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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 dan Optimalisasi Performa Algoritma Gaussian Naive Bayes pada Prediksi Metabolic Syndrome Menggunakan SMOTE Fauziyah, Nadiyah Jihan; Rahmania, Fadilla; Daniyal, Muhammad; Sari, Nur Fitriyah Ayu Tunjung
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.2024.9.2.112-122

Abstract

Metabolic syndrome is a complex global health problem, with symptoms such as abdominal obesity, insulin resistance, high blood pressure, high blood sugar, and abnormal blood lipids. With this global challenge, several studies have attempted to predict these diseases using machine learning methods. However, often, predictions about a disease result in data imbalance where minority classes are underrepresented. To balance the class proportions, the Synthetic Minority Over-sampling Technique (SMOTE) method replicates the minority class samples. In this research, the technique applied to predict is the Gaussian Naive Bayes (GNB) algorithm. The results show an increase in prediction accuracy by 0.2 from 0.81 to 0.83. This study confirms the critical role of the SMOTE oversampling method in machine learning using the Gaussian Naive Bayes (GNB) algorithm in Metabolic Syndrome prediction and its positive impact on diagnostic efficiency and public health.
Ensemble Learning pada Kategorisasi Produk E-Commerce Menggunakan Teknik Boosting Sepbriant, Genta Dwigi; Utomo, Danang Wahyu
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.2024.9.2.123-133

Abstract

The development of e-commerce significantly contributes to technological advancement, especially for businesses adopting the concept. The growth of e-commerce has seen a significant increase, reaching 196.47 million users in 2023. In e-commerce, a wide range of product variations is provided to users, which can lead to errors or confusion in product selection. Product categorization is crucial in e-commerce to assist users in navigating efficiently. However, manual categorization is less effective as it can be time-consuming. This study aims to clarify the factors of concern in grouping using the K-Nearest Neighbors (KNN) algorithm in product categorization on the e-commerce platform. This research focuses on whether the novelty lies in the implemented algorithm, the variables used, or the applied grouping parameters. This work applies the XGBoost algorithm to improve the effectiveness of product categorization in e-commerce through ensemble learning approaches. The research findings indicate that boosting algorithms like XGBoost outperform individual algorithms like KNN regarding classification accuracy. This proves that ensemble learning approaches may greatly enhance product classification in e-commerce. The testing process of the implemented e-commerce system in this study also provides confidence in the theoretical and practical benefits of applying this research to enhance efficiency and user experience in product categorization on the e-commerce platform.
Klasterisasi Jumlah Penduduk Provinsi Jawa Timur Tahun 2021-2023 Menggunakan Algoritma K-Means Aryanto, Risqi Pradana; Nilogiri, Agung; Wardoyo, Ari Eko
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.2024.9.2.134-146

Abstract

Understanding the population data of a region is crucial for policy development and strategic planning. East Java Province, the second-largest province in Indonesia, has undergone significant population growth from 2021 to 2023. Uneven growth poses challenges in resource and infrastructure management. The K-Means algorithm clusters population data into several groups based on specific characteristics. The Elbow method is used to determine the optimal number of clusters, ensuring the accuracy of the analysis. This research aims to analyze and cluster the population distribution in each city in East Java Province, providing a more detailed and accurate depiction. The research findings reveal three significant clusters. Cluster 0 includes 21 towns, Cluster 1 comprises 4, and Cluster 2 encompasses 13. These findings have important implications for targeted development policy formulation at the city level in East Java Province. Additionally, this study contributes to the development of demographic analysis and population management, using valid methods and consistent results between RapidMiner and manual calculations. In conclusion, this research provides a solid foundation for more effective development policy formulation in East Java Province, offering essential information for sustainable population management.
Deteksi Pelanggaran pada Zebra Cross dengan Water Spray dan Buzzer berbasis IoT Firdaus, Dina Uzlifatul; Christanto, Febrian Wahyu
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.2024.9.2.147-158

Abstract

A zebra crossing is a road marking indicating a crossing path for pedestrians. Zebra crossings are directly used to signal drivers to stop at the line boundaries. Because the zebra crossing functions as a crossing area, pedestrians and motorized vehicle drivers must understand and obey existing traffic signs. According to data from the WHO (World Health Organization), 270,000 pedestrians die every year or around 22% of all victims who die due to road accidents. An ESP32-Cam microcontroller, an E18-D80NK Infrared Proximity Sensor, water spray and buzzer approaches, and the prototype development method were used to design a system for detecting crossing violations at zebra crossings to address this issue. The Infrared Proximity sensor will automatically detect when a crossing violation occurs, then the water spray will spray water, and the buzzer will make a sound as a warning sign to obey traffic. ESP32-Cam functions as an image capturer if a crossing violation has occurred and is automatically sent to the Telegram Bot. The confusion matrix test tested the research results with an accuracy value of 83.33%, a precision value of 83.33%, and a recall value of 88.23%.
Analisis Performa Normalisasi Data untuk Klasifikasi K-Nearest Neighbor pada Dataset Penyakit Allorerung, Petronilia Palinggik; Erna, Angdy; Bagussahrir, Muhammad; Alam, Samsu
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.2024.9.3.178-191

Abstract

This study investigates four normalization methods (Min-Max, Z-Score, Decimal Scaling, MaxAbs) across prostate, kidney, and heart disease datasets for K-Nearest Neighbor (K-NN) classification. Imbalanced feature scales can hinder K-NN performance, making normalization crucial. Results show that Decimal Scaling achieves 90.00% accuracy in prostate cancer, while Min-Max and Z-Score yield 97.50% in kidney disease. MaxAbs performs well with 96.25% accuracy in kidney disease. In heart disease, Min-Max and MaxAbs attain accuracies of 82.93% and 81.95%, respectively. These findings suggest Decimal Scaling suits datasets with few instances, limited features, and normal distribution. Min-Max and MaxAbs are better for datasets with numerous instances and non-normal distribution. Z-Score fits datasets with a wide range of feature numbers and near-normal distribution. This study aids in selecting the appropriate normalization method based on dataset characteristics to enhance K-NN classification accuracy in disease diagnosis. The experiments involve datasets with different attributes, continuous and categorical data, and binary classification. Data conditions such as the number of instances, the number of features, and data distribution affect the performance of normalization and classification.
Implementasi Data Augmentation untuk Klasifikasi Sampah Organik dan Non Organik Menggunakan Inception-V3 Bintang, Rahina; Azhar, Yufis
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.2024.9.3.192-204

Abstract

The surge in global waste, particularly in Indonesia, with a total of 36.218 million tons per year, has become an urgent issue. Challenges in waste management are increasingly complex due to the lack of public understanding and awareness in classifying types of waste. One systemic approach to address waste classification issues involves the use of machine learning technology to categorize waste into two main types: organic and non-organic. The data used in this study comes from a Kaggle website dataset comprising 25,500 entries. This research employs a transfer learning approach with the Inception-V3 architecture and data augmentation implementation. Transfer learning is chosen for its proven performance in image data classification, while data augmentation is implemented to introduce diversity to the dataset. The research stages include business understanding, data preprocessing, data augmentation, data modelling, and evaluation. The results show that the use of transfer learning with the Inception-V3 approach and data augmentation implementation achieves an accuracy rate of 94%, which falls into the excellent category.
Implementasi K-Means Clustering pada Pengelompokan Pasien Penyakit Jantung Wala, Jihan; Herman, Herman; Umar, Rusydi
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.2024.9.3.205-216

Abstract

Heart disease is a prominent global health concern, necessitating early identification and patient grouping for effective management. This study employs the K-Means clustering algorithm with a medical dataset of 303 patients, encompassing various attributes. These include Age, Gender, Chest Pain Type, Blood Pressure, Serum Cholesterol Level, Fasting Blood Sugar, Resting Electrocardiographic Results, Maximum Heart Rate, Angina, ST Depression, and Slope of the ST Segment. The goal is to categorize patients into four clusters based on chest pain types, a crucial symptom indicating disease severity. The computation concludes after the sixth iteration, revealing Cluster 1 (27 patients), Cluster 2 (135 patients), Cluster 3 (15 patients), and Cluster 4 (126 patients). Collaborative analysis with medical experts highlights that Cluster 1, mainly comprising older males, exhibits high-risk indicators. While this grouping aids in personalized treatment strategy development, further clinical validation involving more experts and datasets is imperative for enhanced reliability.
Pelabelan Sentimen Berbasis Semi-Supervised Learning menggunakan Algoritma LSTM dan GRU Ayuningtyas, Puji; Khomsah, Siti; Sudianto, Sudianto
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.2024.9.3.217-229

Abstract

In the sentiment analysis research process, there are problems when still using manual labeling methods by humans (expert annotation), which are related to subjectivity, long time, and expensive costs. Another way is to use computer assistance (machine annotator). However, the use of machine annotators also has the research problem of not being able to detect sarcastic sentences. Thus, the researcher proposed a sentiment labeling method using Semi-Supervised Learning. Semi-supervised learning is a labeling method that combines human labeling techniques (expert annotation) and machine labeling (machine annotation). This research uses machine annotators in the form of Deep Learning algorithms, namely the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms. The word weighting method used in this research is Word2Vec Continuous Bag of Word (CBoW). The results showed that the GRU algorithm tends to have a better accuracy rate than the LSTM algorithm. The average accuracy of the training results of the LSTM and GRU algorithm models is 0.904 and 0.913. In contrast, the average accuracy of labeling by LSTM and GRU is 0.569 and 0.592, respectively.
Integrating Retrieval-Augmented Generation with Large Language Model Mistral 7b for Indonesian Medical Herb Firdaus, Diash; Sumardi, Idi; Kulsum, Yuni
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.2024.9.3.230-243

Abstract

Large Language Models (LLMs) are advanced artificial intelligence systems that use deep learning, particularly transformer architectures, to process and generate text. One such model, Mistral 7b, featuring 7 billion parameters, is optimized for high performance and efficiency in natural language processing tasks. It outperforms similar models, such as LLaMa2 7b and LLaMa 1, across various benchmarks, especially in reasoning, mathematics, and coding. LLMs have also demonstrated significant advancements in addressing medical queries. This research leverages Indonesia’s rich biodiversity, which includes approximately 9,600 medicinal plant species out of the 30,000 known species. The study is motivated by the observation that LLMs, like ChatGPT and Gemini, often rely on internet data of uncertain validity and frequently provide generic answers without mentioning specific herbal plants found in Indonesia. To address this, the dataset for pre-training the model is derived from academic journals focusing on Indonesian medicinal herbal plants. The research process involves collecting these journals, preprocessing them using Langchain, embedding models with sentence transformers, and employing Faiss CPU for efficient searching and similarity matching. Subsequently, the Retrieval-Augmented Generation (RAG) process is applied to Mistral 7b, allowing it to provide accurate, dataset-driven responses to user queries. The model's performance is evaluated using both human evaluation and ROUGE metrics, which assess recall, precision, F1 measure, and METEOR scores. The results show that the RAG Mistral 7b model achieved a METEOR score of 0.22%, outperforming the LLaMa2 7b model, which scored 0.14%.
Comparison of KNN and Random Forest Algorithms on E-Commerce Service Chatbot Zamakhsyari, Fardan; Makayasa, Bagas Adi; Hamami, R. Abudullah; Akbar, Muhammad Tulus; Cahyono, Andi; Amirullah, Amirullah; Hisyamuddin, Muhammad Zida; Siregar, Maria Ulfah
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 10 No. 1 (2025): January 2025
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.2025.10.1.100-109

Abstract

Technology has a profound influence on our lives, with the expansion of e-commerce being a significant outcome that warrants attention. Given the prevalence of smartphones equipped with messaging apps and fast networks, people often utilize these platforms to communicate with sellers, offering a convenient way for sellers to engage efficiently with a diverse customer base. Recognizing this trend, there is a need for digital transformation of services to improve operational efficiency. Thus, this study aimed to compare the efficiency of classification algorithms in e-commerce service chatbots. The researcher employed machine learning techniques, specifically KNN and Random Forest algorithms, in this case. To assess the feasibility of the application, the chatbot results will be tested using the confusion matrix method to determine accuracy. From this study, it was found that the KNN method, combined with calculating word weight using TF-IDF, produces an accuracy value of 71.4%, thus confirming its feasibility.

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