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Jurnal Tekinkom (Teknik Informasi dan Komputer)
Published by Politeknik Bisnis Indonesia
ISSN : 26211556     EISSN : 26213079     DOI : https://doi.org/10.37600/tekinkom
Core Subject : Science,
Computer Science & IT
Jurnal TEKINKOM merupakan jurnal yang dimaksudkan sebagai media terbitan kajian ilmiah hasil penelitian, pemikiran dan kajian analisis-kritis mengenai isu Ilmu - ilmu komputer dan sistem informasi, seperti : Pemrograman Jaringan, Jaringan Komputer, Teknik Komputer, Ilmu Komputer/Informatika, Sistem Informasi, dan Multi Disiplin Penunjang Domain Penelitian Komputasi, Sistem dan Teknologi Informasi dan Komunikasi, dan lain-lain yang terkait. Artikel ilmiah dimaksud berupa kajian teori (theoritical review) dan kajian empiris dari ilmu terkait, yang dapat dipertanggungjawabkan serta disebarluaskan secara nasional maupun internasional.
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 407 Documents
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ANALISA DATA MINING ASSOSIASI FP-GROWTH PADA PENJUALAN PRODUK DI TOKO RITEL AGUNG Subagja Putra Pratama
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 1 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i1.744

Abstract

Quick decision-making based on data, facts, and knowledge has become a necessity for companies in implementing strategies to increase competitiveness. To create a fast strategy with measurable analysis, one can use sales data mining techniques from time to time. The research was carried out using the fp-growth association data mining method and the RapidMiner application as a mining tool for processing sales data for a one-year period (June 2021 - May 2022) with a total of 13734 sales transactions and a total of 49360 records. Data mining in this study resulted in 6 rules association with a minimum support value of 0.001 and a minimum confidence value of 0.01 and produced the 42 lowest association rules with a minimum support value of 0.001 and a minimum confidence value of 0.5. The discovery of the association rules can be used as a consideration in making product sales strategy decisions quickly and accurately so that companies can increase sales and competitiveness.
PENERAPAN METODE FORCASTING DALAM MENENTUKAN JUMLAH SISWA BARU MENGGUNAKAN ALGORITMA SIMPLE LINEAR REGRESSION Tajrin Tajrin; Mohammad Irfan Fahmi; Maikel Felix Ginting; Unika Nduru
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 1 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i1.880

Abstract

New student admission is a school activity to recruit new prospective students that occurs regularly every year even in the middle of the teaching year. Madrasah Tsanawiyah (MTs) Al-Ittihadiyah is a school under the auspices of the Ministry of Religion. Where every year the school always accepts a fairly large number of new students around 300 people. This results in the school always having difficulties in preparing infrastructure facilities such as classrooms and teachers because the increase in the number of new students increases every year. This will happen repeatedly in schools from year to year. So that it will be an accumulation of data every year to help transform data into data information into useful information. This large amount of data opens up opportunities to generate useful information for schools. In this study, researchers see an opportunity to create a new technology that answers the needs and problems that have occurred so far. In determining the number of new students at MTs. Al-Ittihadiyah Pkl. Masyhur researchers used 2 dataset scenarios where scenario 2 datasets used a simple linear regression algorithm. In pre-processing data that produces prediction performance, namely Y = 71.9538 + 0.709269X, in the dataset for forecasting estimates for the number of new students if the registrant is 374 students, it will produce a prediction of new students of 337 students.
PENGGUNAAN ALGORITMA K – MEANS CLUSTERING UNTUK MENENTUKAN PENILAIAN KEDISIPLINAN KARYAWAN RUMAH SAKIT ROYAL PRIMA Winda Nia Purba; Michael Kosasih; Donny Kallamas; Michael Wijaya
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 1 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i1.856

Abstract

Hospital is a place that has community service facilities engaged in the health sector, in the hospital provides outpatient and inpatient care. A hospital has several services such as health services for the elderly, health services for children and health services for adults. Before a hospital implements all these services for the community, all employees of the hospital need to do a training process so that the management of the hospital can assess the level of discipline of all employees of the hospital. Apart from the training process, the department of a hospital company will conduct an employee assessment, which the goal is to find out the level of discipline of the hospital employees. The research methodology used in this study is descriptive. a research method used to discuss a problem by researching, processing data, analyzing and describing with regular discussion. In this descriptive method research, the author uses three aspects that are the criteria for discipline of Royal Prima Hospital Employees, namely Discipline, Absence and Appreciation. The data collected by the author is through observation techniques and sampling techniques.
DESAIN SISTEM BACK-END PADA WEBSITE PROPERTI Dahlan Susilo; Pranowo Setiaji
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 1 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i1.844

Abstract

The Clee Ltd prioritizes comfort, speed and customer satisfaction and has a high commitment and professionalism in providing services at relatively affordable prices. The website can reach a wider range of customers and make it easier for customers to find information about products and choose the best offers provided by the company, as well as being a promotion. Business websites require good back-end system support to help create a website as a behind-the-scenes system that processes databases and servers. Design a property website system that has 2 users, namely User and Admin. User as website visitor and Admin as website manager. Users can only access 2 pages, namely the "Login" and "View" pages. Admins can access the "Login", "Manage Email ", "Manage Phone Number ", "Manage Buying Data", "Manage Selling Data", and "Manage Rental Data" pages. Design a back-end website property system by implementing Flask which is a framework that has simplicity, where this trait facilitates the design of the website system and produces an optimal website properly. The implementation of the Flask Framework on the system can be done using the Flask. The Flask route function is a decorator where the URL will be called by the system. Flash serves to display messages. The use of the Flask Framework makes it easier by users to implement the website. The Flask functions used in this system are “login” and “dashboard”. The server can deliver users to do “Buying”, “Selling”, and “Rental” pages. The user can choose one of these systems to move to the selected view.
MODEL RAPID APPLICATION DEVELOPMENT (RAD) PADA PENGEMBANGAN APLIKASI PENJADWALAN MATA KULIAH Arif Amrulloh; Dwi Januarita Ardianing Kusuma; Yudha Saintika; Abednego Dwi Septiadi
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 1 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i1.579

Abstract

Scheduling courses is a common problem in tertiary institutions, including clashes between lecturers' teaching time, students' rooms, and classes. This study proposes the development of a course scheduling application to overcome lecturers' teaching schedule conflicts, classroom, and student. The course scheduling application developed is website-based, making it easier for users to run the application because it can be accessed using a browser. The application uses PHP programming language and MySQL database as data storage media, while the system development method used is Rapid Application Development (RAD). The RAD method is used because the RAD method emphasizes the development cycle with a short time. The test results using white-box testing show that the developed application is in line with expectations, and no errors were found in all features. The trial results used 265 lecturers' teaching assignment data with the availability of 65 classes, it only took 561 seconds, and no clashes occurred. This scheduling application can process quite a lot of course schedules in a reasonably short time. The results of this study are a website-based course scheduling application that can overcome the problems of lecturer schedule conflicts, student schedule conflicts, teaching time conflicts and lecture room conflicts.
IMPLEMENTASI ALGORITMA APRIORI UNTUK MENENTUKAN POLA PEMBELIAN PADA KOLEGA COFFEE via nabila banda; Ade Eviyanti; Uce Indahyanti
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.1103

Abstract

Kolega Coffee is one of the coffee shops that sells a variety of coffee variants and other menus. The sold menus will be recorded in the sales system for the sales report. The increasing number of visitors generates a lot of sales transaction data. And the data only serves as sales reports and archives. However, if the data is processed further, it will acquire new information for decision-making in business strategy. The research aims to find menus that are often purchased simultaneously through sales transaction data from January to June 2023 using a priori algorithm whose rule has the advantage of being able to know patterns of purchasing products at the same time. by defining frequent itemsets that are useful to form association rules. The results of this study resulted in five association rules from 1917 sales transaction data. For example, if someone buys onion rings, they're also likely to buy a mix platter with a 38.33% confidence rate. This study can give advice for business strategies to coffee colleagues for example for promotion of menu packages or other discounts.
PENGGUNAAN METODE KLASTERISASI K-MEANS DALAM MENENTUKAN MINAT JURUSAN PADA PROSES PENERIMAAN PESERTA DIDIK BARU Tajrin Tajrin; Kevin Agape Tampubolon; Ronasib Haryanto Syahputra; Piltodam Luhut Gunawan Silaban
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.934

Abstract

Al Manar High School is one of the schools under the auspices of the Al Munawwarah Al Manar Islamic Education Foundation which is located in Medan City. Al Munawwarah has several educational units, namely, SMA, Aliyah, SMP, MTs and SD. Where every year Al Manar High School always accepts 200 new students each year. This results in schools not utilizing PPDB data properly. However, data utilization for strategic needs for both promotion and marketing evaluation has not been fully carried out using existing data. One way to make it easier to determine marketing promotion is with the K-Means algorithm. This research will recommend the determination of majors for new students of Al -Manar High School by processing data on written test exam scores on new students, student majors consist of 2 namely Science and Social Sciences while the exam variables carried out consist of mathematics, Indonesian language, English, Science and Social Sciences, this research uses new student data as many as 145 students. With this research, the percentage level of grouping new student majors is higher, based on selected attributes with the K-Means Clustering algorithm. The test resulted in a science grouping of 113 students and a social science grouping of 35 students and resulted in an accuracy rate of 52.9%.
IMPLEMENTASI DATA MINING CLUSTERING DALAM MENGUKUR KEPUASAN TERHADAP PELAYANAN PERPUSTAKAAN DI UNIVERSITAS PRIMA INDONESIA Winda Nia Purba; Rinaldi Syahputra; Fine Reza Nainggolan; Gabriel Immanuel Manullang
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 1 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i1.1213

Abstract

This research aims to measure student satisfaction with Prima Indonesia University library services using data mining methods. Clustering techniques are used to group student satisfaction data based on various attributes such as service quality, resource availability, facility comfort, and interaction with library staff. Data was collected through questionnaires distributed to students. The clustering results revealed significant patterns in student satisfaction, which were analyzed to identify key factors influencing satisfaction levels. These results provide library managers with valuable insights for optimizing services and increasing user satisfaction. The application of clustering data mining has proven effective in helping libraries understand student needs and preferences and plan service improvements more accurately and efficiently.
ANALISIS PREDIKSI GENRE FILM PADA INTERNET MOVIE DATABASE INDONESIA MENGGUNAKAN METODE LONG SHORT TERM MEMORY Dwi Novi Marito Tampubolon; Valentina Vincensia Hulu; Ryan Oktavianus Sipahutar; Oloan Sihombing
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.925

Abstract

A film producer is a person who initiates, coordinates, supervises, and manages the managerial and administrative aspects of film production. This research aims to predict the Film Genre in the Indonesian Internet Movie Database using the Long Short-Term Memory method to analyze the historical data of Indonesian film genres in the Internet Movie Database application for the previous 12 years. The research findings indicate that the LSTM algorithm model can generate accurate predictions with an RMSE value below 2.50 for both training and testing data, and it provides graphical results that can capture patterns and trends from the genre data of IMDb Movies Indonesia. The predictions in this study show that the drama genre has a higher predicted value to be released in the coming years.
ANALISIS SENTIMEN ULASAN APLIKASI PEMBELAJARAN DUOLINGGO DI PLAY STORE MENGGUNAKAN DISTILBERT Syindy Mauliddiyah; M.Noer Fadli Hidayat; Fathur Rizal
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 7 No 1 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i1.1395

Abstract

Learning media innovation is currently required to keep up with the development of science and technology. Duolingo is a free language learning application. Duolinggo has been downloaded more than 500 million times and recorded more than 21 reviews in the comments column consisting of positive and negative comments. Duolinggo user reviews are classified into two sentiments, namely positive sentiment and negative sentiment. Sentiment analysis is an activity used to analyze a person's opinion or opinion on a topic, to support the classification, the algorithm used is DistilBERT. DistilBERT is a technique of how to make the BERT model smaller, but has similar qualities to a large model, distilBERT can be termed as 2 running models, namely the teacher model and the student model, the teacher model is a large model and is trained with a complete range of features such as the base (pre-trained model) The results of the DistilBERT algorithm for classifying 1000 reviews of the Duolingo learning application produce precision, recall, f1-score values on class 1 labels are 74%, 96%, and 84%, indicating that this BERT algorithm is very good at predicting label classes. With the accuracy result obtained is 80% in 85 seconds.

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