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All Journal Jurnal Tekinkom (Teknik Informasi dan Komputer) INFOKUM Jurnal Teknologi Informatika dan Komputer
Julfikar Rahmad
Universitas Prima Indonesia
Computer Science & IT Electrical & Electronics Engineering

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Implementation of Greedy Algorithm for Profit and Cost Analysis of Swallow's Nest Processing Dirty to Finished Products Efendi Efendi; Daniel Ryan Hamonangan Sitompul; Stiven Hamonangan Sinurat; Ruben Ruben; Andreas Situmorang; Dennis Jusuf Ziegel; Julfikar Rahmad; Evta Indra
INFOKUM Vol. 10 No. 02 (2022): Juni, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (425.252 KB)

Abstract

Swallow's nest is made from the saliva of swallows, especially species of swallows of the genus Collocalia. Swallow's nest is used traditionally to improve health so it is widely consumed by the community. Swallow nest products are difficult to produce, causing the product to be expensive. This study aims to analyze the costs and benefits of swallow nest production. The analysis uses the Greedy algorithm, which is looking for solutions to each stage of production. The principle of Greedy's algorithm is "take what you can get now". There are 6 processes in the production of swiftlet nests, namely sorting raw materials, cleaning, drying, printing, in process control (IPC) and packaging. In the sorting and cleaning process, employees in the medium and medium to light nest categories were combined. The total costs incurred in the sorting process are reduced by 14% and the costs incurred in the cleaning process are reduced by 8%. The process of drying dense and medium hair nests takes the same time so that they are carried out simultaneously and the required cost is reduced by 11% to Rp 675,000. The stages of printing the original and super types of nests are combined because they have.
SENTIMENT ANALYSIS COMPARE LINEAR REGRESSION AND DECISION TREE REGRESSION ALGORITHM TO DETERMINE FILM RATING ACCURACY Rivaldo Sitanggang; Daniel Ryan Hamonangan Sitompul; Stiven Hamonangan Sinurat; Ruben, Andreas Situmorang; Denis Jusuf Ziegel; Julfikar Rahmad; Evta Indra
INFOKUM Vol. 10 No. 02 (2022): Juni, Data Mining, Image Processing, and artificial intelligence
Publisher : Sean Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (567.849 KB)

Abstract

Rating assessment in a film is the most important thing because it describes the satisfaction of film lovers with the films they have watched. With technological advances like now, we can easily find out the rating of a film by using a platform to accommodate the audience's review results, namely the Internet Movie Database (Imdb). The Machune Learning model that has been created can determine whether the film we watch is good based on ratings and reviews from moviegoers who share their experiences in watching similar films. Based on the results of the analysis of the two algorithms Linear Regression and Dicision Tree Regression, the best accuracy results from the Decision Tree Regression algorithm are 95.47%
ANALISIS BIG DATA PENJUALAN VIDEO GAMES MENGUNAKAN EDA Davit Toramli Husni; Daniel Ryan Hamonangan Sitompul; Stiven Hamonangan Sinurat; Ruben Ruben; Andreas Situmorang; Dennis Jusuf Ziegel; Julfikar Rahmad; Evta Indra
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 5 No 1 (2022)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v5i1.517

Abstract

Advertising is a very effective way for product marketing, this method is often used to disseminate product information to be marketed. Errors in the analysis of products to be marketed resulted in significant losses to the company due to errors in the exploration of Big Data processing. Big data is described as large-scale data that can be presented, processed and analyzed using existing technologies, methods and theories. Therefore, an assessment of the big data of video game operators that is in demand by the market is carried out to determine the highest and lowest sales of video games using the Exploratory Data Analysis method so that a company can determine the games to be promoted and produced. The results obtained in this study that have the highest and lowest sales of video games in the global market by genre are action at 1745.27 and strategy at 174.5. And for sales by platform, PS2 is 1255.64 and PCFX is 0.03. With this method, video game sales can be presented graphically, making it easier for companies to determine which games to market and promote small game sales.
Analisis Perbandingan Decision Tree, Support Vector Machine, dan Xgboost dalam Mengklasifikasi Review Hotel Trip Advisor Hansen Christanto; Julfikar Rahmad; Stiven Hamonangan Sinurat; Daniel Ryan Hamonangan Sitompul; Andreas Sitomorang; Dennis Jusuf Ziegel; Evta Indra
Jurnal Teknologi Informatika dan Komputer Vol 9, No 1 (2023): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v9i1.1429

Abstract

Jaringan media sosial pada saat ini terus berkembang dan berdampak pada industri perhotelan. Pelanggan dan traveler telah memposting hasil review secara online untuk menunjukkan tingkat kepuasan mereka terhadap hotel dan berbagi pengalaman terkait hotel yang dikunjungi dengan pelanggan lain yang ada di seluruh belahan dunia. Situs web yang bergerak dalam pariwisata dan perhotelan berkembang pesat secara online seperti Trip advisor. Trip advisor merupakan platform penyedia layanan perjalanan dan pemesanan hotel. Penelitian menggunakan teknik analisis sentimen untuk mengkategorikan opini pengguna yang bernilai negatif maupun positif dengan bantuan kecerdasan buatan yaitu Machine Learning. Penelitian ini menguji tiga algoritma Machine Learning, yaitu Decision Tree Classifier, Support Vector Machine (SVM) dan Xgboost Classifier, dalam melakukan analisis sentimen terhadap review hotel di platform Trip advisor. Hasilnya menunjukkan bahwa Xgboost memiliki tingkat keakuratan (accuracy) yang paling tinggi, mencapai 99%, dibandingkan dengan Decision Tree (97%) dan Support Vector Machine (98%). Dengan demikian, Xgboost dianggap sebagai algoritma terbaik untuk melakukan analisis sentimen pada review hotel di Trip advisor. 
Perbandingan Algoritma K-Nearest Neighbors (K-NN) dan Random forest terhadap Penyakit Gagal Jantung Fredilio Fredilio; Julfikar Rahmad; Stiven Hamonangan Sinurat; Daniel Ryan Hamonangan Sitompul; Dennis Jusuf Ziegel; Evta Indra
Jurnal Teknologi Informatika dan Komputer Vol 9, No 1 (2023): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v9i1.1432

Abstract

Penelitian ini bertujuan untuk membandingkan akurasi algoritma K-Nearest Neighbor (K-NN) dan Random Forest dalam mengklasifikasikan penyebab penyakit gagal jantung. Penyakit ini menjadi salah satu penyebab utama kematian di seluruh dunia dan kasusnya terus meningkat di Indonesia. Oleh karena itu, penanganan dan klasifikasi dini terhadap penyebab gagal jantung sangat diperlukan untuk mencegah penyakit tersebut. Penelitian ini diharapkan dapat memberikan informasi tentang metode terbaik untuk mengklasifikasikan penyebab penyakit gagal jantung serta memberikan manfaat bagi tenaga medis dan masyarakat umum dalam menjaga kesehatan jantung mereka.
Co-Authors Andreas Sitomorang Andreas Situmorang Daniel Ryan Hamonangan Sitompul Davit Toramli Husni Denis Jusuf Ziegel Dennis Jusuf Ziegel Efendi Efendi Evta Indra Evta Indra Fredilio Fredilio Hansen Christanto Rivaldo Sitanggang Ruben Ruben Ruben, Andreas Situmorang Stiven Hamonangan Sinurat