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Jurnal Teknologi dan Sistem Komputer
Published by Universitas Diponegoro
ISSN : 26204002     EISSN : 23380403     DOI : -
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.
Arjuna Subject : -
Articles 389 Documents
Clothing size recommender on real-time fitting simulation using skeleton tracking and rigging Kurniawati, Arik; Kusumaningsih, Ari; Aliffio, Yanuar
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 2, Year 2020 (April 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.8.2.2020.127-132

Abstract

Virtual fitting room (VFR) is a technology that replaces conventional fitting rooms. The VFR is not only available in shops, malls, and any shopping center but also in online stores, which makes VFR technology more and more developed, primarily to support online garment sales. VFR become a trending research interest since Microsoft has developed a Kinect tracking system. In this paper, we proposed the interactive 3D virtual fitting room using Microsoft's Kinect tracking and the rigging technique from 3D Modeling Blender and to implement the VFR. VFR manages the progress of virtual fitting that forms the three-dimensional simulations and visualization of garments on virtual counterparts of the real prospective buyer (user). Users can view the clothing animation on the various poses that are following the user body movements. The system can evaluate the user’s match, guiding them to choose the suitable size of the clothes using Euclidean distance.
Kombinasi SOM-RBF untuk prediksi drought code berdasarkan data curah hujan dan suhu udara Midyanti, Dwi Marisa
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 1, Year 2020 (January 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.8.1.2020.64-68

Abstract

This study aims to predict Drought Code (DC) in Kabupaten Kubu Raya using a combination of SOM-RBF. The final weight value of SOM was used as a center on the RBF network. The input data variables are rainfall data and air temperature data for three days with three binary outputs to predict DC values. This study also observed the effect of the number of neurons, learning rates, and the number of iterations on the results of the SOM-RBF network training. The smallest MSE of training result from the SOM-RBF network was 0.159933 using 65 neurons in the hidden layer, learning rate 0.007, and epoch 45000. The detection accuracy of SOM-RBF was 91.34 % from 245 test data.
Sistem inferensi fuzzy Mamdani untuk menentukan tingkat kualitas air pada kolam bioflok dalam budidaya ikan lele Pujiharsono, Herryawan; Kurnianto, Danny
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 2, Year 2020 (April 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.8.2.2020.84-88

Abstract

The government has launched a program to increase the production of catfish by using biofloc ponds. The biofloc ponds can maintain the quality of water biologically to maximize the growth of fish. However, the level of water quality monitoring is generally only divided into good or bad categories so that it cannot represent the condition of fish growth. Therefore, this study aims to get the level of water quality (0–100 %) using the Mamdani fuzzy inference system (FIS) algorithm based on pH, temperature, and dissolved oxygen (DO). The level of water quality was correlated based on catfish growth conditions. The results showed that the range of values of the water quality level for each condition of catfish growth was 100 % for normal-living fish, 83–99 % for stunted fish growth, and < 83% for threatened fish. The FIS algorithm had 89.92 % of accuracy.
Algoritme RSA menggunakan pembangkit kunci ESRKGS untuk enkripsi pesan chat dengan protokol TCP/IP Aminudin, Aminudin; Aditya, Gadhing Putra; Arifianto, Sofyan
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 2, Year 2020 (April 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.8.2.2020.113-120

Abstract

This study aims to analyze the performance and security of the RSA algorithm in combination with the key generation method of enhanced and secured RSA key generation scheme (ESRKGS). ESRKGS is an improvement of the RSA improvisation by adding four prime numbers in the property embedded in key generation. This method was applied to instant messaging using TCP sockets. The ESRKGS+RSA algorithm was designed using standard RSA development by modified the private and public key pairs. Thus, the modification was expected to make it more challenging to factorize a large number n into prime numbers. The ESRKGS+RSA method required 10.437 ms faster than the improvised RSA that uses the same four prime numbers in conducting key generation processes at 1024-bit prime number. It also applies to the encryption and decryption process. In the security testing using Fermat Factorization on a 32-bit key, no prime number factor was found. The test was processed for 15 hours until the test computer resource runs out.
Model dan prototipe inkubator mobile menggunakan kontroler PID berbasis Arduino Uno Munadi, Munadi; Pandu, R. Andhika; Wiradinata, Rizky; Julianti, Hari Peni; Setiawan, Rudy
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 1, Year 2020 (January 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.8.1.2020.69-77

Abstract

One of the causes of the high infant mortality rate in Indonesia is the lack of health support facilities in remote areas, including incubators, to keep the baby's body warm at a specific temperature. This research develops a model and prototype of a mobile incubator to carry and maintain the baby's temperature during emergencies to get further treatment to hospitals that have better facilities than incomplete health clinic facilities. The mobile incubator prototype uses a PID controller system with the optimum gain value Kp 1.501, Ki 0.016, and Kd -1,319 from the results of modeling and tuning in Matlab. The results of the bode plot analysis show that system stability was achieved with a gain margin of 109 dB. The incubator's operational mobility can last up to 59.6 minutes with two 12 V, 5 Ah batteries.
K-means-SMOTE untuk menangani ketidakseimbangan kelas dalam klasifikasi penyakit diabetes dengan C4.5, SVM, dan naive Bayes Hairani, Hairani; Saputro, Khurniawan Eko; Fadli, Sofiansyah
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 2, Year 2020 (April 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (73.3 KB) | DOI: 10.14710/jtsiskom.8.2.2020.89-93

Abstract

The occurrence of imbalanced class in a dataset causes the classification results to tend to the class with the largest amount of data (majority class). A sampling method is needed to balance the minority class (positive class) so that the class distribution becomes balanced and leading to better classification results. This study was conducted to overcome imbalanced class problems on the Indian Pima diabetes illness dataset using k-means-SMOTE. The dataset has 268 instances of the positive class (minority class) and 500 instances of the negative class (majority class). The classification was done by comparing C4.5, SVM, and naïve Bayes while implementing k-means-SMOTE in data sampling. Using k-means-SMOTE, the SVM classification method has the highest accuracy and sensitivity of 82 % and 77 % respectively, while the naive Bayes method produces the highest specificity of 89 %.
Klasifikasi citra HSV aksara kuno pada prasasti tembaga Kintamani menggunakan GLRCM dan SVM Purnama Yanti, Christina; Andika, I Gede
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 2, Year 2020 (April 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (179.199 KB) | DOI: 10.14710/jtsiskom.8.2.2020.94-99

Abstract

The problem of inscription physical damage as one of the historical heritages can be overcome using an image processing technique. The purpose of this study is to design a segmentation application for ancient scripts on inscriptions to recognize the character patterns on the inscriptions in digital form. The preprocessing was carried out to convert images from RGB to HSV. The application used the gray level run length matrix (GLRLM) to extract texture features and the support vector machine (SVM) method to classify the results. The inscription image segmentation was carried out through the pattern detection process using the sliding window method. The application obtained 88.32 % of accuracy, 0.87 of precision, and 0.94 of sensitivity.
Kinerja jaringan saraf berbasis backpropagation dan LVQ sebagai algoritme fingerprint RSS LoRa untuk penentuan posisi pada ruang terbuka Misbahuddin, Misbahuddin; Iqbal, Muhamad Syamsu; Wiriasto, Giri Wahyu; Ahmad, L; Akbar, S. Irfan; Irwan, Muhammad
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 2, Year 2020 (April 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.8.2.2020.121-126

Abstract

Outdoor positioning is one of the important applications in the Internet of things (IoT). The usage of GPS is unsuitable for low-power IoT devices. Alternatively, it can use the LoRa devices. This research aims to find a better method as the fingerprint algorithm for determining the outdoor position using RSS LoRa. The methods used as the fingerprint algorithm were two artificial neural network models, i.e. backpropagation (BP) with four types of training methods and learning vector quantization (LVQ) with two types of training methods. The experiment results show the performance of LVQ1 better than those of LVQ2. Besides, the LVQ1 was also better than the BP method. However, both BP and LVQ2 have a performance that is almost similar to about 70 %. Both of the artificial neural network models, BP and LVQ, can be used as a fingerprint algorithm to determine quite accurate the outdoor object position.
Segmentasi pelanggan menggunakan algoritme bisecting k-means berdasarkan model recency, frequency, dan monetary (RFM) Puspitasari, Novianti; Widians, Joan Angelina; Setiawan, Noval Bayu
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 2, Year 2020 (April 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.8.2.2020.78-83

Abstract

Information on customer loyalty characteristics in a company is needed to improve service to customers. A customer segmentation model based on transaction data can provide this information. This study used parameters from the recency, frequency, and monetary (RFM) model in determining customer segmentation and bisecting k-means algorithm to determine the number of clusters. The dataset used 588 sales transactions for PT Dinar Energi Utama in 2017. The clusters formed by the bisecting k-means and k-means algorithm were tested using the silhouette coefficient method. The bisecting k-means algorithm can form the best customer segmentation into three groups, namely Occasional, Typical, and Gold, with a silhouette coefficient of 0.58132.
Optimasi decision tree menggunakan particle swarm optimization untuk identifikasi penyakit mata berdasarkan analisis tekstur Arifin, Toni; Herliana, Asti
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 1, Year 2020 (January 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (286.208 KB) | DOI: 10.14710/jtsiskom.8.1.2020.59-63

Abstract

The problem of visual impairment is a serious problem with increasing cases, ranging from visual impairment to the cause of blindness. This study examines the development of an identification application for the classification of patients with eye disorders using the Decision Tree (DT) method, which is optimized using Particle Swarm Optimization (PSO). This study used 311 eye image data, consisting of 233 normal eye images and 78 eye images with glaucoma, cataracts, and uveitis. The feature extraction used Gray Level Co-occurrence Matrix (GLCM), while the feature optimization used the PSO and the learning method used DT. This optimized visual impairment classification application can improve system accuracy to 88.09 %.

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