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IJID (International Journal on Informatics for Development)
Published by Universitas Islam Negeri Sunan Kalijaga Yogyakarta
ISSN : 22527834     EISSN : 25497448     DOI : -
Core Subject : Science,
Computer Science & IT
One important point in the accreditation of higher education study programs is the availability of a journal that holds the results of research of many investigators. Since the year 2012, Informatics Department has English language. Journal called IJID International Journal on Informatics for Development. IJID Issues accommodate a variety of issues, the latest from the world of science and technology. One of the requirements of a quality journal if the journal is said to focus on one area of science and sustainability of IJID. We accept the scientific literature from the readers. And hopefully these journals can be useful for the development of IT in the world. Informatics Department Faculty of Science and Technology State Islamic University Sunan Kalijaga.
Arjuna Subject : -
Articles 234 Documents
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K-Means Clustering of Social Studies Performance at Junior High School Tundo; Raihanah, Syifa; Wahyudi, Tri; Sugiyono
IJID (International Journal on Informatics for Development) Vol. 13 No. 2 (2024): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2024.4632

Abstract

This study aims to optimize the use of technology in evaluating student performance by grouping students based on their abilities. The main issues include the underutilization of technology, the absence of an appropriate evaluation system for different levels of student ability, and ineffective methods for grouping students. The K-Means Clustering algorithm was chosen because it has proven effective in grouping academic data in various studies. The data used includes Daily Knowledge Scores (DKS), Daily skill scores (DSS), Mid-term Summative Scores (MSS), End-of-Year Summative Scores (ESS), and Grade Report (GR). The data was analyzed using the CRISP-DM methodology with the help of RapidMiner. The results showed that 28.63% of students were classified as having excellent performance, 50.21% as having good performance, and 21.16% as having moderate performance. The Davies-Bouldin Index score of 1.713 for K=3 was considered sufficient for distinguishing the different student performance groups. The results of this study are expected to help schools provide learning support that better aligns with student needs. Future research is recommended to focus on optimizing the number of clusters (K), applying this method to other subjects, and integrating it with e-learning platforms for real-time student performance monitoring.
Comparison of Single Exponential Smoothing and Double Moving Average Algorithms to Forecast Beef Production Tundo; Rachmat Hidayat Insani; Rasiban; Untung Suropati
IJID (International Journal on Informatics for Development) Vol. 13 No. 1 (2024): 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.2024.4663

Abstract

Beef is considered a high-value commodity as it is an important source of protein. Interest in beef continues to rise. Beef production has risen sharply in the past decade, but declined by 7,240.68 tons in 2020 amid coronavirus lockdowns. After that, in 2021, production reached 16,381.81 tons and continued to increase in 2022 and 2023. A precise method is required to forecast beef production. One way to predict beef production in Jakarta is using the Single Exponential Smoothing and Double Moving Average methods. The two algorithms are compared to get the lowest error rate. The methodology used in this research is the SEMMA (Sample, Explore, Modify, Model, and Assess) methodology. According to SAS Institute Inc., there are five stages in developing a system using the SEMMA methodology. After analyzing using MAPE, it is found that the algorithm with the smallest error value is the Single Exponential Smoothing algorithm with a percentage in the monthly period of 16% while for the annual period, it is 27% compared to other algorithms. The forecasting is quite accurate because the MAPE value for each algorithm used has an error of less than 31%.
Analyzing Customer Loyalty Levels through Segmentation in Aesthetic Clinics Using K-Means and RFAM Laga, Sinarring Azi; Hermansyah, Deny; Rithmaya, Chitra Laksmi; Zainuddin, Muhammad; Aji, Geo Ardana Ihsan Purnama; Mukhlis, Iqbal Ramadhani
IJID (International Journal on Informatics for Development) Vol. 13 No. 2 (2024): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2024.4841

Abstract

Effective customer segmentation is crucial in optimizing marketing strategies, particularly in customer-oriented aesthetic clinics. This research aims to enhance customer segmentation in aesthetic clinics using a K-Means approach based on the RFAM (Recency, Frequency, Average-Monetary) model. This approach is utilized to leverage historical customer data to identify customer segments based on their purchasing behavior, including visit frequency, average purchase amount, and the last time they visited the clinic. The K-Means clustering method maps customers into homogeneous groups, enabling aesthetic clinics to adapt more focused and personalized marketing strategies. The research results indicate insights obtained from the analysis and interpretation of RFAM conducted on 493 data points, resulting in the formation of two distinct clusters. In Cluster 1, denoting low loyalty, there are 156 customers, while Cluster 2 comprises 337 customers, reflecting high loyalty. Practical implications of this research include improvements in service customization and promotions tailored to customer needs and preferences. In conclusion, the K-Means approach based on the RFAM model can be utilized as an effective tool to enhance customer segmentation in the aesthetic clinic industry.
Assessing AI Integration in Islamic Higher Education: A Mixed-Methods Fishbone Diagram Analysis Aan Ansori; Damyati, Fitri; Dhestyani, Syifa Amara
IJID (International Journal on Informatics for Development) Vol. 13 No. 2 (2024): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2024.4862

Abstract

The integration of Artificial Intelligence in higher education has shown significant potential to improve the efficiency and effectiveness of learning. The strategic implementation of AI in Indonesian State Islamic Higher Education Institutions fosters innovative pedagogy and improved academic performance. This study employs the Fishbone Diagram approach to systematically analyze Artificial Intelligence's impact on Indonesian State Islamic Higher Education Institutions education, identifying key factors influencing implementation. The method employs a reverse-cause analysis, mapping factors contributing to a primary issue, and identifying underlying causes and sub-factors. Findings highlight the crucial roles of technological infrastructure, human resource readiness, supportive policies, adaptive curriculum design, and organizational culture. This study underscores the necessity of integrated AI adoption frameworks in Indonesian Islamic higher education, harmonizing technological advancement with Islamic pedagogical principles. This study offers a foundational framework guiding Indonesian State Islamic Higher Education Institutions in developing sustainable and ethical AI policies. Comprehensive AI policies and strategies are essential for PTKIN to harmonize innovation with Islamic principles.
Improving Osteosarcoma Detection through SMOTE-Driven Machine Learning Approaches Muhammad Ainul Fikri; Ajie Kusuma Wardhana; Yudha Riwanto; Inggrid Yanuar Risca Partiwi; Fauzia Sekar Anis Sekar Ningrum; Putra, Iqbal Kurniawan Asmar
IJID (International Journal on Informatics for Development) Vol. 13 No. 2 (2024): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2024.4890

Abstract

Osteosarcoma is an aggressive and highly malignant bone cancer primarily affecting adolescents and young adults, with males being more commonly affected. Although deep learning models such as YOLO (95.73% accuracy) and VGG19 (95.25% accuracy), have demonstrated effectiveness in osteosarcoma detection, their large model sizes and extensive computational requirements limit their feasibility in resource-constrained environments. This study proposes a lightweight AI approach that optimizes osteosarcoma detection while maintaining high diagnostic accuracy, leveraging machine learning models under 5MB, manually or semi-automatically extracted features, and SMOTE for data balancing. Experimental results show that Random Forest, SVM, and XGBoost achieve accuracies of 94.70%, 94.23%, and 94.39%, respectively, closely matching the performance of YOLO and VGG19 while maintaining computational efficiency. Furthermore, the inference time for SVM is under one second (0.97s), demonstrating the speed advantage of lightweight models. These findings highlight the potential of small-size (lightweight) machine learning models to deliver high diagnostic accuracy with minimal computational requirements, providing a scalable and practical solution for early osteosarcoma detection in resource-limited settings. By balancing simplicity, efficiency, and high performance, this study establishes a new benchmark for achieving state-of-the-art results with lightweight models and paving the way for improved healthcare accessibility in underserved regions.
Culinary Business Recommendation Application Using Promethee-II Method Setiawan, Eko Budi; Ferdika Bayu Herlambang; Angga Setiyadi
IJID (International Journal on Informatics for Development) Vol. 11 No. 1 (2022): 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.2022.3562

Abstract

This research discusses the application that can help determine consumer segmentation and provide recommendations for culinary business, including its selling price. The technology used in this research is a GPS sensor, Google API and JavaScript Object Notation. An Android-based mobile application platform uses GPS sensors to determine the new business location. This research used the Promethee-II method for the recommendation process, and it was based on certain criteria, particularly the factors influencing consumers to purchase. Five criteria were applied, and eight alternatives were calculated. The application was developed with the waterfall model. Based on the results of the alpha testing and user acceptance test, it is concluded that the application which was successfully built could provide prospective business makers with information regarding recommended types of culinary businesses including their selling price, recommended business location, and information about the consumer segmentation.
Quran Memorization Technologies and Methods: Literature Review Haryono, Kholid; Rajagede, Rian Adam; Negara, Muhammad Ulil Albab Surya
IJID (International Journal on Informatics for Development) Vol. 11 No. 1 (2022): 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.2022.3746

Abstract

The application of the Qur'an for the memorizers in adding and maintaining their memorization continues to grow in number. No less than 200 digital Qur'an applications are available on mobile application providers. In addition, publications on the topic of the digital Qur'an in the last ten years have also increased. Through these applications and publications, it is an opportunity to find patterns and knowledge about current topics and features. Through this knowledge, it is hoped that it can be a recommendation for a better form of digital Al-Qur'an application system, especially providing features that affect increasing the ease and quality of memorizing the Qur'an. This paper aims to explore the application of the Qur'an specifically for memorizing and papers on the topic to provide these recommendations. The method used to get the paper using PRISMA. While the applications being reviewed are taken from the AppStore. As a result, 31 papers were reviewed and 12 main applications regarding the Qur'an for memorization were obtained. Through the answers to each research question, it can be used by subsequent researchers as well as by system developers in developing Al-Qur'an products for better memorization of tense.
Assessing an Innovative Virtual Museum Application using Technology Acceptance Model Puspasari, Shinta; Ermatita; Zulkardi
IJID (International Journal on Informatics for Development) Vol. 11 No. 1 (2022): 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.2022.3758

Abstract

This study discusses the assessment of a virtual museum application development based on machine learning models for Palembang culture education through the Sultan Mahmud Badaruddin II museum cultural heritage by using the Technology Acceptance Model approach. The application was tested to measure the user acceptance of the application and pre-test and post-test to measure the effect size of the application as a learning media. A total of N=32 student participants were involved in testing the Sultan Mahmud Badaruddin II innovative virtual museum application for museum visitors was dominated by students who came to the museum to learn the culture and history of Palembang. Hypothesis testing results show that the perceived usefulness and perceived ease of use variables do not affect the attitude toward the use of the innovative virtual museum application. The attitude toward the use of the variable affects the behavioural intention to use, which directly also has a moderate effect on the actual use of the application where the dependent variables have the value of R2 > 0.5. The developed app is recommended as alternative learning media during a pandemic where the app testing participants express interest in using the app to enhance the Palembang culture learning experience.
Mathematical Modeling to Measure the Level of Terrorism Deradicalization Effectiveness Anatansyah Ayomi Anandari
IJID (International Journal on Informatics for Development) Vol. 11 No. 1 (2022): 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.2022.3777

Abstract

The general aim of deradicalization is to make terrorists or groups of perpetrators of violence willing to leave or break away from acts and activities of terrorism. The method in this study is quantitative-descriptive which will provide mathematical modelling to calculate the number of terrorist prisoners in Indonesia that have been successfully handled by the National Counter-Terrorism Agency (Badan Nasional Penanggulangan Terorisme) through a deradicalization program. The purpose of this study is to determine the effectiveness of the deradicalization program of the National Counter-Terrorism Agency in reducing the number of terrorist prisoners in Indonesia. The deradicalization program carried out by the government through the National Counter-Terrorism Agency aims to neutralize the ideological foundations of both militant and radical groups. The deradicalization used by the BNPT is the formula currently being implemented to deal with the threat of terrorism and also those related to radical groups. The mathematical model for terrorist deradicalization efforts by the National Counter-Terrorism Agency is  (dN(t))/dt=T-E-kN(t)-pN(t)-lN(t) .
Uncovering Insights in Spotify User Reviews with Optimized Support Vector Machine (SVM) Tri Romadloni, Nova; Kurniawan, Wakhid
IJID (International Journal on Informatics for Development) Vol. 14 No. 1 (2025): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

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

Abstract

The rapid growth of user-generated reviews on platforms like Spotify necessitates efficient analytical techniques to extract valuable insights.  This study employs a Support Vector Machine algorithm, optimized using Forward Selection, Backwards Elimination, Optimized Selection, Bagging, and AdaBoost, to effectively classify user reviews. A dataset of approximately 10,000 Spotify reviews was compiled from diverse online sources, ensuring a representative sample. The analysis reveals sentiment patterns across positive, negative, and neutral categories, with positive reviews dominates the landscape. These patterns help highlight Spotify’s strengths while identifying areas for improvement. However, the SVM algorithm faces challenges in classifying minority classes, particularly negative sentiments, due to class imbalance. To address this, advanced optimization techniques are utilized to enhance classification precision and recall. Preprocessing steps, including data cleansing, tokenization, stemming, and stopword removal, refine the dataset, while TF-IDF converts text into numerical features for effective feature selection. The results show that the Optimized Selection method achieves the highest accuracy of 84.5%, outperforming other approaches. This research contributes significantly to developing balanced sentiment analysis models. Future studies may explore deep learning techniques to further improve classification accuracy and mitigate current limitations in data representation.

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