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All Journal KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JIKO (Jurnal Informatika dan Komputer) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer Prosiding National Conference for Community Service Project Jurnal Teknologi Informasi dan Komunikasi Telcomatics Journal of Information System and Technology (JOINT) Conference on Management, Business, Innovation, Education and Social Sciences (CoMBInES) Conference on Business, Social Sciences and Technology (CoNeScINTech) Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer
Syaeful Anas Aklani
Universitas Internasional Batam
Aerospace Engineering Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Mechanical Engineering Social Sciences Other

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Journal : Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer

 1 
TRAVEL ITINERARY RECOMMENDER SYSTEM USING MACHINE LEARNING ANALYSIS AND WEB APPLICATION DEVELOPMENT: A CASE OF BATAM CITY REGION Hosse Fernando; Syaeful Anas Aklani
JURNAL ILMIAH BETRIK Vol. 14 No. 01 APRIL (2023): JURNAL ILMIAH BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : P3M Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/betrik.v14i01.12

Abstract

Batam City is an island with a complete and diverse point of destination (POI). Searching for information is common when someone is making a travel itinerary. With the help of technology, planning a trip should be fast and easy. In this study, the authors will build a web application-based recommender system with the help of machine learning. This study uses the ADDIE (Analysis, Design, Development, Implementation, Evaluation) development method and then the TAM Model (Technology Acceptance Model) to analyze the effectiveness. The results of system testing show a range of scores with the lowest value 0.509 and the highest value 0.572. This score indicates the degree of correlation between the test and the underlying construct it is designed to measure. A score of 0.509 indicates a weak correlation between the test and constructs, while a score of 0.572 indicates a stronger correlation. Score above 0.5 Thus, the authors hope that this research can be used as a reference or knowledge for future readers or researchers.
ANALISIS ALGORITMA MONTE CARLO UNTUK MEMPREDIKSI KEUNTUNGAN PEMBANGUNAN APARTEMEN MENGGUNAKAN SCRUM FRAMEWORK Welliam Ali; Syaeful Anas Aklani
JURNAL ILMIAH BETRIK Vol. 13 No. 03 DESEMBER (2022): JURNAL ILMIAH BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : P3M Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/betrik.v13i03 DESEMBER.35

Abstract

This study aims to predict profits in apartment projects. This research was made using the Scrum framework in making AI applications and predicting the benefits of using Monte Carlo. Besides that, making AI applications uses PHP and Xampp to make AI. This study uses qualitative methods to 5 people at different developers. The findings of this study indicate that the results on the analysis of ai applications on the benefits of apartment projects are in accordance with the results on the benefits of projects in the field. The model used by the author is Scrum to help authors complete work one by one quickly and can be recognized in doing Scrum. Scrum is divided into 3 namely Product Owner, Development Team and Scrum Master. Where the Scrum Master is in charge of ensuring the sprint goes well and the Development Team and Product Owner to analyze and design applications. The findings of this study are useful for developers to predict profits in apartment projects to be built.
ANALISIS KOMPARASI ALGORITMA K-NEAREST NEIGHBOR DAN SUPPORT VECTOR MACHINE DENGAN PENDEKATAN MULTI DATASET Julyan Adi Saputra; Syaeful Anas Aklani
JURNAL ILMIAH BETRIK Vol. 13 No. 03 DESEMBER (2022): JURNAL ILMIAH BETRIK : Besemah Teknologi Informasi dan Komputer
Publisher : P3M Institut Teknologi Pagar Alam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36050/betrik.v13i03 DESEMBER.50

Abstract

Data mining is a process of identifying data that is valid and has the potential to be useful to the person who did it. One of the purposes of data mining is to study previously existing data that composes certain patterns and is used to make predictions. Machine learning works by utilizing data and algorithms to create models with patterns from the data set. There are many algorithms that can be used, such as C4.5, K-Means, Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), Naïve Bayes, and others. Since there are many algorithms in data mining, each has its own advantages and disadvantages. This research will focus on the comparison between the Support Vector Machine algorithm and the K-Nearest Neighbor algorithm in terms of accuracy, precision and processing time.
Co-Authors Aldovanda Haflah Setiawan Alex Setiawan Anson Anson Daniel Lim David David Dayton Dayton Derwin Galen Edwin Charley Eka Sucipto Elvert Elvert Eric Lau Erlina Erlina Erwin Erwin Felix Favian Franky Chainoor Johari Hertianto Hertianto Hosse Fernando Jacky Jacky Jason Aaron Yang Jenary Randa Liling Jenry Winata Jimmy Chandra Jonathan Jonathan Julyan Adi Saputra Julyan Adi Saputra Julyanto Julyanto Juzariah Juzariah Kelvyn Kelvyn Kelvyn Melsen Melsen Novita Putri Parlindungan Tampubolon Petrick Handy Putra Ryan Gunawan Ryo Winata Shanata Limanto Stephanie Stephanie Steven Steven Welliam Ali Welliam Ali Yu Lun