Jurnal Teknologi dan Sistem Komputer
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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<![CDATA[Klasifikasi pendonor darah potensial menggunakan pendekatan algoritme pembelajaran mesin ]]>
<![CDATA[Merinda Lestandy]]>;
<![CDATA[Lailis Syafa'ah]]>;
<![CDATA[Amrul Faruq]]>
<![CDATA[ Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 3, Year 2020 (July 2020) ]]>
Publisher : <![CDATA[Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro ]]>
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DOI: 10.14710/jtsiskom.2020.13619
<![CDATA[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.]]>
<![CDATA[Pengenalan sketsa wajah menggunakan principle component analysis sebagai aplikasi forensik ]]>
<![CDATA[Endina Putri Purwandari]]>;
<![CDATA[Aan Erlansari]]>;
<![CDATA[Andang Wijanarko]]>;
<![CDATA[Erich Adinal Adrian]]>
<![CDATA[ Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 3, Year 2020 (July 2020) ]]>
Publisher : <![CDATA[Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro ]]>
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DOI: 10.14710/jtsiskom.2020.13422
<![CDATA[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 %.]]>
<![CDATA[Navigasi robot bergerak berdasarkan landmark garis menggunakan kontroler Braitenberg dan pengolahan citra ]]>
<![CDATA[Ali Rizal Chaidir]]>;
<![CDATA[Gamma Aditya Rahardi]]>;
<![CDATA[Khairul Anam]]>
<![CDATA[ Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 3, Year 2020 (July 2020) ]]>
Publisher : <![CDATA[Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro ]]>
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DOI: 10.14710/jtsiskom.2020.13643
<![CDATA[Line following and lane tracking are robotic navigation techniques that use lines as a guide. The techniques can be applied to mobile robots in the industry. This research applied the Braitenberg controller and image processing to control and obtain line information around the mobile robot. The robot was implemented using Arduino Uno as a controller. A webcam was connected to a computer that performs image processing using canny edge detection and sends the data to the robot controller via serial communication. The robot can navigate on the side of the line, and the success rate of the system is 100 % at a turn of 135 ° and 80 % at a turn of 90 °.]]>
<![CDATA[Perbandingan kinerja RSA dan AES terhadap kompresi pesan SMS menggunakan algoritme Huffman ]]>
<![CDATA[Laurentinus Laurentinus]]>;
<![CDATA[Harrizki Arie Pradana]]>;
<![CDATA[Dwi Yuny Sylfania]]>;
<![CDATA[Fransiskus Panca Juniawan]]>
<![CDATA[ Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 3, Year 2020 (July 2020) ]]>
Publisher : <![CDATA[Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro ]]>
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DOI: 10.14710/jtsiskom.2020.13468
<![CDATA[Improved security of short message services (SMS) can be obtained using cryptographic methods, both symmetric and asymmetric, but must remain efficient. This paper aims to study the performance and efficiency of the symmetric crypto of AES-128 and asymmetric crypto of RSA with message compression in securing SMS messages. The ciphertext of RSA and AES were compressed using the Huffman algorithm. The average AES encryption time for each character is faster than RSA, which is 5.8 and 24.7 ms/character for AES and AES+Huffman encryption and 8.7 and 45.8 ms/character for RSA and RSA+Huffman, from messages with 15, 30, 60, and 90 characters. AES decryption time is also faster, which is 27.2 ms/character compared to 47.6 ms/character in RSA. Huffman compression produces an average efficiency of 24.8 % for the RSA algorithm, better than 17.35 % of AES efficiency for plaintext of 1, 16, 45, and 88 characters.]]>
<![CDATA[Maturity classification of cacao through spectrogram and convolutional neural network ]]>
<![CDATA[Gilbert E. Bueno]]>;
<![CDATA[Kristine A. Valenzuela]]>;
<![CDATA[Edwin R. Arboleda]]>
<![CDATA[ Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 3, Year 2020 (July 2020) ]]>
Publisher : <![CDATA[Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro ]]>
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DOI: 10.14710/jtsiskom.2020.13733
<![CDATA[Cacao pod's ideal harvesting time is when it is about to be ripe. Immature harvest would result in hard cacao beans not suitable for fermentation, while overripe cacao pods lead to fungal-infected, defective, and poor-quality yields. The demand for high-quality cacao products is expected to rise due to advancing technology in the present. Pre-harvesting needs to provide optimal identification of which amongst the pods are ripened enough and ready for the next stage of the cacao process. This paper recommends a technique to determine the ripeness of cacao. Nine hundred thirty-three cacao samples were used to collect thumping audio data at five different pod's exocarp locations. Each sound file is 1 second long, creating 4665 cacao sound file datasets at 16kHz sample rate and 16-bit audio bit depth. The process of the Mel-Frequency Cepstral Coefficient Spectrogram was then applied to extract recognizable features for the training process. The deep learning method integrated was a convolutional neural network (CNN) to classify the cacao sound successfully. The experimental design model's output exhibits an accuracy of 97.50 % for the training data and 97.13 % for the validation data. While the overall accuracy mean of the classification system is 97.46 %, whether the cacao is unripe or ripe.]]>
<![CDATA[Implementasi vigenere cipher 128 dan rotasi bujursangkar untuk pengamanan teks ]]>
<![CDATA[Rihartanto Rihartanto]]>;
<![CDATA[Riris Kurnia Ningsih]]>;
<![CDATA[Achmad Fanany Onnilita Gaffar]]>;
<![CDATA[Didi Susilo Budi Utomo]]>
<![CDATA[ Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 3, Year 2020 (July 2020) ]]>
Publisher : <![CDATA[Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro ]]>
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DOI: 10.14710/jtsiskom.2020.13476
<![CDATA[Information that can be in the form of text, image, audio, and video, is a valuable asset that needs to be secured from unauthorized parties. This research aims to study the implementation of Vigenere cipher 128 (VC-128) and square rotation to secure text information. The square rotation is applied to increase the security of the encryption results obtained from VC-128. The randomness of the rotation results was measured using Shannon entropy based on the distance between characters, and the Avalanche Effect measured changes in the encryption results compared to the original text. The square rotation can increase the randomness of the VC-128 encryption results, as indicated by an increase in entropy values. The highest increase in entropy of 34.8 % occurs in repetitive texts with the square size that produces optimal entropy was a 9x9 medium-size square. The Avalanche effect for each test data shows inconsistent results ranging from 44.5 % to 49 %.]]>
<![CDATA[Discrimination of civet coffee using visible spectroscopy ]]>
<![CDATA[Graciella Mae L Adier]]>;
<![CDATA[Charlene A Reyes]]>;
<![CDATA[Edwin R Arboleda]]>
<![CDATA[ Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 3, Year 2020 (July 2020) ]]>
Publisher : <![CDATA[Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro ]]>
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DOI: 10.14710/jtsiskom.2020.13734
<![CDATA[Civet coffee is considered as highly marketable and rare. This specialty coffee has a special flavor and higher price relative to regular coffee, and it is restricted in supply. Establishing a straightforward and efficient approach to distinguish civet coffee for quality; likewise, consumer protection is fundamental. This study utilized visible spectroscopy as a non-destructive and quick technique to obtain the absorbance, ranging from 450 nm to 650 nm, of the civet coffee and non-civet coffee samples. Overall, 160 samples were analyzed, and the total spectra accumulated was 960. The data gathered from the first 120 samples were fed to the classification learner application and were used as a training data set. The remaining samples were used for testing the classification algorithm. The study shows that civet coffee bean samples have lower absorbance values in visible spectra than non-civet coffee bean samples. The process yields 96.7 % to 100 % classification scores for quadratic discriminant analysis and logistic regression. Among the two classification algorithms, logistic regression generated the fastest training time of 14.050 seconds. The application of visible spectroscopy combined with data mining algorithms is effective in discriminating civet coffee from non-civet coffee.]]>
<![CDATA[Model deep learning untuk klasifikasi fragmen metagenom dengan spaced k-mers sebagai ekstraksi fitur ]]>
<![CDATA[Nur Choiriyati]]>;
<![CDATA[Yandra Arkeman]]>;
<![CDATA[Wisnu Ananta Kusuma]]>
<![CDATA[ Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 3, Year 2020 (July 2020) ]]>
Publisher : <![CDATA[Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro ]]>
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DOI: 10.14710/jtsiskom.2020.13407
<![CDATA[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.]]>
<![CDATA[Sistem pengenalan wajah dengan algoritme PCA-GA untuk keamanan pintu rumah pintar menggunakan Rasberry Pi ]]>
<![CDATA[Subiyanto Subiyanto]]>;
<![CDATA[Dina Priliyana]]>;
<![CDATA[Moh. Eki Riyadani]]>;
<![CDATA[Nur Iksan]]>;
<![CDATA[Hari Wibawanto]]>
<![CDATA[ Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 3, Year 2020 (July 2020) ]]>
Publisher : <![CDATA[Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro ]]>
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DOI: 10.14710/jtsiskom.2020.13590
<![CDATA[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.]]>
<![CDATA[Optimasi nilai k dan parameter lag algoritme k-nearest neighbor pada prediksi tingkat hunian hotel ]]>
<![CDATA[Agus Subhan Akbar]]>;
<![CDATA[R. Hadapiningradja Kusumodestoni]]>
<![CDATA[ Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 3, Year 2020 (July 2020) ]]>
Publisher : <![CDATA[Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro ]]>
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DOI: 10.14710/jtsiskom.2020.13648
<![CDATA[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.]]>
