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Contact Name
Achmad Choiron
Contact Email
journal.inform@unitomo.ac.id
Phone
+6281332765765
Journal Mail Official
journal.inform@unitomo.ac.id
Editorial Address
Jl. Semolowaru no 84, Surabaya, 60118
Location
Kota surabaya,
Jawa timur
INDONESIA
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi
ISSN : 25023470     EISSN : 25810367     DOI : 10.25139
Inform: Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi is One of the journals published by the Informatics Engineering Department Dr. Soetomo University, was established in January 2016. Inform a double-blind peer-reviewed journal, the aim of this journal is to publish high-quality articles dedicated to the field of information and communication technology, Published 2 times a year in January and July. Inform with p-ISSN:2502-3470 and e-ISSN:2581-0367 has been accredited by the Ministry of Research and Technology of the National Research and Innovation Agency of the Republic of Indonesia Number 85/M/KPT/2020 dated April 1, 2020. Accreditation is valid for 5 years Vol.3 No.2 2018 to Vol.8 No.1 2023. Focus and Scope that is Scientific research related to information and communication technology fields, including Software Engineering, Information Systems, Human-Computer Interaction, Architecture and Hardware, Computer Vision, Pattern Recognition, Computer Application and Artificial intelligence, Game Technology, and Computer Graphics, but not limited to informatics scope.
Articles 261 Documents
Model Warna HSCbCrAB untuk Deteksi Kulit Menggunakan PCA-kNN Afirianto, Tri; Amalia, Faizatul
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol 2, No 2 (2017)
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (795.605 KB) | DOI: 10.25139/ojsinf.v2i2.312

Abstract

Deteksi kulit merupakan suatu proses untuk menentukan suatu wilayah apakah termasuk kulit atau bukan kulit. Beberapa kamera digital menghasilkan citra RGB. Dalam berbagai kasus deteksi kulit dilakukan transformasi dari RGB ke ruang warna lainnya, seperti HSV, YCbCr, dan CIELAB. Beberapa ruang warna memiliki dua komponen yang terpisah, yaitu komponen luminan dan krominan, sedangkan warna kulit manusia lebih sering berada pada komponen krominan. Dalam paper ini, kami melakukan penelitian deteksi kulit menggunakan komponen krominan dari ruang warna HSV, YCbCr, dan CIELAB, dengan nama HSCbCrAB. Kami menggunakan PCA untuk mengurangi dimensi dank NN sebagai klasifier. Hasil dari penelitian menunjukkan performa yang bagus pada ruang warna HSCbCrAB untuk deteksi kulit
Deteksi Pemain Basket Terklasifikasi Berbasis Histogram of Oriented Gradients Hafidhoh, Nisa ul; Sukmana, Septian Enggar
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol 3, No 1 (2018)
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (511.809 KB) | DOI: 10.25139/ojsinf.v3i1.635

Abstract

Pada olahraga basket jaman modern ini, kebutuhan analisis pergerakan pemain pada calon tim lawan olahraga basket perlu didukung oleh teknologi informasi yang mampu mengupayakan sistem yang otomatis. Analisis pergerakan pemain yang otomatis perlu didukung oleh sistem deteksi pemain yang handal dan akurat sehingga pemetaan pergerakan dapat dilakukan secara optimal. Tujuan dari penelitian ini adalah untuk mengembangkan metode Histogram of Oriented Gradients (HOG) menjadi sebuah metode deteksi yang handal untuk kasus deteksi pemain basket pada media. Tantangan pada penelitian ini adalah deteksi pemain tidak hanya pada saat berjalan dan berlari namun juga pada saat melompat. Untuk memperkuat fokus dan konsistensi terhadap objek yang terdeteksi, pemanfaatan metode klasifikasi Support Vector Machine (SVM) digunakan melalui kolaborasi terhadap HOG descriptor serta warna kostum pemain sehingga pembeda tim dari masing-masing pemain juga dapat dikenali. Tingkat akurasi dari evaluasi yang dihasilkan adalah 92% untuk true positive rate dan 40% untuk false positive rate.
Application of Fuzzy C-Means in Grouping Districts/Cities Based on Health Service Facilities in East Java Maghfiroh, Wardatul; Ulinnuha, Nurissaidah
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol 3, No 2 (2018)
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (375.101 KB) | DOI: 10.25139/inform.v3i2.1070

Abstract

Health is a very important thing for every human being because without good health, then humans will be difficult to do activities. We need health facilities that can support human health or society. This study discussed use of clustering algorithm in grouping districts or cities in East Java according to the number of health care facilities using Fuzzy C-Means. The data source of this research got from Central Bureau of Statistics of East Java. The cluster results obtained then validated with sillhoute coefficient and purity. With the centroid gained in the last iteration, four districts/cities were included in the first cluster, 26 districts/cities included in the second cluster, and 8 districts/cities included in the third cluster. The results of clustering validation is the value of sillhoute coefficient of 0.695 and the purity value of 1. This can be a suggestion to the East Java provincial government, districts / municipalities that are more concerned with having the number of health facilities based on the cluster that has been done.Keywords— data mining; health facilities; clustering; fuzzy c-means; sillhoute coefficient; purity
Perbandingan Metode Single Linkage, Complete Linkage Dan Average Linkage dalam Pengelompokan Kecamatan Berdasarkan Variabel Jenis Ternak Kabupaten Sidoarjo Mu'afa, Sulthan Fikri; Ulinnuha, Nurissaidah
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol 4, No 2 (2019)
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (297.183 KB) | DOI: 10.25139/ojsinf.v4i2.1696

Abstract

Livestock products are widely used by the community in their daily lives, for example as food ingredients, industrial material sources, labor resources, fertilizer sources and energy sources. This study aims to cluster livestock potential with data on livestock population in Sidoarjo Regency in 2017 with single linkage, complete linkage and average linkage method and comparing performance of the methods. In this cluster, the data will be grouped into 3 clusters. The results of the three clusters were obtained by sixteen sub-districts in the first cluster with the potential for low livestock and each one in the second and third clusters for single linkage and average linkage. While complete linkage obtained fifteen sub-districts in the first cluster with high potential for livestock, two sub-districts in the second cluster with the potential of medium livestock and one sub-district in the third cluster with the potential for high farm animals. In the comparison of the standard deviation ratio value, the smallest value of 0.222 is obtained by complete linkage, which shows that complete linkage is better than single linkage and average linkage in the case of subgrouping based on Sidoarjo regency livestock types.
Pneumonia Classification of Thorax Images using Convolutional Neural Networks 1
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 5 No. 2 (2020)
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2362.742 KB) | DOI: 10.25139/inform.v5i2.2707

Abstract

The digital image processing technique is a product of computing technology development. Medical image data processing based on a computer is a product of computing technology development that can help a doctor to diagnose and observe a patient. This study aimed to perform classification on the image of the thorax by using Convolutional Neural Network (CNN).  The data used in this study is lung thorax images that have previously been diagnosed by a doctor with two classes, namely normal and pneumonia. The amount of data is 2.200, 1.760 for training, and 440 for testing. Three stages are used in image processing, namely scaling, gray scaling, and scratching. This study used Convolutional Neural Network (CNN) method with architecture ResNet-50. In the field of object recognition, CNN is the best method because it has the advantage of being able to find its features of the object image by conducting the convolution process during training. CNN has several models or architectures; one of them is ResNet-50 or Residual Network. The selection of ResNet-50 architecture in this study aimed to reduce the loss of gradients at certain network-level depths during training because the object is a chest image of X-Ray that has a high level of visual similarity between some pathology. Moreover, several visual factors also affect the image so that to produce good accuracy requires a certain level of depth on the CNN network. Optimization during training used Adaptive Momentum (Adam) because it had a bias correction technique that provided better approximations to improve accuracy. The results of this study indicated the thorax image classification with an accuracy of 97.73%.
Social Media Analysis Using Probabilistic Neural Network Algorithm to Know Personality Traits 1
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol. 6 No. 1 (2021)
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (237.903 KB) | DOI: 10.25139/inform.v6i1.3307

Abstract

The Internet creates a new space where people can interact and communicate efficiently. Social media is one type of media used to interact on the internet. Facebook and Twitter are one of the social media. Many people are not aware of bringing their personal life into the public. So that unconsciously provides information about his personality. Big Five personality is one type of personality assessment method and is used as a reference in this study. The data used is the social media status from both Facebook and Twitter. Status has been taken from 50 social media users. Each user is taken as a text status. The results of tests performed using the Probabilistic Neural Network algorithm obtained an average accuracy score of 86.99% during the training process and 83.66% at the time of testing with a total of 30 training data and 20 test data.
Data Warehouse Analisa Prestasi Akademik Siswa di SMP Roudlotul Jadid Lumajang Dayati, Yusi Dwi; Choiron, Achmad; Kacung, Slamet
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol 1, No 1 (2016)
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (474.969 KB) | DOI: 10.25139/ojsinf.v1i1.218

Abstract

Sistem Informasi Standard Bill Of Material Quantity Genset di PT Conductorjasa Suryapersada Cahyono, Dwi
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol 2, No 1 (2017)
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (148.579 KB) | DOI: 10.25139/ojsinf.v2i1.404

Abstract

Aplikasi Pembelajaran Bahasa Isyarat Berbasis Android Hikmalansya, Jauharul Khikam
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol 1, No 2 (2016)
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (654.163 KB) | DOI: 10.25139/inform.v1i2.849

Abstract

Pengenalan Nomor Ruangan Menggunakan Kamera Berbasis OCR Dan Template Matching muharom, syahri
Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi Vol 4, No 1 (2019)
Publisher : Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (370.747 KB) | DOI: 10.25139/inform.v4i1.1371

Abstract

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