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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi CESS (Journal of Computer Engineering, System and Science) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Teknoinfo Riau Journal of Empowerment Jurnal Informatika Kaputama (JIK) Jusikom: Jurnal Sistem Informasi Ilmu Komputer JATI (Jurnal Mahasiswa Teknik Informatika) JTIK (Jurnal Teknik Informatika Kaputama) Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) Journal of Computer Networks, Architecture and High Performance Computing Jurnal Anugerah: Jurnal Pengabdian Kepada Masyarakat Bidang Keguruan dan Ilmu Pendidikan INCODING: Journal of Informatics and Computer Science Engineering Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Ilmiah Teknik Informatika & Elektro (JITEK) JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia)
Rizki Muliono
Universitas Medan Area
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Social Sciences Other

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Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

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Analysis of Recommendation System on Travel Platform Using Content-Based Filtering and Collaborative Filtering Algorithms at PT. Angkasa Tour & Travel Prasetyo , Dewo; Muliono, Rizki
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14429

Abstract

This study aims to evaluate the effectiveness of the recommendation system on the PT Angkasa Tour & Travel travel platform using content-based filtering and collaborative filtering algorithms. The background of the identified problem is the need to improve the accuracy and relevance of recommendations in the travel platform, which functions to assist users in choosing travel services that suit their preferences. This research method includes an analysis of the application of the content-based filtering algorithm that focuses on the characteristics of individual users and products, as well as the collaborative filtering algorithm that utilizes collective user behavior patterns. The results of the study indicate that content-based filtering is effective in providing recommendations based on specific user preferences and product attributes, while collaborative filtering is able to produce recommendations based on collective user behavior patterns. This study also reveals that the combination of the two approaches can improve the accuracy and relevance of recommendations, thus better meeting user needs. The conclusion of this study is that the integration of content-based and collaborative filtering in the recommendation system can provide a more comprehensive solution to meet user preferences and needs on the PT Angkasa Tour & Travel travel platform.
The Implementation of Random Forest to Predict Sales a Case Study at Chatime Binjai Supermall Sandy, Boy; Muliono, Rizki
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14431

Abstract

In an increasingly competitive business environment, retail industries like Chatime Binjai Supermall must quickly adapt. Changes in consumer trends, preferences, and technological advancements significantly impact business strategies. To stay competitive, Chatime Binjai Supermall needs to optimize sales, marketing, and inventory management through accurate data analysis and prediction. Random Forest, a powerful machine learning algorithm, is used to process historical data and more accurately predict sales. This study evaluates the performance of Random Forest in predicting daily, weekly, and monthly sales. The analysis shows that products like "Jasmine Green Tea (L)" have the highest daily demand, "PEARL (L)" leads weekly sales, and there is an increase in demand for specific products monthly, such as "CT RAINBOW JELLY (L)." The implementation of the Random Forest algorithm at Chatime Binjai Supermall demonstrates significant potential in enhancing sales efficiency and data-driven decision-making, helping the company remain relevant and competitive amidst market changes.
The Application of Genetic Algorithm in Construction Project Planning System At Cv. Haza Mulia Engineering Harahap , Ryanda Fadillah; Muliono, Rizki
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14432

Abstract

Project scheduling is a crucial aspect in construction project management that aims to ensure that all tasks are carried out in an optimal sequence to maximize efficiency and reduce completion time. This study has three main objectives: (1) to build a web-based construction project planning system at CV. Haza Mulia Engineering, (2) to apply genetic algorithms to the construction project planning system at CV. Haza Mulia Engineering, and (3) to analyze the performance of genetic algorithms in generating optimal project schedules. This study was motivated by the need to complete a final assignment or thesis and used genetic algorithms as the main method. The research process begins with the identification of tasks and dependencies in a construction project. An initial population consisting of random schedules is generated and evaluated using a genetic algorithm. The selection, crossover, and mutation processes are carried out to gradually produce a new, better population. The fitness of each individual is calculated based on the number of unconnected activity dependencies, and the algorithm stops when the best mutually continuous schedule is found. The main result of this study is a web-based application built using PHP. This application is able to produce more efficient project scheduling compared to conventional methods. The schedule generated by genetic algorithm shows significant reduction in project completion time by minimizing unmet dependencies. The conclusion of this study confirms that the application of genetic algorithm in web-based project planning scheduling can avoid conflicts between activities and make the schedule more structured.
Co-Authors -, Muhathir Akbar Siregar, Muhammad Alwi, Buchori Bunaya Arthavia Sitorus Darkani, M. Farhan Dedi Sahputra Effiati Juliana Hasibuan Esrayanti Simanjuntak Fikri, Ridho Ahmad Harahap , Ryanda Fadillah Hasibuan, Samsul Abdul Rahman Sidik Hasudungan, Andre Hidayah, Safrina Ida Royani Irfansyah Iswandi Iswandi Juanda Hakim Lubis Juanda Hakim Lubis Laia, Aldi Irfan Marischa Elveny, Marischa Marpaung, Febriady Merri Hafni Muhammad Farhan Darkani Muhammad Hamdani Santoso Muhathir Muhathir Muhathir Muhathir, Muhathir Muhathir, Muhathir Nanda Novita, Nanda Napisah, Napisah Nurul Hidayani Nurul Khairina Nurul Khairina Prasetyo , Dewo Putri, Riza Dwi Razaq, Faisal Sandy, Boy Siahaan, Ricardo Fransdoli Siregar, Yusril Izza Haholongan Sitopu, Essay Puspita Sri Juwita Sri Wahyuni Suhaira, Zatin Supriatno, Supriatno Susilawati Susilawati Susilawati Susilawati, Susilawati Theofil Tri Saputra Sibarani Tino Hermanto Y. Syah, Rahmad B. Yopan Rahmad Aldori Zebua, Meniati Zulfikar Sembiring Zulfikar Sembirirng