such as product quality, price, customer reviews, and platform usability. However, predicting purchase intention poses a significant challenge due to the large and complex nature of consumer data. Smote used for imbalance data. This study aims to combine CNN (Conv1D) and BiLSTM for high-accuracy purchase intention prediction. The research focuses on analyzing model accuracy and the effectiveness of the algorithms in handling imbalanced data. The results indicate that the combined CNN(Conv1D) + BiLSTM model achieves 97% accuracy with balanced evaluation metrics, although the True class recall (96%) is slightly lower than that of the False class (95%). Further optimization is needed to enhance overall model performance.
Audit Sistem Informasi Terhadap Sistem Pembelian Di Aplikasi Shopee Menggunakan Framework Cobit 5 Dwi, Rosmawati; Astriyani, Erna; Salsabila, Fadia; Fitriani, Hidayah; Gracia, Gracia
Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Vol 11 No 1 (2025): Journal CERITA : Creative Education of Research in Information Technology and Ar
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/cerita.v11i1.3714

Abstract

Aplikasi e-commerce seperti Shopee menjadi simbol penting dalam kemajuan perdagangan daring, menawarkan kemudahan akses, variasi produk yang luas, dan pengalaman berbelanja yang memuaskan. Di balik kecanggihan tersebut, keamanan dan integritas sistem informasi menjadi faktor krusial yang harus dijaga. Penulis melakukan penelitian ini berdasarkan studi pustaka tentang audit sistem informasi menggunakan framework COBIT 5 sub part DSS. Penulis menyebar kuesioner terhadap pengguna shopee. Hasil kuisioner lalu dinilai berdasarkan tingkat kematangan dan dianalisis. Hasil dari rekapitulasi tingkat model maturity level penelitian audit sistem terhadap pembelian di aplikasi Shopee, yaitu berada di level 3 dan 4. Kelemahan terdapat pada submain DSS05, dimana memiliki nilai kematangan paling kecil dari domain lainnya yaitu 3,42.
Sistem Pemantauan Kedisiplinan Santri Berbasis Citra Raspberry Pi Dan Internet Of Things Qadri, Khaerul; Razak, Mashur; Jalil, Abdul
Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Vol 11 No 2 (2025): Journal CERITA : Creative Education of Research in Information Technology and Ar
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/cerita.v11i2.3500

Abstract

The pesantren, as a traditional Islamic educational institution, faces significant challenges in maintaining discipline due to the large number of students and the extensive area. To address these challenges, an innovative discipline monitoring system using Internet of Things (IoT) technology, Raspberry Pi, and image recognition has been developed. This system employs a webcam connected to the Raspberry Pi to capture and analyze students' faces, activating a buzzer in response to detected disciplinary patterns. The system was installed in strategic locations, with real-time audio responses provided by the buzzer and data processed and recorded via a web-based platform. Analysis of the system's performance reveals that violations most frequently occur in the afternoon, accounting for 45.7%, followed by daytime violations at 30.4%. The system demonstrates high accuracy, efficiency, and reliability in detecting and managing disciplinary issues. These findings, illustrated in the accompanying charts and pie diagram, underscore the system’s operational efficiency, high detection accuracy, and effective data management capabilities, significantly enhancing discipline management and the overall quality of education in the pesantren.
Sistem Pendukung Keputusan Mata Pelajaran Pilihan Siswa Menggunakan Metode Simple Additive Weighting dan Weighted Product Fadila, Rischa Nuril
Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Vol 11 No 2 (2025): Journal CERITA : Creative Education of Research in Information Technology and Ar
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/cerita.v11i2.3507

Abstract

Sistem pendukung keputusan memiliki peran krusial dalam memfasilitasi pengambilan keputusan yang tepat, terutama dalam bidang pendidikan. Di SMA Negeri 2 Bojonegoro, penerapan Kurikulum Merdeka Belajar memberikan siswa kebebasan untuk memilih mata pelajaran yang sesuai dengan minat dan kemampuan mereka. Namun, siswa masih sering mengalami kesulitan dalam menentukan jurusan yang tepat, yang bisa berdampak buruk pada karier mereka di masa depan. Untuk mengatasi masalah ini, dikembangkanlah sebuah sistem pendukung keputusan yang memanfaatkan metode Simple Additive Weighting dan Weighted Product. Penelitian menunjukkan bahwa penerapan metode Simple Additive Weighting dan Weighted Product dalam sistem pendukung keputusan sangat efektif, dengan skor pengujian system usability scale masing-masing sebesar 84,75 dan 86. Skor ini masuk dalam kategori excellent dengan grade scale B, menandakan bahwa sistem ini layak digunakan. Sistem pendukung keputusan tidak hanya membantu siswa dalam memilih mata pelajaran yang sesuai, tetapi juga meningkatkan efisiensi guru dalam memberikan bimbingan yang lebih tepat dan terstruktur. Sistem pendukung keputusan yang dikembangkan di SMA Negeri 2 Bojonegoro dapat memudahkan penyajian informasi nilai rapor siswa saat kelas 10, memberikan rekomendasi jurusan kuliah, serta menentukan mata pelajaran pilihan yang sesuai dengan kemampuan dan minat siswa. Dengan demikian, sistem ini memiliki potensi untuk meningkatkan kualitas keputusan siswa serta prestasi akademik mereka.
Analisis Sentimen Komentar Instagram Terkait Persepsi Hidup Sehat Menggunakan Algoritma BERT-LSTM Putri, Audiva Tartila Daning
Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Vol 11 No 2 (2025): Journal CERITA : Creative Education of Research in Information Technology and Ar
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/cerita.v11i2.3526

Abstract

The perception of a healthy lifestyle is crucial for physical and mental well-being, especially with the rising cases of non-communicable diseases in Indonesia. Instagram is an effective platform for education, enabling sentiment analysis of the perception of a healthy lifestyle using comment data from posts on the @ayosehat.kemkes account. This involves data collection and preprocessing, labeling, and splitting the data into training, validation, and testing sets. By combining the BERT-LSTM algorithm to classify sentiment into positive, negative, and neutral, the testing results showed that the best model achieved an accuracy of 89.20%, with a precision of 89.49%, recall of 89.20%, and an F1-score of 88.74%, with an 80:10:10 dataset split ratio.
Analisis Ulasan E-commerce Menggunakan Fine Grained Sentiment Analysist dan Convolutional Neural Network Harun, Rusni; Razak, Mashur; Jalil, Abdul
Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Vol 11 No 2 (2025): Journal CERITA : Creative Education of Research in Information Technology and Ar
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/cerita.v11i2.3529

Abstract

Beragam ulasan dan komentar dari konsumen Aplikasi e-Commerce seringkali ditinggalkan pada kolom komentar merupakan pengalaman mereka saat mengadakan transaksi jual beli pada platform e-commerce dari yang sangat positif hingga sangat negatif dapat memberikan informasi berharga tentang kepuasan atau ketidakpuasan pelanggan. Ulasan seringkali di tulis dalam bahasa alami yang tidak terstruktur sehingga sulit dianalisis secara manual karena dalam skala besar. Penelitian ini dilakukan untuk menganalisis ulasan pada aplikasi e-commerce platform Bukalapak dan Tokopedia menggunakan metode Fine Grained Sentiment Analysis dan Convolutional Neural Network dengan 1000 dataset yang di scrawling dari google play store menggunakan google colab sebagai toolsnya. Penelitian ini bertujuan untuk memberikan informasi bagi perusahaan dari analisis sentimen yang diperoleh sehingga dapat merespons dengan cepat terhadap umpan balik pelanggan, dan kemudian bisa meningkatkan kualitas layanan, dan mengoptimalkan pengalaman belanja secara online. Penelitian ini menggunakan 5 kelas sentimen yaitu : sangat positif, positif, sangat negatif, negatif dan netral. Dari hasil eksperimen yang telah dilakukan hasil akurasi yang diperoleh dari aplikasi e commerce Tokopedia dengan epoch 10, 20, 40, 60, 80, 100 adalah 62.50 %, 59.26 %, 57.58 %, 48.39%, 51.85%, 65.62%, pada aplikasi Bukalapak adalah 62.50 %, 55.17 %, 62.86 %, 50.00%, 75.00%, 51.72%.

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