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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of Telematics and Informatics Jurnal sistem informasi, Teknologi informasi dan komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) Infotech Journal Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Komputer Terapan JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Justek : Jurnal Sains Dan Teknologi Jurnal Sisfokom (Sistem Informasi dan Komputer) JURNAL TEKNOLOGI DAN OPEN SOURCE Jurnal Manajemen Informasi Kesehatan Indonesia (JMIKI) Building of Informatics, Technology and Science GUYUB: Journal of Community Engagement Jurnal Teknologi Informasi Cyberku COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Nusantara Journal of Computers and its Applications JAR'S (Journal of Advanced Research in Informatics) Babakti : Journal of Community Engangement Transaction on Informatics and Data Science SINAR: Sinergi Pengabdian dan Inovasi untuk Masyarakat JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Babakti: Journal of Community Engangement
Fathorazi Nur Fajri
Universitas Nurul Jadid
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

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Penerapan Machine Learning untuk Penentuan Mata kuliah Pilihan pada Program Studi Informatika Fathorazi Nur Fajri; Abu Tholib; Wiwin Yuliana
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 3 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i3.3990

Abstract

Informatics study program at Nurul Jadid University does not have a general concentration of knowledge, so that sometimes the selection of elective courses by students is not quite right. This study aims to classify the concentration of knowledge with a data mining approach which can then be used as a recommendation for selecting elective courses by students. In this study, we implement a machine learning algorithm to provide recommendations to students regarding what interests are more suitable to be taken based on the values ​​of prerequisite courses in previous semesters. Student data was obtained from the Head of the Center for Data and Information Systems (PDSI) at Nurul Jadid University with 70 student data from Nurul Jadid University batch 2018. The machine learning algorithm used is Neural Network with Python programming language, the tools used are Google Collab. At the beginning of data collection, then pre-processing is carried out to prepare the dataset in order to get good results, and model training is carried out. After training on the model, then further testing is carried out on the model to determine the performance of the model. The result of the accuracy value in the training model process is 0.83 or 83% and the accuracy of the test data is 0.79 or 79%.
Pengembangan Sistem Identifikasi Ancaman Keamanan pada Sistem Penerimaan Mahasiswa Baru dengan Framework Laravel Halimi, Ahmad; Fajri, Fathorazi Nur; Rizal, Fathur
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 6, No 1 (2025): Kecerdasan Buatan dalam Meningkatkan Efisiensi Bisnis
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/coreai.v6i1.11537

Abstract

Perkembangan transformasi digital yang semakin pesat telah digitalisasi mendorong institusi pendidikan mengadopsi sistem Penerimaan Mahasiswa Baru (PMB) berbasis web. Namun, sistem ini rentan terhadap berbagai serangan siber seperti SQL Injection, XSS, dan bot attack. Penelitian ini bertujuan mengembangkan keamanan berbasis Laravel yang mampu mendeteksi dan menangkal ancaman tersebut secara otomatis. Metode yang digunakan adalah Research and Development (R&D), melalui tahap studi pendahuluan, perancangan, pengembangan, pengujian, dan evaluasi. Middleware yang dikembangkan memiliki fitur validasi input, deteksi pola serangan berbasis regex, pembatasan permintaan, logging aktivitas, serta geo-tracking. Hasil pengujian menunjukkan middleware mampu mendeteksi dan memblokir seluruh simulasi serangan dengan tingkat keberhasilan 100%, tanpa menurunkan performa sistem secara signifikan. Evaluasi pengguna juga menunjukkan peningkatan kepercayaan terhadap sistem PMB yang lebih aman dan tangguh. Penelitian ini membuktikan bahwa integrasi keamanan berbasis dapat menjadi solusi efektif untuk melindungi aplikasi web akademik dari ancaman siber.
Edukasi Cyber untuk Peningkatan Literasi Digital: Menuju Desa Smart People Fajri, Fathorazi Nur; Moh. Dzikrillah; Ahmad Khairi
Babakti: Journal of Community Engangement Vol. 2 No. 1 (2025): April
Publisher : Universitas Singaperbangsa Karawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35706/babakti.v2i1.67

Abstract

Kegiatan Pengabdian kepada Masyarakat (PKM) di Desa Karanganyar bertujuan meningkatkan literasi digital masyarakat, khususnya dalam penggunaan media sosial yang bijak dan bertanggung jawab. Program ini penting karena tingginya penggunaan media sosial belum diimbangi pemahaman tentang keamanan, etika, dan dampak negatif seperti hoaks dan cyberbullying. Edukasi ini dirancang untuk mewujudkan Desa Karanganyar sebagai "Desa Smart People" yang adaptif terhadap perkembangan teknologi. Analisis situasi menunjukkan rendahnya literasi digital dan minimnya edukasi formal di desa tersebut. Untuk mengatasi tantangan ini, diterapkan metode berupa ceramah, diskusi interaktif, simulasi praktis, serta pendampingan pasca-program. Keberhasilan kegiatan ini didukung oleh partisipasi aktif pemerintah desa dan tokoh masyarakat. Hasil program menunjukkan peningkatan signifikan dalam pemahaman peserta terkait literasi digital, bahaya hoaks, dan pentingnya menjaga privasi. Perubahan perilaku masyarakat terlihat dalam sikap yang lebih selektif terhadap informasi yang disebarkan. Program ini juga mendorong pemanfaatan media sosial untuk promosi produk lokal dan pariwisata desa. Secara keseluruhan, program ini meletakkan dasar transformasi digital di Desa Karanganyar dan dapat direplikasi untuk membangun masyarakat yang cerdas teknologi di desa lain.
Detection of Eight Skin Diseases Using Convolutional Neural Network with MobileNetV2 Architecture for Identification and Treatment Recommendation on Android Application Furqon, Ainul; Malik, Kamil; Fajri, Fathorazi Nur
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 2 (2024): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i2.28817

Abstract

Skin diseases are common in Indonesia due to the tropical climate, high population density, and low public awareness about skin health. These diseases are often caused by infections, chemical contamination, or other external factors and typically develop internally before becoming visible, with contact dermatitis being the most frequently reported condition. To address this issue, this research proposes the use of Artificial Intelligence (AI), specifically Convolutional Neural Network (CNN) with the MobileNetV2 architecture, to detect eight types of skin diseases, namely cellulitis, impetigo, athlete's foot, nail fungus, ringworm, cutaneous larva migrans, chickenpox, and shingles. MobileNetV2 was chosen for its efficiency and high accuracy in mobile applications. The methodology involves developing a detection system using CNN MobileNetV2, integrated into an Android application to identify skin diseases and provide treatment recommendations. The dataset was collected, labeled, resized, and normalized to meet the model requirements. After training, the model was tested using a separate dataset to ensure its generalization ability and was finally integrated into the Android application. This application allows users to detect skin diseases and receive treatment advice directly. The research results show that the CNN MobileNetV2 model achieves high accuracy in classifying the eight types of skin diseases, with stable performance over several training epochs. Evaluation of the test dataset revealed an overall accuracy of 97%, with high precision, recall, and F1-score for all disease classes. The application achieved an accuracy of 84% on general data, demonstrating its practical utility. However, the need for real-time updates of treatment information was identified as a limitation. This research advances skin disease detection technology and improves public access to accurate healthcare services. Future studies should focus on real-time treatment information updates and expanding the range of detectable diseases to enhance skin disease application.
Validation of New Student Registration Documents at Nurul Jadid University Using Convolutional Neural Network Fajri, Fathorazi Nur; Pratamasunu, Gulpi Qorik Oktagalu; Malik, Kamil
Transactions on Informatics and Data Science Vol. 1 No. 2 (2024)
Publisher : Department of Informatics, Faculty of Da'wah, UIN Saizu Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24090/tids.v1i2.12281

Abstract

Every year, Nurul Jadid University admits new students by registering them using the website. Each prospective new student can fill in data independently and upload documents such as Deeds, Family Register, Identity Cards, Diplomas, and SKHU. Often, prospective new students need clarification in uploading documents; for example, the place for uploading ID cards is filled with uploading diplomas and vice versa. It causes the uploaded data not to match the place or group. Today, no document validation technique can match these types of documents. Therefore, a way is needed to overcome this problem. One way to recognize the document type is by its visual form or image. There are several methods for identifying an image, namely deep learning and neural network models. Where the convolutional neural network is known to be fast in processing data in images, this research aims to validate documents on new student registration data with a deep learning method, namely convolutional neural network (CNN). The experimental results show that the proposed method can classify the Nurul Jadid University new student registration documents with an accuracy rate of 0.91, such as the birth certificate at 0.97, diploma documents at 0.88, Family card documents at 0.88, identity cards at 0.84, exam result certificate with an accuracy 0.94.
GAME EDUKASI SIMULASI PENGENALAN REAKSI UNSUR KIMIA DENGAN LINGKUNGAN BERBASIS VIRTUAL REALITY Hasan, Muhammad Fadil; Fajri, Fathorazi Nur; Muafi, Muafi
NJCA (Nusantara Journal of Computers and Its Applications) Vol 9, No 1 (2024): June 2024
Publisher : Computer Society of Nahdlatul Ulama (CSNU) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36564/njca.v9i1.365

Abstract

Pengembangan game simulasi untuk memfasilitasi pembelajaran reaksi unsur kimia telah menjadi topik yang semakin menarik dalam dunia pendidikan. Jurnal ini membahas penerapan teknologi Virtual Reality (VR) dalam pengembangan permainan simulasi untuk memperkenalkan konsep-konsep kimia kepada siswa Sekolah Menengah Atas (SMA) atau sederajat. Metode yang digunakan adalah pendekatan Scrum dalam pengembangan perangkat lunak, memungkinkan pengembangan aplikasi secara iteratif dan efisien. Aplikasi ini bertujuan untuk mengatasi hambatan dalam pembelajaran reaksi unsur kimia di sekolah, seperti ketidaktersediaan bahan praktik, keterbatasan anggaran, dan risiko kecelakaan, dengan menyediakan pengalaman interaktif yang mendalam dan aman bagi siswa. Dengan menggunakan Unity Game Engine dan Oculus VR Quest 2, lingkungan simulasi yang realistis dan menarik bagi pengguna berhasil diciptakan. Hasil pengujian yang melibatkan ahli media dan pengguna menunjukkan tingkat kepuasan yang tinggi terhadap aplikasi ini. Sebagian besar pengguna menyatakan bahwa aplikasi ini membantu mereka memahami konsep kimia dengan lebih baik dan meningkatkan minat mereka terhadap pelajaran unsur kimia. Temuan menunjukkan bahwa 95,45% dari seluruh responden merasa aplikasi game simulasi ini membantu mereka dalam memahami konsep eksperimen kimia dengan lebih baik. Dengan demikian, dapat disimpulkan bahwa pengembangan aplikasi permainan simulasi kimia berbasis VR ini berhasil memberikan kontribusi positif terhadap pembelajaran kimia di sekolah dan memperluas potensi penggunaan teknologi VR dalam pendidikan.
Classification of Final Project Titles Using Bidirectional Long Short Term Memory at the Faculty of Engineering Nurul Jadid University Warda, Faridatul; Fajri, Fathorazi Nur; Tholib, Abu
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1723

Abstract

Every year, the Faculty of Engineering at Nurul Jadid University forms a committee to manage the process of students' final projects from the title selection stage to the final examination process until graduation. The process of selecting the final project title is still done manually, namely by checking the titles one by one, which takes a long time and allows errors because there is a lot of data to check, so human errors can also occur. Therefore, this research proposes to use the Bidirectional Long Short Term Memory (BiLSTM) method to classify the final project title based on its grade category. Several experiments were conducted to generate the most appropriate labels. The first experiment produced 4 labels and the second experiment produced 2 labels. From the results of several experiments, it was concluded that the second experiment had the best accuracy results with the 'good enough' and 'good' classes. The oversampling technique was then applied to overcome overlapping data, and the turning process was then performed on several parameters that could re-optimize the previous accuracy result of 75.24% to 91.15%. With a configuration of 10 random state parameters, using 64 batch sizes and 50 epochs. In addition, model adjustments were made to the hidden layer by adding a dropout layer and relu activation.
Pemberdayaan UMKM Gula Aren melalui Inovasi Kemasan dan Pemasaran Digital untuk Meningkatkan Daya Jual Fajri, Fathorazi Nur; Malik, Kamil; Arifin, Miftahul; Badir, Muhammad
SINAR: Sinergi Pengabdian dan Inovasi untuk Masyarakat Vol 2 No 01 (2025): Oktober
Publisher : CV. Laskar Karya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65678/sinar.v2i01.297

Abstract

UMKM gula aren memiliki potensi besar dalam mendukung kemandirian ekonomi masyarakat, namun sering terkendala pada aspek kemasan dan strategi pemasaran yang masih konvensional. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk memberdayakan pelaku UMKM gula aren melalui pelatihan inovasi kemasan dan pemanfaatan digital marketing guna meningkatkan daya jual produk. Metode pelaksanaan meliputi sosialisasi, pelatihan desain dan pengemasan produk, pendampingan pembuatan label dan identitas merek, serta pelatihan pemanfaatan media digital (WhatsApp Business, marketplace, dan media sosial) sebagai sarana promosi. Evaluasi dilakukan dengan membandingkan pengetahuan mitra sebelum dan sesudah kegiatan, serta melihat perubahan kualitas kemasan dan jangkauan pemasaran. Hasil kegiatan menunjukkan adanya peningkatan pemahaman pelaku UMKM terkait pentingnya kemasan dan branding, kemampuan mendesain kemasan yang lebih menarik dan informatif, serta mulai aktifnya penggunaan platform digital dalam mempromosikan produk gula aren. Kegiatan ini diharapkan dapat meningkatkan daya saing produk gula aren lokal dan menjadi model pemberdayaan UMKM berbasis inovasi kemasan dan pemasaran digital.
Digital Fish Image Segmentation Using U-Net for Shape Feature Extraction Fajri, Fathorazi Nur; Dzikrillah, Mohammad; Khairi, Ahmad
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 7 No. 2 (2024): Jurnal Teknologi dan Open Source, December 2024
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v7i2.3968

Abstract

Segmentation of digital images of fish is an important challenge in image processing in the field of marine biology and aquaculture. Extraction of fish shape features through image segmentation can improve accuracy in species identification and fish population monitoring. The U-Net method, which is based on deep learning, has been proven effective in medical image segmentation and is beginning to be applied in fish image segmentation. This study aims to develop a fish digital image segmentation method using U-Net architecture for accurate and efficient fish shape feature extraction. The dataset used consists of 500 fish images of various shapes and sizes collected from various sources. The fish images were processed using a U-Net artificial neural network, which was trained and tested to obtain the best segmentation results, with evaluation using Intersection over Union (IoU). The segmentation results show that the U-Net method can produce precise segmentation, with a high degree of accuracy in extracting fish shape features. Evaluation of the segmentation metrics resulted in an IoU value of 0.88, indicating excellent performance in distinguishing the fish object from the background and accurately mapping the fish shape. The fish digital image segmentation method using U-Net is effective for fish shape feature extraction and can be applied in fish species identification and aquatic ecosystem monitoring.
Interactive Web and Open Data for Optimizing Village With A Design Science Research and Technology Acceptance Model Fajri, Fathorazi Nur; Rizal, Fathur; Halimi, Ahmad
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 6, No 2 (2025): Pemanfaatan Artificial Intelligence dalam Transformasi Digital
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/coreai.v6i2.13255

Abstract

Penelitian ini merespons rendahnya pemanfaatan open data serta keterbatasan interaktivitas pada profil potensi desa dengan merancang dan mengevaluasi sebuah website desa interaktif. Pendekatan Design Science Research (DSR) digunakan untuk menghasilkan prototipe sistem yang relevan dengan kebutuhan pemangku kepentingan, sedangkan evaluasi penerimaan dilakukan menggunakan Technology Acceptance Model (TAM) mencakup Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude Toward Using (ATU), dan Behavioral Intention (BI). Uji pengguna menunjukkan rerata konstruk berada pada kategori tinggi: PU = 4,23, PEOU = 4,11, ATU = 4,18, dan BI = 4,09. Temuan ini mengindikasikan bahwa pengguna menilai prototipe sistem bermanfaat, mudah digunakan, menumbuhkan sikap positif, serta mendorong intensi penggunaan dan rekomendasi. Secara substantif, integrasi open data ke dalam antarmuka yang responsif melalui peta interaktif dan visualisasi meningkatkan keterbacaan serta transparansi informasi publik desa. Hasil evaluasi selaras dengan prediksi TAM, yakni kemudahan penggunaan berkontribusi pada peningkatan persepsi kebermanfaatan yang pada gilirannya memperkuat niat menggunakan. Implikasi praktis mencakup pedoman implementasi platform desa berbasis data, sementara kontribusi teoretis memperluas bukti penerapan DSR dan TAM pada konteks tata kelola desa digital.
Co-Authors Abd Rahman Abda Abda Abdur Rohman Sholeh Abu Tholib Afiqur Rahman Agung Maulana Agung Wahyu Firdaus Ahmad Khairi Ahmad Khairi Arie Wahyudi Arifah, Indah Inayatul Badir, Muhammad Dewi Sholehatin Diyah Ayu Aprilingga Dzikrillah, Mohammad Fadli Hidayat, M. Noer Fajrullah Fajrullah Fathur Rizal Furqon, Ainul Gulpi Qorik Oktagalu Pratamasunu Habibah Habibah Hadiyanto, Ridwan Halimatus Sa'diya Halimi, Ahmad Hasan, Muhammad Fadil Hasbul Bahar Ibadi, Suhdil Indri Ratna Sugianti Kamil Malik Khairi, Ahmad Khoirun Nisa' Malik, Kamil Matbulul Khairi Maulana, Agung Maulidiansyah Maulidiansyah Miftahul Huda Milzamah Elvi Laily Moh Jasri Moh. Budi Utomo Setiawan Moh. Dzikrillah Moh. Rusli Azizi Mohammad Dzikrillah Muafi Muhammad Roziqin Musfiroh Musfiroh Nadiyatus Sholihah Nurul Hidayattun Nasyihin Priambodo, Wahyu Galih Puji Kurnia Sari Purwanto Purwanto Purwanto, Zendi Ari Ricardus Anggi Pramunendar Shodiq, Wali Jafar Siti Aisyah Suci Rahayu Agustini Suhdil Ibadi Syaiful . Syaiful Syaiful Uky Oktavia Risti Permatasari Uswatun Hasanah Wahab Sya’roni Wali Ja’far Shudiq Warda, Faridatul Wiwin Yuliana Yaqin, Moh. Ainol Yuliana, Wiwin Zainal Arifin Zainal Arifin