Article Per Year (5 Year)
p-Index From 2020 - 2025
1.383
P-Index
This Author published in this journals
All Journal Proceedings of KNASTIK Jurnal Teknologi Informasi dan Ilmu Komputer Seminar Nasional Informatika (SEMNASIF) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Ilmiah FIFO JUTEI (Jurnal Terapan Teknologi Informasi) Seminar Nasional Teknologi Informasi Komunikasi dan Administrasi [SEMINASTIKA] JOINCS (Journal of Informatics, Network, and Computer Science) Journal of Software Engineering and Multimedia (JASMED)
Sri Suwarno
Universitas Kristen Duta Wacana
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

Published : 10 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 10 Documents
Search

 1 
DETEKSI KEMIRINGAN ALUR POLA SIDIK JARI DENGAN HAMMING NET SEBAGAI DASAR KLASIFIKASI Suwarno, Sri; Hartati, Sri
Seminar Nasional Informatika (SEMNASIF) Vol 1, No 1 (2010): Computatinal
Publisher : Jurusan Teknik Informatika

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

Abstract

Sidik jari memiliki dua fitur penting yang disebut fitur lokal (minutiae) dan fitur global (singularities). Fitur lokal terdiri dari bagian-bagian detail dari sidik jari, sedangkan fitur global terdiri alur (ridge), titik pusat (core), dan titik persimpangan (delta). Fitur lokal biasanya digunakan sebagai dasar pencocokan sidik jari, sedangkan fitur global biasanya dipakai untuk dasar klasifikasi. Klasifikasi berdasarkan keberadaan dan posisi core dan delta memerlukan proses komputasi yang rumit dan panjang. Pada penelitian ini sudut kemiringan alur diteliti, dengan harapan dapat dipakai sebagai dasar klasifikasi yang lebih sederhana dan lebih cepat proses komputasinya. Pertama, citra grayscale sidik jari dikonversi menjadi citra biner, kemudian dilakukan proses thinning dan selanjutnya dikonversi menjadi citra bipolar. Kedua, dibuat 12 definisi sudut potongan alur yang melewati blok berukuran 3x3 piksel. Nilai dari piksel-piksel pada ke 12 blok tersebut digunakan sebagai target pelatihan bagi Hamming Network. Dengan memproses setiap blok berukuran 3x3 piksel dari citra sidik jari dengan Hammning Network maka sudut kemiringan potongan alur pada blok tertentu dapat ditentukan. Penelitian ini menghasilkan klasifikasi dengan ketepatan 63% dari 80 sampel pola sidik jari.
Deteksi Klas Pola Sidik Jari dengan Self-Organizing Map Suwarno, Sri; Hartati, Sri
Proceedings of KNASTIK 2010
Publisher : Duta Wacana Christian University

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

Abstract

Klasifikasi sidik jari merupakan proses yang sangat penting sebelum dilakukan proses identifikasi maupun proses verifikasi. Pencarian pola sidik jari pada suatu klas tertentu jauh lebih cepat dibandingkan dengan pencarian pada seluruh basis data. Sidik jari memiliki dua fitur penting yang disebut fitur lokal (minutiae) dan fitur global (singularities). Fitur lokal biasanya digunakan sebagai dasar pencocokan sidik jari, sedangkan fitur global biasanya dipakai sebagai dasar klasifikasi. Pada umumnya klasifikasi didasarkan pada keberadaan dan posisi core dan delta. Pada penelitian ini komposisi sudut kemiringan alur diteliti, dengan harapan dapat dipakai sebagai dasar klasifikasi yang lebih sederhana dan lebih cepat proses komputasinya. Pertama, citra grayscale sidik jari dikonversi menjadi citra biner, kemudian dilakukan proses thinning. Kedua, diambil blok-blok citra berukuran 3x3 piksel. Nilai dari piksel-piksel pada blok tersebut digunakan sebagai input pelatihan bagi jaringan Self Organizing Map (SOM) dengan arsitektur 9 neuron input dan 10 neuron output. Dengan memproses setiap blok berukuran 3x3 piksel dari citra sidik jari dengan SOM  maka akan diperoleh komposisi cluster untuk sidik jari yang bersangkutan.  Sistem yang dibuat mampu mendeteksi klas LOOP dengan baik.
REKAYASA FITUR SIDIK JARI BERBASIS HISTOGRAM SEBAGAI DASAR KLASIFIKASI GENDER Suwarno, Sri
PROSIDING SEMINASTIKA Vol 5 No 1 (2024): 5th SEMINASTIKA 2024
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/seminastika.v5i1.772

Abstract

Fitur sidik jari merupakan ciri atau pola unik pada sidik jari yang dapat digunakan sebagai acuan untuk mengidentifikasi atau memverifikasi pemiliknya. Pengambilan atau pemisahan fitur dari sidik jari disebut ekstraksi fitur. Secara teknis proses esktraksi fitur ada dua model pendekatan, yaitu berdasarkan posisi relatif setiap piksel dan berdasarkan frekwensi kemunculan setiap nilai intensitas piksel pada sidik jari. Penelitian ini membangun fitur sidik jari berdasarkan frekwensi kemunculan nilai-nilai intensitas piksel yang disusun dalam bentuk histogram. Mula-mula dipilih posisi ROI (Region of Interest) yang berada tepat ditengah-tengah citra sidik jari. Ukuran ROI ditentukan 1616 dan 3232 piksel. Selanjutnya nilai-nilai intensitas piksel dalam ROI tersebut disusun dalam bentuk histogram dengan ukuran bin bervariasi dari 2 sampai 16. Banyaknya piksel pada setiap bin disusun dalam bentuk vektor dan digunakan sebagai fitur. Penelitian ini menggunakan dataset sidik jari dari NIST (National Institute of Standart and Technology), terdiri dari 500 sidik jari pria dan 500 sidik jari wanita. Sebagai tolok ukur kwalitas fitur yang dihasilkan, fitur diuji untuk klasifikasi gender dengan algoritma SVM (Support Vector Machine). Dari penelitian yang dilakukan, fitur yang dibangun dapat menghasilkan akurasi klasifikasi maksimal 60.9%. Penelitian ini masih dapat dikembangkan dengan memilih lokasi dan ukuran ROI yang berbeda-beda.
Gender Classification Based on Fingerprint Using Wavelet and Multilayer Perceptron Suwarno, Sri
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 1 (2023): Articles Research Volume 7 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.11925

Abstract

Fingerprint-based gender classification is beneficial for speeding up the fingerprint identification of criminals, accident victims, and natural disaster victims that are difficult to be recognized based on their physical characteristics. The biggest obstacle to digitally classifying fingerprints is the image's poor quality. Some methods have been developed to improve image quality through various preprocessing, such as noise removal, background segmentation, thinning, and binarization. However, as these processes increase the classification time, some methods have been developed to classify fingerprints without preprocessing. One of them that has shown excellent success is CNN (Convolutional Neural Network). The method does not require preprocessing, but the computation time is very long and requires large amounts of training data. This study proposed a new method that did not need any preprocessing by using wavelet decomposition combined with the max-pooling process to generate features. Firstly, the fingerprint image was decomposed with a Haar wavelet of 4 levels, and each level was followed by a max-pooling process with a 2´2 filter. After that, the resulting feature was used as training data for the Multilayer Perceptron (MLP) network. In this study, the training data was a dataset from NIST (National Institute of Standart and Technology), with 750 fingerprints consisting of male and female fingerprints, each as many as 375. The method could produce a total accuracy of 80.1%.
Evaluasi Usabilitas Dan Rekomendasi Desain Antarmuka Website Kesehatan (Studi Kasus: Hellosehat.Com) Pasaribu, Paul Ronaldo Yonathan; Suwarno, Sri; Setiadi, Hendro
Journal of Software Engineering and Multimedia (JASMED) Vol 1 No 1 (2023): Journal of Software Engineering and Multimedia (JASMED)
Publisher : Institut Teknologi Telkom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/jasmed.v1i1.1080

Abstract

The Covid-19 pandemic is an encouragement for all people to be able to transform from previously having to meet face-to-face, forced by circumstances to turn into online meetings. All aspects of human life are forced to change, including in terms of health. In the current pandemic era, people are afraid to travel for treatment or even consult a doctor in person. With health websites such as hellosehat.com or other health websites, people can be helped to access health information. This health website can be an alternative for patients who have minor illnesses that do not need medical treatment or medical equipment assistance from the hospital. Therefore, a website interface that is easy to understand by users is needed so as not to be confused when accessing and using the website. This study aims to evaluate the interfaces of existing health sites. In this case study, researchers used hellosehat.com site to evaluate. The method used in this study is the goal directed design method, where in this method there are six steps, namely research, modelling, requirements, framework, refinement, and support. The development of hellosehat.com site using the goal directed design method resulted in an increase in usability, namely effectiveness which increased from 62.22% to 97.78%, which was an increase of 35%. In the efficiency factor, there was an increase of 44%, where in the initial test it scored 49.88% increasing to 94.77%. In the satisfaction factor, there was an increase of 26%, from 65% to 91.25%.
Implementasi Transfer Learning Pada AlexNet Untuk Klasifikasi Motif Batik Yogyakarta Cahyaningtyas, Angela Gracia; Suwarno, Sri; Susanto, Budi
Jurnal Terapan Teknologi Informasi Vol 9 No 2 (2025): Jurnal Terapan Teknologi Informasi
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21460/jutei.2025.92.432

Abstract

This study discusses the implementation of Transfer Learning using the AlexNet architecture for classifying Yogyakarta Batik motifs, specifically Kawung, Parang, and Truntum. The dataset consists of 1,110 batik images that underwent preprocessing, augmentation, and data splitting. The research was conducted in two main experimental scenarios: applying Average Pooling and Max Pooling with a more complex classifier, and training the model without additional pooling layers using a simpler classifier. Furthermore, the experiments compared the model’s performance across different frozen layers and two optimizers (Adam and SGD). The results show that the best configuration was obtained using the SGD optimizer with Average Pooling and three frozen layers, achieving a test accuracy of 99.10%. In contrast, the Adam optimizer tended to produce lower and less stable performance. Experiments without pooling also reached high accuracy, but were less optimal than those with pooling. Therefore, this study demonstrates that the choice of pooling technique, classifier complexity, frozen layers, and optimizer plays a crucial role in achieving optimal performance of AlexNet for Batik classification.
Analisis Dan Perancangan Antarmuka Situs Portal Belajar Bagi Penyandang Dyschromatopsia Sumolang, Philip Andrew; Delima, Rosa; Suwarno, Sri
Jurnal Ilmiah FIFO Vol 15, No 2 (2023)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/fifo.2023.v15i2.004

Abstract

Currently, many learning websites have been developed to assist people in self-learning from home. However, the developed learning websites are not yet user-friendly for individuals with dyschromatopsia. This is due to the design process not considering using colors that are easily distinguishable for users with dyschromatopsia. Based on the issues, a learning portal website interface that is friendly to individuals with dyschromatopsia is needed. In the design of this application, the method used is User-Centered Design (UCD). Through the UCD method, the needs of users with dyschromatopsia become the main focus of the research. Testing is conducted through usability testing using the Overall Relative Efficiency, Completion Rate, and the System Usability Scale (SUS) questionnaire to measure user satisfaction. The test results show an Overall Relative Efficiency value ranging from 72.675% to 92.130%, Completion Rate testing yields an effectiveness value range of 82% to 96%, and SUS testing yields a score of 81.25. In addition, the author also conducted testing for color-blind accessibility, which resulted in the finding that the contrast ratio of all the pages on the CourseHack website meets the minimum contrast ratio standards.
Estimasi Gender Berbasis Sidik Jari dengan Wavelet dan Support Vector Machine Suwarno, Sri; Mahastama, Aditya Wikan
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 5: Oktober 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2024117972

Abstract

Estimasi gender berbasis sidik jari sering diperlukan untuk identifikasi jenazah tanpa identitas, sebelum dipastikan dengan tes DNA.  Untuk kepentingan tersebut sidik jari jenazah diambil secara digital dan selanjutnya diidentifikasi. Kesulitan yang dihadapi dalam memproses sidik jari secara digital adalah menentukan fitur yang handal dan tidak dipengaruhi oleh kwalitas citra dan masalah translasi dan rotasi.  Selain itu untuk mendapatkan akurasi yang tinggi diperlukan sejumlah preprocessing pada dataset. Penelitian ini bertujuan untuk mengestimasi gender berbasis sidik jari dengan fitur yang dibangkitkan dari transformasi wavelet. Fitur diambil dari nilai Energy yang dihasilkan melalui transformasi Haar wavelet sebanyak enam level. Selanjutnya fitur tersebut dipakai sebagai data latih bagi Support Vector Machines (SVM) untuk diestimasi. Penelitian ini menggunakan dataset dari NIST (National Institute of Standart and Technology) sebanyak 1000 sampel terdiri dari 500 sidik jari pria dan 500 sidik jari wanita.  Berdasarkan hasil percobaan yang dilakukan, metode ini menghasilkan akurasi sampai 70.3% dengan tingkat TPR (True Positive Rate) sebesar 80.6% untuk sidik jari wanita dan 60.0% untuk sidik jari pria. Metode ini menunjukkan waktu komputasi yang cepat karena tidak memerlukan preprocessing, komputasinya sederhana dan dengan jumlah sampel data yang tidak banyak.   Abstract   Gender estimation based on fingerprints is often required to identify unidentified corpses before being confirmed by DNA testing. For this purpose, fingerprints of the body are taken digitally and subsequently identified. The difficulty in digital fingerprint examination is determining reliable features unaffected by translation or rotation. In addition, some preprocessing is required on the dataset to obtain high accuracy. This study aims to estimate gender based on fingerprints using wavelet transform and Support Vector Machines (SVM). The features are the  Energy values generated by the Haar wavelet transform of six-level. The features are then used as training data for the SVM to be classified. This study used datasets from NIST (National Institute of Standards and Technology), as many as 1000 samples consisting of 500 male fingerprints and 500 female fingerprints. Based on the experiments' results, this method produces an accuracy of up to 70.3% with a TPR (True Positive Rate) of 80.6% for female fingerprints and 60.0% for male fingerprints. This method shows a fast computational time because it does not require preprocessing; the computation is simple and with a small amount of sample data.
Testing The Accuracy of Fingerprint Recognition using Levenshtein Distance and Hamming Distance Methods : Uji Ketepatan Pengenalan Sidik Jari dengan Metode Levenshtein Distance dan Hamming Distance Ginantaka, Gregorius Sakti; Saputra, Laurentius Kuncoro Probo; Suwarno, Sri
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 6 No. 1 (2023): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v6i1.1612

Abstract

The presence or evidence of attendance is crucial in monitoring the presence of every individual working in a particular field. Developing an employee attendance system using fingerprints can expedite the processing of data of employees who have or have not attended. One brand of machine used as a fingerprint attendance tool is Fingerspot Flexcode. The data obtained from the machine comes in the form of bitmap images that are converted into strings using encoding. Although the resulting string sequences are different, there is a possibility of similarity in fingerprint data among employees because the system cannot distinguish data precisely. Therefore, the comparison between the Levenshtein Distance and Hamming Distance methods is used to determine which method has the highest accuracy in processing the system's calculation. The method with the highest accuracy will determine the level of compatibility of the method with the tested tool. For example, 6 fingerprint data are taken from each of the 7 different employees, resulting in a total of 42 data as test data. The calculation results show that the accuracy of the Levenshtein Distance method is 80,76 % with a precision of 46,43 %, while the Hamming Distance method is 78,34 % with a precision of 30,50 % in processing string similarity in fingerprint data. Based on these results, it can be concluded that the Levenshtein Distance method is better in calculating similarity in fingerprint data compared to the Hamming Distance method because it has a higher level of accuracy and precision compared to the Hamming Distance method.
REKAYASA FITUR SIDIK JARI BERBASIS HISTOGRAM SEBAGAI DASAR KLASIFIKASI GENDER Suwarno, Sri
PROSIDING SEMINASTIKA Vol. 5 No. 1 (2024): 5th SEMINASTIKA 2024
Publisher : Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/seminastika.v5i1.772

Abstract

Fitur sidik jari merupakan ciri atau pola unik pada sidik jari yang dapat digunakan sebagai acuan untuk mengidentifikasi atau memverifikasi pemiliknya. Pengambilan atau pemisahan fitur dari sidik jari disebut ekstraksi fitur. Secara teknis proses esktraksi fitur ada dua model pendekatan, yaitu berdasarkan posisi relatif setiap piksel dan berdasarkan frekwensi kemunculan setiap nilai intensitas piksel pada sidik jari. Penelitian ini membangun fitur sidik jari berdasarkan frekwensi kemunculan nilai-nilai intensitas piksel yang disusun dalam bentuk histogram. Mula-mula dipilih posisi ROI (Region of Interest) yang berada tepat ditengah-tengah citra sidik jari. Ukuran ROI ditentukan 16?16 dan 32?32 piksel. Selanjutnya nilai-nilai intensitas piksel dalam ROI tersebut disusun dalam bentuk histogram dengan ukuran bin bervariasi dari 2 sampai 16. Banyaknya piksel pada setiap bin disusun dalam bentuk vektor dan digunakan sebagai fitur. Penelitian ini menggunakan dataset sidik jari dari NIST (National Institute of Standart and Technology), terdiri dari 500 sidik jari pria dan 500 sidik jari wanita. Sebagai tolok ukur kwalitas fitur yang dihasilkan, fitur diuji untuk klasifikasi gender dengan algoritma SVM (Support Vector Machine). Dari penelitian yang dilakukan, fitur yang dibangun dapat menghasilkan akurasi klasifikasi maksimal 60.9%. Penelitian ini masih dapat dikembangkan dengan memilih lokasi dan ukuran ROI yang berbeda-beda.
Co-Authors Aditya Wikan Mahastama Budi Susanto Cahyaningtyas, Angela Gracia Ginantaka, Gregorius Sakti Hendro Setiadi Laurentius Kuncoro Probo Saputra, Laurentius Kuncoro Probo Pasaribu, Paul Ronaldo Yonathan Rosa Delima Sri Hartati Sumolang, Philip Andrew