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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 317 Documents
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K-Means Clustering of Social Studies Performance at Junior High School Tundo; Syifa Raihanah; Tri Wahyudi; Sugiyono
IJID (International Journal on Informatics for Development) Vol. 13 No. 2 (2024): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (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, Universitas Islam Negeri (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 Sinarring Azi Laga; Deny Hermansyah; Chitra Laksmi Rithmaya; Muhammad Zainuddin; Geo Ardana Ihsan Purnama Aji; Iqbal Ramadhani Mukhlis
IJID (International Journal on Informatics for Development) Vol. 13 No. 2 (2024): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (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; Fitri Damyati; Syifa Amara Dhestyani
IJID (International Journal on Informatics for Development) Vol. 13 No. 2 (2024): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (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 (AI) in higher education has shown significant potential to improve the efficiency and effectiveness of learning. The strategic implementation of AI in State Islamic Higher Education Institutions (Perguruan Tinggi Keagamaan Islam Negeri/ PTKIN) fosters innovative pedagogy and improved academic performance. This study employs the Fishbone Diagram approach to systematically analyze AI's impact on PTKIN’s 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 PTKIN 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; Iqbal Kurniawan Asmar Putra
IJID (International Journal on Informatics for Development) Vol. 13 No. 2 (2024): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (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.
IT Infrastructure Assessment using the COBIT 2019 Framework Aulia Faqih Rifa'i; Sumarsono; Muhammad Fauzan Al Baihaqi; Yazid Azfa Yasa
IJID (International Journal on Informatics for Development) Vol. 12 No. 2 (2023): IJID December
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2023.5152

Abstract

The Admission Office is responsible for student enrollment, and since 2013, the admission process at UIN Sunan Kalijaga has been supported by information technology. To assess the current state of the IT infrastructure in this university, the COBIT 2019 Framework was used. This study identifies five key domains in need of improvement: APO12 (manage risk), which focuses on managing IT-related risks within an organization, BAI10 (manage configuration), to ensure that IT services are delivered efficiently and effectively, DSS02 (manage service requests & incidents), involves the process of providing quick and efficient responses to user requests and handling various incidents, DSS03 (manage problems), to provide timely and effective support to consumers, ensuring their issues are addressed, their needs are met, and DSS04 (manage continuity), to ensure that the organization can respond effectively to incidents and disruptions, minimizing downtime and maintaining business continuity. The results showed that the capability levels for these domains in UIN Sunan Kalijaga were at Level 1, while the target was Level 4, leading to a capability gap of 3. The gap indicates that considerable effort is required to improve and achieve the desired level of maturity, and this research proposes some recommendations to improve the IT infrastructure.
Modification of the Weighted Product Model: Towards a Fairer and More Rational Ranking Adhie Thyo Priandika; Permata Permata; Sumanto Sumanto; Setiawansyah Setiawansyah
IJID (International Journal on Informatics for Development) Vol. 15 No. 1 (2026): IJID JUNE
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2026.6163

Abstract

The weighted product (WP) method is one of the popular methods in decision support systems (DSSs) due to its simplicity, calculation efficiency, and ability to handle various types of criteria with different weights. This research proposes a modification to the WP model designed to enhance fairness and rationality in the ranking process of alternatives. Therefore, a modified approach, Weighted Product with Averaging and Mean-Normalized Evaluation (WP-A), is adopted by integrating objective weighting methods and more adaptive normalization, so that each criterion can be evaluated proportionally. The supplier selection case study is used to test the effectiveness of the proposed model by comparing it with other MCDM methods. The results of the study show that the WP-A method produces more consistent results and has a stronger correlation with other methods, as indicated by Spearman's correlation test, with a value of 0.9828, indicating a very strong level of consistency with the reference rankings. The main contribution of this research is to provide a new framework for the development of the WP method so that it can be relied upon to support a more transparent and objective decision-making system.

Page 32 of 32 | Total Record : 317

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