Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal Jurnal Informatika Jurnal Telematika Jurnal Terapan Ilmu Manajemen dan Bisnis (JTIMB)
Ria Chaniago
Departemen Teknik Informatika Institut Teknologi Harapan Bangsa Jalan Dipatiukur No. 83–84 Bandung
Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Social Sciences

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
PREDIKSI CUACA MENGGUNAKAN METODE CASE BASED REASONING DAN ADAPTIVE NEURO FUZZY INFERENCE SYSTEM Chaniago, Ria; Liong, The Houw; Wardani, Ken Ratri Retno
Jurnal Informatika Vol 12, No 2 (2014): NOVEMBER 2014
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (416.721 KB) | DOI: 10.9744/informatika.12.2.90-95

Abstract

Weather is one of the nature elements that can influence decision making in human's life. Based on that issue, the author wants to make an application that is able to predict weather with good accuracy. The application is a weather forecasting system, using computer technology that implements expert system. The methods used are Adaptive Neuro Fuzzy Inference System (ANFIS) and Case Based Reasoning (CBR), and a combination of both methods will applied to the system. The system also has learning methods like Backpropagation Error (BPE) and Recursive Least Error (RLSE), to increase its accuracy. Clustering and data cleaning also done inside the system, as it needed by forecasting process to achieve a good result. K-Means is the clustering algorithm, while Box and Whisker Plot is the algorithm for data cleaning. The result from this project is to create a weather forecasting system with high accuracy.
Penggunaan Named Entity Recognition dan Artificial Intelligence Markup Language untuk Penerapan Chatbot Berbasis Teks Christianto, David; Siswanto, Elisafina; Chaniago, Ria
Jurnal Telematika Vol. 10 No. 2 (2015)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v10i2.130

Abstract

Aplikasi chatbot dapat digunakan untuk membantu memberikan kebutuhan informasi pada sistem layanan operator service. Sistem chatbot yang digunakan adalah sistem chatbot yang berbasiskan pada teks. Pada penelitian ini, chatbot dibuat untuk memenuhi kebutuhan informasi di ITHB dengan menggunakan Named Entity Recognition (NER) dan Artificial Intelligence Markup Language (AIML). NER digunakan untuk membantu  mengenali pola (kata kunci) kalimat dari bahasa sehari-hari manusia (Natural Language Processing). AIML digunakan untuk memberikan jawaban yang relevan dan sesuai  dengan pola (kata kunci) kalimat yang telah ditemukan di dalam bahasa manusia.  Selain itu, pada penelitian ini juga dilakukan beberapa optimasi seperti optimasi pada proses perhitungan Naïve Bayes pada NER, proses spelling correction, dan proses pattern matching yang terbukti dapat mempercepat dan meningkatkan akurasi sistem chatbot dalam proses pencarian jawaban. Berdasarkan hasil pengujian, sistem chatbot ini dapat mengenali pola kalimat bahasa manusia dengan akurasi (NER) hingga 97% dan sistem dapat memberikan jawaban yang tepat dengan akurasi hingga 90% berdasarkan pola yang telah ditemukan tersebut.In operator service system area, information is an essential needs for every individuals. Chatbot application can be used to support the fulfilment of information in operation service system. Chatbot system that will be implemented is a text-based chatbot system. In this paper, chatbot was made in order to fulfil the information needs in ITHB by using Named Entity Recognition (NER) and Artificial Intelligence Markup Language (AIML). NER is used to recognize the sentence pattern (keyword) in human natural language (Natural Language Processing). AIML is used to process relevant responses based on the keyword patterns found in human natural language which then will be transformed into data which can be processed and understood by system. This research also covers several  optimizations, such as Naïve Bayes calculation optimization in NER, spelling correction optimization, and pattern matching optimization that has been proven to hasten and increase chatbot system’s accuracy in finding answers as response. Based on the empirical examination, this chatbot system can recognize human sentence pattern (NER process) with accuracy of 97% and system can provide suitable response with accuracy of 90% based on the recognized patterns from NER process.
Penerapan Abstract Syntax Tree dan Algoritma Damerau-Levenshtein Distance untuk Mendeteksi Plagiarisme pada Berkas Source Code Rusdianto, Stephanie; Chaniago, Ria
Jurnal Telematika Vol. 13 No. 2 (2018)
Publisher : Yayasan Petra Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61769/telematika.v13i2.236

Abstract

Plagiarism source code is a program that is made up of other programs that have same syntax structure. In this research, the approach used to detect plagiarism is tree-based by building abstract syntax tree base on grammar on two predefined plagiarism files source code. Damerau-Levenshtein Distance Algorithm will calculate the tree structure formed minimum distance value to get the percentage of similarity. Previously, the application calculated the value of threshold obtained from the average value of plagiarism plot paired pairs, and then were reduced to its standard deviation to be able to declare that both files are plagiarism or not. This research analyzes the best use of grammar between jexer rule or a combination of lexer and parser rule, the best use of preprocessing combination and the best use of distance value of Damerau-Levenshtein Distance Algorithm. Based on the tests performed, the use of grammar lexer and parser rule resulted the highest accuracy of 97.435 % by taking 118,115 seconds and threshold used is 88.2314 %.The combination of preprocessing resulted highest accuracy of 97.435% by using whole preprocessing existing or by using preprocessing comment only. For the best distance value is 4 with highest accuracy 97.435 %.Plagiarisme source code adalah jika sebagai sebuah program yang terbentuk dari program lainnya dan memiliki struktur syntax yang sama. Dalam penelitian ini, pendekatan yang digunakan untuk mendeteksi plagiarisme adalah tree-based dengan membangun abstract syntax tree atas dua berkas source code terduga plagiat berdasarkan grammar yang telah dirancang. Struktur tree yang terbentuk akan dihitung nilai jarak minimumnya dengan Damerau-Levenshtein Distance Algorithm untuk mendapatkan persentase kemiripan. Sebelumnya, aplikasi menghitung nilai threshold yang didapatkan dari nilai rata-rata kemiripan pasangan berkas plagiat yang dikurangi dengan simpangan bakunya untuk dapat menyatakan kedua berkas masukan plagiat atau tidak. Penelitian ini menganalisis penggunaan grammar terbaik antara lexer rule atau kombinasi lexer dan parser rule, penggunaan kombinasi preprocessing terbaik serta penggunaan nilai jarak terbaik pada Damerau-Levenshtein Distance Algorithm. Berdasarkan pengujian yang dilakukan, penggunaan grammar lexer dan parser rule menghasilkan akurasi tertinggi yaitu 97.435 % dengan memakan waktu 118,115 detik dengan nilai threshold 88.2314 %. Kombinasi preprocessing yang menghasilkan akurasi tertinggi 97.435 % menggunakan seluruh preprocessing yang ada atau dengan menggunakan prerpocessing comment saja. Untuk nilai jarak terbaik adalah nilai jarak sebesar 4 dengan akurasi tertinggi, yaitu 97.435 %.
Artificial Intelligence: GEN Z Auditing Students of Universitas Advent Indonesia Sinaga, Judith Gallena; Siagian, Valentine; Chaniago, Ria
Jurnal Terapan Ilmu Manajemen dan Bisnis (JTIMB) Vol. 8 No. 1 (2025): JTIMB | Juni 2025
Publisher : Program Studi Magister Manajemen Universitas Advent Indonesia

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

Abstract

This study explores the relationship between Gen Z auditing students and artificial intelligence (AI), focusing on their competencies, awareness, and frequency of AI tool usage. Utilizing a qualitative research method, questionnaires were distributed to 50 auditing students at Universitas Advent Indonesia through Google Forms. The instrument was designed around four key perspectives: AI competencies, most used AI tools, level of AI awareness, and frequency of AI use in academic tasks. Respondents were selected using purposive sampling to ensure relevance, with ethical standards upheld through voluntary participation and data confidentiality. Findings reveal that while auditing students frequently use AI tools—particularly ChatGPT for tasks like communication, brainstorming, report writing, and presentation creation—their overall AI competence remains moderate, and their awareness tends to be surface-level, focusing more on tool usage than on understanding underlying principles or ethical implications. This highlights a clear gap between usage and mastery. As the future of the auditing profession becomes increasingly intertwined with AI, it is essential to enhance students’ digital literacy and critical understanding of AI’s capabilities and limitations. The study concludes that while Gen Z auditing students are technologically engaged, targeted educational interventions and curriculum development are needed to ensure their future relevance and leadership in a digitally evolving accounting profession.
Co-Authors David Christianto Ken Ratri Retno Wardani Rusdianto, Stephanie Sinaga, Judith Gallena Siswanto, Elisafina The Houw Liong Valentine Siagian