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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.
Arjuna Subject : -
Articles 413 Documents
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Kombinasi metode NER-OCR untuk meningkatkan efisiensi pengambilan informasi di poster berbahasa Indonesia Ahmad Syarif Rosidy; Tubagus Mohammad Akhriza; Mochammad Husni
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 4, Year 2020 (October 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (43.132 KB) | DOI: 10.14710/jtsiskom.2020.13686

Abstract

Penyelenggara acara di Indonesia seringkali menggunakan situs web untuk menyebarkan informasi tentang acara tersebut melalui poster digital. Namun, proses mentransfer informasi dari poster ke situs web secara manual terkendala oleh efisiensi waktu, mengingat makin banyaknya poster yang diunggah. Di sisi lain, metode pengambilan informasi berbasis teknologi informasi, seperti Named Entity Recognition (NER), untuk poster berbahasa Indonesia masih jarang dibahas di literatur, sedangkan penerapan NER terhadap korpus berbahasa Indonesia ditantang dalam peningkatan akurasi karena bahasa Indonesia adalah bahasa dengan sumber daya rendah yang menyebabkan minimnya ketersediaan korpus sebagai referensi. Artikel ini mengusulkan solusi untuk meningkatkan efisiensi waktu ekstraksi informasi dari poster digital. Solusi yang diusulkan merupakan kombinasi antara metode NER dengan Optical Character Recognition (OCR) untuk mengenali teks di poster yang dikembangkan dengan dukungan korpus data latih yang relevan untuk meningkatkan akurasi. Hasil percobaan menunjukkan bahwa sistem mampu meningkatkan efisiensi waktu sebesar 94 % dengan akurasi 82-92 % untuk beberapa entitas informasi yang diekstraksi untuk 50 poster digital uji.
Prediksi konsumsi beras menggunakan metode regresi linear pada sistem kotak beras cerdas Mulia Hanif; Maman Abdurohman; Aji Gautama Putrada
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 4, Year 2020 (October 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2020.13353

Abstract

Currently, the smart rice box has applied the Internet of Things (IoT) but without prediction of rice runs out which shows the amount of rice consumption. This study applies linear regression to predict the rice runs out in an IoT-based smart rice box and analyzes its performance. The prediction used the dataset obtained by measuring a smart rice box equipped with a load cell weight sensor and Hx711 module. The weight sensor accuracy was an RMSE of between 56 and 170 grams. The linear regression method applied to the smart rice box to predict rice running out has an MSE value of 0.2588 with a prediction window of 43 days. An R-squared value of less than one is obtained with a predictive threshold of 24 days.
Model deep learning untuk klasifikasi fragmen metagenom dengan spaced k-mers sebagai ekstraksi fitur Nur Choiriyati; Yandra Arkeman; Wisnu Ananta Kusuma
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.2020.13407

Abstract

An open challenge in bioinformatics is the analysis of the sequenced metagenomes from the various environments. Several studies demonstrated bacteria classification at the genus level using k-mers as feature extraction where the highest value of k gives better accuracy but it is costly in terms of computational resources and computational time. Spaced k-mers method was used to extract the feature of the sequence using 111 1111 10001 where 1 was a match and 0 was the condition that could be a match or did not match. Currently, deep learning provides the best solutions to many problems in image recognition, speech recognition, and natural language processing. In this research, two different deep learning architectures, namely Deep Neural Network (DNN) and Convolutional Neural Network (CNN), trained to approach the taxonomic classification of metagenome data and spaced k-mers method for feature extraction. The result showed the DNN classifier reached 90.89 % and the CNN classifier reached 88.89 % accuracy at the genus level taxonomy.
Sistem pengenalan wajah dengan algoritme PCA-GA untuk keamanan pintu rumah pintar menggunakan Rasberry Pi Subiyanto Subiyanto; Dina Priliyana; Moh. Eki Riyadani; Nur Iksan; Hari Wibawanto
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.2020.13590

Abstract

Genetic algorithm (GA) can improve the classification of the face recognition process in the principal component analysis (PCA). However, the accuracy of this algorithm for the smart home security system has not been further analyzed. This paper presents the accuracy of face recognition using PCA-GA for the smart home security system on Raspberry Pi. PCA was used as the face recognition algorithm, while GA to improve the classification performance of face image search. The PCA-GA algorithm was implemented on the Raspberry Pi. If an authorized person accesses the door of the house, the relay circuit will unlock the door. The accuracy of the system was compared to other face recognition algorithms, namely LBPH-GA and PCA. The results show that PCA-GA face recognition has an accuracy of 90 %, while PCA and LBPH-GA have 80 % and 90 %, respectively.
Optimasi nilai k dan parameter lag algoritme k-nearest neighbor pada prediksi tingkat hunian hotel Agus Subhan Akbar; R. Hadapiningradja Kusumodestoni
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.2020.13648

Abstract

Hotel occupancy rates are the most important factor in hotel business management. Prediction of the rates for the next few months determines the manager's decision to arrange and provide all the needed facilities. This study performs the optimization of lag parameters and k values of the k-Nearest Neighbor algorithm on hotel occupancy history data. Historical data were arranged in the form of supervised training data, with the number of columns per row according to the lag parameter and the number of prediction targets. The kNN algorithm was applied using 10-fold cross-validation and k-value variations from 1-30. The optimal lag was obtained at intervals of 14-17 and the optimal k at intervals of 5-13 to predict occupancy rates of 1, 3, 6, 9, and 12 months later. The obtained k-value does not follow the rule at the square root of the number of sample data.
Penghitung orang pada video CCTV menggunakan metode histogram of oriented gradient dan filter Kalman Faisal Dharma Adhinata; Muhammad Ikhsan; Wahyono Wahyono
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.2020.13660

Abstract

CCTV cameras have an important function in the field of public service, especially for convenience. The objects recorded through CCTV cameras are processed into information to support service satisfaction in the community. This study uses the function of CCTV for people counting from objects recorded by a camera. Currently, the process of detecting and tracking people takes a long time to detect all frames. In this study, the frame selection into keyframes uses the mutual information entropy method. The keyframes processing uses the Histogram of Oriented Gradient (HOG) and Kalman filter methods. The proposed method results F1 value of 0.85, recall of 76 %, and precision of 97 % with winStride parameter (12,12), scale 1.05, and the distance of the human object to CCTV 4 meters.
Sistem pemantauan tanah longsor berdasarkan laju adsorpsi air pada tanah menggunakan sensor kelembapan, kemiringan, dan suhu Faisal Budiman; Erwin Susanto; Doan Perdana; Husneni Mukhtar; Yulius Anggoro Pamungkas; Yakobus Yulyanto Kevin
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 4, Year 2020 (October 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2020.13591

Abstract

This study examines the application of a landslide disaster monitoring system based on soil activity information that utilizes humidity, temperature, and accelerometer sensors. An artificial highland was built as the research object, and the landslide process was triggered by supplying the system with continuous artificial rainfall. The soil activities were observed through its slope movement, temperature, and moisture content, utilizing an accelerometer, temperature, and humidity sensors both in dry and wet conditions. The system could well observe the soil activities, and the obtained data could be accessed in real-time and online mode on a website. The time delay in sending the data to the server was 2 seconds. Moreover, the characteristics of soil porosity and its relevance to soil saturation level due to water pressure were studied as well. Kinetic study showed that the water adsorption to soil followed the intraparticle diffusion model with a coefficient of determination R2 0.99043. The system prototype should be used to build the information center of disaster mitigation, particularly in Indonesia.
Optimalisasi model prediksi kesesuaian lahan kelapa sawit menggunakan algoritme pohon keputusan spasial Andi Nurkholis; Imas Sukaesih Sitanggang
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 (14.805 KB) | DOI: 10.14710/jtsiskom.2020.13657

Abstract

Land suitability evaluation has a vital role in land use planning aimed to increase food production effectiveness. Palm oil is a leading and strategic commodity for Indonesian people, which is predicted consumption will exceed production in the future. This study aims to evaluate palm oil land suitability using a spatial decision tree algorithm that is conventional decision tree modification for spatial data classification with adding spatial join relation. The spatial dataset consists of eight explanatory layers (soil nature and characteristics), and a target layer (palm oil land suitability) in Bogor District, Indonesia. This study produced three models, where the best model was obtained based on optimizing accuracy (98.18 %) and modeling time (1.291 seconds). The best model has 23 rules, soil texture as the root node, two variables (drainage and cation exchange capacity) are uninvolved, with land suitability visualization obtains percentage S2 (29.94 %), S3 (53.16 %), N (16.57 %), and water body (0.33 %).
A proposed method for handling an imbalance data in classification of blood type based on Myers-Briggs type indicator Ahmad Taufiq Akbar; Rochmat Husaini; Bagus Muhammad Akbar; Shoffan Saifullah
Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 4, Year 2020 (October 2020)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2020.13625

Abstract

Blood type still leads to an assumption about its relation to some personality aspects. This study observes preprocessing methods for improving the classification accuracy of MBTI data to determine blood type. The training and testing data use 250 data from the MBTI questionnaire answers given by 250 respondents. The classification uses the k-Nearest Neighbor (k-NN) algorithm. Without preprocessing, k-NN results in about 32 % accuracy, so it needs some preprocessing to handle data imbalance before the classification. The proposed preprocessing consists of two-stage, the first stage is the unsupervised resample, and the second is the supervised resample. For the validation, it uses ten cross-validations. The result of k-Nearest Neighbor classification after using these proposed preprocessing stages has finally increased the accuracy, F-score, and recall significantly.
Sampled and discretized of short-time Fourier transform and non-negative matrix factorization: the single-channel source separation case Jans Hendry; Isnan Nur Rifai; Yoga Mileniandi
Jurnal Teknologi dan Sistem Komputer Volume 9, Issue 1, Year 2021 (January 2021)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2020.13858

Abstract

The Short-time Fourier transform (STFT) is a popular time-frequency representation in many source separation problems. In this work, the sampled and discretized version of Discrete Gabor Transform (DGT) is proposed to replace STFT within the single-channel source separation problem of the Non-negative Matrix Factorization (NMF) framework. The result shows that NMF-DGT is better than NMF-STFT according to Signal-to-Interference Ratio (SIR), Signal-to-Artifact Ratio (SAR), and Signal-to-Distortion Ratio (SDR). In the supervised scheme, NMF-DGT has a SIR of 18.60 dB compared to 16.24 dB in NMF-STFT, SAR of 13.77 dB to 13.69 dB, and SDR of 12.45 dB to 11.16 dB. In the unsupervised scheme, NMF-DGT has a SIR of 0.40 dB compared to 0.27 dB by NMF-STFT, SAR of -10.21 dB to -10.36 dB, and SDR of -15.01 dB to -15.23 dB.

Page 34 of 42 | Total Record : 413

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