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Hapnes Toba
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Fakultas Teknologi Informasi, Universitas Kristen Maranatha Sukawarna, Sukajadi, Bandung City, West Java 40164 Telepon: (022) 2012186
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INDONESIA
jurnal teknik informatika dan sistem informasi
Published by Universitas Kristen Maranatha
ISSN : -     EISSN : 24432229     DOI : https://journal.maranatha.edu/
Core Subject : Science,
Computer Science & IT
Jurnal Teknik Informatika dan Sistem Informasi (JuTISI) menerima topik-topik sebagai berikut, namun tidak terbatas pada : Artificial Intelligence • Business Intelligence • Cloud & Grid Computing • Computer Networking & Security • Datawarehouse & Datamining • Decision Support System • E-System • Enterprise System (SCM, ERP, CRM) • Human Computer & Interaction • Image Processing • Information Retrieval • Information System • Information System Audit • Enterprise Architecture • Knowledge Management • Mobile Computing & Application • Multimedia System • Open Source System & Technology • Semantic Web & Web 2.0 • Internet of Things
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 25 Documents
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Search results for , issue "Vol 7 No 1 (2021): JuTISI" : 25 Documents clear
Modifikasi Skema Teknik Tanam Padi dan Bajak Sawah Berbasis Square Transposition 64-bit Najoan, Nadya Glorya; Pakereng, Magdalena Ariance Irene
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 1 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i1.2973

Abstract

Cryptography is a study discussing mathematic technics that related to information security aspects such as secrecy, data integrity, and authenticity. Cryptography is also a method to secure data or information in the form of a password that makes it hard to understand the meaning. This research discusses how to modify a scheme while looking for random points using different pickup points and income points. While looking for the random point in the modified scheme research using the rice planting and field plow techniques, it has three testing processes which were runtest, monobit, and blockbit tests. This research used square transpositions 64-bit with the size 8x8 and it got a high result with p-value 7.32395E-10, p-value monobit 1.00000000 and p-value blockbit 1 that showed non-random result with the smallest p-value was 0.011662392 with p-value monobit 1.00000000 and the p-value blockbit 0.99990476 that made thisresearch got a random result.
Sistem Rekomendasi Suku Cadang Berdasarkan Item Based Filtering Wibisono, Christian; Haryadi, Lucky Surya; Widyaya, Juan Elisha; Liliawati, Swat Lie
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 1 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i1.3036

Abstract

Replaceable spare part on workshop have many transaction and possibility thus recommender system is needed to simplify the selection process. We propose recommender system with item collaborative filtering, with high data sparsity. With Single Value Decomposition we reduce the matriks to improve the system and decrease “noise” value. Model will be evaluated using MAE, RMSE, and FCP metrics. The results of recommendation model are MAE = 1.2752, RMSE = 1.4882, dan FCP = 0.4947.
Perancangan Sistem E-Reporting Menggunakan ReactJS dan Firebase Panjaitan, Jeremy; Pakpahan, Andrew Fernando
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 1 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i1.3098

Abstract

The head office of west region of Seventh Day Adventist Indonesia in Indonesia is located in Jakarta. Every month employees must report every transaction they have made . The report can be in the form of proof of purchase, shopping receipt, notes and other proof of transactions. The reporting system of the office is done by submitting a hardcopy proof of the transaction to the finance department. The reporting process takes a long time and allows for inaccurate proof of transactions. Another problem that is often found is the unknown status of reports. Employees often do not know whether the reports that have been received by the financial department have been approved or not. The purpose of this study is to design an E-reporting system that will make it easier for employees to report to the financial department so that reporting can be done quickly and accurately. The system to be built uses ReactJS and Firebase using an Agile Software development method. The result of this research is the creation of an E-reporting application that can help to report on the financial department every month.
Perbandingan Algoritma Machine Learning dalam Menilai Sebuah Lokasi Toko Ritel Kristiawan, Kristiawan; Widjaja, Andreas
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 1 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i1.3182

Abstract

Abstract — The application of machine learning technology in various industrial fields is currently developing rapidly, including in the retail industry. This study aims to find the most accurate algorithmic model so that it can be used to help retailers choose a store location more precisely. By using several methods such as Pearson Correlation, Chi-Square Features, Recursive Feature Elimination and Tree-based to select features (predictive variables). These features are then used to train and build models using 6 different classification algorithms such as Logistic Regression, K Nearest Neighbor (KNN), Decision Tree, Random Forest, Support Vector Machine (SVM) and Neural Network to classify whether a location is recommended or not as a new store location. Keywords— Application of Machine Learning, Pearson Correlation, Random Forest, Neural Network, Logistic Regression.
Analisis Klasifikasi Sentimen Terhadap Sekolah Daring pada Twitter Menggunakan Supervised Machine Learning Savitri, Ni Luh Putu Chandra; Rahman, Radya Amirur; Venyutzky, Reyhan; Rakhmawati, Nur Aini
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 1 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i1.3216

Abstract

Covid-19 pandemic urges countries to limit interaction of their people to reduce transmission. Indonesia requires people to do activities at home, one of which is online school. Many people share their thoughts through social media Twitter. Therefore, authors conducted sentiment analysis using supervised machine learning algorithm to determine distribution of words used in commenting on online schools, relationship between sentence, length and sentiment, and best algorithms that can be used to get most accurate results. In this study, authors used the method of crawling with RapidMiner to get data from Twitter. Then authors do data cleansing, data processing with classification methods using Random Forest Classifier , Logistic Regression , BernoulliNB and SVC algorithm. After that authors evaluate using confusion matrix, accuracy rate and classification report. In this research, authors found there are positive, negative, and neutral sentiments expressed on the online school implementation through comments. Authors ranked top three most used words used to express positive sentiments which includes bahagia, rajin and senang. On negative sentiments, top three words are capek, muak and bosen. On neutral sentiments, top three words are tidur, capek, and buka. Lengthy Tweets are usually imbued with negative remarks. On the other hand, the tweet tends to be positive and neutral tweet is usually stable. Authors conclude that the weakness of online school is the amount of workload that makes students tired alongside ineffective teaching method which makes it hard for students to understand the material given by school. However, on the positive side, some people agree with policies that are implemented and they feel like they gained some benefits from the implementation. From the four supervised machine learning algorithms that have been tested, Logistic Regression shows the highest accuracy, 0,87. The analysis shows that society tends to be neutral to the implementation of online school.
Continuous Integration and Continuous Delivery Platform Development of Software Engineering and Software Project Management in Higher Education Ferdian, Sendy; Kandaga, Tjatur; Widjaja, Andreas; Toba, Hapnes; Joshua, Ronaldo; Narabel, Julio
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 1 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i1.3254

Abstract

We present a report of development phase of a platform which aims to enhance the efficiency of software project management in higher education. The platform accommodates a strategy known as Continuous Integration and Continuous Delivery (CI/CD). The phase consists of several stages, followed by testing of the system and its deployment. For starters, the CI/CD platform will be deployed for software projects of students in the Faculty of Information Technology, Universitas Kristen Maranatha. The goal of this paper is to show a design of an effective platform for continuous integration and continuous delivery pipeline to accommodate source code compilation, code analysis, code execution, until its deployment, all in a fully automated fashion.
Evaluasi Tata Kelola Guna Meningkatkan Kinerja Manajemen Teknologi Informasi Menggunakan Framework COBIT 5 Febriani, Fitria; Manuputty, Augie David
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 1 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i1.3260

Abstract

The Salatiga Class I District Court is a government agency that has implemented Information Technology (IT) governance which has problems in IT management, so the purpose of this study is to evaluate information technology governance using the COBIT 5 framework by determining the level of capability and gaps. The results of the recapitulation of the questionnaire with the Likert scale model obtained the average value of the selected domain getting a value of 3.28, which means that the current level of capability (as is) is at level 3 (established process), while the numbers from the range of 0 to 5 levels are expected or target (to be) namely 5 (optimization process). So that a gap or gap is obtained, namely the value of 1.27 which results in a reduction between to be and as is. The selected process domain is in the highest position, namely DSS01 with a value of 3.80 or level 4 (predictable process) while the lowest position is APO12 with a value of 2.82 or level 3 (established process). To achieve the expected conditions, the IT subdivision can improve in dealing with IT risks and IT problems that occur in the Salatiga District Court Class I B. In addition to evaluating IT governance, testing system functionality is also carried out by using the Blackbox Testing method with the Boundary Value Analysis technique. The object is used for testing the system, namely­ e-Court tested on the registration page and login page. From the results of system testing­ e-Court On the registration page, there is a discrepancy in the expected name field and on the login page that the test results ­field - field as expected. Keywords — Information Technology Governance; COBIT 5; e – Court; Blackbox Testing; Boundary Value Analysis.
Pengembangan Knowledge Management System dengan Teknik Information Retrieval Jaya, Try Atmaja Linggan; Ayub, Mewati
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 1 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i1.3316

Abstract

Useful data sets can be used as information to solve problems or share knowledge with others. In the case of companies implementing the new system, many input errors, or not knowing the workflow of the program, are experienced repeatedly by the same person or people in the same department. Besides that, with the entry of new employees, it takes time to adapt and how to solve the problem. To solve it, a place is needed to record problems and their solutions, or share knowledge, both for old and new employees as 'First Aid'. Knowledge Management System application is expected to help solve the problems as a place to collect data which contains errors, cause and solving; business flow; user authorization; etc. The data used, using data from a collection of tickets, personal messages or e-mail, and knowledge owned by the user, will be entered into the database as a storage place for knowledge. In the input process, each word will be broken down based on the character 'space', tokenizing, filtering, and VSM and then entered into the database. Users can search for information or knowledge by entering keywords or sentences according to user needs, then the input will be processed by tokenizing, filtering, and calculating the length using VSM. After getting the input length, the results will use the TF-IDF algorithm and cosine similarity, and the system will display the results in list form and see the details if the results from the list are selected.
Prediksi Penyebaran Informasi di Twitter dengan Metode Pembelajaran Mesin dengan Fitur Linimasa Haryadi, Lucky Surya; Suteja, Bernard Renaldy
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 1 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i1.3324

Abstract

Abstract — Social Media Network has been an important information source, and the information propagation within the network gave an impact on politics, marketing, and entertainment industry. Our aim is to predict a tweet whether the information will be propagated further. The previous research has focused on analyzing this task with a wide range of learning methods and features, such as content and account features. Timeline features are proposed as features that can further predict information propagation and as we compared the performance with content and account features. The dataset consists of 43.229 tweets, we predict the information propagation with logistic regression, support vector machines, and random forest learning method with these features. Our result indicates that the timeline feature can be a good candidate for predicting information propagation and the random forests learning method consistently performs better. From the training result, we further calculate feature importance. Recently tweets, engagement with another user and previous liked tweets on the timeline features contributed to more popular tweets.
Pengaruh Preprocessing Terhadap Klasifikasi Diabetic Retinopathy dengan Pendekatan Transfer Learning Convolutional Neural Network Widyaya, Juan Elisha; Budi, Setia
Jurnal Teknik Informatika dan Sistem Informasi Vol 7 No 1 (2021): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v7i1.3327

Abstract

Diabetic retinopathy (DR) is eye diseases caused by diabetic mellitus or sugar diseases. If DR is detected in early stage, the blindness that follow can be prevented. Ophthalmologist or eye clinician usually decide the stage of DR from retinal fundus images. Careful examination of retinal fundus images is time consuming task and require experienced clinicians or ophthalmologist but a computer which has been trained to recognize the DR stages can diagnose and give result in real-time manner. One approach of algorithm to train a computer to recognize an image is deep learning Convolutional Neural Network (CNN). CNN allows a computer to learn the features of an image, in our case is retinal fundus image, automatically. Preprocessing is usually done before a CNN model is trained. In this study, four preprocessing were carried out. Of the four preprocessing tested, preprocessing with CLAHE and unsharp masking on the green channel of the retinal fundus image give the best results with an accuracy of 79.79%, 82.97% precision, 74.64% recall, and 95.81% AUC. The CNN architecture used is Inception v3.

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