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All Journal TEKNIK INFORMATIKA ELINVO (Electronics, Informatics, and Vocational Education) IJID (International Journal on Informatics for Development) Aplikasia: Jurnal Aplikasi Ilmu-Ilmu Agama EDUMATIC: Jurnal Pendidikan Informatika JURNAL PUSTAKA KOMUNIKASI JISKa (Jurnal Informatika Sunan Kalijaga) Journal of Information Technology and Cyber Security
Maria Ulfah Siregar
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Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Comparison of KNN and Random Forest Algorithms on E-Commerce Service Chatbot Zamakhsyari, Fardan; Makayasa, Bagas Adi; Hamami, R. Abudullah; Akbar, Muhammad Tulus; Cahyono, Andi; Amirullah, Amirullah; Hisyamuddin, Muhammad Zida; Siregar, Maria Ulfah
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 1 (2025): January 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.1.100-109

Abstract

Technology heavily influences our lives, with the expansion of e-commerce being an important outcome that demands attention. Given the prevalence of smartphones equipped with messaging apps and fast networks, people often utilize these platforms to communicate with sellers, offering a convenient way for sellers to engage efficiently with a diverse customer base. Recognizing this trend, there is a need for digital transformation of services to improve operational efficiency. Thus, this study aimed to compare the efficiency of classification algorithms in e-commerce service chatbots. The researcher used machine learning techniques with KNN and Random Forest algorithms in this case. To assess the feasibility of the application, the chatbot results will be tested using the confusion matrix method to assess accuracy. From this study, it was obtained that the KNN method and calculating word weight using TF-IDF produces an accuracy value of 71.4%, thus confirming its feasibility.
An Efficient Journal Articles Searching using Vector Space Model Algorithm Alvriyanto, Azis; Nuruzzaman, Muhammad Taufiq; Siregar, Maria Ulfah; Hidayat, Rahmat
IJID (International Journal on Informatics for Development) Vol. 9 No. 1 (2020): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2020.09104

Abstract

One of the main feature of digital library is a search engine which depends on keywords submitted by a user. However, in the traditional algorithm, the computation performance, searching speed, significantly relies on the number of journal articles stored in the databases. Some irrelevant search results also increase the speed of article searching process. To solve the problem, in this paper we propose vector space model (VSM) algorithm to search for relevant journal articles. The VSM algorithm considers a term frequency - inversed document frequency (TF-IDF). The VSM algorithm will be compared to the baseline algorithm namely traditional algorithm. Both algorithms will be evaluated using combination of keywords which can be a synonym, phrase, error typography, or suffix and prefix. By using the data consist of 635 journal articles, both algorithms are compared in terms of 11 evaluation criteria. The results show that VSM algorithm is able to obtain the intended journal at 5th rank on average as compared to the traditional algorithm which can obtain the intended journal at rank of 171st on average. Therefore, our proposed algorithm can improve the performance to accurately sort the journal articles based on the submitted keywords as compared to traditional algorithm.   
Price Forecasting of Chili Variant Commodities Using Radial Basis Function Neural Network Ramadhan, Ade Umar; Siregar, Maria Ulfah; Nafisah, Syifaun; Anshari, Muhammad; Ndungi, Rebeccah; Mulyawan, Rizki; Nurochman, Nurochman; Gunawan, Eko Hadi
IJID (International Journal on Informatics for Development) Vol. 12 No. 1 (2023): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2023.5129

Abstract

This study addresses the challenge of price instability in chili markets, which can lead to economic losses and inflation. To mitigate this issue, we propose a machine learning model using Radial Basis Function Neural Networks (RBFNN) to predict prices of various chili variants. Our quantitative approach involves a comprehensive data preparation process, including preprocessing and normalization of time series data collected from 2018 to 2022. The RBFNN model is constructed with K-Means clustering for optimal hidden layer configurations and evaluated using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The results demonstrate promising accuracy, with MAPE error rates below 20% and relatively low RMSE values for large red chili (10.37%, 4484) and curly red chili (14.77%, 5590). Our findings indicate the potential for creating a reliable forecast model for predicting chili prices over 7 days, enabling better supply and demand management. The study's results also suggest that increased training data enhances forecasting accuracy. This research contributes to the development of effective price forecasting models, providing valuable insights for policymakers and stakeholders in the chili industry.
Analisis Ketertarikan Pengguna Microsoft Excel Online untuk Pengolahan Data Silsilah Keluarga Menggunakan TAM dan TPB Nufaily, Fathur Rachman; Siregar, Maria Ulfah
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 3 (2025): September 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.3.279-293

Abstract

The use of web-based applications such as Microsoft Excel Online has increased, including for recording family genealogy data. This study aims to analyze the factors influencing the intention and behavior of using this application based on the Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), and their combined framework. The constructs examined include perceived ease of use, perceived usefulness, attitude, subjective norm, perceived behavioral control, intention, and behavior. This quantitative study collected primary data through questionnaires distributed to family members using Microsoft Excel Online. Data analysis was conducted using SEM-PLS (Structural Equation Modeling-Partial Least Squares) with the assistance of SmartPLS version 4.1.0.2. The results indicate that perceived ease of use and perceived usefulness positively and significantly affect attitude, while attitude, subjective norm, and perceived behavioral control positively influence behavioral intention. Furthermore, behavioral intention has a positive effect on actual usage behavior. These findings suggest that Microsoft Excel Online is reliable for recording family genealogy data and supports technology acceptance among users.
Co-Authors Akbar, Muhammad Tulus Alvriyanto, Azis Amalia, Alifah Amirullah -, Amirullah Andi Cahyono Bahar , Afdi Fauzul Bambang Sugiantoro Barkah, Nida Muhliya Derrick, John Derrick, John Dewi , Bella Tasya Kumala Gunawan, Eko Hadi Hamami, R. Abudullah Hisamuddin, Muhammad Zida Hisyamuddin, Muhammad Zida Mahardika, Devara Eko Katon Makayasa, Bagas Adi Mansur, Ejah Said Muhammad Anshari, Muhammad Mulyawan, Rizki Ndungi, Rebeccah Nufaily, Fathur Rachman Nurochman Nurochman, Nurochman Nuruzzaman, Muhammad Taufiq QOMARIYAH Rahmat Hidayat Ramadhan, Ade Umar Saifulloh, Muhammad Syifaun Nafisah Yazid, Ahmad Subkhan Zamakhsyari, Fardan