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Selection of External Factors for enhanced Technological Acceptance Model for E-Learning Mohamed Nafrees Abdul Cader
TIERS Information Technology Journal Vol. 3 No. 1 (2022)
Publisher : Universitas Pendidikan Nasional

A properly regulated e-learning process is one of the utmost needs of this globe these days due to the pandemic. In that sense, researchers have been conducting research but none of those provide a global solution because of selected external factors and sample size. Therefore, this study provides a significant suggestion to select EFs and future research direction by conducting a systematic review study. Therefore, EFs must include not only student perspectives but also staff and parents, similarly, EFs must include not only user-friendliness, system quality, content quality, satisfaction, and self-efficacy but also technical support, anxiety, privacy, and security. Furthermore, the same study analyzes and suggested the most required future research direction for TAM for E-learning such as Developing VR and augmented reality e-learning tools, understanding the use of e-learning from qualitative perspectives through interview or focus group discussions, and Game-based educational tools. Furthermore, selecting the articles for this study was challengeable due to less number of articles published recently and closed access permission.

Security issues of Vehicular Ad Hoc Networks (VANET): A Systematic Review Sahabdeen Aysha Asra
TIERS Information Technology Journal Vol. 3 No. 1 (2022)
Publisher : Universitas Pendidikan Nasional

Vehicular ad hoc networks (VANETs) are a subset of mobile ad hoc networks (MANETs) that refer to a group of connected cars. The various networking layers in common Internet protocol stack topologies are linked to security vulnerabilities in VANETs. A security breach in a VANET is typically serious and harmful. Also Current VANET security standards handle the bulk of the security issues that vehicle networks confront. This paper presents VANET’s different types of security attacks in a systematic review approach. The information was gathered by a systematic examination of existing research articles. Furthermore, the study's shortcomings were the dataset's size and the absence of quality characteristics.

Predictive Modeling Classification of Post-Flood and Abrasion Effects With Deep Learning Approach Finki Dona Marleny; Mambang Mambang
TIERS Information Technology Journal Vol. 3 No. 1 (2022)
Publisher : Universitas Pendidikan Nasional

Floods and abrasion are the most common disasters in Indonesia. A lot of data is collected from post-flood and abrasion disasters. From the data released by BNPB, disaster data is directly based on the occurrence of disasters. From these data, we will test predictive modeling classification with a deep learning approach. Big data can be made through classification and predictive modeling. Our proposed model is a classification of predictive modeling of post-flood and abrasion data using the H2O deep learning approach. Deep learning H2O models can also be evaluated with specific model metrics, termination metrics, and performance charts. This approach is used to optimize the performance and accuracy of predictions during the modeling process using our dataset pool training. The big data to be processed will generate new knowledge for policies in decision making. Big data performance modeled with Deep Learning H2O is used to predict the Survival attributes of post-flood and abrasion sample datasets. The best metric performance is obtained from the maxout activation function with a 200-200 unit layer that gets an accuracy of 93.49% with AUC: 0.808 +/- 0.022 (micro average: 0.808).

Predictive Analysis of Customer Retention Using the Random Forest Algorithm Yogasetya Suhanda; Lela Nurlaela; Ike Kurniati; Andy Dharmalau; Ita Rosita
TIERS Information Technology Journal Vol. 3 No. 1 (2022)
Publisher : Universitas Pendidikan Nasional

Retaining customers is becoming a measurement focus in an industry with increasing competition. The concept of customer retention has become a research study in the sales industry, because it is difficult to retain customers and easily switch to other brands. Customer repurchase decisions in the business world of sales are very competitive. Customer satisfaction is directly proportional to the retention rate, if the customer is not satisfied then the automatic retention rate will be low. If the company is not able to meet customer expectations, it will have a serious impact on the company, namely moving customers to other services. Service factors, price, profit value, satisfaction and trust affect customer retention. One of the factors that influence consumers to become customer retention is service quality. A predictive customer retention plan is needed with data mining using the random forest algorithm. The random forest algorithm is a method that generates a number of trees from sample data, where the creation of one tree during training does not depend on the previous tree, the decision is based on the most voting. The voting results from several decision trees that are formed are the boundaries that are used as class determination in the classification process and the most votes are the winners and determine the classification class. This study aims to determine and analyze customer loyalty, customer trust and customer satisfaction. So that it can make it easier to monitor customers at the company. The results can be seen with the percentage of about 81.12% customer retention and about 18.87% customer churn. The result of feature evaluation shows that customer_activity has the highest influence on customer retention, followed by subtotal and qty.

A Bibliometric Analysis: Computer Science Research From Indonesia Edi Supriyadi
TIERS Information Technology Journal Vol. 3 No. 1 (2022)
Publisher : Universitas Pendidikan Nasional

Bibliometric indicators originally counted ways to measure research quality. This endured for decades following the term's introduction. The purpose of this research is to carry out a bibliometric analysis of the present status and trends in computer science articles written by Indonesian authors that are included in the Scopus database. Bibliographic indicators were analyzed using SciVal (www.scival.com). Elsevier constructed SciVal using Collexis' semantic technology after buying it in 2010. SciVal evaluates scientific performance using Scopus and tracks funding. Between the years 1998 and 2022, the total number of indexed papers in Scopus that discuss the advancement of research outcomes in computer science has greatly expanded. This rise reached its highest point in 2019, with 121 publications. Santoso is the most prolific Indonesian researcher when it comes to releasing research results on computer science in Indonesia. The University of Indonesia has been the most helpful sponsor in terms of sponsoring computer science research.

Sweet Potato Pest Diagnosis Expert System (Ipomoea Batatas) with Forward Chaining Method and Certainty Factor Slamet Prastiyo; Canggih Nailil Maghfiroh; Nur Khafidhoh
TIERS Information Technology Journal Vol. 3 No. 1 (2022)
Publisher : Universitas Pendidikan Nasional

Sweet potato (Ipomoea batatas) growth and development is influenced by biotic and abiotic factors. One of the biotic factors that influence it is a pest attack. Pests are organisms that cause direct damage to plants. Farmers need to make regular observations to minimize the occurrence of pest attacks. Regular observations help farmers find out early on pest populations on plants and can control them immediately. Pests on sweet potato plants are very diverse, therefore, to get the right diagnosis, farmers need to consult with experts where this is often an obstacle due to time and cost constraints. One way to facilitate the identification of these pests is to use a system that can provide conclusions based on the existing symptoms, this system is called an expert system. In this study, the authors build a web-based expert system application using the forward chaining method and certainty factor, while the software development method that the author uses is the waterfall method, and the software testing method uses the black box method. This research produces a web-based application. Based on testing, the application has been successful and runs well.

Selection of External Factors for enhanced Technological Acceptance Model for E-Learning Mohamed Nafrees Abdul Cader
TIERS Information Technology Journal Vol. 3 No. 1 (2022)
Publisher : Universitas Pendidikan Nasional

A properly regulated e-learning process is one of the utmost needs of this globe these days due to the pandemic. In that sense, researchers have been conducting research but none of those provide a global solution because of selected external factors and sample size. Therefore, this study provides a significant suggestion to select EFs and future research direction by conducting a systematic review study. Therefore, EFs must include not only student perspectives but also staff and parents, similarly, EFs must include not only user-friendliness, system quality, content quality, satisfaction, and self-efficacy but also technical support, anxiety, privacy, and security. Furthermore, the same study analyzes and suggested the most required future research direction for TAM for E-learning such as Developing VR and augmented reality e-learning tools, understanding the use of e-learning from qualitative perspectives through interview or focus group discussions, and Game-based educational tools. Furthermore, selecting the articles for this study was challengeable due to less number of articles published recently and closed access permission.

Security issues of Vehicular Ad Hoc Networks (VANET): A Systematic Review Sahabdeen Aysha Asra
TIERS Information Technology Journal Vol. 3 No. 1 (2022)
Publisher : Universitas Pendidikan Nasional

Vehicular ad hoc networks (VANETs) are a subset of mobile ad hoc networks (MANETs) that refer to a group of connected cars. The various networking layers in common Internet protocol stack topologies are linked to security vulnerabilities in VANETs. A security breach in a VANET is typically serious and harmful. Also Current VANET security standards handle the bulk of the security issues that vehicle networks confront. This paper presents VANET’s different types of security attacks in a systematic review approach. The information was gathered by a systematic examination of existing research articles. Furthermore, the study's shortcomings were the dataset's size and the absence of quality characteristics.

Predictive Modeling Classification of Post-Flood and Abrasion Effects With Deep Learning Approach Finki Dona Marleny; Mambang Mambang
TIERS Information Technology Journal Vol. 3 No. 1 (2022)
Publisher : Universitas Pendidikan Nasional

Floods and abrasion are the most common disasters in Indonesia. A lot of data is collected from post-flood and abrasion disasters. From the data released by BNPB, disaster data is directly based on the occurrence of disasters. From these data, we will test predictive modeling classification with a deep learning approach. Big data can be made through classification and predictive modeling. Our proposed model is a classification of predictive modeling of post-flood and abrasion data using the H2O deep learning approach. Deep learning H2O models can also be evaluated with specific model metrics, termination metrics, and performance charts. This approach is used to optimize the performance and accuracy of predictions during the modeling process using our dataset pool training. The big data to be processed will generate new knowledge for policies in decision making. Big data performance modeled with Deep Learning H2O is used to predict the Survival attributes of post-flood and abrasion sample datasets. The best metric performance is obtained from the maxout activation function with a 200-200 unit layer that gets an accuracy of 93.49% with AUC: 0.808 +/- 0.022 (micro average: 0.808).

Predictive Analysis of Customer Retention Using the Random Forest Algorithm Yogasetya Suhanda; Lela Nurlaela; Ike Kurniati; Andy Dharmalau; Ita Rosita
TIERS Information Technology Journal Vol. 3 No. 1 (2022)
Publisher : Universitas Pendidikan Nasional

Retaining customers is becoming a measurement focus in an industry with increasing competition. The concept of customer retention has become a research study in the sales industry, because it is difficult to retain customers and easily switch to other brands. Customer repurchase decisions in the business world of sales are very competitive. Customer satisfaction is directly proportional to the retention rate, if the customer is not satisfied then the automatic retention rate will be low. If the company is not able to meet customer expectations, it will have a serious impact on the company, namely moving customers to other services. Service factors, price, profit value, satisfaction and trust affect customer retention. One of the factors that influence consumers to become customer retention is service quality. A predictive customer retention plan is needed with data mining using the random forest algorithm. The random forest algorithm is a method that generates a number of trees from sample data, where the creation of one tree during training does not depend on the previous tree, the decision is based on the most voting. The voting results from several decision trees that are formed are the boundaries that are used as class determination in the classification process and the most votes are the winners and determine the classification class. This study aims to determine and analyze customer loyalty, customer trust and customer satisfaction. So that it can make it easier to monitor customers at the company. The results can be seen with the percentage of about 81.12% customer retention and about 18.87% customer churn. The result of feature evaluation shows that customer_activity has the highest influence on customer retention, followed by subtotal and qty.

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
Adie Wahyudi Oktavia Gama

Contact Email
journal@undiknas.ac.id

Phone
+6282236805788

Journal Mail Official
journal@undiknas.ac.id

Editorial Address
Jl. Bedugul No.39, Sidakarya, Kec. Denpasar Sel., Kota Denpasar, Bali 80224

Location
Kota denpasar,

Bali

INDONESIA

Abstract

Abstract

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Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Adie Wahyudi Oktavia Gama

Contact Email journal@undiknas.ac.id

Phone +6282236805788

Journal Mail Official journal@undiknas.ac.id

Editorial Address Jl. Bedugul No.39, Sidakarya, Kec. Denpasar Sel., Kota Denpasar, Bali 80224

Location Kota denpasar, Bali INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Adie Wahyudi Oktavia Gama

Contact Email
journal@undiknas.ac.id

Phone
+6282236805788

Journal Mail Official
journal@undiknas.ac.id

Editorial Address
Jl. Bedugul No.39, Sidakarya, Kec. Denpasar Sel., Kota Denpasar, Bali 80224

Location
Kota denpasar,

Bali

INDONESIA