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Scientific Journal of Informatics
Published by Universitas Negeri Semarang
ISSN : 24077658     EISSN : 24600040     DOI : -
Core Subject : Science,
Computer Science & IT
Scientific Journal of Informatics published by the Department of Computer Science, Semarang State University, a scientific journal of Information Systems and Information Technology which includes scholarly writings on pure research and applied research in the field of information systems and information technology as well as a review-general review of the development of the theory, methods, and related applied sciences.
Arjuna Subject : -
Articles 564 Documents
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Web Forensic on Container Services Using Grr Rapid Response Framework Riadi, Imam; Umar, Rusydi; Sugandi, Andi
Scientific Journal of Informatics Vol 7, No 1 (2020): May 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i1.18299

Abstract

Cybercrime on Internet that keeps increasing does not only take place in the environment that running web applications traditionally under operating system, but also web applications that are running in more advance environment like container service. Docker is a currently popular container service in Linux operating system needs to be secured and implements incident response mechanisme that will investigate web server that was attacked by DDoS in fast, valid, and comprehesive way. This paper discusses the investigation using Grr Rapid Response framework on web server that was attacked by DDoS running in container service on Linux operating system, and the attacker using Windows oprating system that runs DDos script. This research has succesfully investigated digital evidence in the form of log file from web server running on container service and digital evidence through netstat on Windows computer.
Penerapan Model Technology Acceptance Model (TAM) untuk Pemahaman Media Pembelajaran Berbasis Multimedia Interaktif Syafrizal, Agusdi; Ernawati, Ernawati; Dwiandiyanta, Yudi
Scientific Journal of Informatics Vol 2, No 1 (2015): May 2015
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v2i1.4524

Abstract

Penerapan model Technology Acceptance Model (TAM) bertujuan untuk mengukur tingkat pemahaman terhadap media pembelajaran yang berbentuk multimedia interaktif. Pada umumnya, dosen hanya mengandalkan metode ceramah dan tanya jawab di dalam proses pembelajaran. Dalam artikel ini dosen yang mengajar meminta untuk menginovasi sistem pembelajaran, yaitu dengan menggunakan multimedia interaktif. Peneliti menggunakan jenis data kuantitatif, dengan metode analisis yang digunakan adalah deskriptif, sedangkan metode pengumpulan data diperoleh dari kuesioner dan observasi. Untuk memahami penerimaan dan penggunaan media pembelajaran berbasis multimedia interaktif bisa diukur dengan menggunakan model penerimaan teknologi (TAM). Model TAM dapat menjelaskan bahwa persepsi pengguna akan menentukan sikapnya dalam penerimaan penggunaan Teknologi Informasi (TI). Model ini secara lebih jelas menggambarkan bahwa penerima penggunaan TI dipengaruhi oleh kemanfaatan (usefulness). 
Utilization of SVM Method and Extraction of GLCM Features in Classifying Fish Images with Formalin Muhathir, Muhathir; Wanti, Eka Pirdia; Pariyandani, Ayu; Idrus, Syed Zulkarnain Syed; Lubis, Andre Hasudungan
Scientific Journal of Informatics Vol 8, No 1 (2021): May 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i1.26806

Abstract

Purpose: Fish is a type of animal protein that can be consumed by humans to supplement protein in the body. Due to the fact that there is an abundance of fish in Indonesia, traders often experience losses because of rotting fish. A small proportion of traders tricked the buyers by mixing fish with formaldehyde to preserve fish in order to prevent fish spoilage until it can be consumed.  Thus, every fish buyer must be aware of fraud by traders. Methods: To be able to find out that the fish has been mixed with formalin, the solution offered is computerized by utilizing the GLCM feature extraction as information extraction on the fish image and the SVM method as a classification method. Result: The results showed an average accuracy of 0.784, precision of 0.799, recall of 0.784, and f-measure of 0.781. Novelty: The effect of the SVM classification method on the performance measurement of the model is not too big compared to previous studies, but it is better. 
Quickpropagation Architecture Optimization Based on Input Pattern for Exchange Rate Prediction from Rupiah to US Dollar Zulkarnaen, Harits Farras; Endah, Sukmawati Nur
Scientific Journal of Informatics Vol 5, No 2 (2018): November 2018
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v5i2.15889

Abstract

Money exchange between countries was done by using exchange rates. One of the examples was the exchange between Rupiah and US Dollar. Exchange rates prediction to US Dollar was an attempt to assist all related economic actors to avoid losses during the process of decision making. The prediction could be done by using artificial neural network method. Quickpropagation was one of artificial neural network models considered suitable for prediction. Quickpropagation network architecture consisted of input layer, hidden layer, and output layer. The input layer of quickpropagation architecture could be determined by using autoregression (AR) for the input pattern. In this research, the authors aim to optimize the quickpropagation network architecture method using Nguyen-Widrow weight initialization to predict the Rupiah exchange rate to US Dollar. The research data were the exchange rate from the BI website from May 2017 to July 2017 with a total of 57 data. The test was performed by using K-Fold Cross Validation with k = 11 values for data without AR and k = 8 for AR data. The results show that quickpropagation method using AR has better performance than quickpropagation method without AR in terms of MSE training and testing. The best parameters are in alpha 0,6 and hidden neuron 5, with MSE training value 0,03272 and MSE testing 0,02873 for selling rate and at alpha 0,9 and hidden neuron 5, with MSE training value 0,03297 and MSE testing 0,02828 for buying rate with maximal epoch 100.000 and target error 0,05.
Metode K-Means untuk Optimasi Klasifikasi Tema Tugas Akhir Mahasiswa Menggunakan Support Vector Machine (SVM) Somantri, Oman; Wiyono, Slamet; Dairoh, Dairoh
Scientific Journal of Informatics Vol 3, No 1 (2016): May 2016
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v3i1.5845

Abstract

Masih sulitnya dalam menentukan klasifikasi tema tugas akhir mahasiswa sering dialami oleh setiap perguruan tinggi. Algoritma SVM digunakan untuk mengklasifikasi jenis tema tugas akhir mahasiswa. SVM merupakan metode yang banyak digunakan untuk klasifikasi. K-Means Clustering merupakan metode pengelompokan paling sederhana yang mengelompokkan data kedalam k kelompok berdasar pada centroid masing-masing kelompok. Optimasi klasifikasi tema tugas akhir mahasiswa menggunakan SVM dan K-Means untuk meningkatkan tingkat akurasi. Hasil yang diperoleh memiliki tingkat akurasi yang lebih baik yaitu 86,21%. 
Digital Evidence Acquisition System on IAAS Cloud Computing Model using Live Forensic Method Sudyana, Didik; Lizarti, Nora
Scientific Journal of Informatics Vol 6, No 1 (2019): May 2019
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v6i1.18424

Abstract

Cloud Computing is a technological development that has been warmly discussed in recent years and has seen significant increases in usage, especially on the IAAS Cloud Computing model. The high rate of development of IAAS Cloud Computing model is in line with the high number of crimes involving IAAS Cloud Computing model on server virtualization. When a computer crime occurs and a digital forensic investigation will be carried out to uncover the case, it raises issues related to the acquisition of digital evidence. Because the acquisition model in general, it is done only to one operating system, while in virtualization there is more than one operating system, so the acquisition technique in general cannot be used because it takes only one operating system involved crime, and cannot acquire the whole data server related privacy data in other virtual operating systems. Therefore, research to make the acquisition system of server virtualization is needed. The focus in this research is to make system acquisition in server virtualization Proxmox using the live forensic method to produce a system that can acquire virtualization without disrupting the overall data server and in accordance with the principle of digital forensics. The resulting acquisition system can be a reference for investigators to investigate the IAAS Cloud Computing model on Proxmox virtualization and facilitate the investigator's work in the use of the system because the investigator simply chooses which virtual operating system to acquire, after which the system will work on its own the acquisition.
Sistem Informasi Tracer Study Alumni Universitas Negeri Semarang Dengan Aplikasi Digital Maps Nugroho, Zulfikar Adi; Arifudin, Riza
Scientific Journal of Informatics Vol 1, No 2 (2014): November 2014
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v1i2.4021

Abstract

Tracer study alumni merupakan salah satu metode yang digunakan untuk menelusuri informasi mengenai alumni. Informasi yang diambil meliputi identitas pribadi alumni, riwayat pendidikan di Universitas Negeri Semarang, riwayat pekerjaan, serta masukan yang diberikan kepada Universitas Negeri Semarang. Salah satu data yang sulit untuk diperoleh adalah data valid mengenai alamat pekerjaan alumni serta cara menyajikan data alamat pekerjaan alumni. Digital Maps adalah representasi fenomena geografik yang disimpan untuk ditampilkan dan dianalisis oleh komputer. Setiap objek pada peta digital disimpan sebagai sebuah atau sekumpulan koordinat. Posisi tempat kerja atau posisi kantor merupakan salah satu data geografis berupa titik, sedangkan titik dalam data geografi merupakan bagian dari sebuah peta. Sehingga titik yang baik adalah titik yang dapat diproyeksikan kedalam sebuah peta. Dalam tulisan ini, akan dibahas rancang bangun sistem informasi Tracer Study alumni Universitas Negeri Semarang dengan aplikasi Digital Maps. 
Prediction of COVID-19 Using Recurrent Neural Network Model Alamsyah, Alamsyah; Prasetiyo, Budi; Hakim, M. Faris Al; Pradana, Fadli Dony
Scientific Journal of Informatics Vol 8, No 1 (2021): May 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i1.30070

Abstract

Purpose: The COVID-19 case that infected humans was first discovered in China at the end of 2019. Since then, COVID-19 has spread to almost all countries in the world. To overcome this problem, it takes a quick effort to identify humans infected with COVID-19 more quickly. Methods: In this paper, RNN is implemented using the Elman network and applied to the COVID-19 dataset from Kaggle. The dataset consists of 70% training data and 30% test data. The learning parameters used were the maximum epoch, learning late, and hidden nodes. Result: The research results show the percentage of accuracy is 88. Novelty: One of the alternative diagnoses for potential COVID-19 disease is Recurrent Neural Network (RNN).
Comparison Between SAW and TOPSIS Methods in Selection of Broiler Chicken Meat Quality Adi, Pungky Tri Kisworo; Sugiharti, Endang; Alamsyah, Alamsyah
Scientific Journal of Informatics Vol 5, No 1 (2018): May 2018
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v5i1.14416

Abstract

Decision support system is a system that can assist semi-structured and unstructured decision making, in which no one knows exactly how decisions should be made. Broiler Chicken farm production is growing very rapidly along with the increasing market demand for Broiler Chicken. Broiler Chickens have fast growth in a relatively short time. The purpose of this research is the selection of chicken meat quality by applying comparison of SAW and TOPSIS method. The variables used are age, ration conversion, weight of chicken weight, and water consumption. The system is created using PHP framework Code Ignitier and database MySQL using waterfall method. That is analyze the user needs on the system, do the database design, by doing a coding and testing the system whether it is what is expected. The result of this research is the application of comparison between SAW and TOPSIS method each consist of 5 criteria. Comparison of these algorithms can facilitate the breeders in choosing a good quality broiler chicken meat.The results of the best farmer recommendation according to comparative method of SAW and TOPSIS. In SAW method of breeder 1 The biggest value is at V2 = 0,341, so alternative A2 is alternatives chosen as good alternative. Breeder 2 The biggest value is at V3 = 0.033, so alternative A3 is the alternative chosen as a good enough alternative. Breeder 3 The biggest value is at V1 = 0.005, so alternative A1 is the alternative chosen as an excellent alternative. Topsis Method of Breeders 1 is the largest value  at V2 = 9.98, so alternative A2 is the alternative chosen as a good alternative. Breeder 2 is the biggest value at V3 = 0.372, so alternative A3 is the alternative chosen as a good enough alternative. Breeder 3 is the biggest value at V3 = 0.982, so alternative A3 is the alternative chosen as a good enough alternative. This system uses only 5 criteria, it would be nice if you add other criteria that support the selection of broiler chicken meat quality.
Prediction The Number of Dengue Hemorrhagic Fever Patients Using Fuzzy Tsukamoto Method at Public Health Service of Purbalingga Hikmawati, Zahra Shofia; Arifudin, Riza; Alamsyah, Alamsyah
Scientific Journal of Informatics Vol 4, No 2 (2017): November 2017
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v4i2.10342

Abstract

DHF (Dengue Hemorrhagic Fever) is still a major health problem in Indonesia. One of the factors that led to an increase in dengue cases is uncertain climate that causes dengue fever is difficult to be predicted. Prediction is an important thing that is used to determine future events by identifying patterns of events in the past. When knowing the events that happen, it will make everyone to make better preparation for everything. This research is aimed at determining the accuracy of Tsukamoto Fuzzy method in the number of dengue patients in Puskesmas Purbalingga. Tsukamoto Fuzzy method can be used for prediction because it has the ability to examine and identify the pattern of historical data. Tsukamoto fuzzy that used to predict the number of dengue fever patients at Puskesmas Purbalingga has several stages. The first stage is the collection of climate data includes precipitation, humidity, water temperature and the data of dengue fever patients in Puskesmas Purbalingga. The next stage is processing the data that has been obtained. The last stage is to make predictions. Based on the results of the implementation by Tsukamoto Fuzzy method in predicting the number of dengue fever patients in Purbalingga for twelve months in 2016, it was obtained a percentage error (MAPE) of 8.13% or had an accuracy rate of 91.87 %. With the small value of MAPE and high accuracy, it shows that the system can predict well. 

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