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All Journal Proceedings of KNASTIK Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics Jurnal Teknologi Informasi dan Ilmu Komputer Sisforma: Journal of Information Systems Bulletin of Electrical Engineering and Informatics JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Technomedia Journal International Journal of Economics, Business and Accounting Research (IJEBAR) Journal of Applied Engineering and Technological Science (JAETS) Jurnal Informatika dan Sistem Informasi IJIIS: International Journal of Informatics and Information Systems Teknika Journal of Applied Data Sciences Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Pena Dimas: Jurnal Pengabdian Masyarakat JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Adi Suryaputra Paramita
Universitas Ciputra Surabaya
Religion Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences

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EXPLORATORY FACTOR ANALYSIS OF TEAM CLIMATE INVENTORY (TCI) ON TECHNOLOGY START-UP Tony Antonio; Amanda Teonata; Trianggoro Wiradinata; Adi Suryaputra; Agoes Tinus Lis Indrianto
International Journal of Economics, Business and Accounting Research (IJEBAR) Vol 5, No 4 (2021): IJEBAR : Vol. 05, Issue 04, December 2021
Publisher : LPPM ITB AAS INDONESIA (d.h STIE AAS Surakarta)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijebar.v5i4.3312

Abstract

Team climate in organization is an important element to make the innovation process in an organization works. Study on team and its behaviour is done extensively around the world. It shows the importance of a team. Team climate is one of the characteristics of an innovative team. Team Climate Inventory is a measurement scale to examine the climate factors in a team. Earlier TCI was developed by West in 1990 and then extended in 1995 and 1998. Kivinaki and Eloainio made a shorter version of West’s which consists only 14 items. The shorter version is administered to a total five teams of co-working space start-up. The technology start-up has an intensive program every day within a month under a supervision of tutor from international wellknown company and an entrepreneurial-based university. The quantitative survey was followed by interviewing some of the member and leaders of the start-ups. The item analysis shows that all items are accepted with CITC value are above 0.3. And high reliability with Cronbach’s alpha value is above 0.8. The analysis shows that TCI has 3 factors, which consist of vision, participatory safety, and support for innovation. Keywords: Team Climate, Innovation, Technology start-up
Workshop Pengenalan Sistem Informasi dan Implementasi SOP Pada Siswa-Siswi SMA Rajawali Makassar Kartika Gianina Tileng; Adi Suryaputra Paramita; Rinabi Tanamal; Yosua Setyawan Soekamto
Abdiformatika: Jurnal Pengabdian Masyarakat Informatika Vol. 1 No. 1 (2021): Mei 2021 - Abdiformatika: Jurnal Pengabdian Masyarakat Informatika
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1152.322 KB) | DOI: 10.25008/abdiformatika.v1i1.131

Abstract

Pelatihan workshop yang dilakukan oleh Program Studi Sistem Informasi, Fakultas Teknologi Informasi, Universitas Ciputra bertujuan untuk memperluas wawasan para siswa-siswa yang bersekolah di SMA Katolik Rajawali Makassar dalam hal pengetahuan akan pengantar dalam dunia Sistem Informasi yang semakin gencar digunakan pada era sekarang ini. SMA Katolik Rajawali Makassar dipilih berdasarkan hasil penawaran dari SMA tersebut dan koordinasi dari tim Universitas Ciputra dan guru yang ada disana. Koordinasi dilakukan melalui WhatsApp messenger maupun menelpon langsung. Hal dilakukan karena pelatihan ini dilakukan online pada masa pandemic Covid-19. Pelatihan ini diperuntukkan untuk pada siswa-siswi SMA Katolik Rajawali Makassar. Melalui kegiatan ini, para murid mendapatkan pemaparan mengenai software, hardware, brainware, serta coaching dalam pembuatan “Standard Operating Procedure” dari dosen Universitas Ciputra dibantu dengan asisten dosen yaitu mahasiswa-mahasiswa jurusan Sistem Informasi di Universitas Ciputra. Setelah mengikuti kegiatan ini, diharapkan para siswa telah dibekali dengan pengetahuan dasar Sistem Informasi dan bisa membuat SOP untuk menetapkan sebuah standar dalam suatu proses bisnis atau kegiatan.
Implementing Machine Learning Techniques for Predicting Student Performance in an E-Learning Environment Adi Suryaputra Paramita; Laura Mahendratta Tjahjono
International Journal of Informatics and Information Systems Vol 4, No 2: September 2021
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v4i2.112

Abstract

The pandemic of COVID-19 has altered the way people learn. Learning has moved from offline to online throughout this pandemic. Predicting student performance based on relevant data has opened up a new field for educational institutions to improve teaching and learning processes, as well as course curriculum adjustments. Machine learning technology can assist universities in forecasting student performance so that necessary changes in lecture delivery and curriculum can be made. The performance of the pupils was predicted using machine learning techniques in this research. Open University (OU) educational data is examined. Demographic, engagement, and performance metrics are used. The results of the experiment. The k-NN strategy outperformed all other algorithms on the OU dataset in some circumstances, but the ANN approach outperformed them all in others.
Property Rental Price Prediction Using the Extreme Gradient Boosting Algorithm Marco Febriadi Kokasih; Adi Suryaputra Paramita
International Journal of Informatics and Information Systems Vol 3, No 2: September 2020
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v3i2.65

Abstract

Online marketplace in the field of property renting like Airbnb is growing. Many property owners have begun renting out their properties to fulfil this demand. Determining a fair price for both property owners and tourists is a challenge. Therefore, this study aims to create a software that can create a prediction model for property rent price. Variable that will be used for this study is listing feature, neighbourhood, review, date and host information. Prediction model is created based on the dataset given by the user and processed with Extreme Gradient Boosting algorithm which then will be stored in the system. The result of this study is expected to create prediction models for property rent price for property owners and tourists consideration when considering to rent a property. In conclusion, Extreme Gradient Boosting algorithm is able to create property rental price prediction with the average of RMSE of 10.86 or 13.30%.
Social Commerce Purchase Intention Factors in Developing Countries : A systematic literature review Adi Suryaputra Paramita
Journal of Applied Engineering and Technological Science (JAETS) Vol. 4 No. 2 (2023): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v4i2.1585

Abstract

Over the last decade, research on social commerce has grown exponentially, reflecting the widespread adoption of social commerce strategies and practices. Social commerce encompasses a broad range of distinct concepts. Recent reviews of the literature detail the numerous factors of social commerce adoption. This paper has an objective to investigate the important factor of social commerce adoption in developing countries. 149 articles from high quality repository collected to be review, in this study the systematic literature review conducted through Kitchenham methodology which consist of developing research question, determining the sources as well as research string, categorizing inclusion and exclusion criteria, choosing the primary studies, extracting the data then synthesizing the data. After careful quality assessment process, 49 articles selected to process in depth review. The result of this study found that there ae several important factors and lead by trust factor for social commerce adoption in developing countries.
Data Governance Model For Nation-Wide Non-Profit Organization Adi Suryaputra Paramita; Harjanto Prabowo; Arief Ramadhan; Dana Indra Sensuse
Journal of Applied Engineering and Technological Science (JAETS) Vol. 5 No. 1 (2023): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v5i1.2415

Abstract

According to Connolly's (2017) research, the context of nonprofit organizations exhibits variations when compared to commercial organizations or businesses, as supported by Zhang's (2010) study. Hence, it is imperative for both theoretical and empirical studies to contribute towards enhancing our comprehension of the strategy, implementation, and utilization of information systems in the specific context of nonprofit organizations. The investigation of information systems within the context of non-profit organizations offers a promising avenue for advancing the field of information systems research. This study focuses on the development of an information systems framework using the soft systems methodology, which has already been established. One opportunity for the advancement of information systems in non-profit organizations lies in the establishment of a comprehensive framework that facilitates adoption and is accompanied by robust data governance. This framework enables the analysis of data and the generation of valuable insights, thereby contributing to the development of information systems in the non-profit sector. The choice of data governance was informed by Zhang's (2010) research, which demonstrated that non-profit organizations face significant obstacles in the form of privacy and data security concerns. Furthermore, it is apparent that the preservation of data privacy plays a crucial role in the acceptance and utilization of information systems within non-profit entities. This research aims to contribute to the resolution of the issue by establishing a governance framework for information systems that effectively communicates to users the absence of data privacy risks associated with the systems employed by organizations. The objective of this study is to create a data governance model that will fill the research gap mentioned earlier and make a valuable contribution to the field of information systems research. The formation of the data governance model will involve the integration of soft systems methodology and the DAMA framework. The outcome of this study will be a data governance model specifically designed for a nationwide non-profit organization that utilizes microservices as its cutting-edge technology.
Rancang Bangun Aplikasi Business Intelligence Berbasiskan Arsitektur Aplikasi Akuntansi Accurate Philbert Lukito Setiawan; Adi Suryaputra Paramita
Jurnal Informatika dan Sistem Informasi Vol. 1 No. 2 (2015): Jurnal Informatika dan Sistem Informasi
Publisher : Universitas Ciputra Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (850.914 KB)

Abstract

Technological developments had an impact on all aspects including in business. There is a problem when technology can’t be regulated properly cause a negative impact, this called big data. The solution for this problem is business intelligence which providing additional value in the data. When BI implemented, businesses can perform an analysis of the business and can be used as a strategy formulation. On the other hand, the development of technology in the business world also began in Indonesia. One of the applications most used by user is accurate. Due to the target market of accurate application of general market, accurate doesn’t have some special features that are required by the company. The purpose of this research is expecte to create busineess accurate intelligence on an architecture that can provide additional value for the company. This research focuses on the design and manufacture of business intelligence applications query the data that converts data from the database accurate data is ready to be analyzed. Making an application using Visual Basic programming language and database firebird. The process of needs analysis was conducted by interview, application studies, comparative studies and database studies. Based on the analysis, accurate database is mapped into four major subjects, namely sales, purchase, inventory and the person who becomes the source of the data from business intelligence made. For the test, the research uses a database of real companies that have used accurate application. From the test results, can disumpulkan that the application has successfully made the analysis based on source data that has been created.
A Comparative Study of Feature Selection Techniques in Machine Learning for Predicting Stock Market Trends Paramita, Adi Suryaputra; Winata, Shalomeira Valencia
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.99

Abstract

This study aims to compare the effectiveness of three feature selection techniques, namely Principal Component Analysis (PCA), Information Gain (IG), and Recursive Feature Elimination (RFE), in predicting stock market conditions. This research uses three distinct Kaggle datasets that contain data for predicting stock market values. The results show that RFE performs better than PCA and IG in predicting market value with fairly precise accuracy. By using the RFE technique, this study was able to identify the most influential features in prediction, reduce the dimensionality of the data, and improve the performance of the prediction model. These provide significant benefits in the world of stocks, including improved investment decisions, reduced investment risk, improved trading strategy performance, and identification of promising investment opportunities. For future research, further comparative studies between other feature selection techniques can be conducted. This research has novelty in several aspects. First, it applies different feature selection techniques, namely Principal Component Analysis (PCA), Information Gain (IG), and Recursive Feature Elimination (RFE), in the context of stock market prediction. Utilizing these techniques to select the most relevant features in predicting stock market conditions provides a deeper understanding of the influence of these features on stock price movements. Furthermore, this research utilizes different datasets from Kaggle, which represent various stock market value predictions. The utilization of diverse datasets provides variation in the data and allows this research to examine the performance of feature selection techniques in multiple stock market contexts. In conclusion, this research provides insight into the effectiveness of feature selection techniques in stock market value prediction. It also provides actionable guidance for market participants to improve investment decisions and trading performance in the stock market.
Modelling Data Warehousing and Business Intelligence Architecture for Non-profit Organization Based on Data Governances Framework Paramita, Adi Suryaputra; Prabowo, Harjanto; Ramadhan, Arief; Sensuse, Dana Indra
Journal of Applied Data Sciences Vol 4, No 3: SEPTEMBER 2023
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v4i3.117

Abstract

Information systems research for non-profit organizations is an opportunity to make a contribution to the field of information systems, the adoption of information systems in this field is relatively tedious and there are few studies that examine this area; consequently, there are several research gaps in the domain of non-profit organizations that need to be solved. This research will focus on the development of data warehouse architecture and business intelligence for non-profit organizations. In this study, the Soft Systems Methodology (SSM) technique will be employed to develop a data warehouse architecture and business intelligence. This research will interview twenty individuals to collect primary data, review organizational policy documents, and conduct an open-ended survey. The obtained data will then be qualitatively analyzed, resulting in the formation of rich picture diagrams, CATWOE analysis, and conceptual models, which will ultimately form a data warehouse architecture and business intelligence. This research has produced a microservices-enhanced data warehouse architecture and business intelligence for non-profit organizations.
Implementation of the K-Nearest Neighbor Algorithm for the Classification of Student Thesis Subjects Paramita, Adi Suryaputra; Maryati, Indra; Tjahjono, Laura Mahendratta
Journal of Applied Data Sciences Vol 3, No 3: SEPTEMBER 2022
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v3i3.66

Abstract

Students who have studied for a considerable amount of time and will complete a lecture process must complete the necessary final steps. One of them is writing a thesis, a requirement for all students who wish to graduate from college. Each student's choice of topic or specialization will be enhanced if it not only corresponds to their interests but also to their skills. K-Nearest Neighbor is one of the classification techniques used. K-Nearest Neighbor (KNN) operates by determining the shortest distance between the data to be evaluated and the K-Nearest (neighbor) from the training data. K-Nearest Neighbor is utilized to classify new objects based on the learning data closest to the new object. Therefore, KNN is ideally suited for classifying data to predict student thesis topics. This research concludes that optimizing the k value using k-fold cross-validation yields an accuracy rate of 79.37% using k-fold cross-validation = 2 and the K-5 value. Based on the K-Nearest Neighbor Algorithm classification results, 45 students are interested in computational theory thesis (RPL) topics, 32 students are interested in artificial intelligence (AI) thesis topics, and 21 students are interested in software development topics.
Co-Authors Adrie Oktavio Amanda Teonata Arief Ramadhan Arief Ramadhan christian christian Dana Indra Sensuse Dimas Kurnia Pratama EVELYN EVELYN Harjanto Prabowo Harjanto Prabowo Hendry Cipta Husada Indra Maryati Isviandhy, Gwynneth Jusak Jusak Kartika Gianina Tileng Laura Mahendratta Tjahjono Marco Febriadi Kokasih Minny Elisa Yanggah Nicoline Odelia, Lisandra Philbert Lukito Setiawan Rinabi Tanamal Tony Antonio Trianggoro Wiradinata Winata, Shalomeira Valencia Yosua Setyawan Soekamto Yulius Hari