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Darius Andana Haris
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JIKSI (Jurnal Ilmu Komputer dan Sistem Informasi)
Published by Universitas Tarumanagara
ISSN : 23028769     EISSN : 23032529     DOI : -
Core Subject : Science, Education,
Computer Science & IT Mathematics Other
Jurnal Ilmu Komputer dan Sistem Informasi (JIKSI) diterbitkan oleh Fakultas Teknologi Informasi Universitas Tarumanagara (FTI Untar) Jakarta sebagai media publikasi karya ilmiah mahasiswa program studi Teknik Informatika dan Sistem Informasi FTI Untar. Karya-karya ilmiah yang dihasilkan berupa hasil penelitian kualitatif dan kuantitatif, perancangan sistem informasi, analisis dan perancangan progam aplikasi. Jurnal ini terbit dua kali dalam setahun yaitu pada bulan Januari dan Agustus.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 937 Documents
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APLIKASI PREDIKSI NILAI DAN REKOMENDASI MATAKULIAH MENGGUNAKAN METODE COLLABORATIVE FILTERING DAN ALGORITMA C4.5 PADA PROGRAM STUDI TEKNIK INFORMATIKA UNIVERSITAS X Filbert; Bagus Mulyawan; Tri Sutrisno
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 10 No. 2 (2022): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v10i2.22548

Abstract

A common problem with lectures and colleges is that students have difficulty choosing a course due to the large number of choices, or the student credits are limited due to poor course results. We hope that by designing this value prediction application and course recommendations, we will help students get the best course recommendations according to their abilities and grades. The program uses the C45 algorithm to display the selection of courses with the lowest score, and the scores generated by collaborative filtering calculations are advanced by determining the value of similarity between students and predicting grades. The system will identify each course with the highest average score / highest pass rate and it will be used as a student course recommendation. Test results show that the accuracy in determining a student's recommended course using the C45 algorithm is 70%, and the calculation error in determining a student's value prediction is 40-56%.
Sistem Pengenalan Covid-19 Berdasarkan Foto X-ray Paru dengan Metode EfficientNet-B0 Jourdan Stanley; Chairisni Lubis; Teny Handhayani
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 10 No. 2 (2022): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v10i2.22549

Abstract

Covid-19 is a viral infection disease severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Covid-19 is a group of viruses that attack the respiratory system in humans which can cause symptoms ranging from mild symptoms to severe symptoms. Currently, to detect whether a person is infected with the Covid-19 virus or not, several tests can be carried out, one of which is the polymerse chain reaction (PCR) examination. This type of examination has a high level of accuracy but this examination requires quite expensive costs, adequate laboratories and requires a long time. So from these problems there is another alternative, namely radiological examination. From these problems, a system was built that can perform classification based on x-ray images of the lungs using the convolutional neural network (CNN) method of Efficientnet-B0 architecture. This system is expected to assist medical personnel in pre-diagnosing a patient's lung condition based on their lung x-ray without changing the role of the medical personnel. After successfully building a Covid-19 recognition system, the system will be tested using the confusion matrix method where in this test there are 2 scenarios. In the first scenario, the data trained using the CLAHE preprocessing method obtained an accuracy rate of 98%, while in the second scenario the data was trained without using the CLAHE preprocessing method, the results obtained an accuracy rate of 97%. Previous research was conducted using the resnet-18 method and obtained an accuracy rate of 92%. From the results obtained prove that Efficientnet is able to increase the level of accuracy from previous studies.
Sistem Rekomendasi Kamera Mirrorless Dengan Metode Simple Multi Attribute Rating Technique (SMART) Marvellino; Viny Christanti Mawardi; Manatap Dolok Lauro
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 10 No. 2 (2022): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v10i2.22550

Abstract

Photography are an activity that can be done through a lot of other media, some uses DSLR (Digital Single Lens Reflex) camera, Mirrorless and even from their smartphones. Choosing a mirrorless camera by those who doesn’t have the knowledge of the specifications of a camera might be a problem by itself. The problem itself rose because there’re a lot of criterias that need to be considered in getting the right one and due to lack of information. That is why recommendation system based on website are developed which can help users choosing the right product using SMART (Simple Multi Attribute Rating Technique) method. SMART method are a method for taking decisions that are multi attribute used for supporting decisions which have other alternatives. In developing this website it uses 5 brands of camera that is Canon, Fujifilm, Nikon, Panasonic and Sony. The validity test based from UAT (User Acceptance Rate) of 35 respondents gives a result of 85.71% accuracy that agrees that the recommendation system is appropriate to what user wants and proves that the system gaves the recommendation based on what user wants and beneficial to be used.
KLASIFIKASI BUAH SEGAR DAN BUSUK MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK BERBASIS ANDROID Prinzky; Chairisni Lubis
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 10 No. 2 (2022): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v10i2.22551

Abstract

Fruit is a food and a good source of vitamins for the body's metabolic processes, but fruit is quickly damaged by the effects of physics, chemistry and microbiology if not given special treatment. Fresh fruit is one of the main needs in the health of the human body because the fruit contains nutrients and vitamins. Therefore, it is proposed to design an application that can classify fresh and rotten fruit. The method that will be used in this design is Convolutional Neural Network (CNN). The architecture that will be used in this design is AlexNet. The fruits that will be classified are apple, banana, grape, guava, jujube, orange, pomegranate, strawberry, mango and tamarillo. The test results on the training data produce an accuracy of 99% and the test on the test data or validation is 98% with the use of the adam optimizer. The confusion matrix shows that the trained model has an accuracy value of 98%, precision of 98%, recall of 98%, and F1-score of 98%. The output of the application is the introduction of fruit names and classification in the form of fresh or rotten.
PENGENALAN AKTIVITAS MANUSIA DI SUPERMARKET DENGAN METODE LONG SHORT TERM MEMORY Kristian Davidson Runtu; Lina
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 10 No. 2 (2022): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v10i2.22552

Abstract

Since a long time ago, supermarkets have become people's destinations for shopping for various things such as food, cooking ingredients, cleaning products and others. Supermarkets are known for their very large and crowded places, making it difficult to monitor. Therefore, supermarkets need a system to help monitoring. With the development of technology, monitoring systems are increasingly advanced and one of the results of these technological developments is a system for recognizing human activities. By using OpenPose to obtain human skeleton data on the image and using the Long Short Term Memory method to perform recognition, testing of the training data was carried out so as to produce a precision value of 99%, recall 99%, and f1-score 99%. And real-time testing using a camera resulted in an accuracy value of 73% for the picking class, 87% for the standing class and 81% for the walking class.
PEMBUATAN PROGRAM SISTEM PENJUALAN HELM BERBASIS WEB PADA TOKO HELM KARTINI Donni Kurniawan; Ery Dewayani
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 10 No. 2 (2022): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v10i2.22554

Abstract

Helmet Sales System Program at the Kartini Helmet Shop, located in Bekasi City, aims to create a helmet sales program in the form of a website that can process transactions online. Customers can process orders, payments and deliveries online through the helmet sales website which is opened with a web browser. Store owners can print sales reports to see the results of their product sales, customer reports to see customers who bought the most and financial reports to see revenue earned. In making this program using the Waterfall method. In designing this program using Context Diagrams, Data Flow Diagrams, Entity Relationship Diagrams, Relationships Between Tables and Table Specifications. The programming languages used in making this program are HTML and PHP with the CodeIgniter Framework and use the MySQL database.
SISTEM PENDUKUNG KEPUTUSAN DALAM KELAYAKAN PENGAJUAN KREDIT KENDARAAN BERMOTOR DI PERUSAHAAN X MENGGUNAKAN METODE NAÏVE BAYES Ricky Hansen Kurnia; Desi Arisandi; Tri Sutrisno
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 10 No. 2 (2022): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v10i2.22636

Abstract

The purpose of this research is to analyze and design a program that can assist decision making in the process of accepting a motor vehicle loan application in accordance with the given criteria so as to produce a prediction of the value of opportunities in the feasibility of granting motor vehicle loans and minimize human errors that occur in the process Credit Application data analysis. The method used in this research is one of the classification methods, is the Naive Bayes algorithm menthod. The results of the research that has been carried out are applications that are designed to calculate the probability value of the data criteria that have been entered, so that a decision suggestion can be obtained whether the data can be accepted or rejected, from research conducted the more training data provided, the more accurate the value will be. generated probabilities, and the designed application is executed through the Microsoft SQL Server Management Studio (SSMS). From the results of the experiments that have been carried out, it can be seen that the program can provide probability values ​​according to the Naive Bayes method according to the criteria data provided.
APLIKASI POINT OF SALE PADA RUMAH MAKAN AYAM BAKAR 7 SAUDARA Franky Wijaya; Ery Dewayani; Manatap Dolok Lauro
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 1 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i1.24063

Abstract

The Ayam Bakar 7 Saudara restaurant has been established since 1978 and still uses a conventional system in terms of managing its sales. Every month, the number of customers at the restaurant also increases significantly, therefore the risk of mistakes is higher. To overcome this, a Point of Sale application was created which is a software system designed as a cashier which is a point of sale, where the transaction process between buyers and sellers is carried out to completion. This application was created using the OutSystems platform which is a low-code platform that allows the process of making applications faster with minimal typing of programming code. The applied application development method is using the Systems Development Life Cycle (SDLC). The resulting system is divided based on its users, namely Cashier and Admin/Owner. Cashiers can input customer purchases, process payments, and print receipts. Admin/Owner can perform the same functions as the cashier with the addition of product management, view dashboards, print sales reports, manage cashier accounts, view and manage sales history.
Deteksi Penggunaan Masker dan Klasifikasi Secara Real Time Melalui Video Webcam Dengan Metode YOLO Saddhananda; Chairisni Lubis
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 1 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i1.24064

Abstract

The use of masks is currently important to prevent the spread of the virus. However, there are often people who do not wear masks in public places. Therefore, a real-time mask detection system is needed via webcam video. This system uses the You Only Look Once (YOLO) method to detect faces and classify whether the person is wearing a mask or not. The YOLO model is used to detect and classify masks in images and is trained using datasets from kaggle. The results for YOLO show the detection accuracy for masks is 92% using training data.
Klasifikasi Ujaran Kebencian Menggunakan Metode FeedForward Neural Network (IndoBERT) Steven Dharmawan; Viny Christanti Mawardi; Novario Jaya Perdana
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 11 No. 1 (2023): JURNAL ILMU KOMPUTER DAN SISTEM INFORMASI
Publisher : Fakultas Teknologi Informasi Universitas Tarumanagara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24912/jiksi.v11i1.24066

Abstract

Everyone in Indonesia has freedom of speech, both in real life and on social media. However, freedom of speech carried out without filtering can lead to hate speech. Hate speech is a form of discrimination directed against individuals or groups of individuals based on race, religion, gender, sexual orientation, or other identities. Hate speech can harm other parties which as a result can trigger conflict, violence, and can even cost a person's life. Therefore, it is important to be able to identify and manage this hate speech effectively. One way to manage hate speech on social media is to classify it. In this study, a web-based application was created that can classify a sentence to determine whether the sentence is hate speech or a normal sentence. The model created for classification uses the feedforward neural network method with IndoBERT. Based on the test results, the model created using the feedforward neural network method with IndoBERT provides the best accuracy of 89.52%.

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