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JISKa (Jurnal Informatika Sunan Kalijaga)
Published by Universitas Islam Negeri Sunan Kalijaga Yogyakarta
ISSN : 25275836     EISSN : 25280074     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Library & Information Science
JISKa (Jurnal Informatika Sunan Kalijaga) adalah jurnal yang mencoba untuk mempelajari dan mengembangkan konsep Integrasi dan Interkoneksi Agama dan Informatika yang diterbitkan oleh Departemen Teknik Informasi UIN Sunan Kalijaga Yogyakarta. JISKa menyediakan forum bagi para dosen, peneliti, mahasiswa dan praktisi untuk menerbitkan artikel penelitiannya, mengkaji artikel dari para kontributor, dan teknologi baru yang berkaitan dengan informatika dari berbagai disiplin ilmu
Arjuna Subject : -
Articles 231 Documents
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Analisis Cluster untuk Pengelompokan Kemampuan Penguasaan ICT Menggunakan K-Means dan Autoencoder Prasetyawan, Daru; Gatra, Rahmadhan
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 2 (2025): May 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.2.145-157

Abstract

Information and Communication Technology (ICT) skills are essential in today’s digital age. However, numerous new students possess varying levels of ICT proficiency and may lack the necessary skills expected by universities. ICT training is essential for enhancing students’ ICT skills. Nevertheless, delivering the same training to all students proves to be less effective. Therefore, grouping students’ ICT skills is crucial to ensure that the training provided aligns with the fundamental abilities of the students. Cluster analysis is a common method for grouping data. This study employs k-Means and an autoencoder for cluster analysis, with the autoencoder utilized to reduce data dimensions and k-Means to perform the clustering process. The Elbow method is utilized to identify the ideal number of clusters. The optimal number of clusters determined was three. Model evaluation was conducted using the Silhouette coefficient and the Davies-Bouldin Index (DBI). The evaluation results revealed that the combination of k-Means and autoencoder yields superior performance compared to using k-Means alone, as evidenced by a higher Silhouette value and a lower DBI value.
Analisis Sentimen Ulasan Pengguna Aplikasi Alfagift Menggunakan Random Forest Prayogi, M. Bagus; Masitoh, Gustina
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 2 (2025): May 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.2.158-170

Abstract

Alfagift is a mobile application developed by Alfamart to support online ordering, featuring promotions, transactions, ordering, and delivery from the nearest point based on the consumer’s address. User feedback on the Google Play Store reveals mixed sentiments, including both positive and negative responses, which developers can use as material to improve the application’s quality. This study focuses on assessing the sentiment of Alfagift app user reviews using the Random Forest algorithm. A total of 4,379 review data points were collected from the Google Play Store and grouped into two categories: positive and negative sentiment. The research steps include data collection, data labeling, data preprocessing, word weighting, dividing the data into training and testing sets, implementing the Random Forest algorithm, and model evaluation. The test results show that the Random Forest algorithm achieves an accuracy of 97.6% and an AUC of 0.98, which falls into the category of excellent classification. This research is expected to contribute to application developers’ understanding of user perceptions, enabling them to improve application quality and increase overall user convenience.
Imputasi Data Temperatur Maksimum Menggunakan Metode Support Vector Regression Sukhna, Isa Kholifatus; Miftahurrohmah, Brina; Wulandari, Catur; Amelia, Putri
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 2 (2025): May 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.2.171-185

Abstract

Temperature is a crucial element affecting various aspects, from agriculture to natural disasters. Temperature data imputation is also important because, in some cases, temperature data is not always complete. This study aims to predict missing temperature data in the East Nusa Tenggara (NTT) region using the Support Vector Regression (SVR) method. The data used comes from six BMKG observation stations in NTT and ERA-5 Reanalysis data. The choice of the SVR method is based on its ability to handle data with complex structures. Modeling is conducted separately for each station using the Radial Basis Function (RBF) kernel. Model evaluation employs the metrics Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R²), presenting the evaluation results with low error. The results show that among several parameter tests, the parameter ranges [C = 1, 5, 10, 15], [ε = 0,1, 0,3, 0,6, 0,9], and [γ = 1, 5, 10, 15] in the SVR method are the best parameter ranges across all stations. The prediction graphs display different temperature fluctuation patterns at each station. This study contributes to enhancing the availability of accurate climate data, supporting sustainable decision-making in the NTT region.
Perbandingan Random Forest dan Convolutional Neural Network dalam Memprediksi Peralihan Pelanggan Kusuma, Dewa Adji; Dewi, Atika Ratna; Wijaya, Andreas Rony
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 2 (2025): May 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.2.186-194

Abstract

The rapid growth of the telecommunications industry has increased competition among companies for customers. As a result, customers often switch to other services or terminate their subscriptions. Retaining customers is very important as it is 10 times cheaper than acquiring new customers. This study compares Random Forest (RF) and Convolutional Neural Network (CNN) algorithms in predicting customer switching, using Correlation-based Feature Selection (CFS) and Recursive Feature Elimination (RFE) for data partitioning. Model evaluation using Confusion Matrix and Area Under Curve (AUC). The evaluation results show that the performance of CNN models with optimization parameters is superior. Using the CFS dataset, the test data evaluation results yielded an accuracy of 98%, AUC of 0.96, precision of 99%, recall of 92%, and F1-score of 96%. The best tuning result for CNN is achieved with three combinations of filter and kernel sizes {[64, 7], [32, 3], [16, 2]} and a pool size of 2. A limitation of this research is determining how to compare the two algorithms being evaluated effectively. Both use different approaches, namely Supervised Learning and Deep Learning.
Penggunaan Teknik Transfer Learning pada Metode CNN untuk Pengenalan Tanaman Bunga Mufidatuzzainiya, Agustina; Faisal, Muhammad
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 2 (2025): May 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.2.195-206

Abstract

This study investigates the impact of employing the transfer learning method on improving flower recognition performance using Convolutional Neural Network (CNN) models. The dataset used consists of 4242 flower images divided into five classes: daisy, tulip, rose, sunflower, and dandelion. This research implements three models: basic CNN, VGG16, and EfficientNetB3, to test the effectiveness of transfer learning in flower classification. The basic CNN model achieved a training accuracy of 73.38% and a validation accuracy of 71.76%, but it generally fails to generalize to new data. The VGG16 model achieved perfect training accuracy but experienced overfitting, with validation accuracy stabilizing around 85-90%. Meanwhile, the EfficientNetB3 model with transfer learning reached a training accuracy of 98.50% and a validation accuracy of 94.00%, demonstrating strong generalization without significant overfitting. The experiment was conducted using data augmentation techniques, and performance evaluation was carried out using accuracy, precision, and recall metrics. The results show that transfer learning with the EfficientNetB3 model provides the best performance in flower classification compared to the basic CNN and VGG16 models. For future research, further development can be done by expanding the types of flower datasets and applying additional optimization techniques to improve accuracy in more complex models.
Perbandingan Sensitivitas Metode SAW, MAUT dan WSM pada Anugerah Mutu Non-Akademik Universitas Wonoseto, Muhammad Galih; Hunaif, Muhammad Abu Shaker
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 2 (2025): May 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.2.207-220

Abstract

A Decision Support System works best with a suitable method. Unfortunately, not all methods are equally used. Two rarely used methods are the MAUT and WSM methods. To determine whether a method is more suitable for a case than another, a sensitivity test is conducted. By conducting sensitivity tests between the two methods and other commonly used methods, such as SAW, in the same case, it’s possible to compare the sensitivity percentages of the three. One case that can be helped by a Decision Support System using the three methods is the ANOMIK assessment at universities. The three methods produced the same best alternative, namely Faculty 9. After conducting a sensitivity test, the results showed that the WSM method was the most sensitive, with a value of 4.954%, followed by the SAW method with a value of 4.901%, and finally the MAUT method with a value of 3.844%.
Algoritma Random Forest dan Synthetic Minority Oversampling Technique (SMOTE) untuk Deteksi Diabetes Nurussakinah, Nurussakinah; Faisal, Muhammad; Santoso, Irwan Budi
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 2 (2025): May 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.2.221-234

Abstract

Diabetes is one of the challenges in global health. Indonesia ranks 5th in the world with the highest rate of diabetes. This research uses the Random Forest algorithm for diabetes detection. The purpose of this study is to detect diabetes using the Random Forest algorithm, which provides accurate and efficient results in the early diagnosis of diabetic patients. The data used is secondary data, specifically the “Diabetes Dataset,” which consists of 952 data points and has 17 features. The test scenario in this study divides the data into three parts, namely scenario 1 (90:10 ratio), scenario 2 (70:30 ratio), and scenario 3 (50:50 ratio). In each scenario, a comparison is made between using SMOTE and not using it. The best performance results are obtained in scenario 1, which uses SMOTE, producing 97% accuracy, 100% precision, 94% recall, and an F1-score of 97%.
Klasifikasi Penyakit pada Tanaman Berdasarkan Citra Daun Menggunakan Metode Convolutional Neural Network Pangestu, Denis Aji; Aziz, Okta Qomaruddin; Crysdian, Cahyo
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 2 (2025): May 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.2.235-248

Abstract

The agricultural sector is a vital part of the economy, providing food, raw materials, and employment opportunities. In Indonesia, this sector faces significant challenges, such as low interest from younger generations and plant disease issues. Plant disease identification typically requires the expertise of experienced professionals, but this process is time-consuming and costly. This research aims to develop a plant disease classification model using a Convolutional Neural Network (CNN) to assist farmers in identifying diseases in rice, corn, tomato, and potato plants based on leaf images. Testing was conducted with data splitting ratios of 70:30, 80:20, and 90:10, using both single-stage and multi-stage classification methods. The best results were achieved with an 80:20 data ratio using single-stage classification, with an average accuracy of 80%, precision of 80%, recall of 81%, and F1-score of 79%. This study demonstrates that the CNN method is effective in plant disease classification, achieving optimal performance at a 80:20 data ratio and in single-stage classification. It is hoped that this research can help farmers quickly and accurately identify and manage plant diseases, as well as encourage innovation in the agricultural sector. The implementation of CNN in plant disease classification shows great potential in enhancing the efficiency and accuracy of disease detection, ultimately supporting the sustainability and development of the agricultural sector.
Prediksi Kualitas Udara Menggunakan Metode CatBoost Syukur, Mohamad Arif Abdul; Suhartono, Suhartono; Chamidy, Totok
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 2 (2025): May 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.2.249-258

Abstract

Air is essential for life, but industrial activities, forest fires, cigarette smoke, and transportation contribute to air pollution. AirVisual AQI 2024 data ranks Jakarta in 11th place globally, with the highest level of pollution, reaching 127, which is unhealthy for sensitive groups and poses a risk of causing serious illnesses, including skin and respiratory diseases. This research uses the CatBoost method to predict the air quality index using Jakarta SPKU data taken from Kaggle. The data is processed through pre-processing and divided into four models with different comparisons of training and testing data. Each model was tested with the parameters iteration, depth, learning rate, and l2_leaf_reg, using GridSearchCV to find the optimal combination. The results show that the model with 90% training data and 10% testing data provides the best accuracy of 97%, due to the larger proportion of training data. This research demonstrates that the CatBoost method can yield accurate air quality predictions, which is crucial in supporting efforts to mitigate the impact of pollution and enhance public health.
Peramalan Nilai Saham BBCA Melalui Pendekatan Time Series Menggunakan Teknik Exponential Smoothing Liantoni, Febri; Simanjuntak, Ondihon
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.2025.10.3.259-266

Abstract

Forecasting stock prices plays a crucial role in shaping investment strategies within the financial market. This article aims to predict the stock prices of Bank Central Asia (BBCA), a prominent entity in the Indonesian banking sector. Employing a time series methodology, this study utilizes the Exponential Smoothing technique to anticipate the fluctuations in BBCA's share prices. Meanwhile, the dataset used is the BBCA share price data from April 2001 to early January 2023. The final error rate in this forecast is 10%.

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