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Writer Identification Based on Hand Writing using Artificial Neural Network Rosalia Arum Kumalasanti
International Journal of Applied Sciences and Smart Technologies Volume 03, Issue 02, December 2021
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v3i2.3920

Humans are social beings who depend on social interaction. Social interaction that is often used is communication. Communication is one of the bridges to connect social relations between humans. Communication can be delivered in two ways, namely verbal or nonverbal. Handwriting is an example of nonverbal communication using paper and writing utensils. Each individual's writing has its own uniqueness so that handwriting often becomes the character or characteristic of the author. The handwriting pattern usually becomes a character for the writer so that people who recognize the writing will easily guess the ownership of the related handwriting. However, handwriting is often used by irresponsible people in the form of handwriting falsification. The acts of writing falcification often occur in the workplace or even in the field of education. This is one of the driving factors for creating a reliable system in tracking someone's handwriting based on their ownership.In this study, we will discuss the identification of a person's handwriting based on their ownership. The output of this research is in the form of ID from the author and accuracy in the form of percentage of system reliability in identifying. The results of this study are expected to have a good impact on all parties, in order to minimize plagiarism. Identification of handwriting to be built consists of two main processes, namely the training phase and the testing phase. At the training stage, the handwritten image is subjected to several processes, namely threshold, wavelet conversion, and then will be trained using the Backpropagation Artificial Neural Network. In the testing phase, the process is the same as in the training phase, but at the end of the process, a comparison will be made between the image data that has been stored during training with a comparison image.Backpropagation ANN can work optimally if it is trained using input data that has determined the size, learning rate, parameters, and the number of nodes on the network. It is expected that the offered method can work optimally so that it produces an accurate percentage in order to minimize handwriting falcification.

Design of Someone's Character Identification Based on Handwriting Patterns Using Support Vector Machine Rosalia Arum Kumalasanti
International Journal of Applied Sciences and Smart Technologies Volume 04, Issue 02, December 2022
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v4i2.5417

Image processing has a fairly broad scope and is rich in innovation. Today, image processing has developed with various reliable methods in almost all aspects of life. One of the uses of technology in the field of image processing is biometric identification. Biometric is a system that utilizes specific data in the form of individual physical characters in the process of identifying and validating data. There is also a biometric attribute that will be developed in this study is handwriting. The handwriting pattern of each individual has a different character and uniqueness so that it can be used as an identity. The uniqueness of this handwriting will be studied with the aim of recognizing a person's character or personality. If someone's personality data has been obtained, this can help the process of recruiting prospective employees in a company by simply reading from handwriting patterns. Handwriting can be studied by combining the science of Psychology so that it can provide output in the form of a person's characteristics or personality. This research will be developed using the multi class Support Vector Machine (SVM) classification. The preprocessing stage in the form of binarization, thinning and data extraction will also greatly affect the reliability of the system. Simulations with variations of variables and parameters are expected to obtain optimal accuracy.

Teknologi Tepat Guna Untuk Meningkatkan Kapasitas Produksi serta Kesehatan Lingkungan Di UKM Jamur Raya Purwanto, Yuli; Sukmawati, Paramita Dwi; Kumalasanti, Rosalia Arum
DHARMA BAKTI Dharma Bakti-Vol 5 No 1-April 2022
Publisher : LPPM IST AKPRIND Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34151/dharma.v5i1.3903

In the village of Gesikan, Subdistrict of gantiwarno, Regency of Klaten, there is a home industry in the form of mushroom cultivation. Mushroom SME in Gesikan Village was established 9 years ago and is still capable of meeting the needs of the food industry. Jamur Raya SME has several types of mushrooms that are cultivated such as oyster mushrooms, straw mushrooms, ear mushrooms, Lingzhi mushrooms and so on. Jamur Raya SMEs carry out production starting from nurseries to ready for planting. The results of these mushroom seeds are usually sold to farmers up to 1000 mushroom seeds every month. A portion of the seedling will be planted on its own and then sold according to customer needs. Mushroom Raya SMEs have a fairly high potential in production in the food industry, but there are also obstacles that are felt by these SME producers.Some of these obstacles are the lack of ability of Mushroom Raya SME to meet consumer needs caused by the limitations of production tools that are still simple/manual. The filling of planting media in the form of baglog and mixing of planting media is still done manually, so it takes a lot of time and energy to process it. In addition, there are environmental constraints, namely in the form of former planting media waste that accumulates, resulting in an unhealthy environment. Another perceived obstacle is from the marketing side which is still conventional or offline. Of course, this also needs to be supported by qualified human resources. Appropriate technology in the form of baglog filling equipment, planting media mixer & straw chopper is expected to be a solution to accelerate mushroom production in meeting consumer needs. Baglog waste treatment in improving environmental quality is also expected to provide solutions to maintain environmental health & cleanliness by using brisket maker and composter. Mushroom Raya SMEs are also expected to survive in the dynamics of the global market so that online marketing is an option to maintain this food industry. Marketing using website media is expected to be able to provide wider information in all circles of society.

Pemodelan Identifikasi Objek Kendaraan Bermotor Menggunakan Faster Region based Convolutional Neural Network (R-CNN) Berbasis Python Arum Kumalasanti, Rosalia; Susanti, Erma
Jurnal Teknologi Vol 17 No 1 (2024): Jurnal Teknologi
Publisher : Jurnal Teknologi, Fakultas Teknik, Universitas AKPRIND Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34151/jurtek.v17i1.4727

The vehicles are currently experiencing a surge in number and variation. This is evident from the kinds of vehicles that are passing through the highway area. The rise in the number of motorized vehicles will surely give a squeeze to the traffic density. The increase in the number of motor vehicles is one of the biggest factors in the impact of the congestion. The congestion can also cause damage to the highway. It's supposed to be the focus of the local government in dealing with the problem. Each road point has its own potential, so it is necessary to have a calculation in identifying the number of vehicles and the type of vehicles that are slipped on the road. Motor vehicle identification can be solved using the Faster Region based Convolutional Neural Network approach. Faster R-CNN is a deep learning architecture used to detect inside computers. Research will run at several highway points to take samples of video at a certain time, for identified the type of vehicle. Vehicle labelling will facilitate the calculation of the number of vehicles crossing the road in a given unit of time. The vehicle identification needs are used to see the density of the highway so that it can help the local government in making the right decision or solution to reduce the traffic density. The results of research such as quantitative data can be easily used to give the right picture and decision.

Pemodelan Identifikasi Objek Kendaraan Bermotor Menggunakan Faster Region based Convolutional Neural Network (R-CNN) Berbasis Python Arum Kumalasanti, Rosalia; Susanti, Erma
Jurnal Teknologi Vol 17 No 1 (2024): Jurnal Teknologi
Publisher : Jurnal Teknologi, Fakultas Teknik, Universitas AKPRIND Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34151/jurtek.v17i1.4727

The vehicles are currently experiencing a surge in number and variation. This is evident from the kinds of vehicles that are passing through the highway area. The rise in the number of motorized vehicles will surely give a squeeze to the traffic density. The increase in the number of motor vehicles is one of the biggest factors in the impact of the congestion. The congestion can also cause damage to the highway. It's supposed to be the focus of the local government in dealing with the problem. Each road point has its own potential, so it is necessary to have a calculation in identifying the number of vehicles and the type of vehicles that are slipped on the road. Motor vehicle identification can be solved using the Faster Region based Convolutional Neural Network approach. Faster R-CNN is a deep learning architecture used to detect inside computers. Research will run at several highway points to take samples of video at a certain time, for identified the type of vehicle. Vehicle labelling will facilitate the calculation of the number of vehicles crossing the road in a given unit of time. The vehicle identification needs are used to see the density of the highway so that it can help the local government in making the right decision or solution to reduce the traffic density. The results of research such as quantitative data can be easily used to give the right picture and decision.

Real-Time Vehicle Detection and Air Pollution Estimation Using YOLOv9 Suparwito, Hari; Prakoso, Bernardus Hersa Galih; Kumalasanti, Rosalia Arum; Polina, Agnes Maria
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 1 (2025): JANUARY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i1.2329

Pollution of air, particularly in cities, is becoming an issue to be taken seriously owing to the health and environmental risks associated with it, and the major contributor to air pollution is car emissions. The objective of the study is to identify and classify vehicles such as motorbikes, cars, buses, trucks in order to monitor live traffic and potentially determine the extent to which the pollution level elevates, utilizing the YOLOv9 model. Traffic CCTV camera footage was gathered under a wide range of circumstances including different lighting and varying traffic intensity. Folders were particularly structured and images annotated, in the manner, which served the purpose of meeting the requirements of the YOLO structure. Once it was trained with a labeled dataset, the vehicle identification by YOLOv9 model was found to be quite satisfactory. Overall vehicle identification accuracy was calculated to be mAP50:95 of 0.826. In contrast, it had a harder time with smaller items like motorcycles, with a mAP50:95 of 0.682. Findings indicate that larger items were detected more than smaller items. Camera angles and the small size of the objects often make small objects appear to blend in to the background. This research indicates that AI can be of help when dealing with the urban structure. It offers a way of measuring traffic volume to predict the amount of CO emissions that can be avoided or controlled. The rest are keen in enhancing the effectiveness of recognizing small objects within the system and deploying it in multiple settings.

Puzzle Game Tokoh Wayang Punakawan sebagai Media untuk Meningkatkan Pemahaman Budaya Jawa pada Anak Ariyana, Renna Yanwastika; Kumalasanti, Rosalia Arum; Mansyur, Muhamad
Prosiding Simposium Nasional Rekayasa Aplikasi Perancangan dan Industri 2019: Prosiding Simposium Nasional Rekayasa Aplikasi Perancangan dan Industri
Publisher : Universitas Muhammadiyah Surakarta

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

Di era globalisasi budaya bangsa dihadapkan pada dinamika perkembanagn budaya barat yang dianggap lebih popular oleh masyarakat. Kesenian wayang kulit yang merupakan salah satu bagian budaya, kini sangat jarang di pertunjukkan sehingga banyak anak-anak yang tidak mengetahunya kesenian tradisional ini. Mengangkat tokoh wayang punakawan melalui media game merupakan salah satu alternative untuk memperkenalkan budaya Jawa pada anak. Melalui tokoh pewayangan anak-anak dapat mengetahui tradisi dan budaya yang melekat pada masyarakat, khususnya kebiasaan adat masyarakat Jawa. Dalam penelitian ini, digunakan jenis game puzzle dimana model permainan yaitu memilih dan mencocokkan gambar acak agar menjadi satu kesatuan yang utuh. Terdapat 3 level pada game, masing-masing level memiliki tingkat kesulitan yang berbeda-beda. Pada tiap level yang diminkan akan diberikan teks dan narasi singkat tentang karakter tokoh wayang punakawan sebagai pengetahuan budaya Jawa pada anak. Penelitian menghasilkan sebuah aplikasi puzzle game tentang tokoh wayang punakawan sebagai media untuk meningkatkan pemahaman budaya, agar anak lebih paham tentang budaya Jawa dan lebih termotivasi untuk mengenal serta mencintai budaya lokal.

Perbandingan Identifikasi Tanda Tangan Offline Menggunakan Backpropgation berdasarkan Learning Rate Kumalasanti, Rosalia Arum; Yanwastika, Renna Ariyana
Prosiding Simposium Nasional Rekayasa Aplikasi Perancangan dan Industri 2019: Prosiding Simposium Nasional Rekayasa Aplikasi Perancangan dan Industri
Publisher : Universitas Muhammadiyah Surakarta

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

Era modern telah banyak merubah pola kehidupan masyarakat mulai dari komunikasi hingga transaksi. Transaksi di era modern ini telah beranjak dari transaksi offline menjadi transaksi online walaupun masih ada beberapa transaksi offline yang dipertahankan. Transaksi offline yang masih dipertahankan hingga saat ini merupakan transaksi yang melibatkan verifikasi keabsahan di dalamnya. Salah satu verifikasi keabsahan yang hingga saat ini digunakan adalah tanda tangan. Tanda tangan sering digunakan sebagai bukti keabsahan suatu berkas atau dokumen penting. Menilik dari kepentingan tanda tangan tersebut, maka besar kemungkinan tanda tangan dapat pula dimanfaatkan oleh oknumyang tidak bertanggung jawab untuk memalsukan dokumen dengan memberikan tanda tangan palsu.Pada penelitian ini akan dibahas mengenai pentingnya memberikan keamanan pada tanda tangan sebagai bukti keabsahan. Identifikasi tanda tangan menjadi pilihan untuk memberikan keamanan biometric berupa tanda tangan sesuai kepemilikannya. Proses ientifikasi ini terdiri dari dua bagian utama yaitu fase pelatihan dan fase pengujian. Fase pelatihan ini citra tanda tangan akan dikenai beberapa proses yaitu threshold, alihragam wavelet , kemudian akan dilatih dengan menggunakan Jaringan Syaraf Tiruan (JST) Backpopagation. Masuk pada fase pengujian memiliki proses yang sama seperti pada fase pelatihan namun pada akhir proses akan dilakukan perbandingan antara citra input dengan citra uji. Akurasi yang optimal dapat dimaksimalkan pada pemilihan parameter dan juga learning rate. JST dapat bekerja optimal apabila dilatih dengan menggunakan data input yang sudah disesuaikan pada saat simulasi. Parameter dan learning rate disini menjadi hal yang penting dalam mencapai akurasi yang optimal. Learning rate berhubungan langsung dengan beban komputasi yang akan berdampak dengan kecepatan pemrosesan pelatihan dan pengujian citra. Ukuran citra yang digunakan adalah 256x256 piksel da teknik-teknik yang digunakan diharapkan dapat mendukung pencapaian akurasi pada verifikasi tanda tangan dengan optimal

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