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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Simetris Jurnal Simantec Prosiding SNATIKA Vol 01 (2011) Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Jurnal IPTEK-KOM (Jurnal Ilmu Pengetahuan dan Teknologi Komunikasi) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Journal of Information Technology and Computer Science (JOINTECS) Network Engineering Research Operation [NERO] JATI (Jurnal Mahasiswa Teknik Informatika) JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer International Journal of Robotics and Control Systems Joutica : Journal of Informatic Unisla Digital Transformation Technology (Digitech) Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Jurnal Maklumatika NERO (Networking Engineering Research Operation) JISCOMP (Journal of Information System and Computer)
Resty Wulanningrum
(SINTA ID : 5986827) Prodi Teknik Informatika, Universitas Nusantara PGRI Kediri
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences

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Ekstraksi Fitur Pada Aksara Kawi Moh Imam Yusuf Mustofa; Resty Wulanningrum; Julian Sahertian
JUKOMPSI (Jurnal Komputer dan Sistem Informasi) Vol 1 No 2 (2023): Juni
Publisher : Teknik Komputer Fakultas Teknik Universitas Islam Kadiri (UNISKA)

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Abstract

The Kawi script is a derivative of the post-palawa language. Kawi itself in Sanskrit means poet. The Kawi script itself is found in many ancient manuscripts from ancient times. Kawi script itself nowadays is no longer used, many people don't know Kawi script. In this modern era, where everything is digital, it needs preservation, one of which is by using computers to recognize kawi script patterns. Before identifying characters, it is necessary to have digital image information, one of which is the extraction process. This research will create a feature extraction system for the kawi script which will later be used as input for the classification of the kawi script. This study uses data sourced from books and in this research, the data taken is only 6 types of data. In the process of making this system using the Matleb application. In the testing phase, the GLCM (Gray Level Co-Occurrence Matrix) feature extraction will be used which includes Contrast, Correlation, Energy, and Homogeneity, then identification will be processed. The results of this study produce values ​​from the GLCM method, namely values ​​from Contrast, Correlation, Energy, and Homogeneity. It is expected that the values ​​of the 4 features can be used as input data from the classification from in further research.
EKSTRAKSI CIRI BENTUK PADA AKSARA JAWA KAWI MENGGUNAKAN METODE L*A*B dan K-Means Clustering Achmad Iqbal Maulana; Resty Wulanningrum; Julian Sahertian
JUKOMPSI (Jurnal Komputer dan Sistem Informasi) Vol 1 No 2 (2023): Juni
Publisher : Teknik Komputer Fakultas Teknik Universitas Islam Kadiri (UNISKA)

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Abstract

Kawi Javanese script is one of the many cultural assets belonging to Indonesia that must be preserved and protected, one of which is by introducing it with a computer-based system, namely pattern recognition. In pattern recognition, shape extraction is a process that identifies and extracts shape features in digital images which can then be used as the initial classification process. This study aims to create a form extraction system for Kawi Javanese script which can then be used to classify Kawi Javanese script images so that they can be used for the process of reading Kawi Javanese script. Data collection in this study was taken from books using Javanese Kawi script with as many as 6 characters. In making this system using Matlab R2020a. Testing is carried out by processing 6 character images using the L*A*B and K-Means Clustering methods which will produce segmentation values ​​and then take shape feature values ​​including Area, Perimeter, Metric, and Eccentricity which can then be processed using the Artificial Neural Network method for classification. It is hoped that the values ​​of these parameters can be used as input values ​​for the classification of the Kawi Javanese script.
Ekstraksi Ciri Bentuk pada Huruf Kawi Cholid Ilham Isniawan; Resty Wulanningrum; Julian Sahertian
JUKOMPSI (Jurnal Komputer dan Sistem Informasi) Vol 1 No 2 (2023): Juni
Publisher : Teknik Komputer Fakultas Teknik Universitas Islam Kadiri (UNISKA)

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Abstract

The Javanese kawi script is basically an ancient script that appeared in the 18th to 16th centuries. Which the kawi script is also a derivative of the Pallawa script, considering the very importance of the Kawi language because previous texts mainly used the history of Hindu texts still using the Kawi language. (Surada, 2018). And to preserve the kawi language, it can be developed through a kawi identification system. For image identification, shape feature extraction is used for the identification system to find information from digital images (Herdiansah, 2022). In this study, an image identification system was created by extracting shape features to get the initial value of the shape of the Kawi letters and then going through a classification process so that they could recognize Kawi letters. There was some data collected from 2 data sources with a total of 6 data collected. In this manufacture using the matlab application (Prayoga, 2019) by conducting tests to process image data that has been obtained using shape feature extraction with metric and eccentricity parameters which are then processed using an artificial neural network method. From the results of this study it is hoped that the extraction value of shape features with these parameters can be used for the next step for classifying kawi letters.
Klasifikasi Siswa Berprestasi Pada SDN Puncu 3 Ahmad Fakhruddin Luthfi; Daniel Swanjaya; Resty Wulanningrum
JUKOMPSI (Jurnal Komputer dan Sistem Informasi) Vol 1 No 1 (2022): Desember
Publisher : Teknik Komputer Fakultas Teknik Universitas Islam Kadiri (UNISKA)

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Abstract

In the world of education, the increasing success and failure of students is a reflection of the world of education. Education is currently required to be able to compete with all the utilization of existing natural resources. This study aims to determine which students will be ranked in the top 5 in grade 6 semester 2 every year at SDN Puncu 3. This study uses the method of self organization maps (SOM). The data that has been obtained from the data recap of the student data values ​​uses the SOM method. The SOM method analyzed data from 75 students of SDN Puncu 3 with a silhouette coefficient value of 0.7399. Keywords: Clustering, Education, Self Organizing Map.
Integrasi Prediksi Pendapatan Pesantren Al-Fuukat Menggunakan Metode K-Means Clustering Dan Backpropagation salma - alawiyah; Daniel Swanjaya2; Resty Wulanningrum
JUKOMPSI (Jurnal Komputer dan Sistem Informasi) Vol 1 No 2 (2023): Juni
Publisher : Teknik Komputer Fakultas Teknik Universitas Islam Kadiri (UNISKA)

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Abstract

Pesantren Al-Fukaat merupakan salah satu tempat pembibitan bibit alpokat yang berada di Desa Trayang, Kecamatan Ngronggot, Kabupaten Nganjuk Kecamatan Ngeronggot . Pesantren Al-Fukaat berdiri sejak tahun 2018. Merupakan salah satu sentra pembibitan buah alpokat yang terbesar di Kabupaten Nganjuk. Adapun varietas bibit yang dijual terdiri dari alpokat lokal dan impor. Untuk alpokat lokal mereka menjual jenis Aligator, Markus, Miki, Hass, dan Pangeran. Sedangkan untuk jenis alpokat impor seperti Cuba, Bokhong Teen, Yellow, Red Vietnam, dan Buchaneir, permasalahan yang dihadapi oleh pemilik adalah pemilik sering ragu dalam memprediksi pendapatan mereka di masa depan. Maka dari itu dibuatkanlah sebuah sistem aplikasi website yang dapat memprediksi pendaptan Pesantren Al-Fuukat dimasa depan dengan owner atau pemilik memfilter tanggal atau memilih tanggal yang diinginkan sistem ini juga memiliki validasi tanggal seperti tanggal to tidak boleh kurang dari tanggal from sebaliknya tanggal from tidak boleh melebihi tanggal to, sistem juga dapat melakukan perekapan data secara otomatis dan customer dapat memesan secara online. Sistem yang dibangun menggukan metode K-Means dan Backpropagation agar lebih flexsibel serta efiesn dalam perhitungan data. hasil akhir clustering / penge-lompkkan mulai dari cluster 1 – 3 dan berbagai macam jenis Kategori ukuran Al-fukaat. Nilai Exp juga bervarian mulai dari 0.03, 0.045 dst sesuai dengan record data pendapatan penjualan Pesantren Al-Fuukat
Deteksi dan Klasifikasi Kue Tradisional Indonesia Menggunakan YOLOv8 Mustofa, Arin Ayu Silvyani; Wulanningrum, Resty; Sahertian, Julian
NERO (Networking Engineering Research Operation) Vol 10, No 1 (2025): Nero - 2025
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v10i1.30177

Abstract

Indonesian traditional cakes are part of the cultural heritage, characterized by their rich flavors, unique forms, and significant historical value. However, the lack of recognition among younger generations necessitates a new approach to preservation efforts. This study aims to develop an image processing-based detection system for traditional cake types using the YOLOv8 algorithm. The five types of cakes identified in this research are lumpur cake, lapis cake, wingko cake, dadar gulung cake, and putu ayu cake. The image dataset was obtained through a combination of direct image capture and public datasets, and was manually annotated using the Roboflow platform. The model was trained using the PyTorch framework and evaluated based on precision, recall, F1-score, and mean Average Precision (mAP) metrics. Experimental results show that the system achieved an average mAP of 89.9% and an F1-score of 86.5%, with a relatively low classification error rate. These findings indicate that the YOLOv8 algorithm is effective in detecting visually similar objects and holds significant potential for application in the digital preservation of culinary heritage. The system can also be further developed as a technology-based educational medium to support the conservation of Indonesia’s local culinary wealth.Keywords: YOLOv8, Object Detection, Cake Traditional, Image Processing, Computer Vision
Penerapan MobileNet Architecture pada Identifikasi Foto Citra Makanan Indonesia Wijayanto, Muhammad Farid; Swanjaya, Daniel; Wulanningrum, Resty
Digital Transformation Technology Vol. 4 No. 1 (2024): Periode Maret 2024
Publisher : Information Technology and Science(ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/digitech.v4i1.4449

Abstract

Pengenalan gambar makanan secara otomatis telah menjadi area penelitian yang menarik, terutama dalam pengembangan MobileNet berbasis kuliner. Penelitian ini membahas penerapan arsitektur MobileNet dalam mengidentifiksi foto citra makanan Indonesia. MobileNet, sebagai jaringan saraf konvolusional yang efisien dan ringan, memungkinkan pengenalan gambar dengan cepat dan akurat pada perangkat dengan keterbatasan komputasi. Tujuan dari penelitian ini yaitu untuk mengetahui hasil dari identifikasi foto citra makanan Indonesia menggunakan Arsitektur dari MobileNet dan untuk mencapai performa terbaik dari model Convolutional Neutral Network menggunakan arsitektur MobileNet. Penelitian ini melibatkan pengumpulan dataset gambar makanan Indonesia, pelatihan model MobileNet, dan evaluasi kinerja model dalam mengklasifikasikan gambar tersebut. Hasil penelitian menunjukkan bahwa arsitektur MobileNet dapat diimplementasikan dengan efektif untuk identifikasi makanan Indonesia, dengan tingkat akurasi yang memuaskan dan waktu pemrosesan yang relatif singkat. Temuan ini diharapkan dapat berkontribusi dalam pengembangan aplikasi pengenalan gambar di bidang kuliner, khususnya untuk makanan Indonesia. Hasil akhir dari penelitian ini menunjukkan bahwa MobileNet berhasil mencapai akurasi sebesar 98.99% dan loss terkecil sebesar 0.05 dalam mengidentifikasi gambar. Keberhasilan dalam penelitian ini membuka peluang untuk pengembangan MobileNet berbasis pengenalan gambar makanan yang lebih canggih. Selain itu, penelitian ini juga dapat berkontribusi pada pengembangan teknologi visi komputer secara umum, khususnya dalam bidang klasifikasi gambar objek kecil dan kompleks.
Comparison of C4.5 and Naive Bayes for Predicting Student Graduation Using Machine Learning Algorithms Tholib, Abu; Fadli Hidayat, M Noer; yono, Supri; Wulanningrum, Resty; Daniati, Erna
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 2 No. 2 (2023): September 2023
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v2i2.3364

Abstract

Student graduation is a very important element for universities because it relates to college accreditation assessment. One of them is at the Faculty of Engineering Nurul Jadid University, which has problems completing the study period within a predetermined time. So that it can be detrimental because accreditation is less than optimal, and the number of active students makes it less ideal in teaching and learning activities. This study aimed to compare the level of accuracy using the C4.5 algorithm and Naïve Bayes method in predicting graduation on time. The C4.5 and Naïve Bayes algorithms are one of the methods in the algorithm for classifying. Tests were carried out using the C4.5 and Naïve Bayes algorithms using Google Colab with Python programming language, then validated using 10-fold cross-validation. The results of this study indicate that the Naïve Bayes method has a higher accuracy value with an accuracy rate of 96.12%, while the C4.5 algorithm method is 93.82%.
Deteksi dan Klasifikasi Kue Tradisional Indonesia Menggunakan YOLOv8 Mustofa, Arin Ayu Silvyani; Wulanningrum, Resty; Sahertian, Julian
NERO (Networking Engineering Research Operation) Vol 10, No 1 (2025): Nero - 2025
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v10i1.30177

Abstract

Indonesian traditional cakes are part of the cultural heritage, characterized by their rich flavors, unique forms, and significant historical value. However, the lack of recognition among younger generations necessitates a new approach to preservation efforts. This study aims to develop an image processing-based detection system for traditional cake types using the YOLOv8 algorithm. The five types of cakes identified in this research are lumpur cake, lapis cake, wingko cake, dadar gulung cake, and putu ayu cake. The image dataset was obtained through a combination of direct image capture and public datasets, and was manually annotated using the Roboflow platform. The model was trained using the PyTorch framework and evaluated based on precision, recall, F1-score, and mean Average Precision (mAP) metrics. Experimental results show that the system achieved an average mAP of 89.9% and an F1-score of 86.5%, with a relatively low classification error rate. These findings indicate that the YOLOv8 algorithm is effective in detecting visually similar objects and holds significant potential for application in the digital preservation of culinary heritage. The system can also be further developed as a technology-based educational medium to support the conservation of Indonesia’s local culinary wealth.Keywords: YOLOv8, Object Detection, Cake Traditional, Image Processing, Computer Vision
Optimized yolov8 to identify normal and disabled people Wulanningrum, Resty; Handayani, Anik Nur; Herwanto, Heru Wahyu; Arai, Kohei
International Journal of Advances in Intelligent Informatics Vol 11, No 4 (2025): November 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i4.1977

Abstract

This research aims to develop an object detection model capable of distinguishing the gait of normal and disabled people with high accuracy. Object detection is currently being developed to detect people and is being implemented in normal and gender-based gait recognition. Gait recognition, if further observed, includes recognizing both normal people and people with disabilities. Normal people walk like most people, but people with disabilities have different gaits from normal people. Some use walking aids, while others walk without them. Yolov8 is a platform for detecting people. This research proposes an object detection for normal people and people with disabilities, both those who use assistive devices and those who do not. The dataset used is Disabled gait, comprising 6500 images, and will be split into 3 data splits: 70% for training, 20% for validation, and 10% for testing. Model evaluation uses precision, recall, mAP50, and mAP50-90. The test results for 3 classifications, namely assistive, non-assistive, and normal, show the highest value in the assistive class with an mAP50 value of 0.98 and an mAP50-95 value of 0.996. This research provides a reliable gait-based object detection model that can accurately distinguish normal individuals from people with disabilities, including those using assistive devices. The findings support the development of more inclusive surveillance, healthcare, and mobility-assessment systems through high-performance detection validated by strong mAP scores.
Co-Authors Abu Tholib Achmad Iqbal Maulana Achmad Zainul Karim Aeri Rachmad Afizza Fikri Kurniawan Ahmad Bagus Setiawan Ahmad Fakhruddin Luthfi Aji Prasetya Wibawa Aminuyati Andrean Ferdyana Vabian Eka Sakti Anggi Nur Fadzila Anggi Wahyu Triprasetyo Anik Nur Handayani Ardi Sanjaya Arsyad, Nandito Pramudya Asmoro, Shandy Sadewa Asna Maulian Amroni Maulian Amroni Asri, Puput Puji Bagus Fadzerie Robby Cholid Ilham Isniawan Christa Witta Putra Santoso Dadi Setyawan Danar Putra Pamungkas, Danar Putra Daniel Swanjaya Daniel Swanjaya2 Desi Dwi Kurniawati Dhela Melani Winandari Dimas Eri Kurniawan Doni Abdul Fatah Donny Firdani Ella Okta Viana Ema Utami Erna Daniati Fadli Hidayat, M Noer Fadli Hidayat, M. Noer Fadli, Abi Ihsan Fadzerie Robby, Bagus Fatkur Rhohman, Fatkur Firmansyah, Muhammad Kukuh Frisca Ayu Fatika Sari Gadang Putro Bagus Setiyawan Heffi Awang Cahya Heru Suhartono, Wawan Heru Wahyu Herwanto Hidayah, Alvi Nurul Intan Nur Farida Iswoyo, Yodhi Pratama Jauhari, Nur Mohamad Iqbal Juli Sulaksono Julian Sahertian Kohei Arai Krisnawan, Apreado Gilang Made Ayu Dunia Widyadara Made Ayu Dusea Widya Dara Miftachul Ludfie Millenialdo Yanuar Ilham Moh Imam Yusuf Mustofa Muhaimin, Mohammad Aqil Muhamad Yusup Efendi Muhammad Abdul Aziz Mustofa, Arin Ayu Silvyani Muttaqien, Hidayatul N.S.A, M Mukhlish Nandha Vera Wihra Lelitavistara Nandha Vera Wihra Lelitavistara, Nandha Vera Wihra Naufal Muji Dwicahyo Nugraha, Reza Setya Nur Mohamad Iqbal Jauhari Iqbal Jauhari Nurul Mahpiroh Patmi Kasih Ratih Kumalasari Niswatin Reza Mawarni Risa Helilintar Risky Aswi R, Risky Rohmat Syamsul Huda Roni Heri Irawan Rony Heri Irawan Ruruh Andayani Bekti, Ruruh Andayani salma - alawiyah Sandhi Kurniawan Sari, Lya Rosita Sinta Sanora Siregar, Muhammad Fariz Hardiansyah Siti Rochana Sri Rahayu Supri yono Supri Yono, Supri Teguh, Aji Triyo Kristantio Ulfatus Syaidah Wahyu Cahyo Utomo Wahyuniar , Lilia Sinta Wijayanto, Muhammad Farid Zakaria, Reza Naim