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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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Determination of The Shortest Route Based on BFS Algorithm for Purpose to Disaster Evacuation Shelter Sularno, Sularno; Mulya, Dio Prima; Astri, Renitra; mulya, Dwiki
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.v8i1.27863

Abstract

Purus village in Padang City which has an area of 0.86 km2 consisting of 8 RW and 28 RT has a population of 8,075 people with a density of 11,875. As a result, people are always haunted by fear and always feel threatened if an earthquake occurs. If an earthquake and tsunami occur, what the community needs at that time is information about a safe zone that can be reached to save themselves and their families. For this reason, there needs to be an educational process for the community so that they have a culture of disaster awareness in the form of a system that is able to inform the community where the closest safe zone they can reach is the route they must take when a disaster strikes so that it can provide a sense of security because of safety guarantees. This study aims to determine the shortest route that can be taken by the user (community) during a disaster to reduce the risk of a greater number of victims by using the Breadth first search algorithm which is integrated into a web-based GIS application. By determining the starting point which is the user's position when the disaster occurs, and then determining the end point which is the location of the closest shelter, it will be possible to calculate the shortest distance that can be reached by the user at that time. The method chosen in this study is a waterfall because each step of the research carried out must be sequential and structured to avoid the risk of errors in each sequence of processes carried out. The results of this study can be proven by doing manual calculations to determine the shortest distance which will later be compared with the results of applications that have been designed using the BFS algorithm.
Data-Driven E-commerce Techniques and Challenges in the Era of the Fourth Industrial Revolution Norian, Joma H.; Jama, Abdelazez M.; Eltaieb, Mohammed H.; Adam, Ali A.
Scientific Journal of Informatics Vol 7, No 2 (2020): November 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i2.25760

Abstract

The E-commerce industry has a significant role in the national and international economy. E-commerce is vital in the implementation of the fourth industrial revolution, where information and communication technologies are tools in creating digital channels of trade. Understanding e-commerce is essential for it is development. The objective of this paper is to explore the popular techniques and data sources of e-commerce in addition to the current challenges that face e-commerce in the last five years. We used a literature review as a method for this research.  According to the literature, sales records are the most popular data source used in the research community for e-commerce analytics, then followed by big data and social media. Besides, detecting and predicting customer behavior is the most used technique in e-commerce research followed by personalized recommendations. Also, we reported the main three challenges that face researchers in the field of e-commerce currently: First, e-commerce Security and privacy is a major concern for consumers and industries. Second, understanding the collected data from e-commerce systems and how to create business value from it efficiently. Third, providing personalized offers with the most appropriate items, still a difficult task.
Application of Deep Learning Using Convolutional Neural Network (CNN) Method For Women’s Skin Classification anton, Anton; Nissa, Novia Farhan; Janiati, Angelia; Cahya, Nilam; Astuti, Puji
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.v8i1.26888

Abstract

Facial skin is skin that protects the inside of the face such as the eyes, nose, mouth, and others. Facial skin consists of several types, including normal skin, oily skin, dry skin, and combination skin. This is a problem for women because it is difficult to recognize and distinguish their skin types this is what causes some women to find it difficult to determine the right make-up and care products for their skin types. In this study, the Convolutional Neural Network (CNN) method is the right method for classifying women's skin types from the age of 20-30 years by following several stages using Python 3.5 programming with a depth of three layers and the results of this research using the CNN method get the results of the accuracy value good at 67%
The Real-Time Alert System for Prayers at Smart Masjid Alam, Tanweer; Erqsous, Moath
Scientific Journal of Informatics Vol 7, No 2 (2020): November 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i2.25356

Abstract

Arrange and monitor people in a crowded environment inside masjid is a critical task. It is necessary to fill rows start from first row behind the Imam. Most counting techniques depend on detecting individuals in order to count their number. Counting and arrangement becomes inefficient when it is required in real-time and when the crowd is dense. I am proposing a technique for monitoring and estimating the density of crowd in real-time using infrared technology. The intelligent systems will be designed based on the number of people section wise. The mosque will be divided into sections and each section will be allocated an Infra-Red Camera. Each section will be programmed to contain limited number of people. There will be an LED display allocated to each section. With the people coming into that section, the display will start becoming less GREEN. In other words, the intensity of the GREEN LED display will become weaker. As the section is completely filled, the display will turn red. This way, people could see the section from quite a distance and can easily decide whether to move forward or not. As soon as the people enter the mosque, they will have an overview of each section and can decide to go to the suitable places to get settled easily into the rows. Our pre-programmed thermal camera will recognize people on the basis of their body temperature. The LED display will go less green as the system receives more thermograms. After reaching the highest level of thermograms received, the LED display will automatically go RED. This would naturally stop people to enter that section.
A Combination of K-Means and Fuzzy C-Means for Brain Tumor Identification Sari, Christy Atika; Sari, Wellia Shinta; Rahmalan, Hidayah
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.v8i1.29357

Abstract

Keywords are the labels of your manuscript and critical to correct indexing and searching. MRI or Magnetic Resonance Imaging is one of the health technologies used to scan the human body in order to get an image of an orgasm in the body. MRI imagery has a lot of noise that blends with the tumor object, so the tumor is quite difficult to detect automatically. In addition, it will be difficult to distinguish tumors from brain texture. Various methods have been carried out in previous studies. The method often used in the previous method is segmentation, but the process is quite heavy and the results that are less accurate are still the main obstacles. This study combines the K-Means method and Fuzzy C-Means (FCM) to detect tumors on MRI. The purpose of the combination is to get the advantages of each algorithm and minimize weaknesses. The method used is Contrast Adjustment using Fast Local Laplacian, K-Means FCM, Canny edge detection, Median Filter, and Morphological Area Selection. The dataset is taken from www.radiopedia.org. Data taken were 73 MRI of the brain, of which 57 MRIs with brain tumors and 16 MRIs of normal brain Evaluation of research results will be calculated using Confusion Matrix. The accuracy obtained is 91.78%.
Assess of Forensic Tools on Android Based Facebook Lite with the NIST Method Bintang, Rauhulloh Noor; Umar, Rusydi; Yudhana, Anton
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.v8i1.26744

Abstract

Purpose: The increase in social media use of Facebook lite by using Android-based smartphones is quite high. Activities when communicating through the social media network Facebook Lite. Facebook lite can send a text message, image, or video. Not a few Facebook users lite social media abusing this app to commit fraud crimes, pornographic acts, or defamation actions from social media users Facebook lite. In such cases, it can be a digital forensic benchmark to get results from digital evidence from the Facebook lite application. Methods: In this investigation, National Institute of Standards and Technology (NIST) research methods with various stages, namely Collection, Examination, Analysis, and Reporting. Result: Comparison and results of data conducted with forensic tools Magnet Axiom Forensic and MOBILedit Forensic Express Pro in the form of parameter data specified. Axiom Forensic Magnet data is 57.14%, while MOBILedit Forensic Express Pro data is 85.71%. Novelty: This data is the data of the performance results of both forensic tool applications in obtaining digital evidence on Facebook lite application.
A Systematic Review of Machine-vision-based Smart Parking Systems Abidin, Muhammad Zainal; Pulungan, Reza
Scientific Journal of Informatics Vol 7, No 2 (2020): November 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i2.25654

Abstract

The development of smart city concept, particularly in smart parking systems, has not solved a problem that occurs in metropolitan areas, such as in urban areas where the population has continued to rise, resulting in high demand for private vehicles and parking spaces. Finding a parking space is known as the most common issue the drivers have had specifically on peak hours’ time. During peak hours, the difficulty arises as many people look around to find vacant parking space at once, which causes many negative impacts on cities and drivers themselves, such as pollution, traffic congestion, traffic accidents, waste of time and fuel, emotions and so on. As a solution, smart parking system exist to equip parking lots with many different types of sensors to automatically detect free parking space that would guide drivers to find the nearest car parking space as efficient as possible. An effective smart parking system can solve this problem and make better use of parking resources. However, many smart parking systems still uses embedded sensors that are expensive for installation and inefficient. This paper presents a review of the existing approaches to the smart parking system. This paper focuses on a machine-vision-based technology used for smart parking system and highlights its main features, advantages and disadvantages.
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

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. The current development in Artificial Intelligence-based services enable the possibilities to implement such experience. This research developed a Deep Learning-based mobile tourism recommender system that gives recommendations on local tourism destinations based on the user's favorite traveling photos. To provide a recommendation, we use cosine similarity to measure the similarity score between one's pictures and tourism destination's galleries through their label tag vectors. The label tag is inferred using an image classifier model that runs from a mobile user device through Tensorflow Lite. There are 40 label tags, which refer to local tourism destination categories, activities, and objects. The model is trained using state-of-the-art mobile deep learning architecture EfficientNet-Lite. We did several experiments and got an accuracy result of more than 85% on average, using EfficientNet-Lite as the base architecture. The implementation of the system as an Android application has been proved to give an excellent recommendation with Mean Absolute Percentage Error (MAPE) equals to 5%.
Implementation of Data Mining using Naïve Bayes Classifier Method in Food Crop Prediction Arifin, Oki; Saputra, Kurniawan; Fathoni, Halim
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.v8i1.28354

Abstract

Lampung province has development activity orienting on source potential in the agricultural sector mainly food crops. Yield estimation of food crops is one of the things crucial problems in the agricultural sector, because of the farmers' lack of knowledge about the bountiful harvest, and climate change big impact on the yield of food crops. Then it was needed to be developed modeling to prediction system of food crops by data mining, with Naïve Bayes Classifier (NBC) which expected will give information and can use by the farmer and industrial food crops. On classification, progress attributes that use there is the temperature (°C), humidity (%), rainfall (mm), photoperiodicity (hour), and production result (ton) as a class attribute. The data of research that getting there are climate data and yield of food crops by data from the Central Bureau of Statistics (BPS) and the Meteorology, Climatology and Geophysics Agency (BMKG) from 2010 to 2017 at Lampung Province. Data of food crops used in this research there are paddy, maize, and soybean. The research results about the average accuracy of modeling that development using the 10-fold cross-validation method, that had an accuracy value of 72.78% and Root Mean Square Error (RMSE) there is 0.438.
Classification of Traditional Batik Motifs in Central Java using Gabor Filter andBackpropagationNeural Network Isnanto, R Rizal; Triwiyatno, Aris
Scientific Journal of Informatics Vol 7, No 2 (2020): November 2020
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v7i2.26215

Abstract

Batik has a variety of varied motifs, each region in Indonesia has certain characteristics on batik motifs. Based on literature studies theuse of backpropagation neural network methods to recognize complex patterns has a satisfactory rate of success. The purpose of this research is to develop and apply neural networks that are fast, precise and accurate to classify batik designs and patterns. Types of batik motifs typical of Central Java that are used include; Truntum from Solo, Warak Ngendhog from Semarang, Sekar Jagad from Lasem, Burnt from Pati, and Jlamprang from Pekalongan. The image first undergoes RGB color feature extraction based on mean values of R, G, and B, and Gabor filter texture characteristics. The tests were carried out using 90 batik images, 60 batik images for training data and 30 batik images for testing data. The results of the study concluded that the best parameter settings were, the number of hidden layer 30 neurons in the first layer and 15 in the second layer, with 6 input layers and 5 output layers. Gabor filter with 90º orientation angle and wavelength 4 become the best combination in this study. From the results of training and testing results obtained an average accuracy of 93.3% in all batik classes in Central Java.

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