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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 413 Documents

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Parameter tuning in KNN for software defect prediction: an empirical analysis Modinat Abolore Mabayoje; Abdullateef Olwagbemiga Balogun; Hajarah Afor Jibril; Jelili Olaniyi Atoyebi; Hammed Adeleye Mojeed; Victor Elijah Adeyemo
Jurnal Teknologi dan Sistem Komputer Volume 7, Issue 4, Year 2019 (October 2019)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.7.4.2019.121-126

Software Defect Prediction (SDP) provides insights that can help software teams to allocate their limited resources in developing software systems. It predicts likely defective modules and helps avoid pitfalls that are associated with such modules. However, these insights may be inaccurate and unreliable if parameters of SDP models are not taken into consideration. In this study, the effect of parameter tuning on the k nearest neighbor (k-NN) in SDP was investigated. More specifically, the impact of varying and selecting optimal k value, the influence of distance weighting and the impact of distance functions on k-NN. An experiment was designed to investigate this problem in SDP over 6 software defect datasets. The experimental results revealed that k value should be greater than 1 (default) as the average RMSE values of k-NN when k>1(0.2727) is less than when k=1(default) (0.3296). In addition, the predictive performance of k-NN with distance weighing improved by 8.82% and 1.7% based on AUC and accuracy respectively. In terms of the distance function, kNN models based on Dilca distance function performed better than the Euclidean distance function (default distance function). Hence, we conclude that parameter tuning has a positive effect on the predictive performance of k-NN in SDP.

Kinerja gateway berbasis XMPP untuk komunikasi perangkat IoT Mahar Faiqurahman; Muhammad Malik Madani; Denar Regata Akbi
Jurnal Teknologi dan Sistem Komputer Volume 7, Issue 4, Year 2019 (October 2019)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

This study examines the performance of a communication gateway for IoT devices by utilizing the XMPP protocol so that these devices can be connected and communicate using the Internet. The sensor nodes, which are IoT devices, were implemented using NodeMCU connected to the DHT11 sensor module and LED lights to simulate the incoming data. Sensor nodes can communicate using the XMPP protocol gateway and process the request-response data. Gateway data transmission performance with size variations from 10-100 MB gets an average delay time of 9.3 ms, an average jitter of 0.00178 ms, and an average throughput of 161.4 kbps. The CPU usage parameter has an average increase of 12%, and memory usage tends to be constant when data transmission occurs.

Sistem pelacakan posisi pengguna menggunakan marker-based AR dalam menjelajahi galeri museum VR Pius Dian Widi Anggoro
Jurnal Teknologi dan Sistem Komputer Volume 7, Issue 4, Year 2019 (October 2019)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.7.4.2019.134-140

This study examines the user position tracking system using marker-based AR on smartphones camera. The tracking system uses a homographic algorithm integrated into the Galeri Museum VR application. In the test, the user performed exploration interactions by 6 degrees of freedom in ten different positions in the museum gallery. The physical space used in this study was 4 x 4 m2 and a marker attached to the wall in front of the user. This system results in errors in XYZ field (0.102 m, 0.047 m, 0.044 m). If the camera's orientation is not directing to the marker and the user is moving, jitter appears because of the untracked marker. The use of marker-based AR successfully applied to track the position of users who perform natural locomotion interactions in the VR environment.

Algoritma Naive Bayes, Decision Tree, dan SVM untuk Klasifikasi Persetujuan Pembiayaan Nasabah Koperasi Syariah Nurajijah Nurajijah; Dwiza Riana
Jurnal Teknologi dan Sistem Komputer Volume 7, Issue 2, Year 2019 (April 2019)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

The decision on financing approval in sharia cooperatives has a high risk of the inability of customers to pay their credit obligations at maturity or referred to as bad credit. To maintain and minimize risk, an accurate method is needed to determine the financing agreement. The purpose of this study is to classify sharia cooperative loan history data using the Naïve Bayes algorithm, Decision Tree and SVM to predict the credibility of future customers. The results showed the accuracy of Naïve Bayes algorithm 77.29%, Decision Tree 89.02% and the highest Support Vector Machine (SVM) 89.86%.

Sistem Pendukung Keputusan untuk Subsidi Biaya Perbaikan Kerusakan Kontainer Menggunakan Naive Bayes Agung Prakesakwa; Adhe Suryani; Rendra Gustriansyah
Jurnal Teknologi dan Sistem Komputer Volume 7, Issue 3, Year 2019 (July 2019)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

During the process of using containers by the importer, the shipping company as the owner of the container is often faced with the problem of those who must be responsible for handling containers that are damaged when shipping goods. This study examines the application of the Naïve Bayes method to help the container owner to make a decision in analyzing each case of objection from the importer. The analysis was carried out for each objection case submitted by the importer regarding subsidizing the cost of repairs to be given a FREE or PAID decision by considering 4 factors, which are the damaging side, the damage, the type of damage, and the cost of repairs. From 48 datasets collected and analyzed, the decision has an accuracy rate of 63.3% in subsidizing of container repair costs.

Deteksi Arteri Karotis pada Citra Ultrasound B-Mode Berbasis Convolution Neural Network Single Shot Multibox Detector I Made Gede Sunarya; Tita Karlita; Joko Priambodo; Rika Rokhana; Eko Mulyanto Yuniarno; Tri Arief Sardjono; Ismoyo Sunu; I Ketut Eddy Purnama
Jurnal Teknologi dan Sistem Komputer Volume 7, Issue 2, Year 2019 (April 2019)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Detection of vascular areas (blood vessels) using B-Mode ultrasound images is needed for automatic applications such as registration and navigation in medical operations. This study developed the detection of the carotid artery area using Convolution Neural Network Single Shot Network Multibox Detector (SSD) to determine the bounding box ROI of the carotid artery area in B-mode ultrasound images. The data used are B-Mode ultrasound images on the neck that contain the carotid artery area (primary data). SSD method result is 95% of accuracy which is higher than the Hough transformation method, Ellipse method, and Faster RCNN in detecting carotid artery area in the B-Mode ultrasound image. The use of image enhancement with Gaussian filter, histogram equalization, and Median filters in this method can increase detection accuracy. The best process time of the proposed method is 2.09 seconds so that it can be applied in a real-time system.

Identification of fat-soluble vitamins deficiency using artificial neural network Noviyanti Sagala; Cynthia Hayat; Frahselia Tandipuang
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.8.1.2020.6-11

The fat-soluble vitamins (A, D, E, K) deficiency remain frequent universally and may have consequential adverse resultants and causing slow appearance symptoms gradually and intensify over time. The vitamin deficiency detection requires an experienced physician to notice the symptoms and to review a blood test’s result (high-priced). This research aims to create an early detection system of fat-soluble vitamin deficiency using artificial neural network Back-propagation. The method was implemented by converting deficiency symptoms data into training data to be used to produce a weight of ANN and testing data. We employed Gradient Descent and Logsig as an activation function. The distribution of training data and test data was 71 and 30, respectively. The best architecture generated an accuracy of 95 % in a combination of parameters using 150 hidden layers, 10000 epoch, error target 0.0001, learning rate 0.25.

Pembangkitan interpretasi tekstual berbahasa Indonesia berdasarkan data pemeriksaan kimia darah menggunakan pendekatan berbasis r-template Indra Aulia; Sarah Purnamawati; Junianto Junianto
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.8.1.2020.44-53

The result of the blood chemistry tests is usually presented in the form of a table written in abbreviations, numbers, and units. Unfortunately, the young doctors often require the time and experience for interpreting the blood chemistry tests into a textual representation, which is easy to read and understand. Therefore, this research aimed at developing a system (BTISys) that can generate the textual representation in the Indonesian language automatically based on the blood chemistry test. BTISys generates the representation using Natural Language Generation (NLG) approach based on the r-template method. The reliability of BTISys is measured by considering the naturalness of generated textual representation. The naturalness can be observed by three criteria, such as readability, clarity, and general appropriateness. The reliability of BTISys is quite good to generate the textual representation automatically. It can be seen from the readability, clarity, and general appropriateness, which reach 73 %, 70 %, and 60 % respectively, that implies the naturalness of generated textual representation.

Reduksi bising seismik secara adaptif menggunakan filter Wiener Sesar Prabu Dwi Sriyanto
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.8.1.2020.12-20

Seismic noise disrupts the earthquake observation system due to the frequency and amplitude of seismic noise similar to the earthquake signal. The filter process is one of the methods that can be used to reduce seismic noise. In this study, the Wiener filter algorithm was designed with the Decision-Directed method for Apriori SNR estimation. This filter was chosen because it is adaptive, so it can adjust to environmental conditions without requiring manual parameter settings. The data used are earthquake signals that occur in the Palu area, Central Sulawesi, which are recorded on PKA29 temporary seismic station from February 3 to April 28, 2015. After each signal data has been filtered, then it is evaluated by calculating SNR differences before and after filtering, the signal's dominant frequency, and the cross-correlation of the signal before and after filtering. As a result, the Wiener filter is able to reduce the noise content in earthquake signals according to noisy frequencies before earthquake signals. The impact is that SNR has increased with an average of 8.056 dB. In addition, this filter is also able to maintain the shape of earthquake signals. This is indicated by the normalization value of the cross-correlation between signals before and after the filter which ranges from 0.703 to 1.00.

Perbandingan penghitungan jarak pada k-nearest neighbour dalam klasifikasi data tekstual Wahyono Wahyono; I Nyoman Prayana Trisna; Sarah Lintang Sariwening; Muhammad Fajar; Danur Wijayanto
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.8.1.2020.54-58

One algorithm to classify textual data in automatic organizing of documents application is KNN, by changing word representations into vectors. The distance calculation in the KNN algorithm becomes essential in measuring the closeness between data elements. This study compares four distance calculations commonly used in KNN, namely Euclidean, Chebyshev, Manhattan, and Minkowski. The dataset used data from Youtube Eminem’s comments which contain 448 data. This study showed that Euclidian and Minkowski on the KNN algorithm achieved the best result compared to Chebycev and Manhattan. The best results on KNN are obtained when the K value is 3.

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