cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
jtsiskom@ce.undip.ac.id
Editorial Address
-
Location
Kota semarang,
Jawa tengah
INDONESIA
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.
Arjuna Subject : -
Articles 413 Documents
 30   31   32   33   34 
Model dan prototipe inkubator mobile menggunakan kontroler PID berbasis Arduino Uno Munadi Munadi; R. Andhika Pandu; Rizky Wiradinata; Hari Peni Julianti; Rudy Setiawan
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; Khurniawan Eko Saputro; Sofiansyah Fadli
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 %.
Optimasi decision tree menggunakan particle swarm optimization untuk identifikasi penyakit mata berdasarkan analisis tekstur Toni Arifin; Asti Herliana
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 %.
Sistem pemantau iklim mikro pada kandang ayam pedaging tertutup berbasis internet of things Damar Wicaksono; Taufiq Kamal
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 (49.77 KB) | DOI: 10.14710/jtsiskom.8.2.2020.100-105

Abstract

Smart agriculture has an emerged concept by using IoT sensors capable of providing various information about their field condition and conducting environmental monitoring to improve the yield of efficient crops. This research aims to develop a microclimate monitoring system in a closed house. The microclimate being monitored is the effective temperature, which is the temperature felt by broilers at that time in a fast area. In this research, IoT has been implemented using WeMos D1 R32 by sending sensor data to observe the effective temperature parameters as actual temperature, humidity, and wind speed into an MQTT cloud server. Microclimate control in the cage is based on effective temperature. The data can be displayed on a 16x4 LCD screen and accessed via an Android smartphone from anywhere and at any time.
Clothing size recommender on real-time fitting simulation using skeleton tracking and rigging Arik Kurniawati; Ari Kusumaningsih; Yanuar Aliffio
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 Dwi Marisa Midyanti
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.
Klasterisasi udang berdasarkan ukuran berbasis pemrosesan citra digital menggunakan metode CCA dan DBSCAN Adri Priadana; Aris Wahyu Murdiyanto
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.8.2.2020.106-112

Abstract

The quality of farmed shrimps has several criteria, one of which is shrimp size. The shrimp selection was carried out by the contractor at the harvest time by grouping the shrimp based on their size. This study aims to apply digital image processing for shrimp clustering based on size using the connected component analysis (CCA) and density-based spatial clustering of applications with noise (DBSCAN) methods. Shrimp group images were taken with a digital camera at a light intensity of 1200-3200 lux. The clustering results were compared with clustering from direct observation by two experts, each of which obtained an accuracy of 79.81 % and 72.99 % so that the average accuracy of the method was 76.4 %.
Klasifikasi pendonor darah potensial menggunakan pendekatan algoritme pembelajaran mesin Merinda Lestandy; Lailis Syafa'ah; Amrul Faruq
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 3, Year 2020 (July 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (10.282 KB) | DOI: 10.14710/jtsiskom.2020.13619

Abstract

Blood donation is the process of taking blood from someone used for blood transfusions. Blood type, sex, age, blood pressure, and hemoglobin are blood donor criteria that must be met and processed manually to classify blood donor eligibility. The manual process resulted in an irregular blood supply because blood donor candidates did not meet the criteria. This study implements machine learning algorithms includes kNN, naïve Bayes, and neural network methods to determine the eligibility of blood donors. This study used 600 training data divided into two classes, namely potential and non-potential donors. The test results show that the accuracy of the neural network is 84.3 %, higher than kNN and naïve Bayes, respectively of 75 % and 84.17 %. It indicates that the neural network method outperforms comparing with kNN and naïve Bayes.
Unjuk kerja k-nearest neighbor untuk alihaksara citra aksara Nusantara Anastasia Rita Widiarti
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.8.2.2020.150-156

Abstract

The concept of classification using the k-nearest neighbor (KNN) method is simple, easy to understand, and easy to be implemented in the system. The main challenge in classification with KNN is determining the proximity measure of an object and how to make a compact reference class. This paper studied the implementation of the KNN for the automatic transliteration of Javanese, Sundanese, and Bataknese script images into Roman script. The study used the KNN algorithm with the number k set to 1, 3, 5, 7, and 9. Tests used the image dataset of 2520 data. With the 3-fold and 10-fold cross-validation, the results exposed the accuracy differences if the area of the extracted image, the number of neighbors in the classification, and the number of data training were different.
Pengenalan sketsa wajah menggunakan principle component analysis sebagai aplikasi forensik Endina Putri Purwandari; Aan Erlansari; Andang Wijanarko; Erich Adinal Adrian
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 3, Year 2020 (July 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2020.13422

Abstract

Recognition of human faces in forensics applications can be identified through the Sketch recognition method by matching sketches and photos. The system gives five criminal candidates who have similarities to the sketch given. This study aims to perform facial recognition on photographs and sketches using Principal Component Analysis (PCA) as feature extraction and Euclidean distance as a calculation of the distance of test images to training images. The PCA method was used to recognize facial images from pencil sketch drawings. The system dataset is in the form of photos and sketches in the CUHK Face Sketch database consists of 93 photos and 93 sketches, and personal documentation consists of five photos and five sketches. The sketch matching application to training data produces an accuracy of 76.14 %, precision of 91.04 %, and recall of 80.26 %, while testing with sketch modifications produces accuracy and recall of 95 % and precision of 100 %.

Page 32 of 42 | Total Record : 413

 30   31   32   33   34 

Filter by Year

2013 2023


Reset
Filter By Issues
All Issue [IN PRESS] Volume 11, Issue 1, Year 2023 (January 2023) [IN PRESS] Volume 10, Issue 4, Year 2022 (October 2022) [IN PRESS] Volume 10, Issue 3, Year 2022 (July 2022) Volume 10, Issue 2, Year 2022 (April 2022) Volume 10, Issue 1, Year 2022 (January 2022) Volume 9, Issue 4, Year 2021 (October 2021) Volume 9, Issue 3, Year 2021 (July 2021) Volume 9, Issue 2, Year 2021 (April 2021) Volume 9, Issue 1, Year 2021 (January 2021) 2021: Publication In-Press Volume 8, Issue 4, Year 2020 (October 2020) Volume 8, Issue 3, Year 2020 (July 2020) Volume 8, Issue 2, Year 2020 (April 2020) Volume 8, Issue 1, Year 2020 (January 2020) Volume 7, Issue 4, Year 2019 (October 2019) Volume 7, Issue 3, Year 2019 (July 2019) Volume 7, Issue 2, Year 2019 (April 2019) Volume 7, Issue 1, Year 2019 (January 2019) Publication In-Press (2019) Volume 6, Issue 4, Year 2018 (October 2018) Volume 6, Issue 3, Year 2018 (July 2018) Volume 6, Issue 2, Year 2018 (April 2018) Volume 6, Issue 1, Year 2018 (January 2018) Volume 5, Issue 4, Year 2017 (October 2017) Volume 5, Issue 3, Year 2017 (July 2017) Volume 5, Issue 2, Year 2017 (April 2017) Volume 5, Nomor 1, Tahun 2017 (Januari 2017) Volume 4, Issue 4, Year 2016 (October 2016) Volume 4, Nomor 3, Tahun 2016 (Agustus 2016) Volume 4, Nomor 2, Tahun 2016 (April 2016) Volume 4, Nomor 1, Tahun 2016 (Januari 2016) Volume 3, Nomor 4, Tahun 2015 (Oktober 2015) Volume 3, Nomor 3, Tahun 2015 (Agustus 2015) Volume 3, Nomor 2, Tahun 2015 (April 2015) Volume 3, Nomor 1, Tahun 2015 (Januari 2015) Volume 2, Nomor 4, Tahun 2014 (Oktober 2014) Volume 2, Nomor 3, Tahun 2014 (Agustus 2014) Volume 2, Nomor 2, Tahun 2014 (April 2014) Volume 2, Nomor 1, Tahun 2014 (Januari 2014) Volume 1, Nomor 4, Tahun 2013 (Oktober 2013) Volume 1, Nomor 3, Tahun 2013 (Agustus 2013) Volume 1, Nomor 2, Tahun 2013 (April 2013) Volume 1, Nomor 1, Tahun 2013 (Januari 2013) More Issue