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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Sainteks Proceedings of KNASTIK TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Informatika SPEKTRUM INDUSTRI Jurnal Sarjana Teknik Informatika JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics Seminar Nasional Informatika (SEMNASIF) Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika JTET (Jurnal Teknik Elektro Terapan) Jurnal Penelitian Pendidikan IPA (JPPIPA) Indonesian Journal of Artificial Intelligence and Data Mining Buffer Informatika Jurnal Sains dan Informatika Insect (Informatics and Security) : Jurnal Teknik Informatika JRST (Jurnal Riset Sains dan Teknologi) JITK (Jurnal Ilmu Pengetahuan dan Komputer) PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer JURTEKSI Mobile and Forensics Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat Masyarakat Berdaya dan Inovasi Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Innovation in Research of Informatics (INNOVATICS) Jurnal Teknik Informatika (JUTIF) EDUTECH : Jurnal Inovasi Pendidikan Berbantuan Teknologi Decode: Jurnal Pendidikan Teknologi Informasi Jurnal Mandiri IT Ahsana: Jurnal Penelitian dan Pengabdian kepada Masyarakat Sabangka Abdimas Jurnal Pengabdian Masyarakat Sabangka Control Systems and Optimization Letters Prosiding Seminar Nasional CORISINDO
Murinto Murinto
Program Studi Teknik Informatika Fakultas Teknologi Industri Universitas Ahmad Dahlan Yogyakarta
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry 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 Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Other

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Advanced Chicken Breed Identification Using Transfer Learning Techniques with the VGG16 Convolutional Neural Network Architecture Nagala Wangsa kencana; Rusydi Umar; Murinto
Jurnal Penelitian Pendidikan IPA Vol 11 No 7 (2025): July
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v11i7.11870

Abstract

This study proposes a deep learning-based classification system to identify chicken breeds from image data. A dataset of 2,400 labeled images representing twelve distinct chicken breeds was collected and divided into training, validation, and testing sets. The system employs transfer learning by integrating the Mobile VGG16 convolutional neural network as the feature extraction backbone. The extracted features were then passed through custom classification layers to differentiate among the breeds. The model was trained using 1,800 images, validated with 300 images, and evaluated on a separate test set of 300 images. During testing, the model achieved an accuracy of 81% and a categorical cross-entropy loss of 0.378. These results indicate that the model can effectively recognize subtle visual distinctions between similar-looking chicken breeds. The system demonstrates practical potential for applications in poultry farming, biodiversity documentation, and automated livestock management. The findings confirm that deep convolutional neural networks, particularly VGG16 in a transfer learning setup, are capable of performing fine-grained classification tasks in real-world scenarios. The proposed method provides a reliable and scalable solution for automatic chicken breed identification based on image input.
PREDICTIVE MODEL FOR COOPERATIVE LOAN RECIPIENT ELIGIBILITY USING SUPERVISED MACHINE LEARNING Rajunaidi, Rajunaidi; Yuliansyah, Herman; Sunardi, Sunardi; Murinto, Murinto
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.7235

Abstract

Non-performing loans remain a critical challenge for cooperatives as they can undermine financial stability, erode member trust, and impede institutional growth. This study develops a predictive model for cooperative loan eligibility using supervised machine learning techniques and a novel three-class classification framework, Approved, Consideration, and Rejected, to support more objective and transparent decision-making. A dataset of 1,000 borrower records containing demographic and financial attributes was analyzed using Naive Bayes, Decision Tree, and Random Forest algorithms implemented in RapidMiner. The Random Forest algorithm achieved the best predictive performance with an accuracy of 96.02%, demonstrating its robustness and reliability compared to the other models. The proposed three-class system differentiates this study from conventional binary classification approaches, enabling finer distinctions among borrower categories and promoting fairness in cooperative credit evaluations. The findings provide practical guidance for cooperatives to adopt data-driven, transparent, and accountable decision-making systems that reduce manual bias and strengthen financial inclusion. Overall, the proposed three-class model built through a supervised learning framework offers a reliable, fair, and scalable solution to support sustainable lending practices and enhance risk management in cooperative institutions.
PENERAPAN PRESENSI DARING BERBASIS WEBASSEMBLY DAN MICROSERVICES UNTUK PENGENALAN WAJAH PADA LEARNING MANAGEMENT SYSTEM Sismadi, Wawan; Riadi, Imam; Murinto, Murinto
EDUTECH : Jurnal Inovasi Pendidikan Berbantuan Teknologi Vol. 6 No. 2 (2026)
Publisher : Pusat Pengembangan Pendidikan dan Penelitian Indonesia (P4I)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51878/edutech.v6i2.9474

Abstract

The demand for reliable, real-time online attendance systems capable of handling large-scale users continues to increase alongside the widespread adoption of Learning Management Systems (LMS) in higher education and online training. Conventional attendance methods based on manual input or simple authentication mechanisms suffer from weaknesses such as susceptibility to fraud, limited automation, and degraded performance under high workloads. Face recognition has emerged as a promising alternative, as it enables automatic and non-intrusive user identity verification. However, most face-based attendance systems still rely on centralized server-side processing, which leads to high latency and limited scalability. This study aims to design and evaluate an online attendance architecture that integrates WebAssembly and Microservices by separating computational workloads between the client and server. The Design Science Research method is employed to develop a web-based face attendance application as the research artifact, in which face detection and feature extraction are executed entirely on the client side using OpenCV.js compiled to WebAssembly, while authentication, attendance recording, and session management are handled by a Microservices-based backend. The evaluation includes face recognition accuracy testing, end-to-end latency measurement, and system throughput analysis. Experimental results demonstrate that the proposed architecture reduces attendance latency by approximately 72 percent compared to a monolithic server-side processing approach, while simultaneously increasing request handling capacity without compromising accuracy. These findings indicate that the integration of WebAssembly and Microservices constitutes an effective architectural solution for real-time biometric attendance systems. ABSTRAKKebutuhan akan sistem presensi daring yang andal, real-time, dan mampu menangani skala pengguna besar terus meningkat seiring dengan meluasnya penggunaan Learning Management System (LMS) dalam pendidikan tinggi dan pelatihan daring. Metode presensi konvensional berbasis input manual maupun autentikasi sederhana memiliki kelemahan berupa potensi kecurangan, keterbatasan otomatisasi, serta performa yang menurun pada kondisi beban tinggi. Pengenalan wajah menjadi solusi alternatif yang menjanjikan karena mampu memverifikasi identitas pengguna secara otomatis dan non-intrusif. Namun, sebagian besar sistem presensi berbasis wajah masih bergantung pada pemrosesan terpusat di sisi server, yang mengakibatkan latensi tinggi dan keterbatasan skalabilitas. Penelitian ini bertujuan merancang dan mengevaluasi arsitektur presensi daring berbasis integrasi WebAssembly dan Microservices dengan pendekatan pemisahan beban komputasi antara klien dan server. Metode Design Science Research digunakan untuk mengembangkan artefak berupa aplikasi presensi wajah berbasis web, di mana proses deteksi dan ekstraksi fitur wajah dijalankan sepenuhnya di sisi klien menggunakan OpenCV.js yang dikompilasi ke WebAssembly, sedangkan autentikasi, pencatatan presensi, dan manajemen sesi ditangani oleh backend berbasis Microservices. Evaluasi dilakukan melalui pengujian akurasi pengenalan wajah, pengukuran latensi end-to-end, dan analisis throughput sistem. Hasil pengujian menunjukkan bahwa arsitektur yang diusulkan mampu menurunkan latensi presensi sekitar 72 persen dibandingkan pendekatan monolitik berbasis pemrosesan server, sekaligus meningkatkan kapasitas penanganan permintaan tanpa mengorbankan tingkat akurasi. Temuan ini menunjukkan bahwa integrasi WebAssembly dan Microservices merupakan solusi arsitektural yang efektif untuk sistem presensi biometrik real-time.
Classification of Pineapple Disease Types Using the VGG16 and EfficiennetB7 Model Approaches: Classification Ediansa, Oka; Riadi, Imam; Murinto, Murinto
Insect (Informatics and Security): Jurnal Teknik Informatika Vol. 12 No. 01 (2026): Maret 2026
Publisher : Universitas Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33506/insect.v12i01.5292

Abstract

Penyakit pada buah nanas merupakan salah satu faktor utama penyebab penurunan kualitas hasil panen dan kerugian ekonomi bagi petani. Identifikasi penyakit secara manual seringkali tidak akurat karena subjektivitas pengamat. Penelitian ini bertujuan untuk mengklasifikasikan jenis penyakit pada buah nanas menggunakan pendekatan Deep Learning dengan membandingkan dua arsitektur populer, yaitu VGG16 dan EfficientNetB7. Dataset yang digunakan berjumlah 215 citra yang terbagi ke dalam empat kelas: Busuk Pangkal, Bercak Hitam, Busuk Inti Buah, dan Nanas Sehat. Karena keterbatasan jumlah data, teknik Transfer Learning dan augmentasi data diterapkan untuk meningkatkan performa model. Hasil penelitian menunjukkan bahwa EfficientNetB7 memberikan performa yang lebih unggul dibandingkan VGG16 dengan akurasi validasi sebesar 89,25%, precision 88,50%, dan f1-score 88,20%. Sementara itu, VGG16 mencapai akurasi validasi sebesar 84,50%. Meskipun EfficientNetB7 membutuhkan waktu komputasi yang lebih lama per epoch, keunggulannya dalam mengekstraksi fitur kompleks pada tekstur kulit nanas menjadikannya model yang lebih ideal untuk sistem deteksi penyakit tanaman. Penelitian ini diharapkan dapat menjadi rujukan dalam pengembangan teknologi otomasi pertanian untuk meningkatkan efisiensi penanganan penyakit pascapanen nanas.
Mobile Forensic Investigation of E-Commerce Fraud Using DFRWS Method and Perceptual Hashing Prambudi, Rizal; Riadi, Imam; Murinto, Murinto
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 1, March 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

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

Abstract

Social media platforms have enabled real-time communication and broad user interaction, but they are often exploited for cybercrime. One such vulnerable medium is e-commerce applications, which facilitate transactions and store sensitive user data. This study investigates digital evidence in a simulated fraud case involving an e-commerce application by applying mobile forensic techniques guided by the Digital Forensic Research Workshop framework. The investigation focused on recovering user accounts, text messages, images, and videos from an Android smartphone. Two forensic tools Oxygen Forensic Detective and MOBILedit Forensic Express were used for data extraction and analysis. To improve the reliability of visual evidence, the study incorporated perceptual hashing and wavelet hashing techniques to validate compressed image files. The results showed that Oxygen Forensic Detective recovered 71.4% of digital evidence, while MOBILedit achieved 57%. Although both tools successfully recovered multimedia files, Oxygen performed better in extracting text messages. These findings demonstrate the effectiveness of mobile forensic methods in identifying and validating digital evidence in e-commerce fraud cases. Moreover, integrating the DFRWS methodology with perceptual hashing significantly improves the interpretation of manipulated or compressed images, thus enhancing the evidentiary value for legal proceedings.
Pemberdayaan Masyarakat untuk Deteksi Dini Kanker Payudara Berbasis Aplikasi Web di Kecamatan Berbah Wintolo, Hero; Ayuningtyas, Astika; Yuliansah, Herman; Murinto, Murinto
ABDI: Jurnal Pengabdian dan Pemberdayaan Masyarakat Vol 8 No 1 (2026): Abdi: Jurnal Pengabdian dan Pemberdayaan Masyarakat
Publisher : Labor Jurusan Sosiologi, Fakultas Ilmu Sosial, Universitas Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24036/abdi.v8i1.1728

Abstract

Pengabdian kepada masyarakat ini bertujuan untuk memberdayakan perempuan dalam melakukan deteksi dini kanker payudara melalui edukasi dan pemanfaatan aplikasi berbasis web. Kegiatan dilaksanakan di Kecamatan Berbah, Kabupaten Sleman, dengan melibatkan 99 peserta perempuan dari berbagai usia. Kegiatan dimulai dengan sosialisasi mengenai kanker payudara yang disampaikan oleh dokter dan penyintas kanker, diikuti dengan pelatihan penggunaan aplikasi deteksi dini yang dikembangkan dari hasil penelitian sebelumnya. Aplikasi ini menggunakan teknologi kecerdasan buatan (AI) dengan arsitektur Convolutional Neural Network (CNN) dan Knowledge Growing System (KGS) untuk membantu pengguna mengenali potensi gejala awal kanker payudara melalui citra ultrasonografi. Para peserta mengikuti sesi edukasi dan praktik langsung menggunakan smartphone, serta mengisi survei skrining untuk mendeteksi risiko dan tingkat kepuasan mereka. Hasil survei menunjukkan bahwa mayoritas peserta belum mengalami gejala kanker, namun memiliki risiko berdasarkan usia dan faktor reproduksi. Evaluasi kepuasan menunjukkan respon positif, dengan kelompok usia 15 hingga 25 tahun memberikan tingkat kepuasan yang paling tinggi. Secara keseluruhan, kegiatan ini berhasil meningkatkan pengetahuan, kesadaran, dan keterampilan peserta dalam melakukan deteksi dini secara mandiri. Dengan pendekatan teknologi yang terintegrasi dengan edukasi, program ini berkontribusi dalam mengurangi risiko keterlambatan diagnosis kanker dan meningkatkan kualitas hidup perempuan melalui pencegahan yang lebih dini.
Co-Authors Abdul Fadlil Abdul Jawad Achmad Sahri Ramdhani Adam, Irfan adelia fitriawati zakiyyah Adhi Prahara Adhi Prahara Adhi Prahara, Adhi Aditya Kurniawan Agus Harjoko Agus Harjoko Amin Padmo A.M Angga Prasetio Romadhon Anton Yudhana Arfiani Nur Khusna Arief Yudiyanto Arya Yugi B Astika AyuningTyas, Astika Auzan, Muhammad Azhari, Ahmad B, Arya Yugi Bachrudin Muchtar Bachrudin Muchtar Benny Adrian Bidinnika, Muhammad Kunta Binar Aji Hermawan Caswito Caswito darmanto darmanto Daru Thobrani Furqon Deris Alfiansyah Kurnia Dewi Pramudi Ismi Dyah Apriliani Dyah Apriliani Dyah Apriliani Ediansa, Oka Eko Aribowo Eko Aribowo Elena Yustina Elena Yustina Erik Iman Heri Ujianto Faisal, Ilyas Farajullah Farajullah Ferangga Puguh Furizal, Furizal Gading Surya Lesmana Galang Romadhon Gustava Ardiantoro Habibillah, Ahmad Yasin Habie, Khairul Fathan Hafin, Aqid Fahri Hazar, Siti Herman Yuliansyah, Herman Ikhwan Hawariyanta Imam Riadi Indarto Indarto Indra Dwi Ananto Irfan Adam Irfan Adam Jamhari Widadi Kartika Firdausy Krisna Astianingrum Labib Azhar Janotama Lesmana, Gading Surya Martania Melany Mawarni, Syifa’ah Setya Miftahurahma Rosyda Miftahurrahma Rosyda Muchtar, Bachrudin Muhammad Arif Nuur Hafidz Muhammad Ridwan Murein Miksa Mardhia Nagala Wangsa kencana Nur Rochmah Dyah Pujiastuti Nurkhasanah Nurkhasanah Nurul Istiqomah Nuur Hafidz, Muhammad Arif Padmo A.M, Amin Pawestri, Sheraton Permadi, Yuda Prambudi, Rizal Pratama, Ridho Haikal Puji Triono Pujiyono, Wahyu Putri, Salsabilla Azahra Rajunaidi, Rajunaidi Risnadi Syazali Rizki Muriliasari Royyan Yuni Miladi Rusydi Umar Salsabilla Azahra Putri Sefiyanti, Reza Shireen Panchoo Sismadi, Wawan Siti Hajar Son Ali Akbar Sri Handayaningsih Sri Hartati Sri Winiarti Suhendra Edi Saputra Sunardi Sunardi Sunardi, Sunardi Suyahman Suyahman Syifa'ah Setya Mawarni Taufik Cahya Prayitna Teguh Sudrajat Thoat Khoirudin Tri Kasihno Triono, Puji Wahju Tjahjo Saputro Wahyu Pujiyono Wahyu Pujiyono Wawan Ragil Wibowo Wijayanti, Dedi Willy Permana Putra Wintolo, Hero Wisnu Ahmad Maulana Yan Adhi Permadi Yesiansyah Yesiansyah Yuda Permadi Yuliansah, Herman Yulisasih, Baiq Nikum Yunianti, Rizqi Yustina, Elena Zakiyyah, Adelia Fitriawati Zulkarnain Effendi