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INDONESIA
Journal of Information Systems and Informatics
Published by Asosiasi Doktor Sistem Informasi Indonesia
ISSN : 26565935     EISSN : 26564882     DOI : 10.63158/journalisi
Core Subject : Science,
Computer Science & IT
Journal-ISI is a scientific article journal that is the result of ideas, great and original thoughts about the latest research and technological developments covering the fields of information systems, information technology, informatics engineering, and computer science, and industrial engineering which is summarized in one publisher. Journal-ISI became one of the means for researchers to publish their great works published two times in one year, namely in March and September with e-ISSN: 2656-4882 and p-ISSN: 2656-5935.
Arjuna Subject : -
Articles 653 Documents
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Information Technology Governance Design in Trading Companies Using the COBIT 2019 Framework Leonardo, Kevin; Latuperissa, Rudi
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.798

Abstract

Effective information technology (IT) governance is very important for trading companies in managing their information assets well. The COBIT 2019 has been recognized as an international standard for IT governance that helps organizations achieve their strategic goals through the implementation of structured practices and processes. This research aims to design an IT governance framework based on COBIT 2019, especially for trading companies. The research methodology uses a case study approach by analyzing the unique needs and characteristics of trading companies in the IT context. The result is a design that is tailored to the needs and challenges faced by trading companies, including aspects of information security which are crucial in today's digital era. This research contributes to IT practitioners and senior managers to understand the practical and effective implementation of COBIT 2019 in improving IT governance in trading companies. By using COBIT 2019, trading companies can optimize the management of their information assets while ensuring regulatory compliance and increasing stakeholder trust.
Optimizing Motorcycle Sales: Enhancing Customer Segmentation with K-Means Clustering and Data Mining Techniques Fernando, Luis; Fianty, Melissa Indah
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.799

Abstract

Information plays a crucial role in the sustainability of company operations. The development of information technology, especially in the industry 4.0 era, affects various fields including economics, social, and education. The company faces challenges in declining motorcycle sales due to intense competition and ineffective customer segmentation. To address these issues, this study proposes the use of the K-Means algorithm with Python tools for better customer segmentation. The study aims to identify diverse customer groups and tailor marketing strategies accordingly. By utilizing the Elbow method and Silhouette score, the analysis of customer data is simplified. This study also employs data mining techniques to uncover hidden patterns in motorcycle sales data, aiding companies in improving operational efficiency and decision-making.
Collaborative Filtering Recommendation System Using A Combination of Clustering and Association Rule Mining Annisa, Siti; Rini, Dian Palupi; Abdiansah, Abdiansah
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.802

Abstract

A recommendation system helps collect and analyze user data to generate personalized recommendations for users. A recommendation system for movies has been implemented, considering the vast number of available films and the difficulty users face in finding movies that match their interests. One popular recommendation method is Collaborative Filtering (CF). Although widely applied, CF still has issues. Basic CF uses overlapping user data in evaluating items to calculate user similarity. This study aims to build a collaborative filtering recommendation system using clustering techniques to group users with similar interests into the same clusters. The next step in CF application is to gather recommendation candidate items by finding users with a high level of similarity to the target user. Subsequently, user pattern analysis is carried out by applying association rule mining to predict hidden correlations based on frequently watched items and the ratings given to those movies. This study uses rating data and movie data from the Movielens website. The evaluation of the recommendation results is measured using precision, recall, and f-measure. The evaluation results show that the proposed recommendation system achieves a hit rate of 95.08%, a precision of 81.49%, a recall of 98.06%, and an f-measure of 87.66%.
Transformation of Consumer Behavior Through Smart City Technology: A Literature Review Febiyanti, Widyantari; Nadlifatin, Reny
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.803

Abstract

Once city implements a smart city, the transformation that occurs not only impacts the city's infrastructure and operations, but also significantly influences individual interactions with public facilities and their consumption patterns. This literature review aims to identify changes in consumer behavior and daily activities after the implementation of smart city technology. The methodology used is PRISMA, with references published over the last decade. The research results show changes in various aspects, including mobility, energy efficiency, citizen engagement, environmental awareness, shopping experience, quality of life, education and information, business prospects, and response to the crisis. These findings show that smart city technology brings positive changes in the daily lives of city residents, which are influenced by the use of technology and the way it is implemented by the community. This research provides insight for policy makers and city managers to understand the broad impact of smart cities on community behavior.
Development of Virtual Reality Application for Arachnophobia Using Multimedia Development Life Cycle Method Anggoro, Nugroho Ari; Astuti, Ika Asti
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.804

Abstract

Arachnophobia or known as irrational fear of spiders, can be detrimental to one’s health and overall well-being. Common procedures, such as Cognitive Behavioral Therapy or medication, are often inefficient and take a lot of time to demonstrate results, thus the alternative way of dealing with arachnophobia is urgently needed. Such alternatives can be achieved by utilizing Virtual reality technology, hence the purpose of the following research is to address the matter by developing Virtual reality based application with the help of Multimedia development life cycle method. The MDLC method was chosen due its ability to create multimedia application, and the collected results of the experiment demonstrate that indeed MDLC can be used as a method to develop the arachnophobia therapy application that is both time efficient and works as test shows the decreasing time used for use application as well as two respondents that able to be detected for having arachnophobia. In conclusion the application developed using MDLC is indeed able to be an alternative way of arachnophobia therapy.
Application of Content-Based Filtering Method Using Cosine Similarity in Restaurant Selection Recommendation System Christyawan, Fajar; Rohman, Arif Nur; Hartanto, Anggit Dwi
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.806

Abstract

This research focuses on developing a restaurant recommender system designed to assist users in selecting restaurants based on preferences such as cuisine type and proximity, thereby enhancing the dining experience. The system employs a content-based filtering approach combined with the Cosine Similarity algorithm to calculate similarity values between restaurant addresses and categories, ensuring personalized and accurate recommendations. Data for the system was collected from TripAdvisor and Google Maps using a web scraping method, resulting in a comprehensive dataset that reflects a wide variety of dining options. An experiment involving 30 respondents was conducted to evaluate the system's performance under real-world conditions. The results demonstrated an accuracy rate of 88%, indicating that the recommender system effectively delivers highly relevant restaurant suggestions to users. These findings suggest that the system can serve as a valuable tool for culinary tourists and local residents, simplifying the process of discovering new dining experiences and aligning them with individual preferences.
Stock Grouping Based on Price Earnings Ratio and Price Book Value Using K-Medoids Algorithm Abdillah, Muhammad Oemar; Putri, Raissa Amanda
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.809

Abstract

Investing involves allocating funds to achieve optimal returns by evaluating opportunities and managing risks in asset acquisition. Recently, many news reports have highlighted issues in the Indonesian capital market, such as stock investors using online loan funds for trading, which often leads to debt. This research aims to apply the K-Medoids algorithm for stock clustering, enabling investors to select fundamentally sound stocks based on the Price-Earnings Ratio (PER) and Price-Book Value (PBV). The K-Medoids method results show that Cluster 1 includes 93 stocks with moderate PER and PBV values. Cluster 2 comprises 91 stocks with the lowest PER and PBV values. Cluster 3 contains 113 stocks with the highest PER and PBV values. Developing an information system that classifies stocks based on PER and PBV can help investors analyze and make investment decisions more effectively.
Utilizing IoT-Enhanced Multilayer Perceptron and Run Length Encoding for Classifying Plant Suitability Based on pH and Soil Humidity Parameters Pratama, Yogi Tiara; Sukemi, Sukemi; Tutuko, Bambang
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.811

Abstract

This research proposes an IoT-based system for classifying plant suitability using pH data and soil humidity parameters. The system utilizes Run-Length Encoding (RLE) to compress sensor data, which is transmitted to a database server via the Esp8266 module. A Multilayer Perceptron (MLP) algorithm is employed to classify the data, achieving an accuracy of 0.82 with only two parameters. The classification results are displayed on a website, providing real-time recommendations for farmers. The system's performance is evaluated using a dataset from Kaggle. The Kaggle dataset contains 2200 instances for 22 different plants and the results show that the proposed system can effectively classify plant suitability based on environmental factors. This research contributes to the development of IoT-based recommendation systems for precision agriculture, and future studies can build upon this work to improve accuracy and quality.
Café Recommendation Using the Content-Based Filtering Method Wicaksono, Anggito Whiku; Rohman, Arif Nur; Hartanto, Anggit Dwi
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.813

Abstract

The coffee industry has experienced rapid growth over the last decade. In this research, the content-based filtering approach is employed to suggest cafes by analyzing the similarity of different features or attributes. The degree of similarity is influenced by the similarity of item profiles between cafes. CW Coffee & Eatery had the highest similarity value of 0.4802 because it found 16 item profiles that were similar to Cosan Seturan. In contrast, Kelanaloka has a very low similarity value of 0.1844, because only 7 similar item profiles were identified when compared. This research shows that content-based filtering methods can be effectively applied to cafe recommendation systems.
Sentiment Analysis on Shopee Product Reviews Using IndoBERT Aras, Suhardi; Yusuf, Muhammad; Ruimassa, Reinhard Yohanis; Wambrauw, Elli Agustinus Billi; Pala'langan, Elsa Bura
Journal of Information System and Informatics Vol 6 No 3 (2024): September
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i3.814

Abstract

A marketplace is a place in cyberspace where there are commercial activities between buyers and sellers. Products offered from the marketplace have reviews to review. Shopee is the most visited marketplace by people and offers various products. Product reviews can provide benefits for other consumers in assessing the products offered. By utilizing NLP technology in particular, this study can classify positive sentiment and negative sentiment in product review data. The IndoBERT model is a model that can be used in NLP technology by utilizing the relationship between each input and output element as well as the weights to be calculated simultaneously. By utilizing this technology, sentiment analysis on Shopee product reviews provides maximum accuracy until 93% with different training conditions. This provide that IndoBERT model can show that the performance of the indoBERT model in this research is very good.

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