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All Journal Jurnal Agronomi Indonesia (Indonesian Journal of Agronomy) TEKNIK Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan Agrikultura Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) ASAS : Jurnal Hukum Ekonomi Syariah Jurnal Pamator : Jurnal Ilmiah Universitas Trunojoyo Madura Planta Tropika Jurnal Hukum Novelty Kultivasi METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Biosaintifika: Journal of Biology & Biology Education Journal of Tropical Crop Science Jurnal Kesehatan Viva Medika: Jurnal Kesehatan, Kebidanan dan Keperawatan Buletin Ilmiah Sarjana Teknik Elektro Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Ilmiah Permas: Jurnal Ilmiah STIKES Kendal JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Journal of Energy and Electrical Engineering (JEEE) International Journal of Robotics and Control Systems Jurnal Ilmu Komputer dan Informatika Jurnal Locus Penelitian dan Pengabdian Jurnal Pengabdian Masyarakat Jurnal Pusat Inovasi Masyarakat Jurnal Ilmu Komputer dan Teknologi (IKOMTI) DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY SEMINAR NASIONAL PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT Journal of Advanced Health Informatics Research SmartComp JURNAL MULTIDISIPLIN ILMU AKADEMIK Control Systems and Optimization Letters Jurnal Multidisiplin Pengabdian Masyarakat (JMPM) Jurnal Ilmiah Ekonomi Terpadu Kesmas: Jurnal Kesehatan Masyarakat Nasional (National Public Health Journal)
Purwono Purwono, Purwono
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Pendekatan Transfer Learning dan SMOTE untuk Klasifikasi Kanker Kulit pada Imbalanced Dataset Lutviana, Lutviana; Purwono, Purwono; Imam Ahmad Ashari
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Skin cancer is one of the most commonly diagnosed cancers worldwide, with the incidence increasing every year. While early detection is a key factor in reducing skin cancer mortality, conventional methods such as biopsy have limitations in terms of cost and invasiveness. This research applies a deep learning based approach for skin cancer classification with Convolutional Neural Networks (CNN) model using transfer learning method. 3 CNN architectures namely MobileNetV2, EfficientNetB0, and DenseNet121 are used to evaluate the performance of the model in detecting skin cancer. One of the main challenges in this research is the imbalanced dataset, which can cause bias in classification. The Synthetic Minority Over-Sampling Technique (SMOTE) was applied to improve the representation of minority classes. The dataset used comes from Kaggle and consists of 2,357 images classified into 9 skin cancer categories. The results show that the transfer learning method combined with SMOTE can significantly improve the accuracy of the model, especially in detecting classes with a smaller number of samples. The evaluation was conducted using accuracy, precision, recall, and f1-score metrics. This research is expected to contribute to the development of an artificial intelligence-based skin cancer detection system that is more accurate, efficient, and can be used as a tool for medical personnel in early diagnosis of skin cancer.
Sistem Informasi Uji Kelayakan Kendaraan Bermotor Berbasis Android (Studi Kasus pada CV. Axlindo Telematika Purwokerto) Setyawati, Endang; Adilla, Axl; Purwono, Purwono; Wibowo, Adhi; Santoso, Muhammad Hery
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2286

Abstract

This study discusses the development of a motor vehicle roadworthiness test information system at CV. Axlindo Telematika Purwokerto, which was previously carried out manually through the registration process, testing, recapitulation, and issuance of KIR certificates. This manual system made the service ineffective and time-consuming. The solution developed was an ECU (Electronic Control Unit)-based Electronic Scanner with NodeMCU RS232 support that can automatically read test data and store it on a server for access via the web or Android applications. The development method used a prototype with REST API integration. The test results showed an increase in effectiveness of 98.9%, efficiency of 86.6%, usefulness of 82.2%, and a difference in data transmission time from 19.2 seconds to 1.39 seconds. The main contribution of this study is the design of hardware and software integration that can improve the accuracy and speed of KIR testing based on an intelligent information system.
Pengembangan Keamanan Sistem Rekam Medis Berbasis Blockchain dengan Smart Contract Purwono, Purwono; Dewi, Pramesti; Kurniawan, Safar dwi
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 12, No 2 (2023): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v12i2.5143

Abstract

Manipulasi data kesehatan memicu keresahan masyarakat dan menurunkan tingkat  kepercayaan terhadap langkah antisipatif yang dilakukan pemerintah Indonesia. Teknologi Blockchain menjadi salah satu solusi untuk mencegah data kesehatan yang berpotensi untuk dimanipulasi. Smart contract adalah protokol yang berjalan di jaringan blockchain. Metode ini mengikat suatu kesepakatan antara beberapa pihak dalam suatu perjanjian. Data kesehatan ini dapat dilindungi dari pihak internal dengan membuat kontrak cerdas antara dokter, pasien, dan pengelola website. Data diagnosis yang dibuat oleh dokter baru adalah valid jika pasien setuju. Administrator hanya dapat mengakses data jika disetujui oleh dokter dan pasien.   Pengujian   keamanan   dilakukan   melalui serangan injeksi SQL. Sistem yang belum menerapkan kontrak pintar dapat dikompromikan melalui uji injeksi muatan, sedangkan sistem yang telah menerapkan kontrak pintar hanya dapat memecahkan kueri login. Pengujian manipulasi data 10 kali setelah login berhasil menunjukkan bahwa data yang telah disimpan tidak dapat diubah karena memerlukan kontrak pintar
Pemanfaatan Teknologi Machine Learning pada Klasifikasi Jenis Hipertensi Berdasarkan Fitur Pribadi Dewi, Pramesti; Purwono, Purwono; Kurniawan, Safar Dwi
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 11, No 3 (2022): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v11i3.3721

Abstract

Hipertensi tampaknya menjadi faktor utama dalam perkembangan penyakit seperti stroke, gagal jantung, infark miokard, fibrilasi atrium, penyakit arteri perifer, dan diseksi aorta. Prediksi dini jenis hipertensi dari riwayat kesehatan merupakan hal yang penting agar kita dapat mengetahui penyakit yang disebabkan olehnya. Prediksi ini dapat diperoleh dengan memanfaatkan teknologi machine learning untuk menemukan pengetahuan baru dari data dasar sehingga menemukan pola yang valid, berguna, dan mudah dipelajari. Model klasifikasi random forest diusulkan dalam penelitian ini. Kontribusi kami dalam penelitian ini adalah membuat model klasifikasi random forest dengan teknik baru yaitu perbaikan data untuk melakukan tuning hyperparameter. Kami melihat peneliti sebelumnya hanya mengejar nilai akurasi yang tinggi semata. Berbeda dengan penelitian sebelumnya, kami menggunakan teknik optimasi hyperparameter gridsearch cv pada model klasifikasi random forest. Parameter terbaik untuk model random forest yaitu max_depth = 80, max_features = 3, min_samples_leaf = 3, min_samples_split = 8, dan n_estimators = 1000 yang direkomendasikan dari teknik gridsearch cv. Akurasi sebelum optimasi adalah 72,3%, sedangkan setelah optimasi adalah 86,1%. Hal ini menunjukkan peningkatan akurasi sebesar 13,7% setelah menerapkan metode grid search cv pada klasifikasi jenis hipertensi menggunakan model random forest
Pendekatan Transfer Learning dan SMOTE untuk Klasifikasi Kanker Kulit pada Imbalanced Dataset Lutviana, Lutviana; Purwono, Purwono; Imam Ahmad Ashari
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 9 No. 2 (2025): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol9No2.pp323-331

Abstract

Skin cancer is one of the most commonly diagnosed cancers worldwide, with the incidence increasing every year. While early detection is a key factor in reducing skin cancer mortality, conventional methods such as biopsy have limitations in terms of cost and invasiveness. This research applies a deep learning based approach for skin cancer classification with Convolutional Neural Networks (CNN) model using transfer learning method. 3 CNN architectures namely MobileNetV2, EfficientNetB0, and DenseNet121 are used to evaluate the performance of the model in detecting skin cancer. One of the main challenges in this research is the imbalanced dataset, which can cause bias in classification. The Synthetic Minority Over-Sampling Technique (SMOTE) was applied to improve the representation of minority classes. The dataset used comes from Kaggle and consists of 2,357 images classified into 9 skin cancer categories. The results show that the transfer learning method combined with SMOTE can significantly improve the accuracy of the model, especially in detecting classes with a smaller number of samples. The evaluation was conducted using accuracy, precision, recall, and f1-score metrics. This research is expected to contribute to the development of an artificial intelligence-based skin cancer detection system that is more accurate, efficient, and can be used as a tool for medical personnel in early diagnosis of skin cancer.
A Narrative Review of Privacy Preserving Artificial Intelligence in Nursing Practice Through Federated Learning Iis Setiawan Mangkunegara; Purwono, Purwono
Viva Medika Vol 18 No 3 (2025)
Publisher : LPPM Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/vm.v18i3.2226

Abstract

The rapid integration of artificial intelligence in nursing practice has enhanced predictive analytics, clinical decision support, and workforce management. However, concerns regarding data privacy, data silo fragmentation, and limited model generalizability remain significant challenges. Federated learning has emerged as a privacy preserving distributed machine learning approach that enables collaborative model development without transferring raw patient data across institutions. This narrative review aims to examine the conceptual foundation of federated learning and analyze its relevance for nursing practice and research. A literature search was conducted using Scopus and ScienceDirect databases covering publications from 2015 to 2025. Articles were analyzed through thematic synthesis focusing on technical architecture, clinical applications, ethical implications, and implementation challenges. The review indicates that federated learning has substantial potential to support predictive risk modeling, multicenter nursing outcome research, and integration within clinical decision support systems while maintaining patient confidentiality. Nevertheless, challenges related to non identical data distribution, governance accountability, interoperability, and digital literacy among nurses must be addressed to ensure safe and equitable implementation. Federated learning represents a strategic pathway for developing collaborative and privacy conscious artificial intelligence in nursing, provided that ethical safeguards, standardized data frameworks, and institutional readiness are systematically strengthened.
Scoping Review Kecerdasan Artifisial Dalam Optimasi Dosis dan Pemantauan Keamanan Obat Antidiabetik Meilani, Reina; Purwono, Purwono
Seminar Nasional Penelitian dan Pengabdian Kepada Masyarakat 2025 Prosiding Seminar Nasional Penelitian dan Pengabdian Kepada Masyarakat (SNPPKM 2025)
Publisher : Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/snppkm.v4i1.1423

Abstract

The use of artificial intelligence in diabetes therapy for dose optimization and safety monitoring of antidiabetic drugs has increased substantially over the past decade. This scoping review was conducted to map the types of AI models applied, to evaluate their impact on glycemic control, and to analyze their contribution to strengthening pharmacovigilance systems. Approaches including machine learning, deep learning, and reinforcement learning have been implemented to model nonlinear dose–response relationships and to identify plateau effects. Adaptive dosing recommendations have been generated using clinical data and continuous glucose monitoring inputs. Improvements in time in range and reductions in HbA1c levels have been reported in comparison with conventional therapeutic approaches. In drug safety monitoring, detection and analysis of adverse drug reactions have been enhanced through the application of natural language processing, Bayesian modeling, and generative AI. Data extraction from electronic health records and individual case safety reports has been performed more efficiently and systematically. Causality assessment processes have been accelerated, leading to improved efficiency in risk evaluation. AI integration in diabetes management has also been implemented through closed-loop systems, real-time glucose prediction, and identification of patients at risk of inappropriate dosing.Several methodological and regulatory challenges remain, including data bias, limited external validation, and concerns regarding algorithmic transparency. The need for real-world validation and strengthened ethical and governance frameworks has been identified to ensure safe and accountable clinical implementation
A Scoping Review of Machine Learning Applications in Nursing Practice: Clinical Decision Support, Risk Prediction, and Workflow Optimization Anton Suhendro; Wahyu Caesarendra; Purwono, Purwono
Viva Medika Vol 18 No 3 (2025)
Publisher : LPPM Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/vm.v18i3.2222

Abstract

Machine learning (ML) is rapidly transforming nursing practice by enabling advancements in clinical decision support, risk prediction, and workflow optimization. This scoping review synthesizes evidence from empirical studies, reviews, and implementation reports published between 2018 and 2025, identified through Scopus and ScienceDirect. The findings indicate that supervised learning algorithms, deep learning, and natural language processing are widely utilized for risk assessment, early detection of patient deterioration, and enhancement of administrative efficiency. Natural language processing (NLP) also supports automation of nursing documentation and improved data quality. Despite favorable performance metrics, including AUROC values above 0.85 in many applications, most studies are limited by single-institution data, insufficient external validation, and heterogeneous reporting standards. Major barriers include ethical and legal concerns, data quality issues, algorithmic bias, infrastructural limitations, and limited nurse involvement in model development. Enhancing AI literacy and fostering nurse engagement in system design are highlighted as critical for successful clinical integration. Future research priorities include multicenter validation, development of explainable AI, adoption of standardized reporting guidelines, and interdisciplinary collaboration to address ethical, technical, and regulatory challenges. Overall, this scoping review demonstrates that machine learning offers substantial potential to improve patient outcomes and nursing operations, but responsible adoption requires rigorous validation, transparent governance, and active participation of nursing professionals throughout the technology lifecycle
Penerapan Cloud AI dalam Penyusunan Assessment For Learning Listiyani, Listiyani; Cety Wahyu Muslimah; Asmah Kustati; Lilis Tri Fariyah; Agus Triwidodo; Purwono, Purwono
JURNAL MULTIDISIPLIN ILMU AKADEMIK Vol. 3 No. 2 (2026): JURNAL MULTIDISIPLIN ILMU AKADEMIK (JMIA)  April 2026
Publisher : CV. KAMPUS AKADEMIK PUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61722/jmia.v3i2.9217

Abstract

This study describes the application of Cloud AI technology in the preparation of Assessment for Learning (AfL) in learning at State Islamic Senior High School (Madrasah Aliyah) in Magelang City, State Islamic Senior High School 3 in Sragen, State Islamic Senior High School 1 in Sragen, and State Islamic Senior High School in Sukoharjo, and identifies the challenges and efforts of teachers in its implementation. This research method uses a qualitative approach. The research subjects were teachers at State Islamic Senior High School (Madrasah Aliyah) in Magelang City, State Islamic Senior High School 3 in Sragen, State Islamic Senior High School 1 in Sragen, and State Islamic Senior High School in Sukoharjo. The informants of this study were teachers. Data collection techniques were interviews, observation, and documentation. The results showed that Cloud AI created a more systematic, fast, and data-driven formative assessment workflow. The process began with the analysis of learning objectives and competencies, followed by indicator mapping, and then the design of AfL instruments such as diagnostic quizzes, reflective questions, and formative assignments with the help of Cloud AI to be adaptive, varied, and support differentiated learning. Cloud AI also facilitates the real-time collection of AfL results through a cloud-based platform, including student answers, completion time, and error patterns, which are analyzed to identify misconceptions, gaps in understanding, and student learning progress. These findings enable teachers to provide rapid, meaningful, and personalized formative feedback, along with recommendations for remediation and enrichment. However, the implementation of Cloud AI still faces two main challenges: limited infrastructure and unequal digital access, and teachers' difficulty integrating AI analysis results into daily pedagogical practices in a contextual manner. Teachers' efforts to overcome these challenges include improving digital literacy and understanding of AI through ongoing training such as workshops, webinars, learning communities, and competency development programs. With the support of infrastructure and professional mentoring, Cloud AI has the potential to strengthen AfL's function in supporting student learning more effectively.
Artificial Intelligence Applications in Community and Home Nursing Care: A Systematic Literature Review Berliana Rahmadhani; Purwono, Purwono; Muhammad Ahmad Baballe; Isa Ali Ibrahim
Viva Medika Vol 19 No 1 (2026)
Publisher : LPPM Universitas Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35960/vm.v19i1.2235

Abstract

Healthcare systems face increasing demand for community and home nursing care due to population aging, chronic disease prevalence, and hospital resource limitations. Artificial intelligence (AI) has emerged as a supportive technology with potential to enhance nursing practice in decentralized care environments. This systematic literature review synthesizes recent evidence on AI applications in community and home nursing care. The review followed PRISMA 2020 guidelines and analyzed fifteen peer-reviewed studies published between 2022 and 2025. The findings indicate that machine learning–based predictive analytics and decision-support systems are the most frequently implemented technologies. AI applications primarily support risk prediction, remote monitoring, chronic disease management, and workflow optimization. Reported outcomes include improved clinical vigilance, enhanced care coordination, and increased operational efficiency. However, implementation challenges remain, including infrastructure readiness, digital literacy gaps, ethical governance concerns, and data privacy risks. Overall, AI functions as an augmentative tool that strengthens professional nursing judgment rather than replacing it. Sustainable integration in community and home nursing care requires digital competence, regulatory alignment, and human-centered implementation strategies.
Co-Authors Abdul Matin, Hashfi Hawali Adhi Wibowo Adilla, Axl Adji, Diva Permata Adriani, Vita Afrilies, Marlia Hafny Agung Karuniawan Agus Triwidodo AHMAD JUNAEDI Ainurrofiq, Mohammad Naffah Alfian Ma’arif Anas Dinurrohman Susila Anik Sarminingsih, Anik Annastasya Nabila Elsa Wulandari Annida Unnatiq Ulya Anton Suhendro Ardhi Ristiawan Ardianto, Rian Arfianto, Irfan Arif Setia Sandi A. Arya Rezagama Asmah Kustati Bala Putra Dewa Basil, Noorulden Berliana Rahmadhani Budi Nugroho Budiyono Budiyono Cahyani, Gesa Nur Cety Wahyu Muslimah Deli, Syekh Zulfadli Arofah Dewa, Bala Putra Dharend Lingga Wibisana Dita Purwinda Anggrella Dyah, Dwi Tristining Eka Maulidiya, Sherly Eka Wardhani S., Eka Endang Setyawati Eso Solihin Fadillah, Arvin Muhammad Fakhri Zahi Mumtaza Fathurrahman, Haris Imam Karim Fathurrohman Husen Fatmawati, Puput Yosi Febria Cahya Indriani Firdausi, Eyda Fitriansyah, Muhammad Ramdhani Frisky, Aufaclav Zatu Kusuma Frutos, Roger Garunja, Evis Hadi Jayusman Hadiyanto Hadiyanto Hamdani, Kiki Kusyaeri Haq, Qazi Mazhar ul Hermanto Hermanto Hermawan Hermawan I Ketut Suada Iis Setiawan Mangkunegara Imam Ahmad Ashari Imam Ahmad Ashari, Imam Ahmad Indriyanto, Jatmiko Irdika Mansur Isa Ali Ibrahim Istiqomah, Hani Janu Saptari, Janu Jayusman, Hadi Josef, Hari Kusnanto Kencanawardhani, Larasati Gumilang Ketty Suketi Khairani Khairani KHOIRUN NISA Kurniawati, Ari Lilis Tri Fariyah Listiyani, Listiyani Lutviana Lutviana, Lutviana Mahfud Afandi, Mahfud Mangkunegara, Iis Setiawan Marhoon, Hamzah M. Marlin Sefrila Maulana, Haris Maya Melati Mei Ahyanti Meilani, Reina Mia Yustika, Mia Mochtar Hadiwidodo Mohamad Rahmad Suhartanto Mohammad Fatkhul Mubin, Mohammad Fatkhul Monica Puspa Dewi Muhammad Ahmad Baballe Muhammad Amin Bakri Munif Ghulamahdi murwanto, bambang Nadia Nuraniya Kamaluddin Nandang Hermanto Novieta Hardeani Sari Nurfaiz, Agus Nurhalizah, Ria Suci Nurul Fajri Ramadhani, Nurul Fajri Nurwulan Purnasari Pangesti, Lintang Desy Pascawati, Nur Alvira Prabowo, Zuhda Nur Pramesti Dewi Purwaningsih, Wida Putra, Jessa Syah Putri, Korisa Putri, Lystiana Dewi Rachman Hidayat Rahayu, Nur Laila Rahmaniar, Wahyu Restuono, Joko Rija Sudirja Rumbiwati, Rumbiwati Safa Kiana Safar Dwi Kurniawan Salah, Wael A. Sandra Arifin Aziz Santoso, Dwi Andreas Santoso, Muhammad Hery Saphira, Debby Bella Sarwono Sarwono Satriya Pranata Sefrila, Marlin Septin Puji Astuti Setiyaningrum, Ika Feni Setyo Supratno Sharkawy, Abdel-Nasser Silviani, Wahyu Dian Simanjuntak, Efendi Siti Aisah SITI RACHMAWATI Sudirman Yahya Suryo Wiyono Syaiful Anwar Titik Istirokhatun Tri Baskoro Satoto, Tri Baskoro Tri Yulianti Tri Yulianti, Tri Trigunarso, Sri Indra Tristiyaningrum, Diana Tuny, Nurfitriyana Vranada, Aric Wahyono, Yoyon Wahyu Caesarendra Wiharyanto Oktiawan Wiwit Rahajeng Wulandari, Annastasya Nabila Elsa Y.Paidjo Y.Paidjo, Y.Paidjo Yuris Tri Naili