Articles
<![CDATA[Penerapan Sistem Kriptografi Enkripsi Jamak Dan Tanda Tangan Digital Dalam Mendukung Keamanan Informasi ]]>
<![CDATA[Phie Chyan]]>
<![CDATA[ TEMATIKA: Jurnal Penelitian Teknik Informatika dan Sistem Informasi TEMATIKA Volume 6 Nomor 1 (Maret 2018) ]]>
Publisher : <![CDATA[Fakultas Teknologi Informasi, Universitas Atma Jaya Makassar ]]>
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<![CDATA[The use of cryptographic techniques and digital signatures is widely applied as a method to secure confidentiality and authenticity of data such as sending data over the internet network. on cryptographic, data that intent to be secret is disguised by changing the original data to unreadable format while the digital signature is used to check the authenticity of the message according to the source. Both of these techniques have weaknesses, but by combining these two techniques it will make confidential data more secure in secrecy and authenticity. The main discussion of this research is to create a data security application by combining cryptographic techniques and the use of digital signatures. The algorithm used is a Multi- encryption algorithm consisting of a combination of substitution and transposition algorithms and el gamal algorithm for document verification using digital signatures. The process that is done is to encrypt the message either typed directly into the application or from the text file that is loaded into the application. The results of this study are applications designed to meet information security needs, both protection against the confidentiality of information and protection against counterfeiting and changing unwanted information.]]>
<![CDATA[Perancangan Layanan Mobile Bengkel Online Menggunakan Metode Location Based Service ]]>
<![CDATA[Heri]]>;
<![CDATA[Phie Chyan]]>
<![CDATA[ TEMATIKA: Jurnal Penelitian Teknik Informatika dan Sistem Informasi TEMATIKA Volume 5 Nomor 2 (September 2017) ]]>
Publisher : <![CDATA[Fakultas Teknologi Informasi, Universitas Atma Jaya Makassar ]]>
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<![CDATA[This research focuses on the utilization of Location Based Service services to find the location of the nearest workshop in Makassar. Workshop is a place to fix two-wheeled vehicles and four-wheeled and more. When damage occurs at the time of use, the vehicle owner is generally confused to find the nearest workshop. In addition, the nearest workshop may not necessarily have a good reputation so that vehicle owners prefer to enter the vehicle in the official workshop. The purpose of this research is to design an information service for customers to find the nearest service based on Location Based Service, so that later both general workshop and authorized workshops can compete to provide good service for customers. Location base service is a Service using Google's global positioning service (GPS) and cell-based location technology. In addition, the LBS consists of several components, namely mobile devices, communication networks, position components, and service and content providers. The research method uses a waterfall method consisting of the stage of analysis, design, design and implementation. The results of this study is an application that can receive requests for repair or service periodically by the owner of the vehicle to the workshop. When the repair shop receives requests for repair or maintenance, the workshop will pick up the vehicle.]]>
<![CDATA[Metode Modifikasi Histogram Untuk Peningkatan Kontras dan Kecerahan Citra ]]>
<![CDATA[Phie Chyan]]>
<![CDATA[ JSAI (Journal Scientific and Applied Informatics) Vol. 1 No. 3 (2018): Sceintific and Applied Informatics ]]>
Publisher : <![CDATA[Fakultas Teknik Universitas Muhammadiyah Bengkulu ]]>
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DOI: 10.36085/jsai.v1i3.64
<![CDATA[Histogram Equalization adalah merupakan metode yang paling sering digunakan untuk meningkatkan kontras pada citra digital. Sebagai hasilnya citra yang diproses menggunakan metode HE memiliki efek negatif seperti tampilan yang kelihatan buram dan kontur yang berubah akibat perubahan pada kecerahan gambar. Untuk mengatasi masalah tersebut diperlukan model HE yang dapat memelihara tingkat kecerahan citra. Umumnya, metode tersebut mempartisi histogram dari citra asli ke dalam sub histogram dan kemudian secara independen melakukan ekualisasi terhadap sub histogram tersebut. Penelitian ini menghasilkan suatu kerangka modifikasi histogram sederhana untuk kontras enhancement pada citra tak bergerak untuk meningkatkan kontras citra tanpa kehilangan detail dari fitur citra. Metode yang disajikan terdiri dari 2 tahap. Pertama, histogram dari citra asli di modifikasi terhadap histogram umum. Pada tahap kedua, histrogram yang dimodifikasi pada citra asli dipisahkan kedalam dua sub-histogram berdasarkan rataan dari citra asli dan kemudian melakukan ekualisasi secara independen untuk menjaga kecerahan citra. Dengan menggunakan parameter enhancement, tingkat dari enhancement kontras dapat disesuaikan berdasarkan kontras citra input. Hasil eksperimen menunjukkan bahwa metode ini mempertahankan kecerahan citra lebih baik dan menghasilkan citra yang terlihat lebih alami dibandingkan dengan hasil dari metode konvensional yang lain. Metode yang digunakan telah diuji menggunakan berbagai jenis citra dan memberi kualitas visual yang lebih baik dibandingkan dengan metode lain]]>
<![CDATA[Pemulihan Citra Berbasis Metode Markov Random Field ]]>
<![CDATA[Phie Chyan]]>;
<![CDATA[N. Tri Saptadi]]>
<![CDATA[ JURIKOM (Jurnal Riset Komputer) Vol 9, No 2 (2022): April 2022 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
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DOI: 10.30865/jurikom.v9i2.3966
<![CDATA[Image processing and computer vision today are faced with increasing big data applications. Excessive collection of Image data sometimes can have bad quality due to errors at the time of acquisition or at the time of transmission, so for that problem the method is needed to perform image restoration. Image restoration is a process to make improvements to the image with the aim of obtaining a clean image from noise like the original image. Among the methods that can be used in image restoration, Markov Random Field (MRF) based on a probabilistic representation of image processing problems, namely maximizing the probability size calculated starting from the input data for all candidate solutions can provide a faster sub-optimal solution for image restoration. Based on the implementation this experiment conducted with the noisy test image, the MRF method was capable to improve the noisy image up to 96.75 percent close to the original image without noise]]>
<![CDATA[Desain Model Sistem Keamanan Berbasis Kamera Dengan Image Enhancement Algorithm ]]>
<![CDATA[Phie Chyan]]>;
<![CDATA[Adi Chandra Syarif]]>;
<![CDATA[Sean Coonery Sumarta]]>;
<![CDATA[Fransiskus Eduardus Daromes]]>
<![CDATA[ JURIKOM (Jurnal Riset Komputer) Vol 5, No 4 (2018): Agustus 2018 ]]>
Publisher : <![CDATA[STMIK Budi Darma ]]>
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DOI: 10.30865/jurikom.v5i4.956
<![CDATA[Security is a supporting factor in creating order and stability in the social environment. The issue of environmental security is related to efforts made to avoid and overcome criminal acts in society and now thanks to the advancement of information and communication technology, the function of safeguarding and controlling the environment can be supported by the help of technology to facilitate human work. The security system developed is supported by digital image processing software to process snapshot and recorded images from system cameras with the aim of improving the quality of visual images for the sake of analysis and digital forensic evidence that can be used in law enforcement related to criminal acts. The end result is a product innovation security system that can be utilized by the community in various needs related to security and supervision activities.]]>
<![CDATA[SEGMENTASI KULIT MANUSIA DENGAN EKSTRAKSI FITUR WARNA DAN ALGORITMA GMM-EM ]]>
<![CDATA[Phie Chyan]]>
<![CDATA[ Jurnal Pendidikan Teknologi Informasi (JUKANTI) Vol 5 No 1 (2022): Jurnal Pendidikan Teknologi Informasi (JUKANTI) Edisi April 2022 ]]>
Publisher : <![CDATA[Program Studi Pendidikan Informatika, Universitas Citra Bangsa ]]>
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DOI: 10.37792/jukanti.v5i1.468
<![CDATA[Deteksi kulit manusia merupakan salah satu algoritma dalam visi komputer yang paling banyak digunakan dalam berbagai aplikasi. Klasifikasi kulit manusia merupakan suatu metode membedakanpiksel gambar yang merepresentasikan kulit manusia dengan non kulit manusia. Bagi manusia tugas untuk mendeteksi kulit manusia merupakan tugas yang mudah tetapi bagi mesin atau komputer tugas ini merupakan suatu hal yang menantang terutama karena berbagai faktor seperti pencahayaan yang tidak merata, keterbatasan peralatan akuisisi citra hingga fitur individual dari manusia sendiri seperti gender, etnis dan usia dapat mempengaruhi proses deteksi yang dilakukan komputer. Dalam penelitian ini akan diimplementasikan salah satu metode yang dapat digunakan untuk melakukan deteksi dan segmentasi kulit manusia menggunakan ekstraksi fitur warna yang diimplementasikan dengan metode pembelajaran mesin berbasis statistik yaitu Gaussian Mixture Model (GMM) untuk melatih model sistem yang dapat digunakan untuk melakukan segmentasi kulit pada citra input. Berdasarkan hasil penelitian yang diperoleh model yang diimplementasikan mampu melakukan segmentasi kulit manusia pada citra sampel dengan akurasi yang baik.]]>
<![CDATA[SEGMENTASI KULIT MANUSIA DENGAN EKSTRAKSI FITUR WARNA DAN ALGORITMA GMM-EM ]]>
<![CDATA[Phie Chyan]]>
<![CDATA[ Jurnal Pendidikan Teknologi Informasi (JUKANTI) Vol 5 No 1 (2022): Jurnal Pendidikan Teknologi Informasi (JUKANTI) Edisi April 2022 ]]>
Publisher : <![CDATA[Program Studi Pendidikan Informatika, Universitas Citra Bangsa ]]>
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DOI: 10.37792/jukanti.v5i1.468
<![CDATA[Deteksi kulit manusia merupakan salah satu algoritma dalam visi komputer yang paling banyak digunakan dalam berbagai aplikasi. Klasifikasi kulit manusia merupakan suatu metode membedakanpiksel gambar yang merepresentasikan kulit manusia dengan non kulit manusia. Bagi manusia tugas untuk mendeteksi kulit manusia merupakan tugas yang mudah tetapi bagi mesin atau komputer tugas ini merupakan suatu hal yang menantang terutama karena berbagai faktor seperti pencahayaan yang tidak merata, keterbatasan peralatan akuisisi citra hingga fitur individual dari manusia sendiri seperti gender, etnis dan usia dapat mempengaruhi proses deteksi yang dilakukan komputer. Dalam penelitian ini akan diimplementasikan salah satu metode yang dapat digunakan untuk melakukan deteksi dan segmentasi kulit manusia menggunakan ekstraksi fitur warna yang diimplementasikan dengan metode pembelajaran mesin berbasis statistik yaitu Gaussian Mixture Model (GMM) untuk melatih model sistem yang dapat digunakan untuk melakukan segmentasi kulit pada citra input. Berdasarkan hasil penelitian yang diperoleh model yang diimplementasikan mampu melakukan segmentasi kulit manusia pada citra sampel dengan akurasi yang baik.]]>
<![CDATA[DESAIN MODEL KLASIFIKASI SAMPAH ORGANIK MENJADI BAHAN BAKU BRIKET BIOMASSA MENGGUNAKAN METODE DEEP LEARNING ]]>
<![CDATA[Norbertus Tri Suswanto Saptadi]]>;
<![CDATA[Phie Chyan]]>;
<![CDATA[Valentina Marchella Widjaja]]>
<![CDATA[ JURNAL INFORMATIKA DAN KOMPUTER Vol 6, No 2 (2022): ReBorn -- September 2022 ]]>
Publisher : <![CDATA[Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia ]]>
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DOI: 10.26798/jiko.v6i2.559
<![CDATA[The development of information technology in Makassar has implemented the smart city concept. The mission of urban development is to build the economy of urban communities that have superior capacity, competitiveness, productivity, creativity, innovation, efficiency and use information technology to utilize the waste generated by the community and companies. Cities have problems stemming from development by producing waste in the form of organic and inorganic waste. To empower the community, city governance is needed with a design concept that continues towards green technology. The aim of the research is to design a model that can sort and identify waste based on various types of organic matter that can be used as raw material for making biomass briquettes as a renewable alternative energy source. Research uses deep learning methods as part of machine learning in solving object detection problems in digital image classification in computer vision. The research resulted in a classification model design based on object detection by classifying 5 types of organic waste.]]>
<![CDATA[Analysis of Supermarket Product Purchase Transactions With the Association Data Mining Method ]]>
<![CDATA[Norbertus Tri Suswanto Saptadi]]>;
<![CDATA[Phie Chyan]]>;
<![CDATA[Jeremias Mathias Leda]]>
<![CDATA[ Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023 ]]>
Publisher : <![CDATA[Ikatan Ahli Informatika Indonesia (IAII) ]]>
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DOI: 10.29207/resti.v7i3.4844
<![CDATA[The development of business world is entering the era of big data. In meeting supermarkets' sales and purchase targets, the management needs to improve themselves in managing the goods available in the store. The research aims to determine the pattern of purchases that occur in a transaction, find out related and related products in supermarkets, and improve supermarket services to customers. The method applied uses the association rules approach to data mining. Several purchasing data from customers have been able to be analyzed by displaying a diagram as a visualization of the number of specified association rules. The processing results show a relationship above 90%: sugar and coffee with a confidence of 94.4%, shirts and trousers with a confidence of 93.4%, and sugar, milk, and coffee with a confidence of 92.0%. Decisions that can be taken by supermarket management in providing places and goods need to consider and follow product relationships and proximity based on the highest confidence value to provide services to customers effectively and efficiently.]]>
<![CDATA[Image Restoration Using Deep Learning Based Image Completion ]]>
<![CDATA[Phie Chyan]]>;
<![CDATA[Tri Saptadi]]>
<![CDATA[ Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 3 (2023): NOVEMBER ]]>
Publisher : <![CDATA[ISB Atma Luhur ]]>
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DOI: 10.32736/sisfokom.v12i3.1699
<![CDATA[Digital images can experience various disturbances in acquisition and storage, one of which is a disturbance indicated by damage to certain areas of the image field and causes the loss of some of the information represented by the image. One of the ways to restore an image experiencing disturbances like this is with image completion technology. Image completion is an image restoration technology capable of filling in or completing missing or corrupted parts of an image. Various methods have been developed for this image completion, starting from those based on basic image processing to the latest relying on artificial intelligence algorithms. This study aims to develop and implement an image completion model based on deep learning with the transfer learning method from the completion.net architecture. Using the Facesrub training dataset consisting of a collection of unique facial photos allows the model to understand facial attributes better. Compared to conventional image completion based on image patches, the method developed in this study can perform image filling in image gaps with more realistic results. Based on visual tests conducted on respondents, the results obtained enable respondents to understand all the information represented by the restored image, similar to the original image.]]>
