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Hapnes Toba
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JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Published by Universitas Kristen Maranatha
ISSN : 24432210     EISSN : 24432229     DOI : https://doi.org/10.28932/jutisi
Core Subject : Science,
Computer Science & IT
Paper topics that can be included in JuTISI are as follows, but are not limited to: • Artificial Intelligence • Business Intelligence • Cloud & Grid Computing • Computer Networking & Security • Data Analytics • Datawarehouse & Datamining • Decision Support System • E-Systems (E-Gov, E-Health, E-Commerce, etc.) • Enterprise System (SCM, ERP, CRM) • Human-Computer Interaction • Image Processing • Information Retrieval • Information System • Information System Audit • Enterprise Architecture • Knowledge Management • Machine Learning • Mobile Computing & Application • Multimedia System • Open Source System & Technology • Semantic Web & Web 2.0
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 479 Documents
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Penerapan Kansei Engineering dalam Perbandingan Desain Aplikasi Mobile Marketplace di Indonesia Nucky Vilano; Setia Budi
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 2 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i2.2705

Abstract

The company's application design is very important because it displays the company's image and to attract more users to purchase/utilize the application. This research applies Kansei Engineering Method to analyze the emotion or feelings of the user towards the design of a mobile application interface. Six Kansei Words and three specimens are utilised in this research, where Kansei words are selected from words related to user experience. The participants of this research consist of 54 students from Maranatha Christian University. Participants’ responses are studied using multivariate statistical analysis (e.g., Coefficient Correlation Analysis, Principal Component Analysis, and Factor Analysis). This study explores the emotional factors that occur in designing an application. This analysis shows that there are some major factors that greatly influence the design of a mobile application interface.
Penerapan Metode SCRUM dalam Pengembangan Sistem Informasi Layanan Kawasan Warkim Warkim; Muhamad Hanif Muslim; Farham Harvianto; Setiawan Utama
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 2 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i2.2711

Abstract

The development of technology is very influential in the business processes of an organization to be able to carry out its duties and functions. As a government agency engaged in research, the Indonesian Institute of Sciences (LIPI) needs to make organizational changes to support its vision as a world-class research institution. One of the first steps taken is reorganizing and redistributing employees that have a high impact on the business process of service to employees because the supporting resources are placed corporately and no longer in the work units. To deal with this problem, we developed a Regional Service Information System using the Scrum methodology. The output is a web-based software that facilitates service requests needed by employees, ranging from service submission, processing by the Area Manager and Central Manager, to being received again by the service requester. The Regional Service Information System is expected to be a solution to the problems that arise as a result of the redistribution of employees at LIPI and to improve the effectiveness of employees as the research supporting resources.
Penerapan Estimasi Posisi dan Tracking Wajah Pada Sistem Presensi Mahasiswa Afrillebar Putra Pratama; Agi Prasetiadi; Elisa Usada
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 2 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i2.2730

Abstract

The current presence system can be done with a computerized system, one of which is the face biometric system. This study focuses on the application of position estimation and tracking based on clustering on people's faces to determine the position in three dimensions. Position estimation can be obtained by making a kernel that is ready to be used to predict three-dimensional coordinates of faces based on two-dimensional coordinates of two images. Position estimation can be done by utilizing the Machine Learning algorithm family. In this study, Least Absolute Shrinkage and Selection Operators (LASSO) is used to perform the position estimation. Meanwhile, clustering in this study uses the K-Means algorithm. Based on the test results, the kernel error obtained in estimating the face location is 9.23 cm. The tracking accuracy of an object based on clustering is 100%.
Implementasi Algoritma Apriori Pada Penyusunan Menu Makanan Rumah Makan Prasmanan Asep Budiman Kusdinar; Daris Riyadi; Asriyanik Asriyanik
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 2 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i2.2742

Abstract

A buffet restaurant is a restaurant that provides buffet food that is served directly at the dining table so that customers can order more food according to their needs. This study uses the association rule method which is one of the methods of data mining and a priori algorithms. Data mining is the process of discovering patterns or rules in data, in which the process must be automatic or semi-automatic. Association rules are one of the techniques of data mining that is used to look for relationships between items in a dataset. While the apriori algorithm is a very well-known algorithm for finding high-frequency patterns, this a priori algorithm is a type of association rule in data mining. High- frequency patterns are patterns of items in the database that have frequencies or support. This high-frequency pattern is used to develop rules and also some other data mining techniques. The composition of the food menu in the Asgar restaurant is now arranged randomly without being prepared on the food menu between one another. The result of this research is to support the composition of the food menu at the Asgar restaurant so that it is easier to take food menu with one another.
Pengaruh Metode Penyeimbangan Kelas Terhadap Tingkat Akurasi Analisis Sentimen pada Tweets Berbahasa Indonesia Ivan Nathaniel Husada; Hapnes Toba
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 2 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i2.2743

Abstract

Nowadays internet access is getting easier to get. Because of the ease of access to the internet, almost all internet users have social media. Social media is widely used by users to call out their opinions or even to make complaints about a matter and also discuss a topic with other social media users. From many existing social media, one that is popularly used for that activity is Twitter. Sentiment analysis on Twitter has become possible because of the activities of these Twitter users. In this research, the authors explore sentiment analysis with bag-of-words and Term Frequency Inverse Document Frequency (TF-IDF) features extraction based on tweets from Indonesian Twitter users. The data obtained is in imbalanced condition, so that it requires a method to overcome them. The method for overcoming imbalanced dataset uses a resampling approach which combines over and under sampling strategies. The results of sentiment analysis accuracies with Naïve Bayes and neural networks before and after input data resampling are also compared. Naïve Bayes methods that will be used are Multinomial Naïve Bayes and Complement Naïve Bayes, while the Neural Network architecture that will be used as a comparison are Recurrent Neural Networks, Long Short-Term Memory, Gated Recurrent Units, Convolutional Neural Networks, and a combination of Convolutional Neural Networks and Long Short-Term Memory. Our experiments show the following harmonic scores (F1) of the sentiment analysis models: the Multinomial Naïve Bayes F1 score is 55.48, Complement Naïve Bayes is 51.33, Recurrent Neural Network is 75.70, Long Short-Term Memory is 78.36, Gated Recurrent Unit is 77.96, Convolutional Neural Network is 76.12, and finally the combination of Convolutional Neural Networks and Long Short-Term Memory achieves 81.14.
Pengambilan Keputusan Strategis Pemasaran di Perguruan Tinggi dengan menggunakan Analytics Hierarchy Process (AHP) Arie Tunggal; Setia Budi
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 2 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i2.2748

Abstract

In marketing strategies, it is very important to consider various variables in decision making. With intense competition in higher education, it is important to determine a more appropriate and effective marketing strategy to get prospective students. For this reason, it is necessary to investigate what factors influence prospective students in determining tertiary institutions. This study reveals that the most influencing factors for prospective students in determining academic institutions are the ease of getting a job after graduation, followed by some other supporting factors, such as: scholarships, campus reputation, spiritual activities, and campus lifestyle.
Implementasi DenseNet Untuk Mengidentifikasi Kanker Kulit Melanoma Jasman Pardede; Dwi Adi Lenggana Putra
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 3 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i3.2814

Abstract

Skin is a part of a human body that covers the entire body and protect the lower layer from direct sunlight and another microorganism. Because of that, skin cells are always changing and could be changed because of genetic mutation that causes skin cancer. In general, skin cancer is divided into three groups, namely : skin cancer Basal cell carcinoma, skin cancer Squamous cell carcinoma, and skin cancer Melanoma. Melanoma skin cancer is caused by abnormal growth in melanocyte cells. Several methods are proposed to predict Melanoma skin cancer using ResNet, LeNet, and Support Vector Machine. System performance is measured based on the value of accuracy, precision, recall, and f-measure. This experiment is conducted using a Melanoma skin cancer dataset that obtained the average value in terms of accuracy, precision, recall, and f-measure are 0.94, 0.95, 0.92, and 0.94 respectively. Based on that result, the proposed DenseNet121 performs better with 0.94 accuracy, compared with ResNet, LeNet, and Support Vector Machine method. Keywords— Convolutional Neural Network; Image Classification; Melanoma Classification; DenseNet121.
Implementasi Algoritma Caesar Cipher Dan Steganografi Least Significant Bit Untuk File Dokumen Irvan Maulana Yusup; Carudin Carudin; Intan Purnamasari
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 3 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i3.2817

Abstract

Abstract — Security and confidentiality of a file are important aspects because the owner of the file does not want the data to be known by irresponsible parties. To keep the file secret and secure there are techniques called cryptographic algorithms and steganographic algorithms. Cryptographic algorithms are a way to change the contents of the file to be incomprehensible, while steganographic algorithms are a way of inserting files that you want to keep secret with other file types such as images, sounds, or videos. One type of cryptography is Caesar Cipher and steganography is Least Significant Bit (LSB). Caesar Cipher is a way of securing and keeping the contents of a file secret by shifting letters, while the Least Significant Bit (LSB) is a method of insertion by replacing the rightmost or backmost bits. This research uses waterfall software development with the stages consist of needs analysis, system design, implementation, and testing. The program code is written in Java language and uses the Netbeans 8.2 application. The result of the research is that with 10 research materials, 5 document files (* .doc) and 5 image files (* .png), only 2 files of each research material can be processed by this software. The tests carried out included testing the functions and steganographic criteria such as Fidelity, Recoverable, and Robustness. Keywords — Algorithm, Caesar Cipher; Least Significant Bit (LSB); Netbeans;
Klasifikasi American Sign Language Menggunakan Ekstraksi Fitur Histogram of Oriented Gradients dan Jaringan Syaraf Tiruan Muhammad Ezar Al Rivan; Mochammad Trinanda Noviardy
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 3 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i3.2844

Abstract

Sign languages have various types, one of which is American Sign Language (ASL). In this study, ASL images from the handshape alphabet were extracted using Histogram of Oriented Gradient (HOG) then these features were used for the classification of Artificial Neural Networks (ANN) with various training functions using 3 variations of multi-layer network architecture where ANN architecture consists of one hidden layer. Based on ANN training, trainbr test results have a higher success rate than other training functions. In architecture with 15 neurons in the hidden layer get an accuracy value of 99.29%, a precision of 91.84%, and a recall of 91.47%. The test results show that using the HOG feature and ANN classification method for ASL recognition gives a good level of accuracy, with an overall accuracy of 5 neurons 95.38%, 10 neurons 96.64%, and 15 neurons with 97.32%. Keywords— Artificial Neural Network; American Sign Language; Histogram of Oriented Gradient; Training Function
Pendeteksian Penyakit pada Daun Cabai dengan Menggunakan Metode Deep Learning Rosalina Rosalina; Ardi Wijaya
Jurnal Teknik Informatika dan Sistem Informasi Vol 6 No 3 (2020): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v6i3.2857

Abstract

Chili is one of the most essential horticultural plants in Indonesia. In addition to the lack of supply of plants, the price of chili on the market has increased dramatically. The shortage is affected by unpredictable climate changes, which have to result in many chili plants suffering from crop failure. It was because the disease infects chili plants so that harvests are decreased. This work would incorporate Deep Learning for image processing in Disease Detection Systems. This disease detection method will be used to help users, in particular chili farmers, identify whether or not the leaves of their chili plants are contaminated with the disease. This system would take a picture of chili leaf using a Raspberry Pi camera and implement image processing on the chili leaf image to collect valuable information on the image to find out whether or not the chili leaf is contaminated with the disease. The purpose of this research is to make a desktop application for a disease detection system that has the ability to detect whether or not a chili leaf is infected by several diseases, display the condition of the chili leaves, display the type of disease that infects the chili leaves (if any), and provide a percentage probability of the system in detecting the image of the chili leaves correctly (whether it is healthy chili leaves or sick chili leaves). The system reaches 100 percent accuracy with good brightness and distance less than 1 meter, while the system reaches 68.8 percent accuracy with poor brightness and distance greater than or equal to 100 percent. Keywords— chili leaf; deep learning; disease detection; raspberry pi

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