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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 Emerging Science Journal 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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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
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.
Environmental Impact of Energy Diversification Using Refuse-Derived Fuel in Cement Industry Firdausi, Eyda; Abdul Matin, Hashfi Hawali; Rachmawati, Siti; Wahyono, Yoyon; Purwono, Purwono; Budiyono, Budiyono; Kencanawardhani, Larasati Gumilang
Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan Vol 23, No 1 (2026): March 2026
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/presipitasi.v23i1.175-187

Abstract

Processing municipal solid waste into refuse-derived fuel (RDF) is an alternative solution to the waste problem. This study determined the potential environmental impact of Sleman RDF as an energy diversification agent in the cement industry using life cycle assessment (LCA). The boundary system of this study is gate-to-grave, with a functional unit of co-firing energy requirements for the production of 1 metric ton of clinker. Two clinker co-firing scenarios were developed, involving a combination of coal and alternative fuels. The results showed that the production of 1 metric ton of clinker requires a large amount of resources and contributes significantly to climate change, ecosystem toxicity, and human toxicity. Co-firing clinker in the alternative scenario successfully reduced the potential environmental impact by 14% with a thermal substitution rate of 20%. The findings of this study indicate that RDF is effective in reducing dependence on fossil fuels and lowering emissions and the potential environmental impact.
Understanding Large Language Models: A Review Wulandari, Annastasya Nabila Elsa; Purwono, Purwono; Ma’arif, Alfian; Basil, Noorulden; Marhoon, Hamzah M.
Control Systems and Optimization Letters Vol 4, No 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v4i2.292

Abstract

Large Language Models (LLMs) have experienced rapid development and have been established as the dominant paradigm in modern Natural Language Processing (NLP), with high performance demonstrated across various language understanding and generation tasks. Increasing architectural complexity has led to the need for a structured conceptual framework to explain how architectural design, training paradigms, and inference mechanisms are collectively associated with model behavior. A conceptual and analytical review of LLMs is presented in this article through an examination of the relationship between Transformer-based architectures, multi-stage training processes, and the resulting capabilities and limitations. Encoder-only, decoder-only, and encoder–decoder architectural variants are examined in relation to structural characteristics and functional implications. The roles of pretraining, supervised fine-tuning, and instruction tuning are analyzed to clarify how output characteristics are shaped during model development. This study emphasizes how architectural and training strategies causally influence generative capabilities and inherent limitations. Fundamental issues, including hallucination, bias, data dependency, computational cost, and evaluation challenges, are critically examined as consequences of the probabilistic modeling paradigm adopted in LLMs. This review contributes a structured analytical perspective for evaluating LLMs design choices and their operational consequences, supporting more informed development and deployment practices.
Developing Data Integrity in an Electronic Health Record System using Blockchain and InterPlanetary File System (Case Study: COVID-19 Data) Riadi, Imam; Ahmad, Tohari; Sarno, Riyanarto; Purwono, Purwono; Ma'arif, Alfian
Emerging Science Journal Vol. 4 (2020): Special Issue "IoT, IoV, and Blockchain" (2020-2021)
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/esj-2021-SP1-013

Abstract

The misuse of health data stored in the Electronic Health Record (EHR) system can be uncontrolled. For example, mishandling of privacy and data security related to Corona Virus Disease-19 (COVID-19), containing patient diagnosis and vaccine certificate in Indonesia. We propose a system framework design by utilizing the InterPlanetary File System (IPFS) and Blockchain technology to overcome this problem. The IPFS environment supports a large data storage with a distributed network powered by Ethereum blockchain. The combination of this technology allows data stored in the EHR to be secure and available at any time. All data are secured with a blockchain cryptographic algorithm and can only be accessed using a user's private key. System testing evaluates the mechanism and process of storing and accessing data from 346 computers connected to the IPFS network and Blockchain by considering several parameters, such as gas unit, CPU load, network latency, and bandwidth used. The obtained results show that 135205 gas units are used in each transaction based on the tests. The average execution speed ranges from 12.98 to 14.08 GHz, 26 KB/s is used for incoming, and 4 KB/s is for outgoing bandwidth. Our contribution is in designing a blockchain-based decentralized EHR system by maximizing the use of private keys as an access right to maintain the integrity of COVID-19 diagnosis and certificate data. We also provide alternative storage using a distributed IPFS to maintain data availability at all times as a solution to the problem of traditional cloud storage, which often ignores data availability. Doi: 10.28991/esj-2021-SP1-013 Full Text: PDF
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 Ardhi Ristiawan Ardianto, Rian Arfianto, Irfan Arif Setia Sandi A. Arya Rezagama Asmah Kustati Bala Putra Dewa Basil, Noorulden 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 Imam Riadi Indriyanto, Jatmiko Irdika Mansur 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 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 Riyanarto Sarno Rumbiwati, Rumbiwati Safa Kiana Safar Dwi Kurniawan 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 Tohari Ahmad Tri Baskoro Satoto, Tri Baskoro Tri Yulianti Tri Yulianti, Tri Trigunarso, Sri Indra Tristiyaningrum, Diana Tuny, Nurfitriyana Vranada, Aric Wahyono, Yoyon Wiharyanto Oktiawan Wiwit Rahajeng Wulandari, Annastasya Nabila Elsa Y.Paidjo Y.Paidjo, Y.Paidjo Yuris Tri Naili