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All Journal Jurnal Pendidikan Teknologi dan Kejuruan MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Prosiding Seminas Competitive Advantage Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Kursor Journal of Development Research JOIV : International Journal on Informatics Visualization Applied Information System and Management IJISTECH (International Journal Of Information System & Technology) Systemic: Information System and Informatics Journal ILKOMNIKA: Journal of Computer Science and Applied Informatics Jurnal Mnemonic International Journal of Advances in Data and Information Systems IJISTECH Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Janitra Informatika dan Sistem Informasi Bulletin of Social Informatics Theory and Application Jurnal Indonesia : Manajemen Informatika dan Komunikasi
Yunifa Miftachul Arif
Department Of Informatics Engineering, Universitas Islam Negeri Maulana Malik Ibrahim Malang
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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Journal : Applied Information System and Management

 1 
Performance of Multi-Criteria Recommender System Using Cosine-Based Similarity for Selecting Halal Tourism Rizqi Aulia Nadhifah; Yunifa Miftachul Arif; Hani Nurhayati; Linda Salma Angreani
Applied Information System and Management (AISM) Vol 5, No 2 (2022): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v5i2.25035

Abstract

Tourism is an activity where people or groups travel voluntarily for relaxation, seeking entertainment, or enjoy cultural diversity both within the city, outside the city, or even abroad. For traveling, information about halal tourism is essential that tourists must know. Tourists can contact a tour guide to find information and recommendations for halal tourism. However, it will cost quite a bit and need for a recommendation system to obtain recommendations and make it easier for tourists to determine which halal tourism to visit. This study aims to obtain the Multi-Criteria Recommender System's (MCRS) performance using cosine-based similarity to select halal tourism in Batu City. MCRS extends the traditional approach by using more than one scoring criteria to generate recommendations. The implementation of MCRS using cosine-based similarity succeeded in producing the five highest recommendations for halal tourist attractions, which were implemented in a game-based system. Through recommendation accuracy testing on two items, three items, four items, and five tourist attractions items, we obtained an average accuracy is 77,95%.
Selecting Tourism Site Using 6 As Tourism Destinations Framework Based Multi-Criteria Recommender System Yunifa Miftachul Arif; Duvan Deswantara Putra; Nauman Khan
Applied Information System and Management (AISM) Vol 6, No 1 (2023): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v6i1.25140

Abstract

Batu City is a place with many types of tourism and had many tourists in 2019. However, there was an imbalance of tourist attractions visited from the total number. Tourists are only fixated on famous tourist spots. Therefore, a recommendation system is needed that can provide recommendations for tourists. In this study, we use the Multi-Criteria Recommender System (MCRS) method based on the rating value between users to obtain recommendations from the system regarding selecting tourist destinations. The authors use the 6 As Tourism Destinations (6AsTD) framework for user assessment criteria in this study. The framework consists of six indicators that assess the success of tourism destinations, including attractions, accessibility, amenities, support services, activities, and available packages. The six components are considered the key to the success of a tourist destination under the marketing approach. This study aimed to obtain a recommendation system for selecting tourist destinations using the multi-criteria concept based on the 6AsTD framework. Based on the experimental results, the proposed method has an accuracy rate of up to 72%.
Analysis of the Use of Artificial Neural Network Models in Predicting Bitcoin Prices Muhammad Sahi; Muhammad Faisal; Yunifa Miftachul Arif; Cahyo Crysdian
Applied Information System and Management (AISM) Vol 6, No 2 (2023): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v6i2.29648

Abstract

Bitcoin is one of the fastest-growing digital currencies or cryptocurrencies in the world. However, the highly volatile Bitcoin price poses a very extreme risk for traders investing in cryptocurrencies, especially Bitcoin. To anticipate these risks, a prediction system is needed to predict the fluctuations in cryptocurrency prices. Artificial Neural Network (ANN) is a relatively new model discovered and can solve many complex problems because the way it works mimics human nerve cells. ANN has the advantage of being able to describe both linear and non-linear models with a fairly wide range. This research aims to determine the best performance and level of accuracy of the ANN model using the Back-Propagation Neural Network (BPNN) algorithm in predicting Bitcoin prices. This study uses Bitcoin price data for the period 2020 to 2023 taken from the CoinDesk market. The results of this study indicate that the ANN model produces the best performance in the form of four input nodes, 12 hidden nodes, and one output node (4-12-1) with an accuracy rate of around 3.0617175%.
Non-Rating Recommender System for Choosing Tourist Destinations Using Artificial Neural Network Yunifa Miftachul Arif; Dyah Wardani; Hani Nurhayati; Norizan Mat Diah
Applied Information System and Management (AISM) Vol 6, No 2 (2023): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v6i2.26741

Abstract

The development of tourist destinations in Batu City makes it hard for the tourists to decide their destinations. The recommender system is a solution that provides a lot of information or tourist attraction data. Collaborative filtering is often used in recommender systems. However, it has drawbacks; one of which is the cold-start problem, where the system cannot recommend items to new users. It was caused by the new user who had no history of rating on any item, or the system had no information. This study aims to apply a non-rated travel destination recommendation system to address the cold-start problem for new users. We use a multi-layer perceptron or artificial neural networks to overcome the problem by training user preference data to produce high training accuracy. Based on four experiments in the training data, the network architecture shows 5 – 7 – 5 – 3 –14, which is the highest accuracy. The architecture uses five variables as inputs and three hidden layers, with each layer was activated using the ReLU activation function. The output layer produces 14 binary outputs and is activated using the sigmoid activation function. The system can give recommendations to new users using feedforward from test data with updated values in weights and biases. The test results from 46 test data showed an accuracy of 67.235%.
Struggling Models: An Analysis of Logistic Regression and Random Forest in Predicting Repeat Buyers with Imbalanced Performance Metrics Mauludiah, Siska Farizah; Arif, Yunifa Miftachul; Faisal, Muhammad; Putra, Dony Darmawan
Applied Information System and Management (AISM) Vol 7, No 2 (2024): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v7i2.39326

Abstract

Predicting repeat buyers is essential for businesses seeking to improve customer retention and maximize profitability. This study examines the effectiveness of logistic regression and random forest algorithms in forecasting repeat buyers, utilizing an e-commerce dataset from Kaggle. Despite the theoretical strengths of these models, our results indicate significant performance challenges. Both models were evaluated on key metrics: accuracy, precision, recall, F1 score, and ROC-AUC. The findings revealed that the models logistic regression and random forest performed poorly, with accuracy hovering around 50%, precision and recall demonstrating imbalanced performance, and ROC-AUC scores barely exceeding random guessing levels. Such metrics highlight the limited discriminative power of these models in identifying repeat buyers. The analysis suggests that issues such as data quality, feature relevance, and class imbalance contribute to these shortcomings. Specifically, the models struggled to effectively learn from the data, leading to suboptimal predictions. These results underscore the need for enhanced feature engineering, better handling of class imbalance, and possibly exploring more advanced algorithms. This study provides a critical assessment of the limitations inherent in using Logistic Regression and Random Forest for predicting repeat buyers, hence implements feature engineering, SMOTE and hyperparameter tuning using RandomSearchCV to get better result.
Enhancing Repeat Buyer Classification with Multi Feature Engineering in Logistic Regression Mauludiah, Siska Farizah; Crysdian, Cahyo; Arif, Yunifa Miftachul
Applied Information System and Management (AISM) Vol 8, No 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.45025

Abstract

This study presents a novel approach to improving repeat buyer classification on e-commerce platforms by integrating Kullback-Leibler (KL) divergence with logistic regression and focused feature engineering techniques. Repeat buyers are a critical segment for driving long-term revenue and customer retention, yet identifying them accurately poses challenges due to class imbalance and the complexity of consumer behavior. This research uses KL divergence in a new way to help choose important features and evaluate the model, making it easier to understand and more effective at classifying repeat buyers, unlike traditional methods. Using a real-world dataset from Indonesian e-commerce with 1,000 records, divided into 80% for training and 20% for testing, the study uses logistic regression along with techniques like SMOTE for oversampling, class weighting, and regularization to fix issues with data imbalance and overfitting. Model performance is assessed using accuracy, precision, recall, F1-score, and KL divergence. Experimental results indicate that the KL-enhanced logistic regression model significantly outperforms the baseline, especially in balancing precision and recall for the minority class of repeat buyers. The unique contribution of this work lies in its synergistic use of KL divergence in both the feature engineering and evaluation phases, offering a robust, interpreted, and data-efficient solution. For e-commerce businesses, the findings translate into improved targeting of high-value customers, better personalization of marketing efforts, and more strategic allocation of resources. This research offers practical tips for enhancing predictive customer analytics and supports data-driven decision-making in digital commerce environments.
Enhancing Repeat Buyer Classification with Multi Feature Engineering in Logistic Regression Mauludiah, Siska Farizah; Crysdian, Cahyo; Arif, Yunifa Miftachul
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.45025

Abstract

This study presents a novel approach to improving repeat buyer classification on e-commerce platforms by integrating Kullback-Leibler (KL) divergence with logistic regression and focused feature engineering techniques. Repeat buyers are a critical segment for driving long-term revenue and customer retention, yet identifying them accurately poses challenges due to class imbalance and the complexity of consumer behavior. This research uses KL divergence in a new way to help choose important features and evaluate the model, making it easier to understand and more effective at classifying repeat buyers, unlike traditional methods. Using a real-world dataset from Indonesian e-commerce with 1,000 records, divided into 80% for training and 20% for testing, the study uses logistic regression along with techniques like SMOTE for oversampling, class weighting, and regularization to fix issues with data imbalance and overfitting. Model performance is assessed using accuracy, precision, recall, F1-score, and KL divergence. Experimental results indicate that the KL-enhanced logistic regression model significantly outperforms the baseline, especially in balancing precision and recall for the minority class of repeat buyers. The unique contribution of this work lies in its synergistic use of KL divergence in both the feature engineering and evaluation phases, offering a robust, interpreted, and data-efficient solution. For e-commerce businesses, the findings translate into improved targeting of high-value customers, better personalization of marketing efforts, and more strategic allocation of resources. This research offers practical tips for enhancing predictive customer analytics and supports data-driven decision-making in digital commerce environments.
Co-Authors AA Sudharmawan, AA Achmad Sabar, Achmad Ady Wicaksono Agariadne Dwinggo Samala Alfachruddin, M. Nabil Fahd Aristantia, Yuliana Bojic, Ljubisa Cahyo Crysdian Coelho, Diogo Pereira Dedy Kurnia Setiawan Duvan Deswantara Putra Dyah Wardani Fachrul Kurniawan Fathir Fathir Fathir Fathurrahman Firmansyah, Rezky Fresy Nugroho Hamid, Abdulhalim Hamid Salih Hani Nurhayati Howard, Natalie-Jane Ihsan, Afif Nuril Ikhlayel, Mohammed Imamudin, M. Janitra, Geovanni Azam Juniardi Nur Fadila Linda Salma Angreani Mauludiah, Siska Farizah Mauridhi H Purnomo Mochamad Hariadi Mochammad Wahyu Firmansyah Mokhamad Amin Hariyadi Muhammad Faisal Muhammad Faisal Muhammad Sahi Nauman Khan Norizan Mat Diah Putra, Dony Darmawan Rawas, Soha Ririen Kusumawati Rizqi Aulia Nadhifah Rony, Zahara Tussoleha Roro Inda Melani Safitri A Basid, Puspa Miladin Nuraida Setiyawan, Niko Heri Sulika Sulika Supeno Mardi S. N, Supeno Mardi Supeno Mardi Susiki Nugroho, Supeno Mardi Supriyono Tsoy, Dana Wahyuliningtyas, Lia Zainal Abidin Zulfiandri Zulfiandri