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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) Indonesian Journal of Artificial Intelligence and Data Mining Buffer Informatika Jurnal Sains dan 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 Masyarakat Berdaya dan Inovasi Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Innovation in Research of Informatics (INNOVATICS) Jurnal Teknik Informatika (JUTIF) 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 Humanities Automotive Engineering 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Social Sciences Other

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Alphabet SIBI sign language recognition using YOLOv11 for real-time gesture detection Putri, Salsabilla Azahra; Murinto, Murinto; Sunardi, Sunardi
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.408

Abstract

Modern gesture recognition systems for sign language face challenges in balancing computational efficiency and detection accuracy in complex and dynamic environments. To address this, this study proposes a SIBI alphabet recognition framework based on YOLOv11, optimized for real-time applications. The model architecture integrates a modified, efficient YOLOv11 backbone to enable accurate hand gesture feature extraction with minimal latency. A custom SIBI dataset comprising alphabet signs and essential vocabulary is used to train the model, supported by data augmentation techniques that enhance robustness against variations in position, lighting, and background. Experimental results demonstrate that the model achieves a high detection accuracy with an mAP50 of 97%, while significantly reducing computational complexity. These findings present a meaningful scientific contribution by showcasing how a lightweight yet highly accurate deep learning model can be effectively applied to sign language recognition, particularly for SIBI in the Indonesian context. From a practical standpoint, this framework offers a real-time gesture detection solution that is suitable for deployment on resource-constrained devices, making it accessible for mobile or embedded systems. The system can replace or complement traditional communication aids, especially in inclusive education, public services, and healthcare. Furthermore, the proposed method can be adapted for gesture-based interaction in other domains such as athletic training, physical education, and app-based fitness programs where accurate and real-time motion recognition is essential.
Identifikasi Jenis Daun untuk Ecoprint Mengunakan Metode Convolutional Neural Network Hajar, Siti; Murinto, Murinto; Yudhana, Anton
Jurnal Sains dan Informatika Vol. 11 No. 1 (2025): Jurnal Sains dan Informatika
Publisher : Teknik Informatika, Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jsi.v11i1.1774

Abstract

Tumbuhan yang berdaun merupakan salah satu kategori tumbuhan yang memiliki berbagai manfaat. Tumbuhan ini dapat dimanfaatkan sebagai bahan dalam produk kecantikan, makanan, obat-obatan, pewarna alami, dan kain. Makalah ini membahas cara mengidentifikasi jenis daun untuk ecoprint dengan menggunakan metode Deep Learning, khususnya Convolutional Neural Network (CNN). Tujuan dari identifikasi ini adalah untuk mempermudah menentukan jenis daun yang bisa dan tidak bisa untuk ecoprint teknik steaming. Metode yang digunakan saat ini masih manual, dengan mengambil beberapa jenis daun dan diproses. Dalam pemrosesan manual sangat lama lebih dari satu hari, dan tidak efisien untuk membuktikan bahwa sampel daun yang dicoba tersebut bisa atau tidak untuk ecoprint. Mengatasi masalah ini, solusi mengunakan Convolutional Neural Network (CNN) algoritma Deep Learning lebih tepat. Penelitian ini menganalisis 400 gambar daun yang diambil dari 10 jenis daun untuk diidentifikasi. Proses pelatihan dilakukan dalam dua tahap: Feature Learning dan Klasifikasi, dengan jumlah epoch sebanyak 15. Hasil training (0,9850) dan valisasi (0,9796) sedangkan hasil pengujian accuracy rata-rata diperoleh (0,9194). Dapat disimpulkan bahwa algoritma Deep Learning yaitu Convolutional Neural Network (CNN) bisa mengidentifikasi jenis daun untuk ecoprint.
Comparison of Transfer Learning Strategies Using MobileNetV2 and ResNet50 for Ecoprint Leaf Classification Hajar, Siti; Murinto, Murinto; Yudhana, Anton
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5266

Abstract

This research focuses on the classification of leaf types used in ecoprint production through the steaming technique by applying transfer learning on two widely recognized convolutional neural network (CNN) architectures, MobileNetV2 and ResNet50. Leaves have diverse applications in various sectors such as medicine, nutrition, and handicrafts. The study utilized a total of 600 leaf images from 15 species were collected from the surrounding environment and divided into 80% training and 20% testing sets. The aim of this study is to classify leaf types suitable for ecoprint quickly and efficiently, based on transfer learning with two CNN architectures, while incorporating fine-tuning. MobileNetV2 was selected for its computational efficiency, while ResNet50 was chosen for its ability to address the vanishing gradient problem and deliver high accuracy. Fine-tuning was employed to optimize model performance. Experimental results demonstrate that both architectures achieved strong performance, with MobileNetV2 reaching 94.12% accuracy and ResNet50 slightly outperforming it at 94.96%. Confusion matrix evaluation further confirmed these results, yielding accuracy, precision, recall, and F1-score values of 0.94, 0.95, 0.95, and 0.94, respectively. These findings highlight ResNet50’s superior performance over MobileNetV2 while affirming the effectiveness of both models in ecoprint leaf classification.
Pengenalan Pola Depresi Berbasis Suara Menggunakan Ekstraksi Fitur Mel-Frequency Cepstral Coefficients Saputro, Wahju Tjahjo; Fadlil, Abdul; Murinto, Murinto
Jurnal PROCESSOR Vol 20 No 2 (2025): Jurnal Processor
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/processor.2025.20.2.2513

Abstract

The identification of depression patterns from human voices is important because depression can interfere with activities, reduce interest in learning, and hinder socialisation. Depression is a significant problem today because there has been a global increase in the number of people suffering from it. The factors contributing to depression are numerous and complex, and can affect all groups, from children to the elderly. The purpose of this study was to identify depression patterns based on voice feature extraction. The feature extraction method used is Mel-Frequency Cepstral Coefficients (MFCC). The MFCC method is capable of extracting features that closely resemble the human auditory system. The dataset used is the EATD-Corpus, which contains 162 recordings of students from Tongji University in China. The results of the study show that depression and healthy patterns can be distinguished using MFCC parameters, namely 25 measurements per frame, 10 frame intervals, an alpha value of 0.97 as the pre-emphasis coefficient, a maximum of 40 Mel filterbank coefficients, and 12 cepstral coefficients. Classification thresholds can be obtained for two classes: healthy with thresholds < 53.00 and depressed ≥ 53.00 using the Self-Rating Depression Scale.
Analysis of the Influence of Number of Segments on Similarity Level in Wound Image Segmentation Using K-Means Clustering Algorithm Furizal, Furizal; Mawarni, Syifa’ah Setya; Akbar, Son Ali; Yudhana, Anton; Kusno, Murinto
Control Systems and Optimization Letters Vol 1, No 3 (2023)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v1i3.33

Abstract

This study underscores the importance of wound image segmentation in the medical world to speed up first aid for victims and increase the efficiency of medical personnel in providing appropriate treatment. Although the body has a protective function from external threats, the skin can be easily damaged and cause injuries that require rapid detection and treatment. This study used the K-Means clustering algorithm to segment the external wound image dataset consisting of three types of wounds, namely abrasion, puncture, and laceration. The results showed that K-Means clustering is an effective method for segmenting wound images. The greater the number of segments used, the better the quality of the resulting segmentation. However, it is necessary to take into account the specific characteristics of each type of wound and the number of segments used in order to choose the most suitable segmentation method. Evaluation using various metrics, such as VOI, GCE, MSE, and PSNR, provides an objective assessment of the quality of segmentation. The results showed that abrasion wounds were easier to segment compared to puncture wounds and lacerations. In addition, the size of the image file also affects the speed of program execution, although it is not always absolute depending on the characteristics of the image.
Perilaku Remaja dalam Menggunakan Internet untuk Mengenali dan Mengindari Phishing pada SMA Muhammadiyah Pacitan Sunardi, Sunardi; Murinto, Murinto; Astianingrum, Krisna; Yulisasih, Baiq Nikum; Putri, Salsabilla Azahra
Ahsana: Jurnal Penelitian dan Pengabdian kepada Masyarakat Vol. 2 No. 3 (2024): Oktober 2024 - Ahsana: Jurnal Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ahsana.v2i3.367

Abstract

SMA Muhammadiyah Pacitan merupakan salah satu SMA swasta yang ada di Kota Pacitan, bertepatan di Jalan Gajahmada No. 20 Kabupaten Pacitan. Penelitian ini bertujuan untuk mengidentifikasi perilaku remaja dalam menggunakan internet serta cara mengenali dan menghindari phishing di SMA Muhammadiyah Pacitan. Seiring dengan meningkatnya akses dan penggunaan internet di kalangan remaja, muncul berbagai risiko keamanan siber, termasuk phishing yang dapat merugikan pengguna. Melalui metode survei dan wawancara mendalam, penelitian ini menganalisis kebiasaan online siswa, tingkat kesadaran terhadap ancaman phishing, dan strategi yang digunakan untuk melindungi diri. Hasil penelitian menunjukkan bahwa sebagian besar remaja di SMA Muhammadiyah Pacitan aktif menggunakan internet untuk keperluan pendidikan dan hiburan, namun masih kurang memahami bahaya phishing. Edukasi mengenai tanda-tanda phishing, seperti email atau situs web palsu yang meminta informasi pribadi dan praktik aman berinternet menjadi sangat penting. Program pemberdayaan umat ini berhasil, dengan 38,9% siswa merasa sangat mampu dan 38,9% merasa mampu mengenali phishing setelah mengikuti kegiatan tersebut, tanpa ada yang merasa tidak mampu. Selain itu, 61,1% siswa menilai program ini sangat efektif dalam memberikan pengetahuan tentang keamanan internet dan phishing.
PREDICTING LOAN ELIGIBILITY WITH SUPPORT VECTOR MACHINE: A MACHINE LEARNING APPROACH Rajunaidi, Rajunaidi; Yuliansyah, Herman; Sunardi, Sunardi; Murinto, Murinto
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 3 (2025): Juni 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i3.3876

Abstract

Abstract: Non-performing loans remain one of the main challenges faced by cooperatives, particularly when the loan eligibility assessment process is still conducted manually. This traditional approach tends to be time consuming, subjective, and prone to inaccurate decisions. This study aims to develop a predictive model for borrower eligibility using the Support Vector Machine (SVM) algorithm as a more efficient and objective machine learning-based solution. A total of 1,000 loan history records were processed using RapidMiner software, taking into account variables such as salary, years of employment, loan amount, monthly installment, employment status, monthly expenses, number of dependents, housing status, age, and collateral value. The model’s performance was evaluated using a confusion matrix and classification metrics including accuracy, precision, recall, and kappa. The results indicate that the SVM model achieved an accuracy of 90.05%, precision of 90.13%, recall of 90.05%, and f1 score of 90,08%, reflecting a strong performance in classifying borrower eligibility. The application of this method makes a significant contribution to the development of data driven decision support systems within cooperative environments. This finding expands the scientific understanding in the field of microfinance and supports the implementation of artificial intelligence technologies in making decisions that are more precise, rapid, and accurate.Keywords: cooperative; eligibility prediction; machine learning; non-performing loan; SVMAbstrak: Kredit macet merupakan salah satu permasalahan utama yang dihadapi koperasi, terutama ketika proses penilaian kelayakan peminjam masih dilakukan secara manual. Pendekatan ini cenderung lambat, subjektif, dan berisiko menghasilkan keputusan yang kurang akurat. Penelitian ini bertujuan untuk membangun model prediksi kelayakan peminjam menggunakan algoritma Support Vector Machine (SVM) sebagai solusi berbasis machine learning yang lebih efisien dan objektif. Sebanyak 1.000 data riwayat pinjaman diolah menggunakan tools RapidMiner dengan mempertimbangkan variabel: gaji, lama bekerja, besar pinjaman, angsuran per bulan, status pegawai, pengeluaran bulanan, jumlah tanggungan, status rumah, umur, dan nilai jaminan. Evaluasi model dilakukan menggunakan confusion matrix dan metrik klasifikasi seperti akurasi, presisi, recall, dan kappa. Hasil menunjukkan bahwa model SVM mencapai akurasi  90,05%, presisi 90,13%, recall 90,05%, dan f1 score 90,08%, yang mencerminkan performa model yang sangat baik dalam mengklasifikasikan kelayakan peminjam. Penerapan metode ini memberikan kontribusi penting dalam pengembangan sistem pendukung keputusan berbasis data di lingkungan koperasi. Temuan ini memperluas wawasan keilmuan di bidang keuangan mikro dan mendukung penerapan teknologi kecerdasan buatan dalam pengambilan keputusan yang lebih tepat, cepat, dan akurat.Kata Kunci: koperasi; kredit macet; machine learning; prediksi kelayakan; SVM  
Klasifikasi Genre Musik Berdasarkan Fitur Mel Frequency Spectral Coefficient Menggunakan Random Forest Sefiyanti, Reza; Murinto, Murinto
CORISINDO 2025 Vol. 1 (2025): Prosiding Seminar Nasional CORISINDO 2025
Publisher : CORISINDO 2025

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/corisindo.v1.5560

Abstract

Klasifikasi genre musik merupakan proses pengelompokan lagu berdasarkan kemiripan elemen-elemen seperti frekuensi, harmoni, dan pola ritme. Proses klasifikasi secara manual menjadi tidak efisien ketika berhadapan dengan volume data yang besar. Penelitian ini bertujuan mengembangkan sistem klasifikasi otomatis genre musik menggunakan algoritma Random forest dengan menggabungkan fitur ekstraksi Mel frequency cepstral coefficient (MFCC) dan fitur spektral seperti Chroma, Spectral contrast, dan Tonnetz. Data yang digunakan berasal dari dataset GTZAN yang berisi 1000 file audio berdurasi 30 detik dan mewakili 10 genre musik berbeda. Tahapan penelitian meliputi ekstraksi fitur audio, preprocessing, pembagian data latih dan data uji, pelatihan model menggunakan Random forest dengan Grid Search, serta evaluasi performa menggunakan Confusion Matrix dan metrik akurasi. Hasil terbaik diperoleh dari kombinasi fitur MFCC, delta, delta-delta dengan perhitungan rata-rata dan standar deviasi, serta fitur spektral dengan perhitungan rata-rata, yang menghasilkan akurasi sebesar 76%. Temuan ini menunjukkan bahwa kombinasi fitur tersebut efektif dalam meningkatkan performa klasifikasi genre musik.
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.
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 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 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 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 Nur Rochmah Dyah Pujiastuti Nurkhasanah Nurkhasanah Nurul Istiqomah Nuur Hafidz, Muhammad Arif Padmo A.M, Amin Pawestri, Sheraton Permadi, Yuda Pratama, Ridho Haikal Puji Triono Pujiyono, Wahyu Putri, Salsabilla Azahra Rajunaidi, Rajunaidi Risnadi Syazali Rizki Muriliasari Royyan Yuni Miladi Sefiyanti, Reza Shireen Panchoo Siti Hajar Son Ali Akbar Sri Handayaningsih Sri Hartati Sri Winiarti Suhendra Edi Saputra Sunardi Sunardi Sunardi, Sunardi Suyahman Suyahman Syifa&#039;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 Wisnu Ahmad Maulana Yan Adhi Permadi Yesiansyah Yesiansyah Yuda Permadi Yulisasih, Baiq Nikum Yunianti, Rizqi Yustina, Elena Zakiyyah, Adelia Fitriawati Zulkarnain Effendi