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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Teknik Informatika UNIKA Santo Thomas J-SAKTI (Jurnal Sains Komputer dan Informatika) Journal of Soft Computing Exploration J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST)
Yulita, Winda
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Energy

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Journal : J-SAKTI (Jurnal Sains Komputer dan Informatika)

 1 
Analisis dan Rancangan User Experience Website OAIL Menggunakan Metode Task Centered System Design (TCSD) Yulita, Winda; Algifari, Muhammad Habib; Rinaldi, Daniel; Praseptiawan, Mugi
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 2 (2021): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i2.384

Abstract

The increase in internet service users in obtaining information and knowledge has made many institutions or organizations begin to create and design websites. One of the institutions that started to build a website is UPT OAIL. In the process of forming the OAIL website, a good User experience (UX) design is needed because it affects user satisfaction in using the website. The analysis and design of user experience in this study uses the Task Centered System Design (TCSD) method. The TCSD method can identify user needs and task needs. The stages in the research are user identification and observation, user and organization requirements analysis, design as scenario and walkthrough evaluate. In this study, identification and observation were carried out by interviewing UPT OAIL staff as well as prospective users. The test is based on the usability method using USE Questionare on the ease of use dimension. The results of the study obtained that the interpretation score for testing the ease of use dimension was 94.2% with the conclusion that the average user or respondent chose the design of each page and the features made were very easy to use.
Pendeteksian Jumlah Bangunan Berbasis Citra Menggunakan Metode Deep Learning Bagaskara, Radhinka; Rizkita, Alya Khairunnisa; Fernandes, Rivaldo; Yulita, Winda
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 1 (2022): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i1.428

Abstract

Counting residential houses is one of the problems faced when determining population density level in Indonesia, therefore it’s required to find a method that’s able to solve said problem. Deep learning method is capable of creating a prediction model for detecting the number of buildings from an image. The deep learning prediction model is created with MobileNetv2 application. The prediction model is trained by using a dataset from Kaggle. The prediction model is tested using satellite photos taken from Way Kandis-Sukarame, Bandar Lampung. The result is a deep learning prediction model with accuracy of 91.30% for SenseFly and 10.34% for Way Kandis dataset. The research can be further improved by using a better training and testing dataset
Automatic Short Answer Assessment Using The Cosine Similarity Method Bahri, Samsu; Praseptiawan, Mugi; Yulita, Winda
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 1 (2023): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i1.608

Abstract

In the learning process, most exams to assess learning achievement have been carried out by providing questions in the form of short answers or essay questions. The variety of answers given by students makes a teacher have to focus on reading them. This process of assigning grades is difficult to guarantee quality if done manually. Moreover, each class is mastered by a different teacher, which can cause inequality in the grades obtained by students due to the influence of differences in teacher experience. Therefore the automated answer assessment research was developed. The automatic short answer assessment is designed to automatically assess and evaluate students' answers based on a trained set of answer documents.  The automated grading system uses the cosine similarity method to determine the degree of similarity of a student's answer to the teacher's answer. While the word weighting used is the Term Frequency-Inverse Document Frequency (TF-IDF) method.  The data used is a question totaling 5 questions with each question answered by 30 students, while the students' answers are assessed by experts to determine the real value. This study was evaluated by Mean Absolute Error (MAE) with the resulting value of 0.22.
Co-Authors Afriansyah, Aidil Algifari, Muhammad Habib Andika Setiawan, Andika Anggraini, Leslie Aprilianda, Mohamad Meazza Atmajaya, Gde KM Bagaskara, Radhinka Bahri, Samsu Ba’its, Alfian Kafilah Drantantiyas, Nike Dwi Grevika Dwi Nugroho, Eko Faisal, Amir Febrianto, Andre Fernandes, Rivaldo Fitrawan, Mhd. Kadar Habib Algifari, Muhammad Harmiansyah, Harmiansyah Listiani, Amalia Mufidah, Zunanik Praseptiawan, Mugi Rahman Indra Kesuma Ramadhani, Uri Arta Ridwan, Naufal Taufiq Rinaldi, Daniel Rizkita, Alya Khairunnisa Solly Aryza Untoro, Meida Cahyo Verdiana, Miranti