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INDONESIA
J-SAKTI (Jurnal Sains Komputer dan Informatika)
Published by STIKOM Tunas Pematang Siantar
ISSN : 25489771     EISSN : 25497200     DOI : -
Core Subject : Science,
Computer Science & IT
JSAKTI adalah jurnal yang diterbitkan oleh LPPM STIKOM Tunas Bangsa Pematangsiantar yang bertujuan untuk mewadahi penelitian di bidang Manajemen Informatika. JSAKTI (Jurnal Sains Komputer dan Informatika) adalah wadah informasi berupa hasil penelitian, studi kepustakaan, gagasan, aplikasi teori dan kajian analisis kritis dibidang Ilmu Komputer terbit 2 kali setahun.
Arjuna Subject : -
Articles 499 Documents
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Pemodelan Gedung Kampus Universitas Muhammadiyah Sidoarjo dengan Metode Augmented Reality Sebagai Media Informasi Utama, Bagas Riski; Rosid, Mochamad Alfan; Hindarto, Hindarto
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.691

Abstract

The purpose of making the UMSIDA Augmented Reality application is as an information medium as well as a promotional medium for prospective new students to introduce the campus during the promotion. The Augmented Reality visualization of the campus 1, campus 2 and campus 3 buildings at Muhammadiyah University of Sidoarjo can combine real and virtual objects in the form of 3-dimensional buildings in real time using the Marker Based Tracking method. This method produces an application that can display the building's visuals in 3 dimensions by scanning the markers on the brochure by directing the Android smartphone camera from various angles.
Pemodelan Arsitektur Sistem Informasi Manajemen Aset Menggunakan Kerangka Kerja Zachman di Kantor Kelurahan Pada Eweta Anakoda, Getrida Loba; Tetik, Yulius Nahak; Momo, Lidia Lali
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.707

Abstract

Management of assets in government or private institutions becomes an obligation that needs to be implemented well and correctly starting from planning, Submission, The purchase, use, maintenance and removal of assets.The office of kelurahan on eweta which in the process of asset inventory is still done conventionally especially in its labeling has a problem that often occurs because it cannot be distinguished between fixed assets or moving assets so it is necessary to design a system model that can later be made as a reference in the development of asset management information system. Zachman's framework with the enterprising approach of the architecture planning is a very systematic phase because it has very comprehensive coverage where in the initial stages it needs to be planned for the architecture design to match the function and needs in the eweta office..In this architectural process, it produced a modeling business that was proposed., data architecture, application architecture, and a model of conceptual information technology that needs to be applied so it can sustain information systems that will be developed based on architectural instructions that have been designed. With an enterprise architecture approach can produce architectural models that correspond to the needs in the office of deployment on the eva, therefore, in the next phase it is necessary to draw a roadmap of implementation plan so that it can be used as a guideline or form for the development of management information systems in supporting the process of asset data management.
Analisis Penerimaan Teknologi Aplikasi Pemesanan Makanan Gofood dengan Technology Acceptance Model dan Pearson Correlation Munna, Aliyatul; Nugroho, Kristiawan; Hadiono, Kristophorus
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.682

Abstract

Technology has proven itself as a powerful tool to ease human work in many ways, including food ordering technology. GoFood is a popular and innovative food ordering application that has brought convenience and comfort to users in Indonesia. This research aims to analyze the technology acceptance of the Gofood food ordering application using the Technology Acceptance Model (TAM). TAM is a framework used to understand the factors that influence the acceptance and use of technology. In the context of food ordering apps, user acceptance of the app is critical to the success and growth of the business. This research method involves collecting data through online surveys among Gofood application users. Respondents were asked to assess relevant factors in the TAM, including perceived usefulness, perceived ease of use, as well as attitudes toward use and behavioral intention to use. ), and test the correlation between constructs using Pearson correlation. The results of the analysis show that these findings indicate that perceived usefulness and perceived ease of use of the GoFood application contribute to attitudes toward use and interest in utilizing and using the application. .
Penyusunan Manajemen Risiko TI Berdasarkan Cobit 2019 I&T Risk Focus Area Untuk Digitalisasi Fintechco Azis, Abdul; Mulyana, Rahmat; Fauzi, Rokhman
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.698

Abstract

The rapid embrace of digital technology is visible in the Fintech industry as a born-digital company. Previous research identified the influence of IT governance (ITG) on organizational performance (OP), mediated by digital transformation (DT). However, more research is needed to understand how IT risk management works in Fintech's digitalization. This study uses Design Science Research (DSR) following ISACA's COBIT 2019 I&T Risk Focus Area. It looks at a case study in FintechCo, using interviews data and document triangulation. Solutions and plans are created based on the gaps found in seven components within FintechCo's three priorities: EDM03 Ensured Risk Optimization, APO12 Managed Risk, and MEA03 Managed Compliance with External Requirements resulting estimated capability improvement 0,8 (34,8%). This study helps us understand how to manage IT-related risks in organizational digitalization and offers practical insights for FintechCo's digitalization journey.
Analisa Segmentasi Customer Pada Perusahaan Bisnis Properties Menggunakan Model RFM (Kasus PT. Pollux Aditama Kencana) Arseta, Gama; Purnomo, Hindriyanto Dwi
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.673

Abstract

The current business development in the property industry is promising, leading to a highly competitive market. As a result, PT. Pollux Aditama Kencana, which operates in the property business, must have strategies in every market competition, especially in gaining customer loyalty. This study uses the Recency, Frequency, and Monetary (RFM) model combined with K-Means. The RFM model is used for customer data clustering based on the number of transaction activities, transaction amount, and transaction time. Meanwhile, K-Means can describe the level of customer loyalty. The data used in this study were taken from sales reports from November 28, 2014 to September 19, 2022, involving 1966 customers in property purchases. The results show that the proposed use of the RFM and K-Means models is superior compared to using only the RFM model. Cluster 1 has 936 customers, indicating customers with high loyalty to the company, while Cluster 2 has 250 customers, indicating customers with low loyalty, and Cluster 3 has 780 customers, indicating customers with medium loyalty. The RFM and K-Means models used successfully produced several loyalty attributes that affect customer evaluations, with 4% in the top customer category, 12% in the high-value customer category, 34% in the medium-value customer category, 31% in the low-value customer category, and 19% in the lost customer category.
Rancang Bangun Aplikasi Verifikasi Keaslian e-Sertifikat dengan Algoritma SHA-512 Javier, J; Dinata, Yuwono Marta
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.664

Abstract

In today's digital age, electronic certificates, also known as e-certificates, are frequently used. The authenticity of these certificates, however, can be a challenge to resolve because they are easily falsified. Rarely do certificates for events like contests, seminars, courses, and awards have serial numbers, barcodes, or QR scanners as authenticity markers. Contrary to certificates for land titles, vehicles, law, education, or academia, which are challenging to counterfeit because they have an indicator and typically have a physical form rather than a digital one. Recruiting committees, institutional memberships, and HR departments could all suffer as a result. Therefore, a solution is required to maintain the validity of these certificates using the Cryptographic Hash Function technique and the SHA-512 algorithm, which is the most recent member of the SHA-2 generation. The findings from this research indicate that the website application developed performs effectively on the user's device and facilitates the verification process of e-certificates that lack authenticity indicators. Therefore, the authors can conclude that this website application provides assistance to both individual and organizational users in verifying digital certificates.
Analisis Sentimen Terhadap Aplikasi Chatgpt Pada Twitter Menggunakan Algoritma Naïve Bayes Rifaldi, Muhamad Ilmar; Ramadhan, Yudhi Raymond; Jaelani, Irsan
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.687

Abstract

Sentiment analysis or opinion mining is the detection of attitudes, opinions and emotions towards an object. The value of sentiment analysis can be divided into 2 types of sentiment, namely positive and negative sentiment. One of the topics that is currently being discussed by the public on Twitter social media is an AI-based chatbot application, ChatGPT. This research makes public tweets on Twitter as sentiment analysis material. Data retrieval is done using the Twitter API with data processing on Rapidminer and visualization using Power BI. The sentiment analysis process uses the Naïve Bayes algorithm by dividing the data of 301 tweets into training data and test data. Then testing using Confusion matrix to calculate the performance of the classification results. From the test results, 80% accuracy was obtained, then the Precission value was 80.95% and the Recall value 89.47%. It can be concluded that public sentiment on Twitter for the ChatGPT application tends to be positive seen from the number of positive tweet data as much as 74%.
Sentiment Analysis of Indonesian Presidential Candidate 2024 on Facebook Fattah, Fardhan Saifullah; Ratnasari, Chanifah Indah
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.703

Abstract

The 2024 Indonesian Presidential Election has become a hot topic in daily life, including on Facebook. Everyone has the freedom to express or speak their opinions on the Indonesian presidential candidate for 2024. As a result, there are a variety of viewpoints, some of which are positive and some of which are negative. This research aims to classify positive and negative sentiments in each presidential candidate's Facebook post comment data. Several processes are involved in this sentiment analysis process, including data scraping, data preprocessing, data labeling, and data classification using the Support Vector Machine (SVM) approach. The data used are comments obtained from each presidential candidate's Facebook page between July 1 and July 31, 2023. Anies Baswedan, with a positive sentiment value of 88.20% and a negative sentiment value of 11.80%, is the presidential candidate with the highest positive sentiment and the lowest negative sentiment. The SVM approach with a linear kernel produced the best precision value for positive sentiment, namely 94%.
Menerapkan Algoritma Neural Network Pada Chatbot Mengenai Pariwisata Di Provinsi Bangka Belitung Mahendra, Ristian; Kamayani, Mia
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.678

Abstract

Bangka Belitung Province, precisely in South Bangka regency, is one of the areas that have the potential to be visited by tourists. However, not all attractions are known by tourists due to lack of information. From these problems, researchers tried to develop a chatbot system. Chatbot is a program that conducts conversations between humans and machines using human language. This chatbot system aims to help tourists do questions and answers automatically to find information about tourist attractions in South Bangka.  The chatbot system applies a model with a natural language processing approach and neural network algorithms. This study aims to create a chatbot model that can provide information with good accuracy about paratourism in Bangka Belitung Province, especially South Bangka district. The data used in this study were the results of interviews and filling out questionnaires to the community. Then the data obtained is stored and converted into JSON format consisting of 173 tags, 618 patterns, and 187 responses. Then preprocessing the data was carried out by taking 25 random test questions. The results of the chatbot system accuracy test got an accuracy score of 92% from 25 questions asked randomly by getting an error value of 8%. From the results of accuracy testing, the chatbot system gets a response by looking at the appropriate questions asked by users based on tags, so that they can get the right answer.
Analisa Kemampuan Algoritma YOLOv8 Dalam Deteksi Objek Manusia Dengan Metode Modifikasi Arsitektur Setiyadi, Aris; Utami, Ema; Ariatmanto, Dhani
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.694

Abstract

Object detection is a skill that can be taught to a machine with the help of a camera sensor to capture a digital image. By using the YOLO algorithm we can teach machines to detect, for example, humans. Much research in object detection has been carried out previously using different algorithms and methods and also on different objects and images. In this research, a method was carried out to modify the architecture of YOLOv8 in the head section to be used to detect human objects in grayscale images. The training process was carried out 4 times using the default architecture, Model 1, 2 and 3 architecture. With the default model results, the mAP value was 76, Model 1 had an mAP value of 66, model 2 had an mAP value of 81 and model 3 produced an mAP value of 80. From the research carried out modifications The YOLOv8 architecture in the head section can influence the training results and produce a better model than the default architecture which only produces an mAP value of 76. The best results were obtained in model 2 with layers used of 40x40x512xW resulting in a model with an mAP value of up to 81.

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