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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Technology and Computer Science JIEET (Journal of Information Engineering and Educational Technology) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Indonesian Journal of Electrical Engineering and Computer Science Indonesian Journal of Engineering and Technology (INAJET) Journal of Informatics and Computer Science (JINACS) Jurnal Ilmiah Edutic : Pendidikan dan Informatika J-Icon : Jurnal Komputer dan Informatika Repeater: Publikasi Teknik Informatika dan Jaringan Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics JURNAL LINTAS KARSA
Ervin Yohannes
Universitas Negeri Surabaya
Arts Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Materials Science & Nanotechnology Mechanical Engineering Social Sciences

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The Web-Based Birth Certificate Validity Information System for Disdukcapil Magetan Services Liswardana Gostafi Arifin; Ervin Yohannes
Journal of Informatics and Computer Science (JINACS) Article In Press
Publisher : Universitas Negeri Surabaya

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Abstract—Digital public services demand strict information security guarantees, particularly in the management of vital civil-registration documents such as birth certificates. This study designs and develops a web-based Birth Certificate Validity System (E-Akta) for the Population and Civil Registration Office (Disdukcapil) of Magetan Regency, replacing an unstructured WhatsApp-based workflow with an application that integrates the principles of an Information Security Management System (ISMS) based on ISO/IEC 27001. The system was built with the Django framework and emphasizes the three pillars of the CIA Triad: Confidentiality, Integrity, and Availability. A custom registration flow records applicant data, followed by mandatory core-data entry, a multi-step document-upload wizard, and a review page prior to permanent storage. The system was evaluated through Black-Box Testing of its functional modules, technical security testing of built-in Django safeguards, and a user-acceptance survey of ten respondents using a five-level Likert scale. The results show that built-in security features—CSRF protection, PBKDF2 password hashing, and server-side validation—successfully preserve data confidentiality and integrity against manipulation attempts, while a real-time status-tracking feature improves service availability for applicants and simplifies monitoring for administrators. Functional testing achieved a 100% success rate, and the user-acceptance survey produced an overall score of 92.8%, categorized as "Very Good." Keywords—Django; birth certificate validity; information security management system; CIA Triad; Black-Box Testing.
Image Captioning Using an InceptionV3 Encoder and a Vanilla Transformer Decoder on the Flickr30k Dataset Titis Dila Fajari; Ervin Yohannes
Journal of Informatics and Computer Science (JINACS) Article In Press
Publisher : Universitas Negeri Surabaya

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Abstract—Image captioning aims to automatically generate textual descriptions that accurately represent visual content. This study proposes a Sequence-to-Sequence image captioning framework that integrates an InceptionV3 encoder with a Vanilla Transformer decoder. The InceptionV3 network is employed to extract visual features from images, while the Vanilla Transformer generates captions by modeling contextual relationships between visual and textual representations. Experiments were conducted on the Flickr30k dataset using two data split scenarios (80:10:10 and 70:15:15) and two training configurations to evaluate the effectiveness of the proposed framework. Model performance was assessed using BLEU, METEOR, ROUGE-L, and CIDEr metrics. The best results were achieved using the 80:10:10 train-validation-test split and training configuration 1, obtaining BLEU-1, BLEU-2, BLEU-3, and BLEU-4 scores of 0.4252, 0.2946, 0.2176, and 0.1601, respectively, along with METEOR, ROUGE-L, and CIDEr scores of 0.4155, 0.4681, and 0.5059. These findings demonstrate that the proposed InceptionV3–Vanilla Transformer architecture is effective in generating accurate and contextually relevant image captions on the Flickr30k dataset. Keywords—Image Captioning, InceptionV3, Vanilla Transformer, Sequence-to-Sequence, Flickr30k.
Fatigue Detection Based on Facial Expressions Using ResNet50 with Grad-CAM Visualization Wike Septiana Vidya Utami; Ervin Yohannes
Journal of Informatics and Computer Science (JINACS) Article In Press
Publisher : Universitas Negeri Surabaya

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Abstract—Fatigue is a condition that can affect a person's performance and concentration, making an automatic system necessary for its rapid and accurate detection. This study applies a Convolutional Neural Network (CNN) architecture based on the pretrained ResNet50 model to classify facial images into two categories, fatigued and non-fatigued, using the Driver Drowsiness Dataset, which consists of approximately 41,700 images. The model was trained and validated using five data-split scenarios, namely 50:50, 60:40, 70:30, 80:20, and 90:10, with varying learning rates, batch sizes, and numbers of epochs. Performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results show that the best performance of the ResNet50 model was obtained with a 60:40 data split, a learning rate of 0.0001, a batch size of 16, and 30 epochs, achieving an accuracy of 99.22%, a precision of 99.67%, a recall of 99.86%, and an F1-score of 99.26%. Visual analysis using Grad-CAM showed that the model focused attention on the eyes, eyelids, cheeks, and forehead during the classification decision, improving the interpretability of the predictions. The ResNet50 model was also implemented in a camera-based system to provide real-time fatigue predictions. These results indicate that ResNet50 is effective and has the potential to be applied in a practical and efficient facial-image-based fatigue detection system. Keywords—ResNet50, Convolutional Neural Network (CNN), Fatigue Detection, Grad-CAM, Facial Image Classification.
Evaluation Of The Buku Pokok Pemakaman Application In Supporting Smart Governance Using The System Usability Scale (SUS) (A Case Study At The Department Of Population And Civil Registration Of Magetan Regency) Yulinda Seven Ningtyas; Ervin Yohannes
Journal of Informatics and Computer Science (JINACS) Article In Press
Publisher : Universitas Negeri Surabaya

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Abstract—Buku Pokok Pemakaman Application is a web-based system developed by the Population and Civil Registration Office of Magetan Regency to support the management of death data. This study aims to evaluate the usability of the application using the System Usability Scale (SUS) method and to provide improvement recommendations based on the evaluation results. The study involved 112 respondents, who are village and sub-district (desa and kelurahan) operators acting as active users of the application. Data were collected through task-scenario testing to measure effectiveness and efficiency, distribution of the SUS questionnaire to measure user satisfaction, and interviews to identify problems in using the application. The data were analyzed using the effectiveness, efficiency, and System Usability Scale (SUS) metrics. The results show an effectiveness level of 98.98%, categorized as very effective. An efficiency value of 0.121 goals/sec indicates that users were able to complete tasks well. Meanwhile, the SUS evaluation obtained an average score of 68.59, which falls into Grade C, with a High Marginal acceptance level and an OK adjective rating. These results indicate that the application has met the aspects of effectiveness and efficiency but still requires improvement to increase user satisfaction. Based on the evaluation results, this study produced five interface improvement recommendations, realized in the form of mockups referring to the Google Material Design guidelines. Kata Kunci— usability, System Usability Scale (SUS), buku pokok pemakaman, smart governance, information system evaluation.
Machine Learning-Based Land Cover Change Analysis in Surabaya Using Google Earth Engine Devika Setyaningrum; Ervin Yohannes
Journal of Informatics and Computer Science (JINACS) Article In Press
Publisher : Universitas Negeri Surabaya

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Abstrak—Penyebaran hoaks di kalangan pelajar Sekolah Menengah Atas (SMA) terus meningkat seiring tingginya intensitas penggunaan media digital yang tidak diimbangi kemampuan literasi digital memadai, sementara metode edukasi konvensional dinilai kurang mampu melibatkan generasi muda secara aktif. Penelitian ini bertujuan mengembangkan aplikasi edukasi mobile bergamifikasi bernama “Detektif Hoaks” yang mengintegrasikan kerangka evaluasi informasi CRAAP (Currency, Relevance, Authority, Accuracy, Purpose) guna meningkatkan kewaspadaan siswa SMA terhadap hoaks. Aplikasi dikembangkan menggunakan model ADDIE (Analysis, Design, Development, Implementation, Evaluation) dengan Flutter sebagai kerangka kerja front-end dan Supabase sebagai backend. Mekanisme gamifikasi yang diterapkan meliputi tiga level berjenjang, bank soal dinamis, sistem poin dan bintang, serta fitur ulasan jawaban. Pengujian fungsionalitas melalui black box testing menunjukkan tingkat keberhasilan 100% pada 17 skenario uji. Validasi ahli materi memperoleh persentase kelayakan 98,29% dan ahli media 96,7%, keduanya berkategori “Sangat Layak”. Uji coba kepada 24 siswa SMA menghasilkan tingkat penerimaan pengguna sebesar 98,83%, juga berkategori “Sangat Layak”, dengan 90,48% responden menyatakan merasa lebih mampu mengidentifikasi hoaks setelah menggunakan aplikasi. Temuan ini menunjukkan bahwa integrasi kerangka CRAAP ke dalam mekanika gamifikasi berbasis mobile merupakan pendekatan yang valid dan diterima baik sebagai media literasi digital preventif bagi siswa SMA. Kata Kunci—Gamifikasi, Literasi Digital, Hoaks, Kerangka CRAAP, Model ADDIE, Aplikasi Mobile, Flutter.
Co-Authors Ahmad Fikri Hanafi Asyrofa Rahmi, Asyrofa Avirmed Enkhbat Devika Setyaningrum Esti, Esti Yogiyanti Febriansyah, Aldin Liswardana Gostafi Arifin Muhammad Dzaki Salman Muhammad Farrell Saifulloh Mulyono, Alfito Nafisah, Nurun Paramitha Nerisafitra Putri, Windy Chikita Cornia Ramadhany, Sinta Dhea Rayyan Nur Fauzan Reza Pahlevi Ricky Eka Putra Ronggo Alit Shih, Timothy K. Titis Dila Fajari Utaminingrum, Fitri Wayan Firdaus Mahmudy Wike Septiana Vidya Utami Windy Chikita Cornia Putri Wiyli Yustanti Wiyli Yustanti Yogiyanti, Esti Yoyok Prastyo Yulinda Seven Ningtyas YUNI YAMASARI