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All Journal SISFOTENIKA Indonesian Journal on Software Engineering (IJSE) Jurnal Pilar Nusa Mandiri Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Informasi dan Teknologi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Infortech Jurnal Sistem Informasi SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Pengabdian Teknik dan Ilmu Komputer (PETIK) Jurnal Pengabdian Masyarakat Bangsa Jurnal Pariwisata, Bisnis Digital dan Manajemen Journal Medical Informatics Technology International Journal of Computer Technology and Science
Daniati Uki Eka Saputri
Universitas Nusa Mandiri
Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Optimization of Melanoma Skin Cancer Detection with the Convolutional Neural Network Kekal, Harming Puja; Saputri, Daniati Uki Eka
Journal Medical Informatics Technology Volume 1 No. 2, June 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i2.10

Abstract

Currently, skin cancer is a very dangerous disease for humans. Skin cancer is classified into many types such as Melanoma, Basal and Squamous cell carcinoma. In all types of cancer, melanoma is the most dangerous and unpredictable disease. Detection of melanoma cancer at an early stage is useful for effective treatment and can be used to classify types of melanoma cancer. New innovations in the classification and detection of skin cancer using artificial neural networks continue to develop to assist the medical and medical world in analyzing images precisely and accurately. The method used in this research is Convolutional Neural Network (CNN) with MobileNet model architecture. Skin cancer detection consists of five important stages, namely image database collection, preprocessing methods, augmentation data, model training and model evaluation. This evaluation was carried out using the MobileNet method with an accuracy of 88%.
Enhancing Skin Cancer Classification Using Optimized InceptionV3 Model Daniati Uki Eka Saputri; Nurul Khasanah; Aziz, Faruq; Taopik Hidayat
Journal Medical Informatics Technology Volume 1 No. 3, September 2023
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v1i3.14

Abstract

Skin cancer is a disease that starts in skin cells characterized by uncontrolled growth that can attack skin tissue. Although it has a high cure rate if treated in a timely manner, a delay in diagnosis can have serious consequences. The use of computer technology, especially Artificial Intelligence (AI), has played an important role in improving health services, including in the context of skin cancer. New innovations in the classification and detection of skin cancer using artificial neural networks have led to significant improvements in diagnosis and treatment. One promising approach is using the InceptionV3 algorithm, which has high accuracy and is capable of processing high-resolution images. This study aims to implement InceptionV3 to classify two types of skin cancer, namely malignant and benign, with an emphasis on improving accuracy performance. With the pre-processing process, namely augmentation and the addition of several features, this study aims to provide accurate and efficient results in skin cancer classification. The results of this study can have a positive impact in increasing the accuracy of early detection of skin cancer, especially by future researchers.
Efficient Skin Lesion Detection using YOLOv9 Network Faruq Aziz; Saputri, Daniati Uki Eka
Journal Medical Informatics Technology Volume 2 No. 1, March 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i1.30

Abstract

Skin lesion detection plays a crucial role in dermatological diagnosis and treatment. In this study, we propose an efficient approach for skin lesion detection using the YOLOv9 network. Leveraging state-of-the-art deep learning techniques, our model demonstrates robust performance in accurately identifying various skin lesion types, including acne, atopic dermatitis, keratosis pilaris, leprosy, psoriasis, and wart. We conducted comprehensive experiments using a curated dataset comprising 2721 training images, 288 validation images, and 145 test images. The model was trained and evaluated based on standard metrics such as Precision, Recall, and mean Average Precision (mAP). Our results indicate promising detection accuracy, with an overall Precision of 60.5%, Recall of 86.0%, and an mAP of 81.4%. Class-wise analysis reveals varying levels of performance across different disease classes, highlighting the model's proficiency in detecting common dermatological conditions such as acne and wart lesions. Furthermore, we provide insights into potential challenges and limitations, including dataset size and class imbalance, and discuss avenues for future research to address these issues. Our study contributes to the advancement of AI-driven solutions for dermatological diagnosis and underscores the efficacy of the YOLOv9 network in skin lesion detection
TELEMARKETING BANK SUCCESS PREDICTION USING MULTILAYER PERCEPTRON (MLP) ALGORITHM WITH RESAMPLING Masturoh, Siti; Nugraha, Fitra Septia; Nurlela, Siti; Saelan, M. Rangga Ramadhan; Saputri, Daniati Uki Eka; Nurfalah, Ridan
Jurnal Pilar Nusa Mandiri Vol 17 No 1 (2021): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v17i1.2168

Abstract

Telemarketing is a promotion that is considered effective for promoting a product to consumers by telephone, other than that telemarketing is easier to accept because of its direct nature of offering products to consumers. Telemarketing is also considered to help increase a company's revenue. The problem of predicting the success of a bank's telemarketing data must be done using machine learning techniques. Machine learning used in the available historical data is a bank dataset of 45211 instances at 17 features using the multilayer perceptron algorithm (MLP) with resampling. The use of resampling aims to balance the unbalanced data resulting in an accuracy value of 90.18% and a ROC of 0.89%. Meanwhile, if the data resampling is not used in the multilayer perceptron (MLP) algorithm, the accuracy value is 88.6 and ROC is 0.88%. The use of resampling data becomes more effective and results in higher accuracy values.
APPLICATION OF FUZZY LOGIC AND GENETIC ALGORITHM APPROACHES IN EVALUATION OF GAME DEVELOPMENT Saputri, Daniati Uki Eka; Aziz, Faruq; Khasanah, Nurul; Hidayat, Taopik; Septian, Rendi
Jurnal Pilar Nusa Mandiri Vol. 20 No. 1 (2024): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v20i1.5532

Abstract

The gaming industry is undergoing rapid evolution, presenting developers with intricate challenges in selecting compelling and successful game concepts. To tackle these challenges, decision support systems (DSS) play an increasingly crucial role in facilitating accurate decision-making. Despite their growing importance, the adoption of DSS within the gaming sector remains limited. Therefore, scientific research focused on developing DSS to evaluate optimal game concepts is essential to foster innovation in gaming industries. This study aims to construct a decision support system utilizing fuzzy logic and optimized with genetic algorithms to assess and identify game concepts with the highest potential for success in the market. Evaluation results highlight the system's effectiveness in recommending top-quality games like "Clash of Clans," "Honor of Kings," and "Genshin Impact," renowned for delivering exceptional gaming experiences and receiving high ratings. The system evaluation achieved an average Mean Squared Error (MSE) of 0.0246, indicating accurate prediction of game ratings with minimal error. The significance of this research extends beyond advancing decision support systems in gaming, opening avenues for further advancements in optimizing game evaluations and similar technologies across industries grappling with data-driven decision-making challenges.
Pengembangan dan Peningkatan Keterampilan Guru PAUD melalui Pelatihan Microsoft Word: Studi Kasus di PAUD Tunas Bangsa 05 Saputri, Daniati Uki Eka; Firasari, Elly; Khasanah, Nurul; Cahyanti, F. Lia Dwi
Jurnal Pengabdian Teknik dan Ilmu Komputer (Petik) PETIK : Jurnal Pengabdian Teknik dan Ilmu Komputer Vol. 4 No. 1 Juni 2024
Publisher : Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/petik.v4i1.13133

Abstract

The Microsoft Office training, specifically focusing on Microsoft Word, for the teaching staff of PAUD Tunas Bangsa 05 aimed to enhance technological skills to support educational quality. Conducted by a team of lecturers from Universitas Nusa Mandiri on May 18, 2024, the training was met with high enthusiasm from the participants. The methods employed included material presentation, hands-on practice, and Q&A sessions. Evaluation results indicated a significant improvement in participants' understanding and skills in using Microsoft Word. They were able to utilize the application to create more organized and professional documents, positively impacting the teaching-learning process. The activity successfully achieved its objectives and received positive feedback, also indicating the need for further training. Consequently, this training contributes to the professional development of the teaching staff and the enhancement of educational quality at PAUD Tunas Bangsa 05
Penerapan Metode Design Thinking untuk Perancangan UI/UX: APIW Aplikasi Image Watermarking Aziz, Faruq; Yanto, Yanto; Saputri, Daniati Uki Eka
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): Januari 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6204

Abstract

This study aims to apply the Design Thinking methodology in the design of the user interface (UI) and user experience (UX) for APIW, an image watermarking application based on invisible watermarking for copyright protection. The Design Thinking methodology is applied through five stages: Empathize, Define, Ideate, Prototype, and Test. The innovation in this research lies in the design of the UI/UX for APIW, focusing on the invisible watermarking feature that can still be verified even when the image is resized or reformatted. This study creates an intuitive interface for users without technical skills. Design evaluation using tree testing shows a success rate of 94.71% in finding the desired features, while the search system achieves 98%. Thus, this application is designed to provide ease for users in adding watermarks to their images without compromising aesthetics. This study concludes that the application of Design Thinking in the UI/UX design of APIW successfully creates a solution that is effective, intuitive, and responsive to user needs.
Multiclass Meat Classification Using a Hybrid Machine Learning Approach Taopik Hidayat; Daniati Uki Eka Saputri; Faruq Aziz; Nurul Khasanah
International Journal of Computer Technology and Science Vol. 2 No. 2 (2025): International Journal of Computer Technology and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijcts.v2i2.238

Abstract

Image classification is a key field in digital image processing with broad applications, such as object recognition and disease detection. The use of artificial neural network architectures, such as MobileNetV2, has significantly advanced pattern recognition in large datasets. However, in small datasets, challenges related to accuracy and generalization are often encountered. This study explores an RGB-based approach utilizing MobileNetV2 for image feature extraction and Support Vector Machine (SVM) as the classifier. MobileNetV2 is applied to extract features from RGB images, which are then further processed by SVM to determine image classes. The results indicate that this model achieves an accuracy of 91.67%, precision of 0.9163, recall of 0.9167, and F1-score of 0.9161. Based on the confusion matrix analysis, the model effectively distinguishes between classes, despite slight overlaps. This research contributes to the development of intelligent image classification systems that can be applied in various fields, including the food industry. With these achievements, the RGB approach integrating MobileNetV2 and SVM has proven effective in enhancing image classification accuracy, even with relatively small datasets. These findings open opportunities for applying similar methods in other image processing tasks that require high accuracy in object or disease detection and classification.
PENGEMBANGAN SISTEM REKRUTMEN LOWONGAN KERJA BERBASIS WEB UNTUK OPTIMALISASI BURSA KERJA KHUSUS (BKK) Saputri, Daniati Uki Eka; Sardiarinto, Sardiarinto
Jurnal Pariwisata Bisnis Digital dan Manajemen Vol. 4 No. 1 (2025): Jurnal Pariwisata, Bisnis Digital dan Manajemen Periode Mei 2025
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jasdim.v4i1.6796

Abstract

Bursa Kerja Khusus (BKK) at Vocational High Schools (SMK) plays a crucial role in connecting graduates with the workforce. However, the current manual process of disseminating job vacancies and handling applications leads to suboptimal information distribution and inefficient data management. This study aims to design and develop a web-based job recruitment system to improve the efficiency of information dissemination and the job application process for students and alumni of SMK Negeri 1 Ngawen. The system is developed using the Waterfall model, which includes the phases of requirements analysis, system design, implementation, testing, and maintenance. The results show that the developed system successfully delivers real-time job vacancy information, accelerates the application process, and improves the accuracy of matching candidates with available jobs. Testing conducted using the Black-Box Testing method confirms that the system functions as expected, ensuring proper input validation, secure access control, and high data integrity. It is expected that this system will enhance the employability of SMK graduates and make the recruitment process more efficient and effective.
Pelatihan Pembuatan Curriculum Vitae Berbasis AI untuk Mempersiapkan Karier di Era Society 5.0 Hasanah, Riyan Latifahul; Saputri, Daniati Uki Eka; Saelan, M. Rangga Ramadhan
Jurnal Pengabdian Masyarakat Bangsa Vol. 3 No. 3 (2025): Mei
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v3i3.2372

Abstract

Di tengah arus transformasi digital ini, persiapan karier tidak lagi cukup hanya mengandalkan kemampuan teknis dan akademik semata, tetapi juga memerlukan keterampilan dalam memanfaatkan teknologi untuk mempromosikan diri dan kompetensi yang dimiliki. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan literasi digital dan kesiapan karier generasi muda melalui pelatihan pembuatan curriculum vitae berbasis artificial intelligence (AI). Kegiatan ini diselenggarakan oleh dosen Fakultas Teknologi Informasi Universitas Nusa Mandiri, berkolaborasi dengan Yayasan Kopia Raya Insani, sebuah lembaga sosial yang memiliki fokus pada pengembangan sumber daya manusia melalui pendidikan, keagamaan, dan kegiatan sosial kemanusiaan. Permasalahan yang dihadapi mitra yaitu kurangnya pemanfaatan teknologi digital berbasis kecerdasan buatan (AI), khususnya untuk mempersiapan karier secara profesional. Pelatihan ini mengenalkan dan mengoptimalkan penggunaan Rezi AI sebagai sebuah platform berbasis kecerdasan buatan yang dirancang untuk membantu para pencari kerja dalam membuat resume, surat lamaran, dan mempersiapkan diri untuk wawancara kerja. Metode pelatihan dilakukan melalui ceramah interaktif, diskusi, dan praktik langsung menggunakan Rezi AI. Dengan pelatihan ini, peserta mampu mengembangkan kemampuan mendesain curriculum vitae (CV) secara mandiri sebagai bagian dari upaya mempersiapkan diri menghadapi tantangan dunia kerja masa kini. Luaran yang dihasilkan berupa press release pada media masa elektronik dan artikel ilmiah yang diterbitkan di Jurnal pengabdian masyarakat.
Co-Authors Abdul Latif Achmad Baroqah Pohan Ade Suryadi Agus Salim Annida Purnamawati Aziz, Faruq Cahyanti, F. Lia Dwi Dwi, F Lia Dwiza Riana Eko Arif Riyanto, Eko Arif Faruq Aziz Faruq Aziz Firasari, Elly Fitra Septia Nugraha Gata, Windu Hasanah, Riyan Latifahul Juninisvianty, Tri Kekal, Harming Puja Khultsum, Umi Lili Novita Monikka Nur Winnarto Nugraha, Fitra Septia Nurfalah, Ridan Nurul Khasanah Nurul Khasanah Pratiwi, Risca Lusiana Rangga Pebrianto Risca Lusiana Pratiwi - Universitas Nusa Mandiri Rosiyadi, Didi Saelan, M. Rangga Ramadhan Salsabila, Unik Hanifah Sardiarinto Sardiarinto Seimahuira, Syarah Septian, Rendi Siti Masturoh Siti Masturoh siti nurlela Solly Aryza Sri Hadianti Taopik Hidayat Umi Khultsum Veithzal Rivai Zainal YANTO YANTO