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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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A Novel Construction of Perfect Strict Avalanche Criterion S-box using Simple Irreducible Polynomials Alamsyah, Alamsyah
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.v7i1.24006

Abstract

An irreducible polynomial is one of the main components in building an S-box with an algebraic technique approach. The selection of the precise irreducible polynomial will determine the quality of the S-box produced. One method for determining good S-box quality is strict avalanche criterion will be perfect if it has a value of 0.5. Unfortunately, in previous studies, the strict avalanche criterion value of the S-box produced still did not reach perfect value. In this paper, we will discuss S-box construction using selected irreducible polynomials. This selection is based on the number of elements of the least amount of irreducible polynomials that make it easier to construct S-box construction. There are 17 irreducible polynomials that meet these criteria. The strict avalanche criterion test results show that the irreducible polynomial p17(x) =x8 + x7 + x6 + x + 1 is the best with a perfect SAC value of 0.5. One indicator that a robust S-box is an ideal strict avalanche criterion value of 0.5
Implementasi Sistem Informasi Geografis Daerah Pariwisata Kabupaten Temanggung Berbasis Android dengan Global Positioning System (GPS) Santoso, Kartika Imam; Rais, Muhamad Nur
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.v2i1.4526

Abstract

Pariwisata merupakan aspek yang berharga bagi suatu daerah, dan semakin banyak pengunjung maka dapat memajukan kesejahteraan masyarakat di sekitar obyek pariwisata. Kabupaten Temanggung memiliki banyak obyek pariwisata, penggunaan teknologi informasi seperti menggunakan aplikasi smartphone berbasis Android dapat digunakan untuk membantu wisatawan untuk mengenal daerah pariwisata dan mengetahui rute menuju ke obyek pariwisata yang diinginkan. Implementasi Sistem Informasi Geografis (SIG) di daerah wisata Temanggung bertujuan untuk membangun aplikasi Wisata Temanggung berbasis Android dan menerapkan layanan Google Maps Application Programming Interface (API) untuk memudahkan wisatawan dalam memperoleh informasi pemetaan lokasi objek wisata, rute dan fasilitas pendukung wisata yang ada di Kabupaten Temanggung. Metode yang digunakan adalah model proses air terjun (waterfall). Implementasi Aplikasi Wisata Temanggung menggunakan pemrograman Javascript dengan Eclipse Luna, basis data SQLite, serta peta yang bersumber dari Google Maps API. Hasilnya berupa aplikasi Wisata Temanggung berbasis Android yang membantu memudahkan wisatawan dalam memperoleh informasi tentang obyek wisata alam, buatan, budaya, kuliner, hotel dan rute dari lokasi sekarang ke lokasi obyek wisata yang diinginkan di Kabupaten Temanggung dengan bantuan Global Positioning System (GPS).  
Image Sketch Based Criminal Face Recognition Using Content Based Image Retrieval Adimas, Adimas; Irianto, Suhendro Y; Karnila, Sri; Yuliawati, Dona
Scientific Journal of Informatics Vol 8, No 2 (2021): November 2021
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v8i2.27865

Abstract

Purpose: Face recognition is a geometric space recording activity that allows it to be used to distinguish the features of a face. Therefore, facial recognition can be used to identify ID cards, ATM card PINs, search for one’s committed crimes, terrorists, and other criminals whose faces were not caught by Close-Circuit Television (CCTV). Based on the face image database and by applying the Content-Base Image Retrieval method (CBIR), committed crimes can be recognized on his face. Moreover, the image segmentation technique was carried out before CBIR was applied. This work tried to recognize an individual who committed crimes based on his or her face by using sketch facial images as a query. Methods: We used an image sketch as a querybecause CCTV could not have caught the face image. The research used no less than 1,000 facial images were carried out, both normal as well asabnormal faces (with obstacles). Findings:Experiments demonstrated good enough in terms of precision and recall, which are 0,8 and 0,3 respectively, which is better than at least two previous works.The work demonstrates a precision of 80% which means retrieval of effectiveness is good enough. The 75 queries were carried out in this work to compute the precision and recall of image retrieval. Novelty: Most face recognition researchers using CBIR employed an image as a query. Furthermore, previous work still rarely applied image segmentation as well as CBIR.
The Comparison Combination of Naïve Bayes Classification Algorithm with Fuzzy C-Means and K-Means for Determining Beef Cattle Quality in Semarang Regency Devi, Feroza Rosalina; Sugiharti, Endang; Arifudin, Riza
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.v5i2.15452

Abstract

The beef cattle quality certainly affects the quality of meat to be consumed. This researchperforms data processing to do the classification of beef cattle quality. The data used are196 data record taken from data in 2016 and 2017. The data have 3 variables fordetermining the quality of beef cattle in Semarang regency namely age (month), Weight(Kg), and Body Condition Score (BCS) . In this research, used the combination of NaïveBayes Classification and Fuzzy C-Means algorithm also Naïve Bayes Classification andK-Means. After doing the combinations, then conducted analysis of the results of whichtype of combination that has a high accuracy. The results of this research indicate that theaccuracy of combination Naïve Bayes Classification and K-Means has a higher accuracythan the combination of Naïve Bayes Classification and Fuzzy C-Means. This can be seenfrom the combination accuracy of Fuzzy C-Means algorithm and Naïve Bayes Classifierof 96,67 while combination of K Means Clustering and Naïve Bayes Classifier algorithmis 98,33%, so it can be concluded that combination of K Means Clustering algorithm andNaïve Bayes Classifier is more recommended for determining the quality of beef cattle inSemarang regency.
Analisis Arsitektur Aplikasi Web Menggunakan Model View Controller (MVC) pada Framework Java Server Faces Gunawan, Gunawan; Lawi, Armin; Adnan, Adnan
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.v3i1.5958

Abstract

Aplikasi web yang khususnya memiliki kompleksitas besar dalam melakukan transaksi data sehingga konsep arsitektur (pattern) perlu menjadi perhatian khusus untuk dapat mengoptimalkan kinerja performansi sistem ketika pengguna (user) menggunakan dalam waktu yang bersamaan dengan jumlah yang banyak. Analisis performa arsitektur aplikasi web yang menggunakan model 2 (MVC) dengan menggunakan framework Java Server Faces (JSF) dan model 1 sebagai pembanding. Metode yang digunakan adalah Load dan Scalability Testing dengan dua cara yaitu uji coba terhadap response time karena peningkatan ukuran dari database dan uji coba terhadap response time karena peningkatan jumlah user yang menggunakan sistem secara bersamaan (concurrent users) dan waktu tunggu (ramp-up) yang ditentukan menggunakan Apache Jmeter. Analisis menunjukkan bahwa dalam implementasi arsitektur web yang menggunakan model 1 waktu rata-rata yang dibutuhkan untuk merespon permintaan user lebih cepat dan efisien dibanding model 2 (MVC).  
Inverse Modeling Using Taylor Expansion Approach and Jacobi Matrix on Magnetic Data (Dyke/Magma Intrusion Cases) Agus Suprianto; Wahyudi Wahyudi; Wiwit Suryanto; Ari Setiawan; Aryono Adhi; Nurul Priyantari; Supriyadi Supriyadi; Agus Subekti
Scientific Journal of Informatics Vol 6, No 2 (2019): November 2019
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v6i2.19798

Abstract

The mathematical modelling of geological structures, i.e. magma intrusion or dyke, has been done,  based on magnetic data with inversion techniques using MatLab. The magnetic equation is a non-linear equation, and completion is done using a linear approach to non-linear mathematical models of magnetic data using the Taylor expansion approach and Jacobi Matrix. The first step of this research is to make synthetic data forward modelling from the magnetic equation of magma intrusion or dyke cases without errors, and the next stepping then add errors to the data. The next step is to do an inversion to get the parameters sought, i.e. depth and angle of the magma intrusion, by giving initial guesses, and then re-correct iteratively until convergent results are obtained. Finally, parameters of slope dyke or thin magma intrusion and its depth can be determined. The results obtained indicate that this technique can be used to get physical parameters sought from magnetic data for simple geological cases, i.e. dyke and magma intrusion.
Pengembangan E-Lecture menggunakan Web Service Sikadu untuk Mendukung Perkuliahan di Universitas Negeri Semarang Putra, Anggyi Trisnawan
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.v1i2.4023

Abstract

Proses penjadwalan di Universitas Negeri Semarang yang sedemikian rumit menghasilkan data penjadwalan yang tersimpan di dalam database Sikadu (Sistem Informasi Akademik Terpadu) berupa keterkaitan antara data dosen, mahasiswa, dan mata kuliah. Namun, data ini tidak diintegrasikan secara langsung ke dalam aplikasi/sistem e-learning yang disediakan oleh Unnes, mengakibatkan adanya proses/kegiatan yang tidak perlu sebelum dapat menggunakan aplikasi e-learning. Dengan fakta bahwa data penjadwalan dapat diakses secara online, dapat dirancang aplikasi pendukung e-lecture dengan memanfaatkan data tersebut. Pertama-tama, dirancang web service yang akan menyajikan akses aman ke dalam data Sikadu. Lalu, dirancang database e-lecture yang akan memanfaatkan web service yang telah dibuat tersebut. Data akan disajikan dalam interface yang dibuat dengan HTML, bermesin PHP. Dosen dan mahasiswa dapat menggunakan akses login yang sama dengan Sikadu untuk dapat langsung memanfaatkan aplikasi ini. Dengan adanya aplikasi ini, proses perkuliahan meliputi sharing bahan ajar, pemberian tugas/aktivitas kuliah, integrasi pengumpulan tugas, koreksi nilai tugas, pembatasan waktu pengumpulan tugas secara tegas (tersistem) dan lain sebagainya dapat dilakukan secara mudah dan efisien. 
Deep Learning-based Mobile Tourism Recommender System Fudholi, Dhomas Hatta; Rani, Septia; Arifin, Dimastyo Muhaimin; Satyatama, Mochamad Rezky
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.v8i1.29262

Abstract

Purpose: This study developed a deep learning-based mobile travel recommendation system that provides recommendations for local tourist destinations based on users' favorite travel photos. To provide recommendations, use cosine similarity to measure the similarity score between a person's image and a tourism destination gallery through the tag label vector. Label tags are inferred using an image classifier model run from a mobile user device via Tensorflow Lite. There are 40 tag labels that refer to categories, activities and objects of local tourism destinations. Methods: The model is trained using state-of-the-art mobile deep learning architecture EfficientNet-Lite, which is new in the domain of tourism recommender system. Result: This research has conducted several experiments and obtained an average model accuracy of more than 85%, using EfficientNet-Lite as its basic architecture. The implementation of the system as an Android application is proven to provide excellent recommendations with a Mean Absolute Percentage Error (MAPE) of 5.2%. Novelty: A tourism recommendation system is a crucial solution to help tourists discover more diverse tourism destinations. A content-based approach in a recommender system can be an effective way of recommending items because it looks at the user's preference histories. For a cold-start problem in the tourism domain, where rating data or past access may not be found, we can treat the user's past-travel-photos as the histories data. Besides, the use of photos as an input makes the user experience seamless and more effortless. 
Associative Analysis Data Mining Pattern Against Traffic Accidents Using Apriori Algorithm Ruswati, Ruswati; Gufroni, Acep Irham; Rianto, Rianto
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.v5i2.16199

Abstract

Traffic accidents are one of the causes of high mortality in the community. Based on information from the World Health Organization (WHO) the number of accident victims in each year amounts to 1,300,000 fatalities, this is caused by traffic accidents that exist throughout the world. The police recorded data on accidents that occurred in several regions of East Priangan namely Ciamis and Tasikmalaya Regencies for the 2016-2017 period reaching an accident rate of ± 1500. The analysis that can be done to reduce the intensity of the occurrence of these events is to use data mining processing techniques. The right method is used by looking at the condition of the data obtained, namely the Association Rules method with the calculation of the Apriori Algorithm. This method will look for patterns of data relations that are formed from combinations of an itemset, so that knowledge will appear from large datasets. The pattern of the relationship sought is the linkages of itemset variables involved in the accident by involving 4 variables that describe the identity of the perpetrators, namely gender, age, profession and level of education and 22 attributes of the dataset. The minimum limit of support, confidence and lift ratio values used in the Apriori Algorithm calculation rules is 15%, 70% and 1.1. This value is used to get many rules that have a high level of occurrence accuracy. The results of the combination pattern calculation were 3 times iterations on each number of data in each region, the pattern of associations found in the Tasikmalaya region were the relation of the professional variables and the age of the perpetrator with the attribute of the Student profession dataset and the boundary group ages 16 to 30 years, while for the pattern associations found in the area of Ciamis Regency, namely the relation between age and education level with the attribute dataset of the 16 to 30 year age group and high school education level. The accuracy of the value obtained is calculated manually and uses one of the data mining applications as a comparison of value accuracy, namely Tanagra 1.4.
Expert System Diagnosis of Bowel Disease Using Case Based Reasoning with Nearest Neighbor Algorithm Vedayoko, Lucky Gagah; Sugiharti, Endang; Muslim, Much Aziz
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.v4i2.11770

Abstract

Expert System is a computer system that has been entered the base of knowledge and set of rules to solve problems like an expert. One method in the expert system is Case Based Reasoning. To strengthen the retrieve stage of this method, the Nearest Neighbor algorithm is used. Bowel is one of the digestive organs susceptible to disease. The purpose of this study is to implement expert systems using Case Based Reasoning with Nearest Neighbor algorithm in diagnosing bowel disease and determine the accuracy of the system. Data used in this research are 60 data, obtained from medical record RSUD dr. Soetrasno Rembang. Variables used are general symptoms and types of diseases. The level of system accuracy resulting from scenario are 40 data as source case,  and 20 data as target case  that is equal to 95%.

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