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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 33 Documents
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Search results for , issue "Vol 6 No 1 (2023)" : 33 Documents clear
PENGKLASIFIKASIAN SENTIMEN ULASAN APLIKASI WHATSAPP PADA GOOGLE PLAY STORE MENGGUNAKAN SUPPORT VECTOR MACHINE Indah Aida Sapitri; Yusra Yusra; Muhammad Fikry
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.v6i1.773

Abstract

The Google Play Store is a platform commonly used to download applications, one of which is WhatsApp. The Google Play Store also provides a feature so that users can provide reviews in the form of comments containing both positive and negative points of view. The method used in this study is the Support Vector Machine method. The purpose of this study is to apply the SVM method in classifying sentiments and knowing the accuracy test of the method. This study uses 1000 reviews collected from the scrapping process and uses two comparisons, namely 90:10 and 80:20. A comparison of 90:10 produces an accuracy of 82%, while a comparison of 80:20 produces an accuracy of 81%, a comparison of 90:10 produces a precision value of 58%, 35% recall, f1-score 44% for the negative class and a precision value of 85 %, 94% recall, 89% f1-score for the positive class, while the 80:20 ratio produces 62% precision, 34% recall, 44% f1-score for the negative class and 84% precision value, 94% recall, f1- score 89% for the positive class. The best parameter pairs are at C=1.0 and γ = 1.0 with an accuracy of 68% at a ratio of 90:10, while at a comparison of 80:20 the best parameter pairs are at C=0.9 and γ=0.7 with an accuracy of 67%.
SISTEM PENDUKUNG KEPUTUSAN PENENTUAN PRIORITAS PERBAIKAN JALAN MENGGUNAKAN METODE GAP Nurahman Nurahman; Andry Wardana
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.v6i1.862

Abstract

Roads are very important infrastructure for economic growth in each region because roads are a means of connecting one place to another. all information related to damaged roads is recorded and collected by the public works and spatial planning offices to be managed. but from all the damaged road data, the responsible agency is sometimes wrong to determine the priority order of roads that will be repaired first so that it is not on target. Therefore, in this study a decision support system for determining the priority of road repair using the gap method was built which is expected to help the public works and spatial planning offices in determining which road priorities will be repaired first based on the criteria of road condition, daily traffic, road surface type, road length and road width. based on the results of the calculation that became the first rank or top priority to be repaired was JLM002 (Mt.Haryono Barat) which obtained the highest value of 4.4. Therefore, the road deserves to be a priority to get repairs and handling first.
PENERAPAN ALGORITMA C4.5 UNTUK PENENTUAN JURUSAN SISWA SEKOLAH MENENGAH ATAS Koko Handoko; Pastima Simanjuntak; Ellbert Hutabri; Erlin Elisa
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.v6i1.788

Abstract

Specialization is a process that is carried out at the high school level in the form of a specialization in science and social studies, the purpose of which is for students to receive education according to their interests and abilities. If majoring is done manually it is not effective and efficient so that students do not get the lessons and interests that are in accordance with those taken during the major. The purpose of this research is to help high school (SMA) level students in determining the major they will choose so that they can overcome the problem of confusion that often occurs. The problem of choosing a major is solved by utilizing data mining with the C4.5 method which will produce a decision tree in the form of a suitable major for students to choose from. The results of implementing the C4.5 algorithm can help schools in Batam determine the direction of students to get the right education in high school. From the results of this study, the prediction accuracy of student majors was obtained with a value of 83.33% and a precision of 92.31%.
ANALISIS SPARE PART HARBOUR TAG PADA DIVISI WORKSHOP MENGGUNAKAN ALGORITMA KNN MIN-MAX SCALING Oloan Sihombing; Edwin Sitanggang; Erik Luis; Kevin Wilmar Winata
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.v6i1.742

Abstract

This study aims to simplify and assist the workshop division in making decisions regarding the amount of stock that must be prepared in large quantities by PT. Multi Jaya Samudera which is a company engaged in shipping services. This study discusses the prediction of supply of ship spare parts which are very important for the workshop division. If spare parts are not available it can hamper the ship's operations which will result in delays in ship repairs and reduce work efficiency. The data used in this study is data for 2021 where the data to be tested is 225 data from the data tested can be used as a guide for planning spare parts inventory. The method used is K-Nearest Neighbor (KNN) which produces an accuracy of 93.33%. The results of the application of the KNN Min-Max scaling method can help and facilitate companies in supplying ship spare parts so that they do not hinder ship operations.
PENERAPAN SISTEM PENDUKUNG KEPUTUSAN DENGAN METODE SIMPLE ADDITIVE WEIGHTING UNTUK REKOMENDASI PENERIMA KREDIT Julianto Simatupang; Purjumatin Purjumatin
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.v6i1.887

Abstract

In the midst of uncertainty and economic upheaval after the Covid-19 pandemic had an impact on the order of life and the national economy. The community, especially MSME actors who feel the direct impact, have to rack their brains to survive through it. Most of them have to take advantage of credit in order to continue to exist. The banking sector as a provider of credit services is the main target of the community. KSP. Tama Mandiri as one of the banking sector institutions experienced a surge in customers applying for loans. So that at this time they have difficulty in determining customers who are eligible to receive credit. In an effort to optimize service for granting credit, a selection is made based on the criteria of employment, income, number of dependents, status of residence and collateral. This study intends to build a decision support system for selecting credit recipients by applying the Simple Additive Weighting method. This method was chosen because it can carry out a ranking process based on criteria and weights and is able to determine the best alternative. The results of the application are in the form of customer reports and the results of the overall selection of credit recipients as well as recommendations for customers who are most deserving of credit based on the highest preference value.
SISTEM PENDUKUNG KEPUTUSAN PENENTUAN PRIORITAS PENGADAAN BUKU PERPUSTAKAAN MENGGUNAKAN METODE K-MEANS DAN ELECTRE Arifin Tua Purba; Heru Sugara; Hengki Mangiring Parulian Simarmata; Doris Yolanda Saragih; Erikson Damanik
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.v6i1.778

Abstract

This study aims to solve the problem of grouping books that are most often borrowed at the library and provide priority recommendations for procuring the right books. Currently the priority for procuring books at the Sekolah Tinggi Akuntansi dan Manajemen Indonesia (STAMI) Library is still done manually. To overcome this problem, two methods are used, namely the K-Means method which functions to group books based on the number of book titles and the ELECTRE method which is used to prioritize books to be purchased. The decision support system built using the ELECTRE method utilizes criteria consisting of the number of books borrowed, copies, book prices, and majors. The results of this study are a web-based decision support system consisting of 11 alternatives grouped, namely: Buku-013, Buku-063, Buku-072, Buku-074, Buku-075, Buku-076, Buku-084, Buku - 092, Book-102, Book-122, and Book-125. With this Decision Support System, the Library of the Indonesian College of Accounting and Management can easily prioritize procuring books in the library.
IMPLEMENTASI DATA MINING UNTUK MEMPREDIKSI MEMBER BARU MENGGUNAKAN LINEAR REGRESSION PADA PT. GSI Agus Rizkiawan; Tri Wahyudi
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.v6i1.707

Abstract

PT.GSI is a company that provides digital marketing learning courses. Companies market their products online to get audience data. However, it turns out that there is a discrepancy between the number of audiences and the number of those who register as new members and this makes it difficult for the company to estimate the number of new members in the future. The purpose of this study is to implement data mining using the Linear Regression algorithm in order to be able to predict new members in the future, and find out what the RMSE value is to find out the error value of the model applied in making predictions. In this study, the variables used consisted of 7 independent variables in the dataset, there were only 5 variables that affected the prediction results, these variables were Outgoing Call, Answer, Call Duration, Gold Package and Silver Package. Meanwhile, the No Answer and Candidate variables had no effect. Based on the test results with RapidMiner, it shows that the performance generated by the Linear Regression algorithm model has good performance with accurate prediction results, showing an RMSE value of 0.098.
PENDUKUNG KEPUTUSAN PENILAIAN KINERJA DOSEN MENGGUNAKAN TEKNIK PRINCIPLE COMPONENT ANALYSIS (PCA) Edgar Bagus Adytia Sianipar; Roy Wahyudi Hutasoit; Imanuel Eastherio L Wokamaw; Ibnu Iqrom; Marlince Nababan
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.v6i1.877

Abstract

Lecturer performance learning requires knowledge, namely a process to evaluate lecturer performance during the learning process, learning is carried out every semester using 20 variables. The research objective is to analyze the most influential lecturer performance variables with the concept of Principle Component Analysis (PCA) technique with the help of Minitab software. Where is the principle of PCA namely reducing data to make it easier to get information and evaluation for lecturers by using 20 variables to be analyzed in the Principle Component Analysis (PCA) technique, of the 20 variables there are 4 PC variables (Principle Components) to be analyzed namely PC1, PC9 , PC11 and PC16, namely PC1(0.24), PC9(0.5), PC11(0.35) and PC16(0.4) with maximum eigenvalue (1.6978), from the results of 4 PC variables it turns out that PC9 and PC11 are the most related or influential variables of all variables or components
IDENTIFIKASI NILAI ESENSIAL DARI OUTLIER NON-EXTREME MENGGUNAKAN METODE MINIMUM VOLUME ELLIPSOID Risna Yuliani
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.v6i1.572

Abstract

In many cases, outliers are considered to have a negative effect because they can cause the test to miss significant findings or distort the results in the data. Outliers are often discarded because they are considered an anomaly. Currently, some outliers carry essential information that cannot be discarded immediately. This study uses the Minimum Volume Ellipsoid estimator to treat the identified outliers differently. In this study, strong evidence was found that outliers do not have a completely negative connotation. Outliers should be treated differently because they carry essential information. This observation namely non-extreme outlier. The case study in this research uses house advertisement data from 5 districts in North Kalimantan and Berau district in East Kalimantan. The house in Tanjung Selor, Bulungan Regency, North Kalimantan, and Jalan Purnawirawan No. 21, RT 06, Karang Anyar, West Tarakan are suspected to be non-extreme outliers.
PENERAPAN DATA MINING UNTUK PENGELOLAAN DATA REKAM MEDIS MENGGUNAKAN METODE K-MEANS CLUSTERING PADA RUMAH SAKIT ROYAL PRIMA MEDAN Winda Nia Purba; Gamaliel Armando Sembiring; Mawar Theresia Turnip; Andreas Saputra; Ben Jua Ivand Manihuruk
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.v6i1.857

Abstract

In this digital era, medical record data in hospitals has grown to be very large and complex. This medical record data includes information about the patient, diagnosis, treatment, and other medical history. Efficient and effective management of medical record data is essential to improve the quality of health services, appropriate decision-making, and medical research. This study uses data mining techniques with the K-Means Clustering method to cluster patient medical record data. Cluster 1 consists of 1827 people suffering from Emergency, Orthopedics, Obgyn, Internal Medicine, Pulmonary, NICU/PISU, Heart Disease, Perinatology, Neonatal and Growth and Development, Obstetrics Oncology, as well as male and female 9227 and 8990 respectively. Cluster 4 consists of 417 people who suffer from Urology, ENT, General, Neurology, Rheumatology diseases, and the male gender is 195 people and the female gender is 112 people. by using data mining, researchers can find new information about how royal prima medan hospital manages various types of care. Researchers hope to be a reference for hospitals, to be able to socialize and prevent sources of disease based on gender and treatment.

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