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Jurnal Teknologi dan Sistem Komputer

JTSISKOM
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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 9 Documents

Search results for , issue "Volume 5, Issue 4, Year 2017 (October 2017)" : 9 Documents clear

Analisis Model Penelusuran Backward Chaining dalam Mendeteksi Tingkat Kecanduan Game pada Anak Anastasya Latubessy; Ahmad Jazuli
Jurnal Teknologi dan Sistem Komputer Volume 5, Issue 4, Year 2017 (October 2017)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Game addiction rate can be detected by applying expert system. This study developed a model of game addiction analysis using backward chaining. This model uses six types of game addiction behavior, among others, salience, euphoria, conflict, tolerance, withdrawal, relapse and reinstatement. Someone is said to be addicted to the game if it meets at least three types of game addiction behavior. Testing the validity of the model is done by testing the closeness of the agreement between the model analysis and expert analysis, resulting in a value of 0.78 which means having a strong agreement.

Sistem Pendukung Keputusan dalam Merekomendasikan Unit Terbaik di PDAM Tirta Lihou Menggunakan Metode Promethee Tia Imandasari; Agus Perdana Windarto
Jurnal Teknologi dan Sistem Komputer Volume 5, Issue 4, Year 2017 (October 2017)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

This research aims to develop decision support system in determining the best production unit in PDAM Tirta Lihou, Simalungun District. The research data were sourced from the institution in 2014. The alternatives used were 34 units of production and 6 criteria as the ranking of the best production unit such as the criteria for selling of water supply (C1), evaluation of arrears achieved (C2), evaluation of leakage rate (C3), running bill evaluation (C4), number of new customers (C5) and sales / use of water per customer (C6). The method used in this research was Promethee. The research showed that the best unit in PDAM Tirta Lihou was the Tanah Jawa unit with Net Flow 0.16274. With this system, agency leaders can produce a better decision to select the best unit in a more objective way.(The data set and data results in this study are available in the supplementary section available online and downloadable)

Sistem Kontrol Frekuensi Optimal Pada Pembangkit Hibrid Wind-Diesel Dengan Imperialist Competitive Algorithm Muhammad Ruswandi Djalal; Andi Imran; Herlambang Setiadi
Jurnal Teknologi dan Sistem Komputer Volume 5, Issue 4, Year 2017 (October 2017)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Frequency load arrangement on wind-diesel hybrid power plants (PLTH) becomes a very important thing in order to support the performance of the plant. One method of setting the load frequency is to use additional controllers such as Proportional Integral Derivative (PID). The use of PID also has its own problems, such as adjusting the value of unoptimal gain. In practice the PID gain is observed by trial-error, making it difficult to obtain the optimal value of the PID. In this research, we proposed an intelligent method of Imperialist Competitive Algorithm (ICA) in finding the optimal value of PID. The ICA is a smart method inspired by the competition in power struggles. ICA works based on the objective function, which minimizes Integral Time Absolute Error (ITAE). From the results of ICA optimization obtained value Kp = 63.1087, Ki = 43.2682, Kd = 9.6038. Response system, among others, for the settling time of 5.8 and overshoot of -9.098e-05 to 1.111e-09 pu which is the smallest settling time and overshoot value.

Klasifikasi Algoritma Swarm Intelligence Dalam Perspektif Complex Adaptive System dengan Metode Uji Komparasi Statistik Ketut Bayu Yogha Bintoro; Silvester Dian Handy Permana
Jurnal Teknologi dan Sistem Komputer Volume 5, Issue 4, Year 2017 (October 2017)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

This research aims to classify which SI algorithms have CAS or non-CAS criteria. The statistical comparative test method with 5 (five) characteristic test parameters was used as the proof approach that produces the classification. Based on the hypothesis that has been tested from 15 (fifteen) algorithms compared in this study, It was obtained that 8 of 15 (53.33%) algorithms has the majority of CAS characteristics, 3 of 15 (20%) algorithms has a minority of characteristics of CAS, and 4 out of 15 (26.66%) algorithms did not have CAS characteristics. The result can be a reference to understanding the characteristics of SI algorithms in the CAS and vice versa.

Layanan Infrastruktur Komputasi Multitenant dengan OpenStack di Lingkungan MaaS Joan Ricard Panggabean; Agung Budi Prasetijo; Eko Didik Widianto
Jurnal Teknologi dan Sistem Komputer Volume 5, Issue 4, Year 2017 (October 2017)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

The growth of high-quality computing needs triggers the development of infrastructure that requires huge costs and resources. This research applied OpenStack in Metal as a Service (MaaS) environment to provide multitenant infrastructure services in the form of virtual machines (IaaS). The capacity of this IaaS system can be configured based on the needs of the computing resources (CPUs, memory, storage, and network interfaces). Users within the tenant can request infrastructure services as their needs without requiring human interaction with each service provider.

Front Matter - JTSiskom Volume 5 Nomor 4 Tahun 2017 JTSiskom, Ketua Editor
Jurnal Teknologi dan Sistem Komputer Volume 5, Issue 4, Year 2017 (October 2017)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (415.063 KB)

This article contains front-matter of JTSiskom Volume 5 Number 4 Year 2017, which includes 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.

Peningkatan Akurasi Klasifikasi Tingkat Penguasaan Materi Bahan Ajar Menggunakan Jaringan Syaraf Tiruan Dan Algoritma Genetika Oman Somantri; Slamet Wiyono
Jurnal Teknologi dan Sistem Komputer Volume 5, Issue 4, Year 2017 (October 2017)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Decision support systems can be applied to perform a lecturer's performance assessment. This research aims to develop a hybrid model using the artificial neural network (ANN) and genetic algorithm (GA) that can be implemented and used as a model of decision support data analysis that produce better accuracy, specifically to assess the lecturer's comprehension of their teaching materials. The use of GA in determining the ANN parameter has increased the accuracy from 85.36% to 85.73%. The training cycle is also reduced to 624 from 1000. The use of this JST-GA model can be applied to provide a better lecture's performance assessment system.

Back Matter - JTSiskom Volume 5 Nomor 4 Tahun 2017 JTSiskom, Ketua Editor
Jurnal Teknologi dan Sistem Komputer Volume 5, Issue 4, Year 2017 (October 2017)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (508.821 KB)

This article contains back-matter of JTSiskom Volume 5 Issue 4 Year 2017, which includes the author's index, author guidelines, copyright notice and its transfer agreement, publication ethics statements and journal content licenses.

Aplikasi Diagnosis Penyakit Kanker Payudara Menggunakan Algoritma Sequential Minimal Optimization Agung Wibowo
Jurnal Teknologi dan Sistem Komputer Volume 5, Issue 4, Year 2017 (October 2017)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Various methods for the diagnosis of breast cancer exist, but not many have been implemented as an application. This study aims to develop an application using SMO algorithm assisted by Weka to diagnose breast cancer. The application was web-based application and developed using Javascript. Test dataset and model formation used original Breast Cancer Database (WBCD) data without missing value. Test mode used 10-fold cross-validation. This application can diagnose breast cancer with an accuracy of 97.3645% and has a significant increase in accuracy for the diagnosis of malignant cancer.

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