cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
sji@mail.unnes.ac.id
Editorial Address
-
Location
Kota semarang,
Jawa tengah
INDONESIA
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
 40   41   42   43   44 
Analisis Hubungan Proses Pembelajaran Dengan Kepuasan Mahasiswa Menggunakan Logika Fuzzy Widaningrum, Ida
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.v2i1.4532

Abstract

Proses belajar mengajar dalam suatu perkuliahan merupakan kegiatan rutin yang dilakukan oleh dosen dalam melaksanakan salah satu tridharmanya yaitu pengajaran. Hal ini memerlukan interaksi aktif dari kedua belah pihak yaitu dosen dan mahasiswa, sehingga pada akhirnya mahasiswa akan dapat mengerti apa yang disampaikan dan membuka wawasan tentang materi tersebut. Analisis hubungan proses pembelajaran dengan kepuasan mahasiswa bertujuan untuk menentukan seberapa besar faktor-faktor penilaian mahasiswa tentang proses pembelajaran diantaranya tentang mengerti tidaknya materi yang disampaikan, kesesuai target, implementasi, peningkatan wawasan dan keahlian, proses interaksi, tanya jawab atau diskusi, efektivitas waktu perkuliahan, motivasi untuk meningkatkan ilmu/pemahaman, dan nilai yang diperoleh untuk mata kuliah tersebut terhadap kepuasan mahasiswa menggunakan logika fuzzy. Logika fuzzy digunakan karena memiliki keunikan dalam hal kemampuannya mengolah data yang bersifat linguistik. Tidak seperti metode logika klasik yang memerlukan pemahaman mendalam tentang sistem, persamaan matematis dan ketepatan nilai numeris, logika fuzzy menggabungkan berbagai macam cara berfikir yang memungkinkan pemodelan system kompleks berdasarkan pengetahuan dan pengalaman manusia. Logika fuzzy memberikan cara sederhana untuk menentukan suatu kesimpulan dari informasi yang tidak jelas, ambigu (bermakna ganda) dan tidak tepat (presisi). Kinerja fuzzy menyerupai cara manusia membuat keputusan berdasarkan pendekatan data yang diketahui kemudian menentukan solusinya.
Comparative Analysis of ADASYN-SVM and SMOTE-SVM Methods on the Detection of Type 2 Diabetes Mellitus Ramadhan, Nur Ghaniaviyanto
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.v8i2.32484

Abstract

Most people with diabetes in the world are type 2. We can detect diabetes early to prevent things that are not desirable by checking sugar and insulin levels with the doctor. In addition to using this method, people with diabetes can also be grouped based on data from diabetes examination results. However, most of the data on health examination results have several parameters that are difficult for the public to understand. These problems can be done by means of automatic classification. In addition to these problems, there is another problem in the form of an unbalanced amount of data for diabetics and non-diabetics. This problem can be done by balancing the amount of data using the model to increase the ratio of the amount of data that is small or decrease the ratio of the amount of data that is too much. Purpose: This study aims to detect type 2 diabetes mellitus using the SVM classification model and analyze the results of the comparison using the SMOTE and ADASYN data balancing technique which is the best. Methods/Study design/approach: The research method starts from collecting the diabetes dataset, then the dataset cleaning process is carried out whether there is a null value or not. After applying two oversampling methods to analyze which method is the most appropriate. After the oversampling technique was carried out, data classification was carried out using a support vector machine model to see the accuracy results. Result/Findings: The results obtained by the ADASYN-SVM method are superior to SMOTE-SVM. The ADASYNSVM method has an accuracy of 87.3%, while the SMOTE-SVM has an accuracy of 85.4%. Novelty/Originality/Value: The data used in this study came from the Karya Medika clinic, Indonesia which contains parameters related to type 2 diabetes.
Push-Up Detector Applications Using Quality Function Development and Anthropometry for Movement Error Detection Muzakir, Ari; Kusmindari, Christofora Desi
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.v5i2.16332

Abstract

Push-up is the simplest and most widely performed sport. Although simple, it also has a high risk of injury risk if done not in accordance with the rules. Push-up detector is a good push-up motion monitoring solution. In this way, nonstandard movements can be detected and corrected immediately. It has two motion sensors integrated with Arduino-based microcontroller. From this detector tool got the data of push-up result from sensor mounted. Sensor data will be displayed in the application in real-time. Quality function development is used to determine the criteria of the user. The sample data involved 200 participants who followed the testing of this tool and got 90% who can do the push-up correctly. Factors that affect the height, age, and weight. Tests conducted on adolescent boys aged 18-23 years. The results of this study is an application capable of monitoring each push-up movement to position in accordance with the provisions to minimize injuries resulting from movement errors.
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.
Decision Support Systems with AHP and SAW Method for Determination of Cattle with Superior Seeds Josaputri, Clarissa Amanda; Sugiharti, Endang; Arifudin, Riza
Scientific Journal of Informatics Vol 3, No 2 (2016): November 2016
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v3i2.7908

Abstract

Department of Animal Husbandry and Fisheries of Semarang District is an institution in charge of livestock and animal health. Basically the Animal Husbandry Department has provided standardization for quality livestock cattle with superior seeds that usually can be judged or measured by various criteria.They are weight, age and value of BCS (Body Condition Score).They needed a system that could help the Department of Livestock and Fisheries of Semarang District in determining the electoral process cattle with superior seeds.  In this research, the manufacture of Decision Support Systems in the determination cattle with superior seedsis using a combination of two methods is Analytical Hierarchy Process (AHP) and the Simple Addictive Weighting (SAW). In AHP will perform an importance value calculation criteria that will be paired up with an alternative to the SAW the next process is the sum of the weight from performance rating of all the attributes to each alternative, a ranking conducted to determine the result of cattle with superior seeds. Suggestions on this system, can be developed further by combining other methods to determine the recommendation that more effective.
Automatic Detection of Motorcycle on the Road using Digital Image Processing sutikno, sutikno; Wibawa, Helmie Arif; Saputra, Ragil
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.v6i2.20143

Abstract

Traffic accident is one of the causes of death in the world. One of them is traffic accidents on motorcyclist not wearing helmets. To overcome this problem, several researchers have developed detection system of motorcyclist not wear helmet. This system consists of motorcycle detection and motorcyclist head detection. On motorcycle detection, accuracy still needs to be improved. For this reason, this paper proposed motorcycle detection by adding image improvement processes that are enhancing contrast and adding object positioning features.The techniques proposed in this study are divided into 3 stages of image enhancement, feature extraction, and classification. The image enhancement stage consists of enhance contrast, convert RGB image to gray scale, background subtraction, convert gray scale image to binary, closing operation, and small object removal. The features used in this paper are the features of the object area, the circumference of the object, and the location of the object, while the method for classification process using back-propagation neural network and SVM. The proposed method resulted in an accuracy of 96.97%. Error occurs in all image test data not motorcycle objects detected as motorcycle objects. This error is caused because the pixel value between the objects in the image with the background color has a level of difference is too small, so it is detected as an object not a motorcycle.
Penerapan Algoritma Greedy Pada Mesin Penjual Otomatis (Vending Machine) -, Alamsyah; Putri, Indriani Tiara
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.v1i2.4608

Abstract

Dalam memasarkan produk minuman dan makanan ringan, Indonesia masih banyak menggunakan tenaga manusia untuk menyalurkan produk tersebut dari pabrik sampai ke konsumen akhir. Jika setiap toko membeli produk dalam jumlah banyak, maka didapatkan keuntungan yang besar dalam penjualan produk tersebut. Hal ini menyebabkan harga penjualan produk yang sampai ke konsumen akhir lebih mahal daripada harga asli yang diberikan oleh pabrik. Dengan permasalahan tersebut, penulis mencoba membuat aplikasi mesin penjual otomatis (vending machine). Pada umumnya vending machine tidak memberikan uang kembalian. Disini penulis mencoba membuat vending machine dengan menerapkan algoritma Greedy agar dapat memberikan uang kembalian sehingga harga penjualan produk sesuai dengan harga asli pabrik. Algoritma Greedy diterapkan untuk menentukan pecahan berapa saja yang muncul dalam proses pengembalian uang dengan meminimalkan jumlah uang logamnya. Penulis menggunakan aplikasi Visual Basic untuk menerapkan program vending machine. 
In Silico Molecular Docking Analysis of Limonene with The Fat Mass and Obesity-Associated Protein by Using Autodock Vina Ahmed, Muhammad Zeeshan; Hameed, Shahzeb; Ali, Mazhar; Hizbullah, Syed; Zaheer, Ammad
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.v8i1.29051

Abstract

Purpose: This study aimed to predict the binding affinity, orientation, and physical interaction between limonene and fat mass and obesity-associated protein. Methods: The mechanism of limonene and protein association was explored by molecular docking, a bioinformatic tool. The association results were compared with the reported results of the anti-obesity drug such as orlistat and with the flavonoids. AutoDock Vina tools were used for the molecular docking of limonene with fat mass and obesity-associated protein. PyMol and Discovery Studio Visualizer was used to visualize the results of this docking. Result: The binding affinity of limonene was higher (Least negative G) than the orlistat and flavonoids such as Daidzein, Exemestane, Kaempherol, Letrozole, And Rutin. Novelty: In this study, the limonene can alleviate obesity by interacting with the fat mass and obesity-associated protein. This inhibitory interaction was more significant as compared to other reported phytochemicals and drugs.
Use of K-Means Clustering and Analytical Methods Hierarchy Process in Determining the Type of MSME Financing in Semarang City Sukmadewanti, Irahayu; Arifudin, Riza; Sugiharti, Endang
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.v5i2.16221

Abstract

The Indonesian government launched an entrepreneurial program to encourage economic growth, one of which is MSME(micro, small and medium enterprises). The constraints commonly faced by MSME are limited enterprises capital. The government has also tried to provide assistance financing for MSMEs in the form of CSR (Corporate Social Responsibility), KUR (Credit People's Enterprises) and KTA (Unsecured Credit). For this type of financing or credit determined based on the type of enterprises accompanied by criteria including number of assets, turnover annually, number of employees, current enterprises period and net income. Based on background behind this research aims to help provide recommendations on types MSME capital financing based on assets, turnover, number of employees, enterprises period and net income of a MSME. This research uses data from MSME in the Semarang City, which has been registered with the Semarang City Cooperatives and MSME Office. K-Means Clustering Method is used to cluster net profit criteria. Then the Analytical Hierarchy Process (AHP) method is used to search recommendations on the types of MSME financing based on each weighted criteria. The results of this application are recommendations for types of capital financing MSME is based on assets, turnover, number of employees, enterprises period and every net profit of MSME. For testing of the system being built, it is carried out by means of a blackbox test. From the test results obtained show that the actual results are appropriate with the expected results so that the functional system is running well. Suggestions from this research, it is necessary to develop further systems regarding grouping data to be more specific.
Decision Support System for Household Labor Services Selection “Best Helper” Using AHP and TOPSIS Methods Pirnanda, I Kadek Aditya; Pradnyana, I Made Ardwi; Wirawan, I Made Agus
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.v6i1.18050

Abstract

Selection of household labor services was an important aspect in families who have a lot of activity. The existence of helpful household labour services would avoid the occurrence of problems that should be caused. Types of household labour services which were often used were maids, baby sitters, elderly nurses, gardeners, and drivers. The main problem arose in the difficulty of service seekers in choosing the desired service and how to minimize the time spent. To overcome this, a decision support system was used using the Analytical Hierarchy Process (AHP) method which was used to find the criteria for each alternative weight, and ranking calculations using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. This research was implemented using PHP language with CodeIgniter framework. The result shows that the usability test obtains the average value with the system usability scale (SUS) method of 71.09%. It shows that the level of system usability is classified as good and can be accepted and used easily by the users. Meanwhile, the result of the user response test shows a percentage of 87.5%, so it can be concluded that the system belongs to good category and feasible to use.

Page 42 of 57 | Total Record : 564

 40   41   42   43   44 

Filter by Year

2014 2024


Reset
Filter By Issues
All Issue Vol 11, No 1 (2024): February 2024 Vol 10, No 4 (2023): November 2023 Vol 10, No 3 (2023): August 2023 Vol 10, No 2 (2023): May 2023 Vol 10, No 1 (2023): February 2023 Vol 9, No 2 (2022): November 2022 Vol 9, No 1 (2022): May 2022 Vol 8, No 2 (2021): November 2021 Vol 8, No 1 (2021): May 2021 Vol 7, No 2 (2020): November 2020 Vol 7, No 1 (2020): May 2020 Vol 6, No 2 (2019): November 2019 Vol 6, No 1 (2019): Mei 2019 Vol 6, No 1 (2019): May 2019 Vol 5, No 2 (2018): November 2018 Vol 5, No 2 (2018): November 2018 Vol 5, No 1 (2018): May 2018 Vol 5, No 1 (2018): May 2018 Vol 4, No 2 (2017): November 2017 Vol 4, No 2 (2017): November 2017 Vol 4, No 1 (2017): May 2017 Vol 4, No 1 (2017): May 2017 Vol 3, No 2 (2016): November 2016 Vol 3, No 2 (2016): November 2016 Vol 3, No 1 (2016): May 2016 Vol 3, No 1 (2016): May 2016 Vol 2, No 2 (2015): November 2015 Vol 2, No 2 (2015): November 2015 Vol 2, No 1 (2015): May 2015 Vol 2, No 1 (2015): May 2015 Vol 1, No 2 (2014): November 2014 Vol 1, No 2 (2014): November 2014 Vol 1, No 1 (2014): May 2014 Vol 1, No 1 (2014): May 2014 More Issue