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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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Comparison of K-Nearest Neighbor, Support Vector Machine, Random Forest, and C 4.5 Algorithms on Indoor Positioning System Astari, M Rizky; Nuruzzaman, Muhammad Taufiq; Sugiantoro, Bambang
IJID (International Journal on Informatics for Development) Vol. 12 No. 1 (2023): 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.2023.3991

Abstract

Today’s most common Positioning System applied is the Global Positioning System (GPS). Positioning System is considered accurate when outdoors, but it becomes a problem when indoors making it difficult to read the GPS signal. Many academics are actively working on indoor positioning solutions to address GPS's drawbacks. Because WiFi Access Point signals are frequently employed in multiple studies, they are used as research material. This study compares the classification algorithms KNN, SVM, Random Forest, and C 4.5 to see which algorithm provides more accurate calculations. The fingerprinting method was employed in the process of collecting signal strength data in each room of the Terpadu Laboratory Building at UIN Sunan Kalijaga using 30 rooms and a total dataset of 5,977 data. The data is utilized to run experiments to determine the location using various methods. According to the experimental data, the Random Forest algorithm achieves an accuracy rate of 83%, C4.5 81%, and KNN 80%, while the SVM method achieves the lowest accuracy rate of 57%.
The WASPAS Method in Determining BSM Recipients Objectively Tundo, Tundo; Wijonarko, Panji; Raffiudin, Muhammad
IJID (International Journal on Informatics for Development) Vol. 12 No. 1 (2023): 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.2023.4089

Abstract

This research was conducted due to complaints from several parents regarding the determination of BSM at SDN Karanganyar 02 which still contains subjectivity in its selection so that some students are less fortunate. SDN Karanganyar 02, once a year always carries out activities related to determining the selection of BSM recipients. With this activity, it is hoped that students who are underprivileged but have fairly good achievements can receive this BSM so that the activities they carry out do not feel burdened with financial needs. The fact is that in institutions there are still many students who do not get BSM, even though according to the requirements these students should be eligible to get BSM. So in the selection that occurs there is a very irrational subjectivity. To solve this problem, the researcher tries to make a solution through an application that applies the Weight Aggregated Sum Product Assessment (WASPAS) method, which is a method of determining with predetermined criteria. The criteria in question are activities, achievements, report cards, parental income, home conditions, and parental dependents. After analyzing and implementing the WASPAS Decision Support System, it was found that the results were detrimental to students where the criteria scores and final determination were lower than some other students, but the SD carried out an assessment by obtaining BSM. To prevent this incident from recurring, WASPAS is very capable of answering objective determinations with the results obtained at 79.88% and the previous subjective determination at 20.12%.
Comparative Analysis of The Combination of Metaheuristic and Machine Learning Algorithms Sirmayanti; Rahman, Farhan; Hendro Prastyo, Pulung; Mahyati
IJID (International Journal on Informatics for Development) 2026
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.2026.4888

Abstract

Diabetes affects about 1.9% of the global population, mainly through Type 2 diabetes. Machine learning (ML) serves a pivotal role in enhancing diabetes prediction by analyzing complex datasets. Feature selection, a crucial ML pre-processing step, improved prediction accuracy by identifying relevant data and discarding irrelevant features. This study investigates the combination of metaheuristic algorithms and ML techniques to enhance diabetes prediction accuracy and computational efficiency. Utilizing the PIMA, Early Stage, and Vanderbilt datasets, experiments evaluated ten algorithm-model combinations based on metrics like accuracy, precision, the Wilcoxon test, and convergence curves. Key findings included that Firefly Algorithm-Logistic Regression, Bat Algorithm-Logistic Regression, and Cuckoo Search-Logistic Regression achieved 74.72% accuracy on PIMA; Firefly Algorithm-Support Vector Machine and Cuckoo Search-Naïve Bayes achieved 83.39% accuracy and 96.15% precision on Early Stage; and Firefly Algorithm-Naïve Bayes achieved 92.88% accuracy and precision on Vanderbilt. These results highlighted the potential of integrating metaheuristics with ML methods to improve clinical diagnostics. Future research is recommended to validate algorithm robustness across diverse datasets to further optimize diabetes prediction strategies.
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, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2025.4903

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.
The Impact of Algorithms on Decision-Making in Daily Life: A Polling Study of Technology Users Dwi Yuniarto; Akbar, Yopi Hidayatul; Abd. Rahman, Aedah; Herdiana, Dody
IJID (International Journal on Informatics for Development) Vol. 14 No. 1 (2025): 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.2025.4973

Abstract

Algorithms have become an integral part of everyday life, particularly in entertainment, shopping, and navigation. This study examines how algorithms influence individual decision-making. Data were collected through an online poll involving 200 respondents, selected using a statistical sampling method. The results indicate that 55% of respondents perceive algorithms as having a significant influence on their decisions, while 28% report a moderate impact. A confidence interval analysis (95%) has been included to ensure statistical accuracy. The study highlights the importance of digital literacy in mitigating algorithmic bias and suggests future research on how socio-cultural factors shape algorithmic perceptions. This research contributes to understanding the extent of algorithmic influence on daily decision-making and raises user awareness of technology’s impact. The implications include the importance of digital literacy to mitigate dependency and bias in algorithm usage and the potential to develop more transparent and ethical algorithmic systems. Future research could explore the relationship between users' awareness of algorithms and their behaviors in various contexts and evaluate ways to enhance public understanding of how algorithms function in the evolving digital ecosystem.
Implementation and Performance Analysis of PVD Method in Concealing Encrypted Data on Images Hanif, Ardhan; Astuti, Nur Rochmah Dyah Puji; Aribowo, Eko
IJID (International Journal on Informatics for Development) Vol. 14 No. 1 (2025): 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.2025.4984

Abstract

This research aims to secure text data by combining steganography and cryptography. The Pixel Value Differencing (PVD) method allows for higher data insertion capacity with minimal distortion, thereby increasing resistance to steganalysis. However, the PVD steganography method is vulnerable to variation in image areas and to the accuracy of Pixel Difference Histogram (PDH) analysis. In addition, this method is susceptible to statistical tools such as the chi-square and RS, which can be used to analyze the distribution of pixel value differences, allowing data to be detected. To address the limitations of the PVD method, we employed a cryptographic technique called XOR-VLSB, which combines XOR as the primary encryption method, Vigenère Cipher for key generation, and Least Significant Bit (LSB) for key embedding. The results showed that the fully encrypted data could be recovered and had good image quality, as indicated by the metric results, which included a low MSE value, a PSNR above 35 dB, and an SSIM value close to 1. In this study, the process of encrypting text data still uses a simple encryption algorithm, namely XOR. Future research may involve replacing cryptographic algorithms with AES, which offers stronger protection and better resistance to advanced security threats.
Implementation of Federated Learning for Alzheimer's Disease Classification Using FedAdagrad Algorithm Arini, Arini; Fahrianto, Feri; Ramadhan, Adil
IJID (International Journal on Informatics for Development) Vol. 14 No. 2 (2025): 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.2025.5045

Abstract

Federated Learning (FL) offers a promising solution for training machine learning models on decentralized data while preserving privacy, making it particularly valuable for sensitive applications such as healthcare. This study implements FL for the classification of Alzheimer’s disease using MRI images, addressing two critical challenges: data heterogeneity and class imbalance. The research evaluates the performance of the FedAdagrad optimization algorithm against the standard FedAvg approach under varying data distribution scenarios. The methodology employs a CNN trained on a dataset of 6,400 MRI images across four severity classes, partitioned non-IID using Dirichlet distributions (α = 0.1, 0.5, 0.9) to simulate real-world heterogeneity. Experiments were conducted using the Flower framework with four clients over ten communication rounds. Results indicate that FedAdagrad achieves a superior F1-score of 50.33% compared to FedAvg’s 48.14%, though both fall short of centralized CNN performance (55%). High data heterogeneity (α = 0.1) leads to a 13.35% accuracy decline, underscoring FL’s sensitivity to uneven data distributions. Class imbalance emerges as the primary bottleneck, affecting all models. The findings contribute to the growing body of research on adaptive optimization in federated settings, offering insights for future improvements in decentralized healthcare AI.
Towards Fair and Efficient Timetabling: A Genetic Algorithm Model Integrating Lecturer Day-Off Requests Khaeroni, Khaeroni; Muqdamien, Birru; Hestiningtyas, Ajeng
IJID (International Journal on Informatics for Development) Vol. 14 No. 1 (2025): 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.2025.5067

Abstract

This study tackles the complex challenge of lecture timetabling by incorporating lecturer day-off preferences, a crucial constraint often neglected in traditional scheduling methods. Given the NP-hard nature of the problem and the need for scalable solutions, a Genetic Algorithm (GA) was employed with a population size of 10, a crossover probability of 0.70, a mutation probability of 0.20, and a maximum generation of 10000. The proposed GA-based method, implemented using PHP and MySQL, is applied to a real-world scenario involving 25 courses, 22 lecturers, and six classrooms over a 5-day weekly schedule at the Faculty of Education and Teacher Training for the Even Semester of the 2023/2024 Academic Year. Experimental results, validated through the Mann-Whitney test, show that incorporating lecturer preferences enhances scheduling flexibility without significantly increasing computational time. Comparative analysis with Simulated Annealing and Tabu Search demonstrates the competitive performance of the GA-based method in optimizing lecture schedules. This study provides a practical solution for educational institutions seeking to improve their timetabling processes.
Sentiment Analysis on Shopee Xpress Delivery Time Reviews Using Support Vector Machine and Logistic Regression Sewin Fathurrohman; Irfan Ricky Afandi; Irma Wahyuningtyas; Azis Styo Nugroho; Firman Noor Hasan
IJID (International Journal on Informatics for Development) Vol. 14 No. 2 (2025): 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.2025.5073

Abstract

This study examines user sentiment towards Shopee Xpress delivery times using machine learning techniques. We collected 497 reviews from platforms like X and the Google Play Store, leveraging the valuable feedback despite its unstructured and informal nature. After labelling 398 reviews for model training and reserving 99 for sentiment prediction, we implemented two classification algorithms: Support Vector Machine (SVM) and Logistic Regression. These models categorised sentiments into negative, neutral, and positive classes. Despite class imbalance in the training data, SVM outperformed Logistic Regression with an accuracy of 93%, demonstrating a more balanced performance across sentiment categories compared to Logistic Regression's 90% accuracy. Both models showed consistent sentiment prediction on new data. Our findings highlight the potential of sentiment analysis as a valuable tool for Shopee Xpress to understand customer perceptions and improve delivery experiences. By providing actionable insights, this study can inform logistics improvements and enhance customer satisfaction. Future research could benefit from collaborating with Shopee to access internal data and integrating additional data sources for more comprehensive insights, ultimately driving business growth and customer loyalty. This study contributes to the growing body of research on sentiment analysis in logistics and e-commerce.
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, Universitas Islam Negeri (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.

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