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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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Klasifikasi Buah dan Sayuran Segar atau Busuk Menggunakan Convolutional Neural Network Munfaati, Eka Aenun Nisa; Witanti, Arita
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 1 (2024): Januari 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.1.27-38

Abstract

Fresh fruits and vegetables contain many nutrients, such as minerals, vitamins, antioxidants, and beneficial fiber, superior to those found in rotten or almost rotten produce. On the other hand, fruits and vegetables that are nearly spoiled or already rotten have significantly lost their nutritional value. Rotten produce also harbors bacteria and fungi that can lead to infections and food poisoning when consumed. Convolutional Neural Network (CNN) offers a programmable solution for classifying fresh and rotten fruits and vegetables. Image processing using the TensorFlow library is employed in this classification process. During testing on the training data, the CNN achieved an accuracy of 90.42%. In comparison, the validation accuracy reached 94.21% when using the SGD optimizer, 20 epochs, a batch size 16, and a learning rate of 0.01. For the testing data, the accuracy obtained was 80.83%.
Improving Stock Price Prediction Accuracy with StacBi LSTM Diqi, Mohammad; Hamzah, Hamzah
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 1 (2024): Januari 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.1.10-26

Abstract

This research aimed to enhance stock price prediction accuracy using the Stacked Bidirectional Long Short-Term Memory (StacBi LSTM) model. The study addressed the challenge of capturing long-term dependencies and temporal patterns inherent in stock price data. The research objectives were to evaluate the model's performance across different input sequence lengths and identify the optimal length for prediction. Leveraging a dataset from the Indonesian Stock Exchange, the model's predictions were evaluated using key metrics such as RMSE, MAE, MAPE, and R2. Results indicated that the StacBi LSTM model excelled in capturing stock price trends and demonstrated strengths over traditional methods. The optimal input sequence length was identified, balancing computational efficiency and prediction accuracy. This research contributes valuable insights into improving stock price prediction techniques and offers practical implications for traders and investors. Future research directions encompass hybrid models and integrating external factors to enhance predictive capabilities further.
Analisa Jejaring Sosial Terhadap Fenomena Cyberbullying Fandom K-Pop pada Sosial Media Twitter Ghufron, Mohammad Iqbal; Supriyati, Endang; Listyorini, Tri
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 2 (2024): Mei 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.2.79-93

Abstract

This study examines cyberbullying among K-pop fandoms through social network analysis (SNA) using data from Twitter, a social media platform. The phenomenon of K-pop gaining global popularity also brings negative impacts, such as cyberbullying, which can affect the psychological well-being of victims. Using R Studio and Gephi analysis tools, this study applied centrality values, including degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality, to identify influential accounts in the spread of the cyberbullying phenomenon. This analysis provides insight into the interaction and influence between Twitter user accounts in the context of cyberbullying. The main objective of this research is to paint a picture of the cyberbullying phenomenon involving various K-pop fandoms and identify the accounts that play an essential role in the related communication network.
Implementasi Load Balancing dengan HAProxy di Sistem Informasi Akademik UIN Sunan Kalijaga Wirawan, Adi; Gatra, Rahmadhan; Hidayat, Hendra; Prasetyawan, Daru
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 1 (2024): Januari 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.1.39-49

Abstract

Efficiently managing academic information systems (AIS) is essential for educational institutions to provide reliable services to students and faculty. This research explores the integration of HAProxy load balancing and file synchronization techniques to optimize the performance of AIS. HAProxy is employed to distribute incoming requests across multiple backend servers, and the backend will call web service to access the data saved in the database to facilitate seamless data sharing and access. Additionally, file synchronization mechanisms are implemented to maintain consistency across scripts used in the backend system. The study conducts performance evaluations and benchmarks to assess the impact of HAProxy load balancing and file synchronization on AIS responsiveness and reliability. The results reveal significant system scalability and fault tolerance improvements, reducing downtime and enhancing user experience. This research contributes to optimizing academic information systems, enhancing their ability to handle increased loads, and ensuring the efficient delivery of educational services.
Pemeringkatan Kinerja Dosen pada Perguruan Tinggi Swasta Menggunakan Algoritma Simple Additive Weighting Mufida, Elly; Iriadi, Nandang; Andriansyah, Doni
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 1 (2024): Januari 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.1.59-69

Abstract

The Tri Dharma of Higher Education is an obligation for lecturers while carrying out their duties as lecturers at higher education institutions, the implementation of which is regulated in Law Number 20 of 2003 concerning the National Education System. The Tri Dharma of Higher Education is a lecturer's obligation, including Education and Teaching, Research and Development, and Community Service. Lecturers need Support and motivation to implement quality Tri Dharma, especially at "X" Private Universities. Providing rewards or awards can motivate lecturers to give their best performance to Tri Dharma. Student feedback is also needed as evaluation material for lecturers to measure their teaching abilities. Rewarding lecturers can be done by ranking lecturer performance, especially at private universities. The SAW (Simple Additive Weighting) algorithm ranks lecturer performance through the criteria of education and teaching, research, community service, and student feedback. An assessment of several subcriteria presents each criterion. The normalized scoring matrix is ​​the ranking preference. From the results of data processing on lecturer performance and feedback from students, with a sample of 25 lecturers, a ranking score was obtained on a scale of 0 to 1, where a score of 1 is the highest ranking. The lecturer performance ranking process involves lecturers, students, and the Study Program Management Unit. A lecturer performance rating information system is needed to facilitate all actors' involvement in the lecturer performance rating process and provide valid and timely rating results to stakeholders.
Server Redundancy: Performa Jaringan Mengunakan DNS Failover MikroTik pada Kasus Private Server dan Public Server Sandi, Tommi Alfian Armawan; Firmansyah, Firmansyah; Fauzi, Ahmad
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 1 (2024): Januari 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.1.50-58

Abstract

The digitization of user services has increased, in line with the need for VPS and cloud computing services, which are rampant among application and platform developers. Quite a few companies that create applications or application users have servers to handle user needs. Testing is carried out using the ICMP Protocol to get real-time results and can be measured. From Scenario 1, carrying out 20 test requests, we get a packet loss of 5% with RTT Avarage of 195,838ms and Mdev 4,103ms. If you apply DNS failover in scenario 2, the client will likely access the web a little slower, as evidenced by the packet loss being 25% greater in value. Compared to scenario 1, having a high standard deviation (Mdev) of round-trip times is not desirable. This variation is also known as jitter. Increased jitter can cause a bad user experience, especially in real-time audio and video streaming applications. However, this is still understandable because it only has a 1-5 second effect on the service. Next, in scenario 3, we can see that private and public servers have relatively high gab with 0% packet loss, which has a small Mdev value of 0.309ms. Therefore, the DNS failover method is a solution for network administrators who have problems related to server migration between public servers and private servers so that services can run even if a server is maintaining or downlinking.
Identifikasi Kematangan Buah Pisang Berdasarkan Variasi Jarak Menggunakan Metode K-Nearest Neighbor Ananda, Rizky Putu; Liantoni, Febri; Prakisya , Nurcahya Pradana Taufik
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 3 (2024): September 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.3.159-169

Abstract

This research aims to identify the level of ripeness of kepok bananas based on the color of their skin using the K-Nearest Neighbor (K-NN) method. Bananas are an important commodity in Indonesia, and various ripeness levels need to be identified. The current process of identifying banana ripeness is still done manually, which requires a lot of labor and tends to be subjective. The K-NN method is used to classify bananas based on their skin color. This research involves the collection of banana images with three ripeness levels (raw, ripe, and overripe) and the extraction of RGB color features from these images. Three distance methods, namely Euclidean, Minkowski, and Manhattan, are also employed to compare accuracy results. The evaluation results of this research show that the accuracy value for the Euclidean distance method is 84%, the Minkowski distance method is 82%, and the Manhattan distance method is 80%. Thus, the findings indicate that the K-NN method and the Euclidean distance method provide good results in identifying the ripeness level of bananas. By implementing the K-NN algorithm, this research attempts to address the weaknesses of the time-consuming and subjective manual identification process, with the hope of providing a more accurate and efficient solution for the banana industry. The results of this research can be used to automate the identification process of banana ripeness levels and improve efficiency in banana sorting. It is expected that this research can provide practical benefits to the community and serve as a basis for further research in this field.
Analisis Keamanan Data Pelanggan dalam Menghadapi Tantangan Penggunaan Marketplace Dewantara, Rizki; Bintang, Rauhulloh Ayatulloh Khomeini Noor; Gatra, Rahmadhan
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 2 (2024): Mei 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.2.94-104

Abstract

The advent of the digital economy makes commerce more accessible to everyone. Example: an online marketplace app that simplifies buying and selling. The marketplace app's useful features and ease of use attract many users. The marketplace app's functionality and convenience have been enhanced to meet consumer expectations and prioritize consumer data protection. This study investigates how customers protect their data when shopping online and utilizing marketplace apps. Environmental and social influences, personal data security facilities, the goal of utilizing the marketplace, and awareness of customer data security when using the marketplace application were asked of 70 random sample participants. The questionnaires had 16 Guttman scale questions. According to the report, 81.42% of customers trust the marketplace app to protect their data. Likewise, 88.57% of customers strongly believe that the marketplace application they use secures their personal information, indicating that this is related to their marketplace service needs.
Deep Learning dalam Prediksi Kebiasaan Merokok di Inggris Guna Mendukung Kebijakan Kesehatan Masyarakat yang Lebih Efektif Prabaswara, Muhammad Arden; Pratama, Kalistus Haris; Majid, Desva Fitranda; Liantoni, Febri
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 2 (2024): Mei 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.2.105-111

Abstract

Smoking is a common practice throughout the world, where a person smokes and inhales the smoke produced from burning tobacco or other tobacco products. This action has become a significant global health issue because of the various health risks. This activity is often considered an addictive habit because nicotine, the psychoactive compound in tobacco, can cause physical and psychological dependence. This research applies Deep Learning methods to predict data on smoking habits in the UK. The dataset used in this research includes information about gender, age, marital status, highest level of education, nationality, ethnicity, income, and region. Through this research using Deep Learning methods, we can examine a complex data set that describes Smoking Habits in the UK. Based on trials with a dataset of 1,691 items, an accuracy of 78% was obtained. This research can provide important insights into the effectiveness of anti-smoking policies that have been implemented and help plan further actions to reduce the prevalence of smoking and its negative impact on society.
Segmentasi Pelanggan E-Commerce Menggunakan Fitur Recency, Frequency, Monetary (RFM) dan Algoritma Klasterisasi K-Means Fauzan, Reyhan Muhammad; Alfian, Ganjar
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 9 No. 3 (2024): September 2024
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2024.9.3.170-177

Abstract

The rapid growth in the e-commerce industry demands the development of smarter and more focused marketing strategies. One approach that can be applied is customer segmentation using various features such as Recency, Frequency, and Monetary (RFM), along with machine learning-based clustering methods. The objective of this study is to design and develop a web-based e-commerce customer segmentation application using a combination of RFM features and clustering methods. The study proposes the K-Means algorithm and compares it with K-Medoids and Fuzzy C Means using publicly available e-commerce datasets. Experimental results showed that the K-Means algorithm outperformed K-Medoids and Fuzzy C Means (FCM) based on the Silhouette Score of 0.67305, Davies Bouldin Index of 0.51435, and Calinski Harabasz Index of 5647.89. Through analysis and testing, the designed application has proven effective in grouping customers into relevant segments. These segments are divided into three categories: Loyal, Need Attention, and Promising, visualized in a web-based application dashboard using Streamlit. The developed application allows e-commerce business owners and users from the business, management, and marketing divisions to categorize customers based on transaction data. The results of this study are expected to provide valuable insights to e-commerce management and marketing professionals who are facing increasingly fierce competition.

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