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All Journal TEKNIK INFORMATIKA Jurnal Teknologi Informasi dan Ilmu Komputer ELINVO (Electronics, Informatics, and Vocational Education) JOIV : International Journal on Informatics Visualization 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) Jurnal Teknik Informatika (JUTIF) Journal of Information Technology and Cyber Security
Maria Ulfah Siregar
Unknown Affiliation
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering 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 has a profound influence on our lives, with the expansion of e-commerce being a significant outcome that warrants 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 employed machine learning techniques, specifically 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 determine accuracy. From this study, it was found that the KNN method, combined with 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.
Suatu Pendekatan Hibrid Menggunakan Topsis - Entropi pada Penentuan Siswa Penerima Beasiswa Prestasi Berbasiskan Kriteria Objektif Siregar, Maria Ulfah; Nasiroh, Titik; Mustakim, Muhammad
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8 No 1: Februari 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.0814261

Abstract

Kondisi ekonomi Indonesia yang turun seiring pandemi Covid-19 yang belum juga berakhir menyebabkan sebagian masyarakat menghadapi kesulitan ekonomi dalam pemenuhan kebutuhan hidup sehari-hari, salah satunya adalah dalam pembiayaan sekolah anak. Salah satu solusi bagi permasalahan ini adalah dengan pemberian beasiswa bagi siswa. Keterbatasan dana beasiswa ditambah dengan siswa yang berhak menerima bantuan adalah berjumlah banyak, menghendaki adanya suatu sistem yang dapat membantu dalam penentuan siswa penerima beasiswa.  Penelitian kami menerapkan pendekatan hibrid dari Multi-Attribute Decision Making (MADM) metode Entropi dan Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Metode Entropi digunakan untuk memberikan pembobotan pada kriteria secara objektif. Metode TOPSIS mencari alternatif terbaik dari sejumlah alternatif, yaitu yang semakin mendekati solusi ideal positif dan menjauhi solusi ideal negatif. Penelitian ini diimplementasikan di SMP N 1 Kokap Yogyakarta dengan menggunakan data siswa kelas 8 sejumlah 146 orang siswa. Terdapat sepuluh kriteria untuk menentukan siswa penerima beasiswa prestasi yaitu nilai pengetahuan, nilai keterampilan, catatan prestasi, keaktifan berorganisasi, jumlah ekstrakurikuler, jumlah ketidakhadiran, penghasilan ayah, penghasilan ibu, jumlah tanggungan orang tua dan status beasiswa. Berdasarkan sebaran dari evaluasi nilai kriteria dengan perhitungan bobot Entropi tanpa menggunakan bobot awal dari sekolah, diperoleh bahwa kriteria catatan prestasi adalah kriteria utama dalam penentuan siswa penerima beasiswa ini dan semua kriteria adalah valid. Output dari sistem ini adalah ranking siswa berdasarkan nilai preferensinya. Pe-ranking-an ini bisa dijadikan sebagai rekomendasi siswa penerima beasiswa prestasi di SMP N 1 Kokap. AbstractEconomic condition in Indonesia which is gradually decreased during Covid-19 pandemic which has not stopped yet causes some of citizens face economic difficulty in fulfilling their daily life, one of which is to pay their children’ school fees. One solution for this problem is to provide scholarship for students. The limited money for scholarship and students who are eligible to get the aids are many in number require a system which can ease in determining the merit scholars objectively. This study applies the hybrid approach of Multi-Attribute Decision Making (MADM) method, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Entropy. Entropy method is used to define objectively a weight of each criteria. TOPSIS method finds the best alternative from several alternatives that getting closer to the positive ideal solution and farther from the negative ideal solution. This study is implemented in SMP N 1 Kokap Yogyakarta and use data of 146 students on Kelas 8. There are ten criteria to determine merit scholars, these are a value of knowledge, a value of skills, a record of merits, organizational activities, the number of extracurriculars, for the number of absences, father's income, mother's income, the number of dependents of parents and scholarship status. Based on disperity of evaluation of criteria’ values, the achievements record is the important criteria for this system and all the criteria are valid. The result of this system is a rank of students based on their preference values. This rank could be used as a reference to recommend merit scholars in SMP N 1 Kokap.   
ANALYSIS OF CHATGPT ACCEPTANCE FOR EDUCATION USING MODIFIED TECHNOLOGY ACCEPTANCE MODEL Mahmud Rizal Mustofa; Siregar, Maria Ulfah
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2095

Abstract

The presence of ChatGPT provides various benefits from all sectors including education. However, despite the various benefits obtained, many researchers argue that ChatGPT also has many significant drawbacks. This research aims to analize the effect of perceived threat, perceived ease of use, perceived usefulness, attitude toward using dan behavioral intention to use the system of ChatGPT in education. The TAM modification in this research is the addition of a perceived threat variable which refers to the problem of the research object.The population in this research is active students of Universitas Islam Negeri Sunan Kalijaga Yogyakarta. The sampling technique is carried out using probability sampling or simple random sampling. While the determination the number of samples in this study used a sample table so that 377 respondents were students from various faculties. The data used in this study were obtained by distributing questionnaires and analyzed using SEM-PLS with the help of SmartPLS 3 software. The result of this research show that perceived threat and perceived ease of use affect perceived usefulness, perceived ease of use and perceived usefulness affect attitude toward using and attitude toward using affects behavioral intention to use of ChatGPT in education.
A Better Performance of GAN Fake Face Image Detection Using Error Level Analysis-CNN Siregar, Maria Ulfah; Nurochman, Nurochman; Setianingrum, Anif Hanifa; Larasati, Dwi; Santoso, William; Stefany, Meisia Dhea
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.2698

Abstract

The use of face images has been widely established in various fields, including security, finance, education, social security, and others. Meanwhile, modern scientific and technological advances make it easier for individuals to manipulate images, including those of faces. In one of these advancements, the Generative Adversarial Network method creates a fake image similar to the real one. An error-level analysis algorithm and a convolutional neural network are proposed to detect manipulated images generated by generative adversarial networks. There are two scenarios: a stand-alone convolutional neural network and a combination of error-level analysis and a convolutional neural network. Furthermore, the combined scenario has three sub-scenarios regarding the compression levels of the error-level analysis algorithm: 10%, 50%, and 90%. After training the data obtained from a public source, it becomes evident that using a convolutional neural network combined with compression of error level analysis can improve the model’s overall performance: accuracy, precision, recall, and other parameters. Based on the evaluation results, it was found that the highest quality convolutional neural network training was obtained when using 50% error level analysis compression because it could achieve 94% accuracy, 93.3% precision, 94.9% recall, 94.1% F1 Score, 98.7% ROC-AUC Score, and 98.8% AP Score. This research is expected to be a reference for implementing image detection processes between real and fake images from generative adversarial networks.
Co-Authors Akbar, Muhammad Tulus Alvriyanto, Azis Amalia, Alifah Amirullah -, Amirullah Andi Cahyono Anif Hanifa Setianingrum, Anif Hanifa Bahar , Afdi Fauzul Bambang Sugiantoro Barkah, Nida Muhliya Derrick, John Derrick, John Dewi , Bella Tasya Kumala Dwi Larasati, Dwi Gunawan, Eko Hadi Hamami, R. Abudullah Hisamuddin, Muhammad Zida Hisyamuddin, Muhammad Zida Mahardika, Devara Eko Katon Mahmud Rizal Mustofa Makayasa, Bagas Adi Mansur, Ejah Said Muhammad Anshari, Muhammad Muhammad Mustakim, Muhammad Mulyawan, Rizki Nasiroh, Titik Ndungi, Rebeccah Nufaily, Fathur Rachman Nurochman Nurochman, Nurochman Nuruzzaman, Muhammad Taufiq QOMARIYAH Rahmat Hidayat Ramadhan, Ade Umar Saifulloh, Muhammad Santoso, William Stefany, Meisia Dhea Syifaun Nafisah Yazid, Ahmad Subkhan Zamakhsyari, Fardan