Article Per Year (5 Year)
p-Index From 2020 - 2025
1.037
P-Index
This Author published in this journals
All Journal Jurnal Teknologi dan Manajemen Informatika JOIV : International Journal on Informatics Visualization JITEEHA: Journal of Information Technology Applications in Education, Economy, Health and Agriculture
Maukar, Anastasia L
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 5 Documents
Search

 1 
Analisa Prediksi Varietas Buah Salak yang Sesuai dengan Lahan Daerah Kabupaten Banjarnegara Menggunakan Algoritma C45 Marisa, Fitri; Maukar, Anastasia L
Jurnal Teknologi dan Manajemen Informatika Vol. 8 No. 1 (2022): Juni 2022
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v8i1.7521

Abstract

Salak is a potential horticultural sector that is a leading commodity in Banjarnegara. Salak fruit varieties have fruit categories that have their advantages. Variants of salak fruit include ivory salak, granulated sugar salak, pondoh salak, and honey salak. Based on data released from the relevant government agencies, further research was carried out related to analyzing and conducting research to predict salak fruit varieties. This variety is suitable for land in every area in Banjarnegara with predictive analysis using the C4.5 algorithm. This method has been widely developed to classify and predict a case with a fairly high degree of accuracy. From this study, researchers hope that it can contribute farmers to determining the type of salak fruit that is most suitable for the land they own so that later the harvest obtained by farmers can be maximized
Klasifikasi Jenis Rumah Adat Malaka Menggunakan Metode Convulational Neural Network (CNN) Nahak, Redemtus; Bura, Audyel Umbu; Araujo, Aprilio Demetrius De; Un, Fransiskus Deni; Ladopurab, Bartolomeus Wadan; Marisa, Fitri; Maukar, Anastasia L
Jurnal Teknologi dan Manajemen Informatika Vol. 9 No. 2 (2023): Desember 2023
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v9i2.10352

Abstract

In Indonesia, there is a rich diversity of cultures, one of which is traditional houses. Traditional houses essentially have the potential to represent the way of life, culture, and local economy. Traditional houses in Indonesia, particularly in the Malaka region, are important cultural symbols that can be regarded as cultural icons in Malaka and Indonesia. They provide a historical perspective, heritage, and reflect the progress of society in a civilization. The Convolutional Neural Network (CNN) method is used in this research. In this study, the CNN algorithm is applied to classify traditional house objects. These traditional house objects are divided into two categories: Kolibein Traditional House and Laleik Traditional House. The objective of this research is to classify traditional houses in Malaka, namely Kolibein Traditional House and Laleik Traditional House, and also to determine the accuracy level of CNN classification results. The previously created model is tested using test data to assess its accuracy. The testing is conducted on 20 data points, with 10 data points in each respective class. The testing results show that the classification of Kolibein and Laleik traditional houses is error- free or very accurate. Based on the model developed for classifying Kolibein and Laleik traditional houses using the Convolutional Neural Network method, it is evident that this method is capable of producing accurate results. The obtained results indicate that the accuracy, based on the classification report using images of Kolibein and Laleik traditional houses, reaches 100%. Therefore, it can be concluded that the constructed CNN model has a high level of accuracy.
Analysis of Restaurant Ordering Patterns Using Apriori Algorithm Marisa, Fitri; Badrussalam, Nanda; Ahmad, Sharifah Sakinah Syed; Vitianingsih, Anik Vega; Maukar, Anastasia L
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 2 No. 2 (2025): June
Publisher : Lumina Infinity Academy Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study implements the Apriori algorithm to analyze ordering patterns in home-based restaurants, specifically Dapur Mb Yani. Sales transaction data for three weeks shows that the Geprek Sambal Merah, Geprek Sambal Ijo, and Ayam Crispy menus are the most frequently ordered items, both individually and in combination. The combination of Geprek Sambal Merah, Ayam Crispy, and Es Teh has a high association value, making it a candidate for bundling promotions, while the strong relationship between Geprek Sambal Merah and Geprek Sambal Ijo opens up opportunities for special offers involving both menus. These results help restaurant managers design more effective promotional strategies, manage ingredient stocks efficiently, and improve customer experience. The application of the Apriori algorithm proves its relevance in supporting data-based decisions, especially for small businesses, as well as opening up opportunities for further development in the culinary industry.
Data Mining Application for Classification of Online Transportation Customer Satisfaction Using C4.5 Algorithm Wardhani , Arie Restu; Irawan, Ryan Avrilio; Marpaung, Fhadillah Ain; Saputra, Idris Ivan; Maukar, Anastasia L
Journal of Information Technology application in Education, Economy, Health and Agriculture Vol. 1 No. 1 (2024): February
Publisher : Lumina Infinity Academy Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

In the era of increasing business competition, transportation companies are required to enhance the efficiency and effectiveness of their services. One method that can be employed to optimize fleet management is through Data Mining analysis. This study focuses on optimizing Ojek online transportation services using the C.4.5 Algorithm method. The aim of this research is to group customers and areas based on service demand patterns, thus improving fleet distribution and reducing waiting times. The data used in this study includes location, demand, and trip frequency information. The analysis results show that the C.4.5 algorithm method effectively groups the data, providing optimal fleet distribution and enhancing service performance. This research demonstrates that applying data mining through the C.4.5 algorithm method can be an effective strategy for improving management and operational efficiency in Ojek online transportation services, offering competitive advantages in service efficiency and customer satisfaction.
Strategic Recommendations in Increasing Gen Z User Engagement towards Gamification Elements with Fuzzy AHP and Octalysis Approaches Marisa, Fitri; Istiadi, -; Ahmad, Sharifah Sakinah Syed; Handajani, Endah Tri Esti; NoerTjahyana, Agustinus; Maukar, Anastasia L
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.3324

Abstract

Generation Z (Gen Z), often referred to as the "digital native" generation, interacts extensively with digital technology and social media. E-commerce companies need to adopt the right strategies, such as gamification, to increase user engagement among Gen Z. However, there is limited research evaluating which gamification elements are most effective in engaging Gen Z users. This study addresses this gap by identifying the most impactful gamification elements that enhance Gen Z user engagement and providing strategic recommendations for e-commerce designers and developers. Using the Fuzzy AHP method and Octalysis approach, this study evaluates five gamification elements: Point, Reward, Referral, Leaderboard, and Level across four key parameters: Motivation, Engagement, User Experience, and Retention. The Fuzzy AHP results indicate that the "Reward" element ranks highest with a score of 1.0, followed by "Level" with a score of 0.829. "Leaderboard" comes in third with a score of 0.669, while "Point" and "Referral" score 0.606 and 0.220, respectively. The low score of "Referral" suggests its limited effectiveness in fostering social connectedness among Gen Z users. The Octalysis analysis reveals that "Reward" has the most significant influence on core drives such as "Development and Accomplishment" and "Scarcity and Impatience," with an average score of 7.25, followed by "Level" with a score of 7.125. These findings underscore the importance of prioritizing "Reward" and "Level" to optimize user engagement for Gen Z. The practical implications of this study suggest that e-commerce platforms should integrate these gamification elements to create more engaging and interactive shopping experiences for Gen Z users, aligning with their preferences and motivations.
Co-Authors Agustinus Noertjahyana Ahmad, Sharifah Sakinah Syed Anik Vega Vitianingsih Araujo, Aprilio Demetrius De Badrussalam, Nanda Bura, Audyel Umbu Handajani, Endah Tri Esti Irawan, Ryan Avrilio Istiadi Ladopurab, Bartolomeus Wadan MARIFANI FITRI ARISA Marpaung, Fhadillah Ain Nahak, Redemtus Saputra, Idris Ivan Un, Fransiskus Deni Wardhani , Arie Restu