Article Per Year (5 Year)
p-Index From 2020 - 2025
2.049
P-Index
This Author published in this journals
All Journal Jurnal Jurusan Ilmu Keolahragaan Journal of Software Engineering Journal of Intelligent Systems JAIS (Journal of Applied Intelligent System) JOIV : International Journal on Informatics Visualization JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING ILKOM Jurnal Ilmiah Musamus Journal of Primary Education Abdimasku : Jurnal Pengabdian Masyarakat Asean Journal of Sport for Development and Peace Indonesian Research Journal on Education Jurnal Informatika: Jurnal Pengembangan IT
Catur Supriyanto
Universitas Dian Nuswantoro
Religion Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences Other

Published : 19 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 19 Documents
Search

 1   2 
Bag of Visual Words pada Citra Sidik Jari Berbasis Hierarchical Agglomerative Clustering Aditya, Antony Eka; Supriyanto, Catur
Journal of Applied Intelligent System Vol 1, No 1 (2015): Februari 2015 (Hal. 1-69)
Publisher : Fakultas Ilmu Komputer, Universitas Dian Nuswantoro

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

Abstract

Pengenalan sidik jari adalah salah satu sifat biometric yang popular digunakan untuk mengenali seseorang. Untuk mengenali identitas seseorang melalui sidik jari, perlu adanya sebuah metode yang tepat dalam melakukan identifikasi. Beberapa teknik diusulkan pada penelitian sebelumnya untuk pengenalan sidik jari. Bag of  visual word ini memerlukan sebuah clustering terhadap beberapa keypoint yang dihasilkan dari sebuah algoritma matching point. Kemudian setelah keypoint dikelompokan, maka keypoint tersebut akan digunakan untuk proses klasifikasi. Penelitian ini hanya bertujuan untuk mengetahui kinerja algoritma clustering dalam pengelompokan keypoint. Hierarchical Agglomerative Clustering dan K-Means dipilih untuk proses clustering tersebut. Dalam penelitian yang dilakukan algoritma K-Means mempunyai kelemahan dalam melakukan evaluasi. Sedangkan algoritma Hierarchical Agglomerative Clustering dalam melakukan evaluasu membutuhkan waktu komputasi klastering yang cukup cepat, namun hasil performa clustering keypoint-nya tidak cukup baik. Kata kunci— Sidik jari, Bag of visual words, Clustering, K-Means, Keypoint, Hierarchical agglomerative clustering.
Integrasi Pareto Fitness, Multiple-Population dan Temporary Population pada Algoritma Genetika untuk Pembangkitan Data Tes pada Pengujian Perangkat Lunak Maulana, Mohammad Reza; Wahono, Romi Satria; Supriyanto, Catur
Journal of Software Engineering Vol 1, No 2 (2015)
Publisher : IlmuKomputer.Com

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (571.276 KB)

Abstract

Pengujian perangkat lunak memerlukan biaya yang mahal dan sering kali lebih dari 50% biaya keseluruhan dalam pengembangan perangkat lunak digunakan dalam tahapan ini. Untuk mengurangi biaya proses pengujian perangkat lunak secara otomatis dapat digunakan. Hal yang sangat penting dalam pengujian perangkat lunak secara otomatis adalah proses menghasilkan data tes. Pengujian secara otomatis yang paling efektif dalam menekan biaya adalah pengujian branch coverage. Salah satu metode yang banyak digunakan dan memiliki kinerja baik adalah algoritma genetika (AG). Salah satu permasalahan AG dalam menghasilkan data tes adalah ketiga target cabang dipilih memungkinkan tidak ada satupun individu yang memenuhi kriteria. Hal ini akan menyebabkan proses pencarian data tes memakan waktu lebih lama. Oleh karena itu di dalam penelitian ini diusulkan integrasi pareto fitness, multiple-population dan temporary population di dalam proses pencarian data tes dengan menggunakan AG (AG-PFMPTP). Multiple-population diusulkan untuk menghindari premature convergence. Kemudian pareto fitness dan temporary population digunakan untuk mencari beberapa data tes sekaligus, kemudian mengevaluasinya dan memasukkan ke dalam archive temporary population. Dari hasil pengujian yang telah dilakukan rata-rata generasi metode AG-PFMPTP secara signifikan lebih sedikit dalam menghasilkan data tes yang dibutuhkan dibandingkan metode AG standar ataupun AG dengan multiple-population (AG-MP) pada semua benchmark program yang digunakan. Hal tersebut menunjukkan metode yang diusulkan lebih cepat dalam mencari data tes yang dibutuhkan
Penanganan Fitur Kontinyu dengan Feature Discretization Berbasis Expectation Maximization Clustering untuk Klasifikasi Spam Email Menggunakan Algoritma ID3 Safuan, .; Wahono, Romi Satria; Supriyanto, Catur
Journal of Intelligent Systems Vol 1, No 2 (2015)
Publisher : IlmuKomputer.Com

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (908.039 KB)

Abstract

Pemanfaatan jaringan internet saat ini berkembang begitu pesatnya, salah satunya adalah pengiriman surat elektronik atau email. Akhir-akhir ini ramai diperbincangkan adanya spam email. Spam email adalah email yang tidak diminta dan tidak diinginkan dari orang asing yang dikirim dalam jumlah besar ke mailing list, biasanya beberapa dengan sifat komersial. Adanya spam ini mengurangi produktivitas karyawan karena harus meluangkan waktu untuk menghapus pesan spam. Untuk mengatasi permasalahan tersebut dibutuhkan sebuah filter email yang akan mendeteksi keberadaan spam sehingga tidak dimunculkan pada inbox mail. Banyak peneliti yang mencoba untuk membuat filter email dengan berbagai macam metode, tetapi belum ada yang menghasilkan akurasi maksimal. Pada penelitian ini akan dilakukan klasifikasi dengan menggunakan algoritma Decision Tree Iterative Dicotomizer 3 (ID3) karena ID3 merupakan algoritma yang paling banyak digunakan di pohon keputusan, terkenal dengan kecepatan tinggi dalam klasifikasi, kemampuan belajar yang kuat dan konstruksi mudah. Tetapi ID3 tidak dapat menangani fitur kontinyu sehingga proses klasifikasi tidak bisa dilakukan. Pada penelitian ini,  feature discretization berbasis Expectation Maximization (EM) Clustering digunakan  untuk merubah fitur kontinyu menjadi fitur diskrit, sehingga proses klasifikasi spam email bisa dilakukan. Hasil eksperimen menunjukkan ID3 dapat melakukan klasifikasi spam email dengan akurasi 91,96% jika menggunakan data training 90%. Terjadi peningkatan sebesar 28,05% dibandingkan dengan klasifikasi ID3 menggunakan binning.
Penerapan Metode Average Gain, Threshold Pruning dan Cost Complexity Pruning Untuk Split Atribut Pada Algoritma C4.5 Rahayu, Erna Sri; Wahono, Romi Satria; Supriyanto, Catur
Journal of Intelligent Systems Vol 1, No 2 (2015)
Publisher : IlmuKomputer.Com

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (695.078 KB)

Abstract

C4.5 is a supervised learning classifier to establish a Decision Tree of data. Split attribute is main process in the formation of a decision tree in C4.5. Split attribute in C4.5 can not be overcome in any misclassification cost split so the effect on the performance of the classifier. After the split attributes, the next process is pruning. Pruning is process to cut or eliminate some of unnecessary branches. Branch or node that is not needed can cause the size of Decision Tree to be very large and it is called over- fitting. Over- fitting is state of the art for this time. Methods for split attributes are Gini Index, Information Gain, Gain Ratio and Average Gain which proposed by Mitchell. Average Gain not only overcome the weakness in the Information Gain but also help to solve the problems of Gain Ratio. Attribute split method which proposed in this research is use average gain value multiplied by the difference of misclassification. While the technique of pruning is done by combining threshold pruning and cost complexity pruning. In this research, testing the proposed method will be applied to datasets and then the results of performance will be compared with results split method performance attributes using the Gini Index, Information Gain and Gain Ratio. The selecting method of split attributes using average gain that multiplied by the difference of misclassification can improve the performance of classifiying C4.5. This is demonstrated through the Friedman test that the proposed split method attributes, combined with threshold pruning and cost complexity pruning have accuracy ratings in rank 1. A Decision Tree formed by the proposed method are smaller. Keyword: Decision Tree, C4.5, split attribute, pruning, over-fitting, gain, average gain.
OPTIMASI K-MEANS CLUSTERING UNTUK IDENTIFIKASI DAERAH ENDEMIK PENYAKIT MENULAR DENGAN ALGORITMA PARTICLE SWARM OPTIMIZATION DI KOTA SEMARANG Rustam, Suhardi; Santoso, Heru Agus; Supriyanto, Catur
ILKOM Jurnal Ilmiah Vol 10, No 3 (2018)
Publisher : Program Studi Teknik Informatika Fakultas Ilmu Komputer Univeristas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (585.534 KB) | DOI: 10.33096/ilkom.v10i3.342.251-259

Abstract

Tropical regions is a region endemic to various infectious diseases. At the same time an area of high potential for the presence of infectious diseases. Infectious diseases still a major public health problem in Indonesia. Identification of endemic areas of infectious diseases is an important issue in the field of health, the average level of patients with physical disabilities and death are sourced from infectious diseases. Data Mining in its development into one of the main trends in the processing of the data. Data Mining could effectively identify the endemic regions of hubunngan between variables. K-means algorithm klustering used to classify the endemic areas so that the identification of endemic infectious diseases can be achieved with the level of validation that the maximum in the clustering. The use of optimization to identify the endemic areas of infectious diseases combines k-means clustering algorithm with optimization particle swarm optimization ( PSO ). the results of the experiment are endemic to the k-means algorithm with iteration =10, the K-Fold =2 has Index davies bauldin = 0.169 and k-means algorithm with PSO, iteration = 10, the K-Fold = 5, index davies bouldin = 0.113. k-fold = 5 has better performance.
PUSH UP MONITORING SYSTEM USING ESP32 FOR REAL-TIME PERFORMANCE ANALYSIS Rusdiawan, Afif; Alamsyah, Sayyidul Aulia; Peni, Hapsari; Supriyanto, Catur
ASEAN Journal of Sport for Development and Peace Vol 4, No 1 (2024): Sport for Sustainable Development (January) 2024
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/ajsdp.v4i1.67924

Abstract

Health and physical fitness are great concern to society today. One way to maintain health and physical fitness is by exercising. Recently, people seem very enthusiastic about exercising. We can see this from the frequent appearance of videos and photos of people’s sports activities on social media. One of the sports that many people do is push-ups. Many people are familiar with the push-up and often do it to train their arm muscles, or compete in endurance competitions between individuals. Even though they are often done, until now push-ups do not have an integrated monitoring tool so that they can become a credible medium for showing them off on social media. This research aims to create a push-up monitoring tool that can display data in real-time. The push-up monitoring tool is made using an ESP32 microcontroller which is equipped with a VL53L0X distance sensor. By using ESPNOW communication, the special communication between ESP microcontrollers, data can be sent from the push-up monitoring device to a receiver connected to a PC where the monitoring application is displayed. On the PC side, the application is designed to display graphs of distance chest to the ground, average height when the arms are straight, and average height when the arms are bent in real-time. These three aspects are important aspects for observing the validity of a push-up. The results of this research underscore the feasibility of the monitoring system in collecting push-up data. In addition, this research serves as a foundation for future research aimed at simplifying the push up monitoring systems.
Malware Detection Using K-Nearest Neighbor Algorithm and Feature Selection Supriyanto, Catur; Rafrastara, Fauzi Adi; Amiral, Afinzaki; Amalia, Syafira Rosa; Al Fahreza, Muhammad Daffa; Abdollah, Mohd. Faizal
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.6970

Abstract

Malware is one of the biggest threats in today’s digital era. Malware detection becomes crucial since it can protect devices or systems from the dangers posed by malware, such as data loss/damage, data theft, account break-ins, and the entry of intruders who can gain full access of system. Considering that malware has also evolved from traditional form (monomorphic) to modern form (polymorphic, metamorphic, and oligomorphic), a malware detection system is needed that is no longer signature-based, but rather machine learning-based. This research will discuss malware detection by classifying the file whether considered as malware or goodware, using one of the classification algorithms in machine learning, namely k-Nearest Neighbor (kNN). To improve the performance of kNN, the number of features was reduced using the Information Gain and Principal Component Analysis (PCA) feature selection methods. The performance of kNN with PCA and Information Gain will then be compared to get the best performance. As a result, by using the PCA method where the number of features was reduced until the remaining 32 PCs, the kNN algorithm succeeded in maintaining classification performance with an accuracy of 95.6% and an F1-Score of 95.6%. Using the same number of features as the basis, the Information Gain method is applied by sorting the features from those with the highest Information Gain score and taking the 32 best features. The result, by using this Information Gain method, the classification performance of the kNN algorithm can be increased to 96.9% for both accuracy and F1-Score.
Performance Comparison of k-Nearest Neighbor Algorithm with Various k Values and Distance Metrics for Malware Detection Rafrastara, Fauzi Adi; Supriyanto, Catur; Amiral, Afinzaki; Amalia, Syafira Rosa; Al Fahreza, Muhammad Daffa; Ahmed, Foez
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.6971

Abstract

Malware could evolve and spread very quickly. By these capabilities, malware becomes a threat to anyone who uses a computer, both offline and online. Therefore, research on malware detection is still a hot topic today, due to the need to protect devices or systems from the dangers posed by malware, such as loss/damage of data, data theft, account hacking, and the intrusion of hackers who can control the entire system. Malware has evolved from traditional (monomorphic) to modern forms (polymorphic, metamorphic, and oligomorphic). Conventional antivirus systems cannot detect modern types of viruses effectively, as they constantly change their fingerprints each time they replicate and propagate. With this evolution, a machine learning-based malware detection system is needed to replace the existence of signature-based. Machine learning-based antivirus or malware detection systems detect malware by performing dynamic analysis, not static analysis as used by traditional ones. This research discusses malware detection using one of the classification algorithms in machine learning, namely k-Nearest Neighbor (kNN). To improve the performance of kNN, the number of features is reduced using the Information Gain feature selection method. The performance of kNN with Information Gain will then be measured using the evaluation metrics Accuracy and F1-Score. To get the best score, some adjustments are made to the kNN algorithm, where 3 distance measurement methods will be compared to obtain the best performance along with the variations in the k values of kNN. The distance measurement methods compared are Euclidean, Manhattan, and Chebyshev, while the variations of k values compared are 3, 5, 7, and 9. The result is, kNN with the Manhattan distance measurement method, k = 3, and using information gain features selection method (reduction until 32 features remain) has the highest Accuracy and F1-Score, which is 97.0%.
Optimization Chatbot Services Based on DNN-Bert for Mental Health of University Students Dzaky, Azmi Abiyyu; Zeniarja, Junta; Supriyanto, Catur; Shidik, Guruh Fajar; Paramita, Cinantya; Subhiyakto, Egia Rosi; Rakasiwi, Sindhu
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.7403

Abstract

Attention to mental health is increasing in Indonesia, especially with the recent increase in the number of cases of stress and suicide among students. Therefore, this research aims to provide a solution to overcome mental health problems by introducing a chatbot system based on Deep Neural Networks (DNN) and BiDirectional Encoder Representation Transformers (BERT). The primary objective is to enhance accessibility and offer a more effective solution concerning the mental health of students. This chatbot utilizes Natural Language Processing (NLP) and Deep Learning to provide appropriate responses to mild mental health issues. The dataset, comprising objectives, tags, patterns, and responses, underwent processing using Indonesian language rules within NLP. Subsequently, the system was trained and tested using the DNN model for classification, integrated with the TokenSimilarity model to identify word similarities. Experimental results indicate that the DNN model yielded the best outcomes, with a training accuracy of 98.97%, validation accuracy of 71.74%, and testing accuracy of 71.73%. Integration with the TokenSimilarity model enhanced the responses provided by the chatbot. TokenSimilarity searches for input similarities from users within the knowledge generated from the training data. If the similarity is high, the input is then processed by the DNN model to provide the chatbot response. This integration of both models has proven to enhance the responsiveness of the chatbot in providing various responses even when the user inputs remain the same. The chatbot also demonstrates the capability to recognize various inputs more effectively with similar intentions or purposes. Additionally, the chatbot exhibits the ability to comprehend user inputs although there are many writing errors.
Pelatihan Digital Marketing Bagi Ibu-ibu PKK sebagai Upaya Mendukung Pertumbuhan Womenpreneur Paramita, Cinantya; Amalia, Amalia; Supriyanto, Catur; Purwanto, Purwanto
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 2 (2024): MEI 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i2.2154

Abstract

Pemberdayaan perempuan merupakan strategi dalam meningkatkan peran perempuan dalam mengembangkan potensi diri agar dapat mandiri dan bahkan dapat berkontribusi positif terhadap pendapatan ekonomi dan kesejahteraan keluarga. Program pemberdayaan perempuan dan keluarga dapat melalui pelatihan keterampilan, salah satunya adalah pelatihan digital marketing. Tujuan kegiatan pelatihan digital marketing yakni memberikan pengetahuan dan keterampilan perempuan maupun ibu-ibu Pemberdayaan Kesejahteraan Keluarga (PKK) agar dapat memotivasi untuk menjadi womenpreneure, bahkan mengembangkan bisnis di era modern. Sasaran kegiatan ini adalah ibu-ibu PKK Kelurahan Meteseh, Kecamatan Tembalang, Semarang. Pelatihan ini diberikan pemahaman secara teori mengenai pentingnya digital marketing dalam meningkatkan visibilitas dan daya saing bisnis. Peserta pelatihan juga melakukan praktik untuk pemanfaatan teknologi digital dalam mempromosikan produk dan jasa. Keberhasilan pelatihan diukur dari peningkatan pengetahuan yaitu mampu menjawab soal dengan benar dengan nilai diatas 81 sebesar 100%, rerata tingkat kepuasan peserta terhadap kegiatan pelatihan sebesar 90%.
Co-Authors . Safuan, . Abdollah, Mohd. Faizal Abdul Rachman Syam Tuasikal Abu Salam Ahmed, Foez Al Fahreza, Muhammad Daffa Alamsyah, Sayyidul Aulia Amalia Amalia Amalia, Syafira Rosa Amiral, Afinzaki Andreas Wilson Setiawan Antony Eka Aditya, Antony Eka Astuti, Yani Parti Bayu Satria, Zaky Indra Dyan Yuliana Dzaky, Azmi Abiyyu Egia Rosi Subhiyakto, Egia Rosi Erna Sri Rahayu, Erna Sri Fauzi Adi Rafrastara Gede Doddy Tisna MS Guruh Fajar Shidik Hapsari Peni Heru Agus Santoso HIMAWAN WISMANADI Junta Zeniarja Lin, wei Jhe Majid, Nur Kholis Marjuni, Aris Mohammad Reza Maulana, Mohammad Reza Muchamad Arif Al Ardha Muljono Muljono Mulyanto, Edy Nining Widyah Kusnanik Nurhasan Nurhasan, Nurhasan Octaviani, Dhita Aulia Oman Somantri Paramita, Cinantya Pitaloka, Tia Amika Purwanto Purwanto Purwanto, Purwanto Rahadian, Arief Rizka Safriyani Rizki, Ainun Zulfikar Romi Satria Wahono Rusdiawan, Afif Rustam, Suhardi Safar, Noor Zuraidin Mohd Sindhu Rakasiwi Sudibyo, Usman Swanny Trikajanti Widyaatmadja Wahyu Adi Nugroho Wakhidah, Elfa Wahyu Yang, Chung Bing YUSUF FUAD