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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) TELKOMNIKA (Telecommunication Computing Electronics and Control) JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Journal of Information Systems Engineering and Business Intelligence JOIN (Jurnal Online Informatika)
Nur Ulfa Maulidevi
Institut Teknologi Bandung
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Library & Information Science Other

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 1 
Factors Influencing User’s Adoption of Conversational Recommender System Based on Product Functional Requirements Z.K. Abdurahman Baizal; Dwi H Widyantoro; Nur Ulfa Maulidevi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 4: December 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i4.4234

Abstract

Conversational recommender system (CRS) helps customers get products fitted their needs by repeated interaction mechanisms. When customers want to buy products having many and high tech features (e.g., cars, smartphones, notebook, etc.), most users are not familiar with product technical features. The more natural way to elicit customers’ needs is by asking what they really want to use with the product they want (we call as product functional requirements). In this paper, we analyze four factors, e.g., perceived usefulness, perceived ease of use, trust and perceived enjoyment  associated to user’s intention to adopt the interaction model (in CRS) based on product functional requirements. Result of experiment using technology acceptance model (TAM) indicates that, for users who aren’t familiar with technical features, perceives usefulness is a main factor influencing users’ adoption. Meanwhile, perceived enjoyment plays a role on user’s intention to adopt this interaction model, for users who are familiar with technical features of product.
Automatic Data Interpretation in Accounting Information Systems Based On Ontology Irvan Iswandi; Iping Supriana Suwardi; Nur Ulfa Maulidevi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 15, No 4: December 2017
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v15i4.6414

Abstract

Financial transactions recorded into accounting journals based on the evidence of the transaction. There are several kinds of evidence of transactions, such as invoices, receipts, notes, memos and others.  Invoice as one of transaction receipt has many forms that it contains a variety of information.  The information contained in the invoice identified based on rules.  Identifiable information includes: invoice date, supplier name, invoice number, product ID, product name, quantity of product and total price.  In this paper, we proposed accounting ontology and Indonesian accounting dictionary. It can be used in intelligence accounting systems. Accounting ontology provides an overview of account mapping within an organization. The accounting dictionary helps in determining the account names used in accounting journals.  Accounting journal created automatically based on accounting evidence identification.  We have done a simulation of the 160 Indonesian accounting evidences, with the result of precision 86.67%, recall 92.86% and f-measure 89.67%.
Penggunaan Algoritma Genetik untuk Mencari Parameter Rumus EMPI 17 Indikator Felix Gunawan; Nur Ulfa Maulidevi
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2007
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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

Abstract

Sebuah sistem yang dapat mendeteksi krisis ekonomi dan keuangan secara lebih awal merupakan salahsatu kebutuhan yang berkembang, khususnya sejak terjadinya krisis mata uang yang menghantam kawasan Asiasejak 1997. Diharapkan dengan adanya sistem tersebut, maka krisis ekonomi atau keuangan dapat diantisipasisedini mungkin. Saat ini banyak terdapat pendekatan atau model yang dapat digunakan untuk membuat sistemtersebut. Makalah ini menyajikan bagaimana model algoritma genetik (genetic algorithm) mencari parameterdari rumus EMPI (Exchange Market Pressure Index) yang dapat digunakan untuk memprediksi kemungkinanterjadinya krisis mata uang dalam suatu horison peringatan tertentu ke depan. Model ini menggunakan sampeldata antara Juli 1997 hingga Desember 2004.Kata kunci: krisis ekonomi dan keuangan, algoritma genetik, rumus EMPI.
Feature selection to increase the random forest method performance on high dimensional data Maria Irmina Prasetiyowati; Nur Ulfa Maulidevi; Kridanto Surendro
International Journal of Advances in Intelligent Informatics Vol 6, No 3 (2020): November 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v6i3.471

Abstract

Random Forest is a supervised classification method based on bagging (Bootstrap aggregating) Breiman and random selection of features. The choice of features randomly assigned to the Random Forest makes it possible that the selected feature is not necessarily informative. So it is necessary to select features in the Random Forest. The purpose of choosing this feature is to select an optimal subset of features that contain valuable information in the hope of accelerating the performance of the Random Forest method. Mainly for the execution of high-dimensional datasets such as the Parkinson, CNAE-9, and Urban Land Cover dataset. The feature selection is done using the Correlation-Based Feature Selection method, using the BestFirst method. Tests were carried out 30 times using the K-Cross Fold Validation value of 10 and dividing the dataset into 70% training and 30% testing. The experiments using the Parkinson dataset obtained a time difference of 0.27 and 0.28 seconds faster than using the Random Forest method without feature selection. Likewise, the trials in the Urban Land Cover dataset had 0.04 and 0.03 seconds, while for the CNAE-9 dataset, the difference time was 2.23 and 2.81 faster than using the Random Forest method without feature selection. These experiments showed that the Random Forest processes are faster when using the first feature selection. Likewise, the accuracy value increased in the two previous experiments, while only the CNAE-9 dataset experiment gets a lower accuracy. This research’s benefits is by first performing feature selection steps using the Correlation-Base Feature Selection method can increase the speed of performance and accuracy of the Random Forest method on high-dimensional data.
PENELITIAN AWAL : OTOMATISASI INTERPRETASI DATA AKUNTANSI BERBASIS NATURAL LANGUAGE PROCESSING Irvan Iswandi; Iping Supriana Suwardi; Nur Ulfa Maulidevi
Jurnal Sistem Informasi Vol 5, No 2 (2013)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (205.012 KB) | DOI: 10.36706/jsi.v5i2.900

Abstract

Proses pencatatan transaksi akuntansi ke dalam sistem akuntansi terkadang terhambat karena lambatnya pemahaman terhadap transaksi yang terjadi.  Keterlambatan ini terjadi karena masih dominannya peran manusia dalam sistem akuntansi, padahal manusia memiliki keterbatasan.  Pemahaman terhadap transaksi akuntansi berkaitan dengan proses klasifikasi terhadap transaksi yang terjadi.  Bila terjadi kesalahan dalam proses klasifikasi maka akan mengakibatkan kesalahan dalam penyajian laporan keuangan.  Penelitian ini bertujuan untuk mengembangkan otomatisasi interpretasi terhadap data akuntansi, baik dalam hal pengenalan transaksi akuntansi, ekstraksi dan kemudian melakukan pengelompokkan terhadap transaksi akuntansi berdasarkan Natural Language Processing.  Langkah utama yang dilakukan dalam pencapaian tersebut melalui analisa dasar terhadap transaksi akuntansi berdasarkan interpretasi bahasa alami.  Simulasi yang dilakukan terhadap beberapa transaksi akuntansi menunjukkan sistem yang dibangun berdasarkan Natural Language Processing dapat meningkatkan kecepatan dan ketepatan dalam interpretasi data akuntansi. Kata kunci: otomatisasi, akuntansi, natural language processing
Deteksi Cyberbullying dengan Mesin Pembelajaran Klasifikasi (Supervised Learning): Peluang dan Tantangan Yudi Setiawan; Nur Ulfa Maulidevi; Kridanto Surendro
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 7: Spesial Issue Seminar Nasional Teknologi dan Rekayasa Informasi (SENTRIN) 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022976747

Abstract

Perkembangan teknologi media sosial tidak hanya memberikan kemudahan dalam berkomunikasi antar individu, akan tetapi juga dapat mengancam kehidupan sosial individu seperti tidakan cyberbullying. Bervariasinya pola dan karakteritik cyberbullying mengakibatkan sulitnya proses deteksi cyberbullying, yang dilakukan oleh pelaku cyberbullying. Penelitian deteksi pola dan karakteristik cyberbullying banyak dilakukan dengan berbagai metode, seperti dengan mengimplementasikan Machine Learning, Natural Language Processing (NLP), dan Sentiment Analysis yang memiliki variasi akurasi yang berbeda, dengan keunggulan dan kelemahan dari masing-masing metode. Implementasi Machine Learning untuk deteksi cyberbullying dapat dilakukan dengan berbagai algoritma, seperti algoritma probabilistik (Naïve Bayes) maupun supervised learning (Support Vector Machine, k-Nearest Neighbour, Decission Tree), dan metode lainnya yang hingga saat ini terus dikembangkan dengan berbagai pendekatan untuk meningkatkan akurasi deteksi cyberbullying atau non-cyberbullying. Adapun peluang dan tantangan penelitian deteksi cyberbullying seperti penerapan pada variasi domain bahasa, dan bentuk ekspresi yang dilakukan pada suatu lingkungan atau budaya, yang masih terdapat ruang untuk dikembangkan dan dijelajah secara luas. Pada artikel ini menjabarkan penelitian berikutnya berupa mengimplementasikan metode pembelajaran klasifikasi (Supervised Learning) dengan modifikasi tahapan untuk meningkatkan akurasi klasifikasi.
Product Review Ranking in e-Commerce using Urgency Level Classification Approach Hamdi Ahmad Zuhri; Nur Ulfa Maulidevi
JOIN (Jurnal Online Informatika) Vol. 5 No 2 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i2.612

Abstract

Review ranking is useful to give users a better experience. Review ranking studies commonly use upvote value, which does not represent urgency, and it causes problems in prediction. In contrast, manual labeling as wide as the upvote value range provides a high bias and inconsistency. The proposed solution is to use a classification approach to rank the review where the labels are ordinal urgency class. The experiment involved shallow learning models (Logistic Regression, Naïve Bayesian, Support Vector Machine, and Random Forest), and deep learning models (LSTM and CNN). In constructing a classification model, the problem is broken down into several binary classifications that predict tendencies of urgency depending on the separation of classes. The result shows that deep learning models outperform other models in classification dan ranking evaluation. In addition, the review data used tend to contain vocabulary of certain product domains, so further research is needed on data with more diverse vocabulary.
Product Review Ranking in e-Commerce using Urgency Level Classification Approach Hamdi Ahmad Zuhri; Nur Ulfa Maulidevi
JOIN (Jurnal Online Informatika) Vol. 5 No 2 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i2.612

Abstract

Review ranking is useful to give users a better experience. Review ranking studies commonly use upvote value, which does not represent urgency, and it causes problems in prediction. In contrast, manual labeling as wide as the upvote value range provides a high bias and inconsistency. The proposed solution is to use a classification approach to rank the review where the labels are ordinal urgency class. The experiment involved shallow learning models (Logistic Regression, Naïve Bayesian, Support Vector Machine, and Random Forest), and deep learning models (LSTM and CNN). In constructing a classification model, the problem is broken down into several binary classifications that predict tendencies of urgency depending on the separation of classes. The result shows that deep learning models outperform other models in classification dan ranking evaluation. In addition, the review data used tend to contain vocabulary of certain product domains, so further research is needed on data with more diverse vocabulary.
Enhancing the Comprehensiveness of Criteria-Level Explanation in Multi-Criteria Recommender System Rismala, Rita; Maulidevi, Nur Ulfa; Surendro, Kridanto
Journal of Information Systems Engineering and Business Intelligence Vol. 11 No. 2 (2025): June
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.11.2.160-172

Abstract

Background: The explainability of recommender systems (RSs) is currently attracting significant attention. Recent research mainly focus on item-level explanations, neglecting the need to provide comprehensive explanations for each criterion. In contrast, this research introduces a criteria-level explanation generated in a content-based pardigm by matching aspects between the user and item. However, generation may fall short when user aspects do not match perfectly with the item, despite possessing similar semantics.  Objective: This research aims to extend the aspect-matching method by leveraging semantic similarity. The extension provides more detail and comprehensive explanations for recommendations at the criteria level.    Methods: An extended version of the aspect matching (AM) method was used. This method identified identical aspects between users and items and obtained semantically similar aspects with closely related meanings.   Results: Experiment results from two real-world datasets showed that AM+ was superior to the AM method in coverage and relevance. However, the improvement varied depending on the dataset and criteria sparsity.  Conclusion: The proposed method improves the comprehensiveness and quality of the criteria-level explanation. Therefore, the adopted method has the potential to improve the explainability of multi-criteria RSs. The implication extends beyond the enhancement of explanation to facilitate better user engagement and satisfaction.  Keywords: Comprehensiveness, Content-Based Paradigm, Criteria-Level Explanation, Explainability, Multi-Criteria Recommender System
Co-Authors Dwi H Widyantoro Felix Gunawan Hamdi Ahmad Zuhri Iping Supriana Suwardi Iping Supriana Suwardi Irvan Iswandi Irvan Iswandi Kridanto Surendro Maria Irmina Prasetiyowati Rita Rismala Setiawan, Yudi ZK Abdurahman Baizal