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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 8 Documents
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Search results for , issue "Volume 6, Issue 4, Year 2018 (October 2018)" : 8 Documents clear
Sistem Pemantau Kelembapan Tanah Akurat dengan Protokol Zigbee IEEE 802.15.4 pada Platform M2M OpenMTC Putu Agus Fredy; Maman Abdurohman
Jurnal Teknologi dan Sistem Komputer Volume 6, Issue 4, Year 2018 (October 2018)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1946.622 KB) | DOI: 10.14710/jtsiskom.6.4.2018.139-145

Abstract

This paper presents a study on an accurate soil moisture monitoring system based on its humidity from 9 sensor nodes using wireless sensor network (WSN) and M2M platform. The system used IEEE 802.15.4 (Zigbee) protocol. The system was connected to the application via the OpenMTC M2M platform. This monitoring system can measure soil moisture accurately and provide soil water content status on the application. The system was effective in measuring soil moisture at a distance of 0-25 meters where there was a barrier between gateway and sensor, and at a distance of 0-50 meter in line of sight. The position of the sensors that are within 3 meters of each other and the depth of each sensor 3 cm can measure soil moisture properly.
Identifikasi Jenis Bambu Berdasarkan Tekstur Daun dengan Metode Gray Level Co-Occurrence Matrix dan Gray Level Run Length Matrix Endina Putri Purwandari; Rachmi Ulizah Hasibuan; Desi Andreswari
Jurnal Teknologi dan Sistem Komputer Volume 6, Issue 4, Year 2018 (October 2018)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (510.891 KB) | DOI: 10.14710/jtsiskom.6.4.2018.146-151

Abstract

Bamboo species can be identified from the bamboo leaf images. This study conducted the identification of bamboo species based on leaf texture using Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) for texture feature extraction, and Euclidean distance for measure the image distance. This study used the images of bamboo species in Bengkulu province, that are bambusa Vulgaris Var Vulgaris, bambusa Multiplex, bambusa Vulgaris Var Striata, Gigantochloa Robusta, Gigantochloa Schortrchinii, Gigantochloa Serik, Schizostachyum Brachycladum, and Dendrocalamus Asper. The bamboo application was built using Matlab. The accuracy of the application was 100% for bamboo leaf test images captured using a smartphone camera and 81.25% for test images downloaded from the Internet.
Paralel Spatial Pyramid Convolutional Neural Network untuk Verifikasi Kekerabatan berbasis Citra Wajah Reza Fuad Rachmadi; I Ketut Eddy Purnama
Jurnal Teknologi dan Sistem Komputer Volume 6, Issue 4, Year 2018 (October 2018)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (616.288 KB) | DOI: 10.14710/jtsiskom.6.4.2018.152-157

Abstract

In this paper, we proposed a parallel spatial pyramid CNN classifier for image-based kinship verification problem. Two face images that compared for kinship verification treated as input for each parallel convolutional network of our classifier. Each parallel convolutional network constructed using spatial pyramid CNN classifier. At the end of the convolutional network, we use three fully connected layers to combine each spatial pyramid CNN features and decided the final kinship prediction. We tested the proposed classifier using large-scale kinship verification dataset, called FIW dataset, consists of seven kinship problems from 1,000 families. In our approach, we treated each kinship problem as a binary classification problem with two output. We train our classifier separately for each kinship problem with same training configuration. Overall, our proposed method can achieve an average accuracy of more than 60% and outperform the baseline method.
Yoruba Handwritten Character Recognition using Freeman Chain Code and K-Nearest Neighbor Classifier Jumoke Falilat Ajao; David Olufemi Olawuyi; Odetunji Ode Odejobi
Jurnal Teknologi dan Sistem Komputer Volume 6, Issue 4, Year 2018 (October 2018)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (685.748 KB) | DOI: 10.14710/jtsiskom.6.4.2018.129-134

Abstract

This work presents a recognition system for Offline Yoruba characters recognition using Freeman chain code and K-Nearest Neighbor (KNN). Most of the Latin word recognition and character recognition have used k-nearest neighbor classifier and other classification algorithms. Research tends to explore the same recognition capability on Yoruba characters recognition. Data were collected from adult indigenous writers and the scanned images were subjected to some level of preprocessing to enhance the quality of the digitized images. Freeman chain code was used to extract the features of THE digitized images and KNN was used to classify the characters based on feature space. The performance of the KNN was compared with other classification algorithms that used Support Vector Machine (SVM) and Bayes classifier for recognition of Yoruba characters. It was observed that the recognition accuracy of the KNN classification algorithm and the Freeman chain code is 87.7%, which outperformed other classifiers used on Yoruba characters.
Perbandingan Unjuk Kerja Algoritme Klasifikasi Data Mining dalam Sistem Peringatan Dini Ketepatan Waktu Studi Mahasiswa Ari Fadli; Mulki Indana Zulfa; Yogi Ramadhani
Jurnal Teknologi dan Sistem Komputer Volume 6, Issue 4, Year 2018 (October 2018)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (431.871 KB) | DOI: 10.14710/jtsiskom.6.4.2018.158-163

Abstract

Observation of growing academic data can be carried using data mining methods, for example, to obtain knowledge related to the determinants of timeliness of students graduation. This study conducted a performance comparison of the classification algorithms using decision tree (DT), support vector machine (SVM), and artificial neural network (ANN). This study used students academic data from Faculty of Engineering, Universitas Jenderal Soedirman in the 2014/2015 odd semester until the 2017/2018 odd semester and the attributes that conform to the academic regulations. The analytical method used is CRISP-DM. The results showed that SVM provided the best performance in an accuracy of 90.55% and AUC of 0.959, compared to other algorithms. A Model with SVM algorithm can be implemented in an early warning system for timeliness of student graduation.
Sistem Pendukung Keputusan untuk Pemutusan Hubungan Kerja Karyawan Menggunakan Metode Elimination and Choice Translation Reality Mesran Mesran; Rusiana Rusiana; Maringan Sianturi
Jurnal Teknologi dan Sistem Komputer Volume 6, Issue 4, Year 2018 (October 2018)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (134.205 KB) | DOI: 10.14710/jtsiskom.6.4.2018.135-138

Abstract

This study aims to develop a decision support system in determining employees which will be laid off. The data used in the research sourced from PT. Mitra Andal Sejati Medan in 2017. This study used the ELECTRE method to conduct an assessment of company employees. The criteria used in the research were absence (C1), appearance (C2), performance (C3), and sales volume (C4). This ELECTRE system can provide an effective decision for termination of employment based on lowest employee rank.
Front Matter - JTSiskom Volume 6 Nomor 4 Tahun 2018 JTSiskom, Editor in Chief
Jurnal Teknologi dan Sistem Komputer Volume 6, Issue 4, Year 2018 (October 2018)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (332.807 KB)

Abstract

This article contains front-matter of JTSiskom Volume 6 Number 4 Year 2018, which includes a cover page, title page, editorial team, acknowledgment, editorial policy and table of contents. JTSiskom's editorial policies include focus and scope, review process statement, publication frequency, open access policy, archiving policy and statement of article processing fee.
Back Matter - JTSiskom Volume 6 Nomor 4 Tahun 2018 JTSiskom, Editor in Chief
Jurnal Teknologi dan Sistem Komputer Volume 6, Issue 4, Year 2018 (October 2018)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (274.09 KB)

Abstract

This article contains back-matter of JTSiskom Volume 6 Issue 4 Year 2018, which includes the author's index, author guidelines, copyright notice and its transfer agreement, publication ethics statements and journal content licenses.

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