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INDONESIA
Tech-E
Published by Universitas Buddhi Dharma
ISSN : -     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal Tech-E dikembangkan dengan tujuan menampung karya ilmiah Dosen dan Mahasiswa, baik hasil tulisan ilmiah maupun penelitian yang berupa hasil studi kepustakaan.
Arjuna Subject : -
Articles 5 Documents
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Search results for , issue "Vol. 7 No. 1 (2023): TECH-E (Technology Electronic)" : 5 Documents clear
Singme Music Entertainment Services Marketing Information System with Content-Based Filtering Method and TAM Testing Darmawan, Ivander; Daniawan, Benny
Tech-E Vol. 7 No. 1 (2023): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v7i1.1500

Abstract

With the times, the website used as marketing and sales media, which developed into E-Commerce. In 2019, there was 16,277 businesses used the E-Commerce concept, and also the value of Gross Domestic Product (GDP) was about 5.07%. Furthermore, because of the Coronavirus Disease (COVID) pandemic, the GDP decreased to -2.07% In 2020 and even impacted 10 out of 17 business sectors. Music entertainment business was also impacted by the pandemic, because during the pandemic, the government restricted certain public activities. Therefore, this system named Singme will help singers or music groups to market their services and provide related information for the public. Searching the services in Singme will be assisted by using Content-Based Filtering (CBF) method, it will give the recommendations of the services which have correlations with the keywords. Using Technology Acceptance Model (TAM) to test 122 feedback data about Singme with SmartPLS application v3.2.9. As the results, all hypotheses are acceptable because each t-statistic value > t-table value (1.981), and also each p-value < 0.05. Which PEOU influences PU by 34.3%, PU and POEU influence ATU by 50.7%, PU and ATU influence BITU by 56.1%, and BITU influences ASU by 52.3%.
Performance Analysis of Classification and Regression Tree (CART) Algorithm in Classifying Male Fertility Levels with Mobile-Based Arif Rahman Hakim; Dewi Marini Umi Atmaja; Amat Basri; Andri Ariyanto
Tech-E Vol. 7 No. 1 (2023): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v7i1.2110

Abstract

Fertility is the ability to produce offspring in a man or the ability of the reproductive organs to work optimally in fertilization. Fertility rates have declined drastically in the last fifty years. Machine Learning is a field devoted to understanding and building learning methods. This study will use machine learning algorithms to classify male fertility levels, namely the Classification and Regression Tree (CART) algorithm and the K-Fold Cross Validation validation method. The fertility dataset used in this study was obtained from the UCI Machine Learning website, with a total of 100 data and the variables used are Age, Childish diseases, Accident or serious trauma, Surgical intervention, High fevers in the last year, Frequency of alcohol consumption, Smoking habit, Number of hours spent sitting per day and Diagnosis. K-Fold Cross Validation can be used together with CART to measure the performance of the CART model on different data, so as to avoid overfitting or underfitting the CART model. Based on the calculation of the CART algorithm and the K-Fold Cross Validation validation method (K = 1 to K = 9), the average accuracy value for training data is 98.70% and the average accuracy value for testing data is 81.16%. The results of this study have proven that the CART algorithm can be used to classify the level of fertility in men well. In addition, the classification model formed can be implemented into a mobile application (android) so that it is easy to use and understand.
Clustering Analysis of Admission of New Students Using K-Means Clustering and K-Medoids Algorithms to Increase Campus Marketing Potential Amin, Hasan
Tech-E Vol. 7 No. 1 (2023): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v7i1.2264

Abstract

Acceptance of new students is a very important activity for a high school or university. The admissions data has not been utilized by the campus in making strategic decisions, marketing potential, and considering invitations through academic admissions. So, to assist in processing the new student admissions data, in this study the design and analysis of new student admissions data was carried out using stages in data mining. The clustering method approach can be applied in analyzing the potential level of PMB quality produced by utilizing the PMB recording dataset for the 2023 period. 86 data records. The K-Means and K-Medoids algorithm models that are applied have results that show a new insight, namely grouping based on 2 clusters, cluster 1 (C0) is a pass category while cluster 2 (C1) has not been determined. The results of the K-Medoids algorithm which has cluster 1 (C0) 60 results, cluster 2 (C1) has 26 results is a potential pass of 60 and has not yet been determined 26 of the data tested 86 while the results of the K-Means cluster 1 algorithm (C0) 40 , cluster 2 ( C1 ) 46 is a potential pass consisting of 40 and 46 undetermined data from the 86 datasets tested. Testing using the RapidMiner Studio application can also produce similar insights, namely each cluster has Davies Bouldin Index or DBI results from each K-Means and K-Medoids algorithm. K-Means has a Davies Bouldin Index result of -0.533 while K-Medoids has a Davies Bouldin Index result of -0.877
The IOT-Based Hydrogen Sulfide Monitoring at PT. Pertamina Geothermal Energy on Lumut Balai Area Dasmen, Rahmat Novrianda; Muhammad Adrian Saputra
Tech-E Vol. 7 No. 1 (2023): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v7i1.2287

Abstract

Geothermal Power Plants produce electricity energy from geothermal sources found in geothermal wells. Inside the geothermal well there is H2S content which is a toxic gas that can cause death to humans when human exposed the H2S content for 500-700 ppm within 30-60 minutes. Based on several literacies, in this research, H2S sensor type MQ-136 was used to monitoring H2S content in the geothermal environment. The Internet of Things system is used to read data parameters as a tool to display PPM values on the LCD of the transmitter and receiver device, as well as Adafruit IO website for reading parameter sensor data.. To transmit data from the transmitter to the receiver, Lora Ra-01 AI Thinker is used. The focus of this research is to be able to remotely monitor H2S content through the Adafruit IO website, the highest data was read is 0.54 ppm and the lowest at 0 ppm. This equipment will give "BHY" signal on the LCD display on the transmitter and from the Adafruit IO website it will send a notification "hazard of high ppm H2S" to mobile phones who installed IFTTT application if the H2S concentration was read for 10 ppm or higher, so that workers avoid being exposed to high concentrations of H2S when they want to monitor the parameters in the Geothermal well area.
Analisis sentiment komentar Instagram bakal calon presiden menggunakan metode Support Vector Machine Alqis Rausanfita; Ramadan, Arip; Dzulfikar Fauzi, Muhammad; Mafidah, Qori Emalia Putri; Ramona, Emilia; Mahardika Putra, Yudha
Tech-E Vol. 7 No. 1 (2023): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v7i1.2289

Abstract

The rising number of Instagram user affecting higher number of comments appear on post especially Instagram accounts of Indonesia's 2024 presidential candidates that made it difficult to understand the public sentiment towards presidential candidate. Therefore, this research aims to classify Indonesian sentiment on Instagram comments of 2024 Indonesian presidential candidates using the Support Vector Machine method. The classified sentiment is divided into three classes, namely positive, negative, and neutral. The results shows that Sentiment Analysis of Comments on Instagram Posts of Indonesia's 2024 Presidential Candidates Using The Support Vector Machine Method has a good accuracy value of 89.41%. This results also obtain recall and precision values of 89% and 87% respectively.

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