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Jurnal Teknologi dan Sistem Komputer
Published by Universitas Diponegoro
ISSN : 26204002     EISSN : 23380403     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Jurnal Teknologi dan Sistem Komputer (JTSiskom, e-ISSN: 2338-0403) adalah terbitan berkala online nasional yang diterbitkan oleh Departemen Teknik Sistem Komputer, Universitas Diponegoro, Indonesia. JTSiskom menyediakan media untuk mendiseminasikan hasil-hasil penelitian, pengembangan dan penerapannya di bidang teknologi dan sistem komputer, meliputi sistem embedded, robotika, rekayasa perangkat lunak dan jaringan komputer. Lihat fokus dan ruang lingkup JTSiskom. JTSiskom terbit 4 (empat) nomor dalam satu tahun, yaitu bulan Januari, April, Juli dan Oktober (lihat Tanggal Penting). Artikel yang dikirimkan ke jurnal ini akan ditelaah setidaknya oleh 2 (dua) orang reviewer. Pengecekan plagiasi artikel dilakukan dengan Google Scholar dan Turnitin. Artikel yang telah dinyatakan diterima akan diterbitkan dalam nomor In-Press sebelum nomor regular terbit. JTSiskom telah terindeks DOAJ, BASE, Google Scholar dan OneSearch.id Perpusnas. Lihat daftar pengindeks. Artikel yang dikirimkan harus sesuai dengan Petunjuk Penulisan JTSiskom. JTSiskom menganjurkan Penulis menggunakan aplikasi manajemen referensi, seperti Mendeley, Endnote atau lainnya. Penulis harus register ke jurnal atau jika telah teregister, dapat langsung log in dan melakukan lima langkah submisi artikel. Penulis harus mengupload Pernyataan Pengalihan Hak Cipta saat submisi. Artikel yang terbit di JTSiskom akan diberikan nomer identifier unik (DOI/Digital Object Identifier) dan tersedia serta bebas diunduh dari portal JTSiskom ini. Penulis tidak dipungut biaya baik untuk pengiriman artikel maupun pemrosesan artikel (lihat APC/Article Processing Charge). Jurnal ini mengimplementasikan sistem LOCKSS untuk pengarsipan secara terdistribusi di jaringan LOCKSS privat.
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Articles 7 Documents
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Search results for , issue "Volume 9, Issue 4, Year 2021 (October 2021)" : 7 Documents clear
Enhanced image security using residue number system and new Arnold transform Akinbowale Nathaniel Babatunde; Afeez Adeshina Oke; Abdulkareem Ayopo Oloyede; Aisha Oiza Bello
Jurnal Teknologi dan Sistem Komputer Volume 9, Issue 4, Year 2021 (October 2021)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2021.14038

Abstract

This paper aims to improve the image scrambling and encryption effect in traditional two-dimensional discrete Arnold transform by introducing a new Residue number system (RNS) with three moduli and the New Arnold Transform. The study focuses on improving the classical discrete Arnold transform with quasi-affine properties, applying image scrambling and encryption research. The design of the method is explicit to three moduli set {2n, 2n+1+1, 2n+1-1}. These moduli set includes equalized and shapely moduli leading to the effective execution of the residue to binary converter. The study employs an arithmetic residue to the binary converter and an improved Arnold transformation algorithm. The encryption process uses MATLAB to accept a digital image input and subsequently convert the image into an RNS representation. The images are connected as a group. The resulting encrypted image uses the Arnold transformation algorithm. The encrypted image is used as input at decryption using the anti-Arnold (Reverse Arnold) transformation algorithm to convert the picture to the original RNS (original pixel value). Then the RNS was used to retransform the original RNS to its binary form. Security analysis tests, like histogram analysis, keyspace, key sensitivity, and correlation coefficient analysis, were administered on the encrypted image. Results show that the hybrid system can use the improved Arnold transform algorithm with better security and no constraint on image width and size.
Deteksi osteoporosis pada citra radiograf panoramik dental menggunakan algoritme J48 dan learning vector quantization Enny Itje Sela
Jurnal Teknologi dan Sistem Komputer Volume 9, Issue 4, Year 2021 (October 2021)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2021.14197

Abstract

Osteoporosis is one type of disease that is not easily detected. This disease can cause fractures for the sufferer. Early detection of osteoporosis is crucial to prevent fractures. This study aims to detect osteoporosis through features extracted from cortical bone and trabeculae in dental panoramic images. The results of the selected feature extraction are trained using an artificial neural network. Based on the study results, the dominant features for osteoporosis detection are radio morphometric index and morphological features. The accuracy, sensitivity, and specificity of the J48 and Learning Vector Quantization (LVQ) are 83.88 %, 78.57 %, and 100 %, respectively.
Algoritme deteksi kedatangan tsunami otomatis untuk sistem observasi tinggi muka air laut Sesar Prabu Dwi Sriyanto; Ping Astony Angmalisang; Lusia Manu; Joshian N. W. Schaduw; Calvyn F. A. Sondak; Rose O. S. E Mantiri; Alfret Luasunaung; Deiske A. Sumilat
Jurnal Teknologi dan Sistem Komputer Volume 9, Issue 4, Year 2021 (October 2021)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2021.14009

Abstract

Agar dapat menginformasikan kedatangan tsunami dengan cepat kepada masyarakat, sistem observasi muka air laut perlu dilengkapi dengan algoritme deteksi tsunami otomatis. Penelitian ini bertujuan merancang algoritme deteksi tsunami yang terdiri dari 3 subalgoritme, yaitu eliminasi spike, pengisian data kosong, dan pendeteksi tsunami. Subalgoritme eliminasi spike dan pengisian data kosong digunakan untuk memperbaiki data observasi tinggi muka air laut yang sering terganggu oleh spike dan data kosong akibat faktor elektronik peralatan. Hasil perancangan diuji dengan data historis tide gauge saat terjadi tsunami antara tahun 2007-2019. Hasilnya, spike telah tereliminasi sebanyak 54,52 % dari 409 kemunculan, sedangkan data kosong berhasil diisi 100%. Pendeteksian tsunami yang menggunakan metode DART (Deep-ocean Assessment and Reporting of Tsunamis) dan TEDA (Tsunami Early Detection Algorithm) mampu mendeteksi 7 dari 10 sinyal tsunami, namun masih ada 3 sinyal yang tidak terdeteksi dan 1 kesalahan deteksi. Selain itu, rata-rata waktu pendeteksian tsunami sekitar 7,7 menit setelah tiba di lokasi tide gauge.
Klasifikasi penyakit citra daun anggur menggunakan model CNN-VGG16 Moh. Arie Hasan; Yan Riyanto; Dwiza Riana
Jurnal Teknologi dan Sistem Komputer Volume 9, Issue 4, Year 2021 (October 2021)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2021.14013

Abstract

Penelitian ini bertujuan melakukan klasifikasi citra penyakit pada daun anggur dengan menggunakan pengolahan citra. Proses pengolahan citra berupa segmentasi menggunakan algoritme k-means clustering dan proses ekstraksi fitur dengan menggunakan teknik transfer learning VGG16 serta klasifikasi menggunakan CNN. Dataset diambil dari Kaggle sejumlah 4000 citra daun anggur untuk empat kelas, yaitu daun dengan campak hitam, bercak daun, daun sehat, dan hawar daun. Citra dari Google sejumlah 100 gambar juga digunakan sebagai data uji di luar dataset. Hasil dari penelitian ini diperoleh akurasi pelatihan model CNN sebesar 99,50 %. Pengujian dengan menggunakan data uji menghasilkan akurasi sebesar 97,25 % sedangkan dengan menggunakan data citra uji di luar dataset diperoleh hasil akurasi sebesar 95 %. Metode pengolahan citra yang dirancang diharapkan dapat diterapkan dalam merancang sistem untuk melakukan identifikasi dan klasifikasi citra penyakit pada daun anggur.
Real-time currency recognition on video using AKAZE algorithm Faisal Dharma Adhinata; Rifki Adhitama; Alon Jala Tirta Segara
Jurnal Teknologi dan Sistem Komputer Volume 9, Issue 4, Year 2021 (October 2021)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2021.13970

Abstract

Currency recognition is one of the essential things since everyone in any country must know money. Therefore, computer vision has been developed to recognize currency. One of the currency recognition uses the SIFT algorithm. The recognition results are very accurate, but the processing takes a considerable amount of time, making it impossible to run for real-time data such as video. AKAZE algorithm has been developed for real-time data processing because of its fast computation time to process video data frames. This study proposes the faster real-time currency recognition system on video using the AKAZE algorithm. The purpose of this study is to compare the SIFT and AKAZE algorithms related to a real-time video data processing to determine the value of F1 and its speed. Based on the experimental results, the AKAZE algorithm is resulting F1 value of 0.97, and the processing speed on each video frame is 0.251 seconds. Then at the same video resolution, the SIFT algorithm results in an F1 value of 0.65 and a speed of 0.305 seconds to process one frame. These results show that the AKAZE algorithm is faster and more accurate in processing video data.
Algoritme decision tree untuk mendeteksi ujaran kebencian dan bahasa kasar multilabel pada Twitter berbahasa Indonesia Fauzi Ihsan; Iwan Iskandar; Nazruddin Safaat Harahap; Surya Agustian
Jurnal Teknologi dan Sistem Komputer Volume 9, Issue 4, Year 2021 (October 2021)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2021.13907

Abstract

Ujaran kebencian dan bahasa kasar mudah ditemukan di dalam komunikasi tertulis di media sosial seperti Twitter, yang dapat memicu terjadinya persengketaan di antara korban dan pengujarnya. Bagaimanapun, akan sulit memeriksa apakah suatu twit mengandung ujaran kebencian dan/atau bahasa kasar bila seseorang berpihak. Penelitian ini bertujuan untuk membangun sistem untuk mengklasifikasi twit apakah mengandung ujaran kebencian dan kata-kata kasar. Apabila terdeteksi mengandung ujaran kebencian, maka level ujaran kebenciannya diukur. Dataset yang digunakan terdiri dari 13.126 twit asli dari Twitter. Word embedding digunakan untuk fitur dari teks. Algoritme Decision Tree digunakan untuk klasifikasi. Rekayasa fitur dan pengaturan parameter menunjukkan peningkatan performa deteksi. Fitur leksikon di klasifikasi Decision Tree menghasilkan akurasi tertinggi untuk deteksi ketiga kelas, yaitu kelas ujaran kebencian, kata-kata kasar dan level ujaran kebencian, daripada rekayasa fitur khusus dan fitur tekstual. Rata-rata akurasi dari ketiga kelas meningkat dari 69,77 % menjadi 70,48 % untuk komposisi data latih-uji 90:10, dan dari 69,35 % menjadi 69,54 % untuk komposisi 80:20.
Malicious URLs detection using data streaming algorithms Kayode Sakariyah Adewole; Muiz Olalekan Raheem; Muyideen Abdulraheem; Idowu Dauda Oladipo; Abdullateef Oluwagbemiga Balogun; Omotola Fatimah Baker
Jurnal Teknologi dan Sistem Komputer Volume 9, Issue 4, Year 2021 (October 2021)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2021.13965

Abstract

As a result of advancements in technology and technological devices, data is now spawned at an infinite rate, emanating from a vast array of networks, devices, and daily operations like credit card transactions and mobile phones. Datastream entails sequential and real-time continuous data in the inform of evolving stream. However, the traditional machine learning approach is characterized by a batch learning model. Labeled training data are given apriori to train a model based on some machine learning algorithms. This technique necessitates the entire training sample to be readily accessible before the learning process. The training procedure is mainly done offline in this setting due to the high training cost. Consequently, the traditional batch learning technique suffers severe drawbacks, such as poor scalability for real-time phishing websites detection. The model mostly requires re-training from scratch using new training samples. This paper presents the application of streaming algorithms for detecting malicious URLs based on selected online learners: Hoeffding Tree (HT), Naïve Bayes (NB), and Ozabag. Ozabag produced promising results in terms of accuracy, Kappa and Kappa Temp on the dataset with large samples while HT and NB have the least prediction time with comparable accuracy and Kappa with Ozabag algorithm for the real-time detection of phishing websites.

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