JISKA (Jurnal Informatika Sunan Kalijaga)

Optimizing K-Means Algorithm Using the Purity Method for Clustering Oil Palm Producing Regions in North Aceh

Novia Hasdyna — 2025-01-31

The K-Means algorithm is a fundamental tool in machine learning, widely utilized for data clustering tasks. This research aims to improve the performance of the K-Means algorithm by integrating the Purity method, specifically focusing on clustering regions renowned for oil palm production in North Aceh. Oil palm cultivation is a vital agricultural sector in North Aceh, contributing significantly to the local economy and employment. This study examines two clustering techniques: the conventional K-Means algorithm and an optimized version, Purity+K-Means. The integration of the Purity method increases the efficiency of K-Means by decreasing the required iterations for convergence. The data used for clustering analysis is sourced from the Department of Agriculture and Food in North Aceh Regency and pertains to oil palm production in 2023. The findings indicate that the Purity+K-Means approach notably reduces the iteration count and improves cluster quality. The average Davies-Bouldin Index (DBI) for standard K-Means is 0.45, whereas the Purity+K-Means method lowers it to 0.30. Furthermore, applying the Purity method reduced the number of K-Means iterations from 15 to just 3. These results highlight an enhancement in clustering performance and overall efficiency.

Predicting Olympic Medal Trends for Southeast Asian Countries Using the Facebook Prophet Model

Bagus Al Qohar — 2025-01-31

The Olympics is a world sporting event held every four years and is a meeting place for all athletes worldwide. The Olympics are held alternately in different countries. The Olympics were first held in Athens in 1896 and have now reached the 33rd Olympics, which will be held in Paris in 2024. A lot of work has been done to develop prediction models emphasizing improving accuracy to predict Olympic outcomes. However, low-performance regression algorithms are the main problems with prediction. By integrating custom seasonality with the Facebook-Prophet prediction model, this study aims to increase the accuracy of Olympic prediction. The proposed new model involves several steps, including preparing the data and initializing and fitting the Facebook-Prophet model with several parameters such as seasonal mode, annual seasonality, and prior scale. The model is tested using the Olympic dataset (1994–2024). The evaluation results show that this prediction model can provide a good value in predicting the total medals earned. On the Olympic Games (1994-2024) dataset, the model has a very low error MAE, MSE, and RMSE and has an R2 score of 0.99, which is close to perfect. This research shows that the model is effective in improving prediction accuracy.

Implementation of Long Short-Term Memory for Chili Price Prediction in East Java Province

Fata Nabil Fikri — 2025-01-31

Di Indonesia, bahan pangan memiliki harga yang berubah-ubah atau tidak stabil seiring berjalannya waktu dan salah satu komoditas bahan pangan yang sering mengalami ketidakstabilan harga yaitu dari komoditas cabai. Untuk itu, langkah prediksi harga cabai dapat ditempuh untuk memperkirakan harga selanjutnya sehingga dapat diambil strategi yang tepat, khususnya melalui proses pembelajaran mesin dengan memanfaatkan jaringan saraf tiruan LSTM. Pada penelitian ini, dilakukan eksperimen pengujian mengenai hyperparameter serta struktur dari jaringan LSTM itu sendiri yang digunakan untuk prediksi pada data harga dua jenis cabai di Provinsi Jawa Timur yaitu cabai merah dan cabai rawit. Hasil dari penelitian ini menunjukkan konfigurasi hyperparameter dan struktur jaringan terbaik sama untuk tiap data harga jenis cabai yang diuji. Data harga cabai merah menghasilkan nilai rata-rata RMSE terbaik yaitu 1751,690, sedangkan data harga cabai rawit menghasilkan nilai rata-rata RMSE terbaik yaitu 1888,741.

Extreme Gradient Boosting Model with SMOTE for Heart Disease Classification

Ahmad Ubai Dullah — 2025-01-31

Heart disease is one of the leading causes of death worldwide. According to data from the World Health Organisation (WHO), the number of victims who die from heart disease reaches 17.5 million people every year. However, the method of diagnosing heart disease in patients is still not optimal in determining the right treatment. Along with the development of technology, various models of machine learning algorithms and data processing techniques have been developed to find models that can produce the best precision in classifying heart disease. machine learning algorithm model in classifying heart disease, so that it can improve the effectiveness of diagnosis and help in determining the right treatment for patients. This research also aims to overcome the limitations of accuracy in existing diagnosis methods by identifying models that are capable of providing the best results in processing and analysing health data, especially in terms of heart disease classification. In this study, the XGBoost model was identified as the most superior, with an accuracy of 99%. These results show that the XGBoost model has a higher accuracy rate compared to previous methods, making it a promising solution to improve the accuracy of heart disease diagnosis and classification in the future.

Class Weighting Approach For Handling Imbalanced Data On Forest Fire Classification Using EfficientNet-B1

Arvinanto Bahtiar — 2025-01-31

Wildfires pose significant threats to ecosystems and human safety, necessitating effective monitoring techniques. Detecting forest fires based on images of forest conditions could be a breakthrough. But, the model built from imbalanced data leads to low accuracy. This research addresses the challenge of class imbalance in multi-class classification for forest fire detection using the EfficientNet-B1 model. This research explores the implementation of class weighting to enhance model performance, particularly focusing on minority classes namely: Fire, Smoke. A dataset of 7,331 training images, categorized into four classes. The results showed that employing the class weighting method achieved an accuracy of 90%. While training duration of 14 minutes and 45 seconds, outperforming the data augmentation method in terms of time efficiency. This study contributes to the development of more effective methods for forest fire monitoring and provides insights for future research in machine learning applications in environmental contexts.

Application of SMOTE in Sentiment Analysis of MyXL User Reviews on Google Play Store

Badriyah — 2025-01-31

Aplikasi di dunia nyata sering kali memiliki kumpulan data teks yang tidak seimbang, yang menyebabkan hasil prediksi algoritma klasifikasi menjadi bias terhadap kelas mayoritas dan mengabaikan kelas minoritas. Akurasi yang tinggi tidak mencerminkan kinerja yang sebenarnya. Penelitian ini menggunakan teknik SMOTE untuk menyeimbangkan kelas dalam analisis sentimen menggunakan kumpulan data ulasan pengguna MyXL dari Google PlayStore. Kami membandingkan kinerja algoritma Regresi Logistik dan Support Vector Machine dengan data yang tidak seimbang dan data yang seimbang dari SMOTE. Fitur teks diekstraksi menggunakan TF-IDF, dan metrik evaluasi didasarkan pada akurasi, presisi, recall dan skor F1, yang dioptimalkan melalui GridSearchCV di Scikit-learn. Kinerja terbaik dicapai dengan menerapkan SMOTE ke algoritma SVM, yang menghasilkan akurasi 73,00%, presisi 67,13%, recall 65,82% dan skor F1 66,30%.

Revitalizing Art with Technology: A Deep Learning Approach to Virtual Restoration

Nurrohmah Endah Putranti — 2025-01-31

This study evaluates CycleGAN's performance in virtual painting restoration, focusing on color restoration and detail reproduction. We compiled datasets categorized by art styles and conditions to achieve accurate restorations without altering original reference materials. Various paintings were degraded, including those with a yellow filter, to create effective training datasets for CycleGAN. The model utilized cycle consistency loss and advanced data augmentation techniques. We assessed the results using PSNR, SSIM, and Color Inspector metrics, focusing on Claude Monet's Nasturtiums in a Blue Vase and Hermann Corrodi's Prayers at Dawn. The findings demonstrate superior color recovery and preservation of intricate details compared to other methods, confirmed through quantitative and qualitative evaluations. Key contributions include employing CycleGAN for art restoration, model evaluation, and framework development. Practical implications extend to art conservation, digital library enhancement, art education, and broader access to restored works. Future research may explore dataset expansion, complex architectures, interdisciplinary collaboration, automated evaluation tools, and improved technologies for real-time restoration applications. In conclusion, CycleGAN holds promise for digital art conservation, with ongoing efforts aimed at its integration across fields for effective cultural preservation.

E-commerce Service Chatbot Application Design using KNN and Random Forest Methods

Fardan Zamakhsyari — 2025-01-31

Our lives are profoundly shaped by technology, with the expansion of e-commerce being a notable outcome that demands attention. Given the prevalence of smartphones equipped with rapid messaging and networking applications, individuals often utilize these platforms to communicate with sellers, offering a convenient means for sellers to efficiently engage with a diverse customer base. Recognizing this trend, there arises a necessity for the digital transformation of services to enhance operational efficiency. In response to this need, the researcher has developed a chatbot application aimed at improving customer service, employing machine learning techniques with the KNN and Random Forest algorithms. To assess the application's viability, the chatbot's results undergo an accuracy test, revealing a satisfactory accuracy value of 71.4%, thereby affirming its feasibility.

Enhancing Abstractive Multi-Document Summarization with Bert2Bert Model for Indonesian Language

Aldi Fahluzi Muharam — 2025-01-31

This study investigates the effectiveness of the proposed Bert2Bert and Bert2Bert+Xtreme models in improving abstract multi-document summarization for the Indonesian language. This study uses the transformer model as a basis for developing the proposed Bert2Bert and Bert2Bert+Xtreme models. The results of the model evaluation with the Liputan6 dataset using ROUGE-1, ROUGE-2, ROUGE-L, and BERTScore show that the proposed models have slight improvements over previous research models with Bert2Bert being better than Bert2Bert+Xtreme. Despite the challenges posed by limited reference summarization for Indonesian documents, content-based analysis using readability metrics, including FKGL, GFI, and Dwiyanto Djoko Pranowo revealed that the summaries generated by Bert2Bert and Bert2Bert+Xtreme are at a moderate readability level, which means they are suitable for adult readers and in line with the target audience of the news portal.

Android Malware Threats: A Strengthened Reverse Engineering Approach to Forensic Analysis

Ridho Surya Kusuma — 2025-01-31

The increasing prevalence of Android devices has made them prime targets for malware attacks. These malicious programs pose a significant threat, causing financial loss through unauthorized premium-rate SMS messages and jeopardizing user privacy by stealing sensitive data like login credentials and contact information. The potential for downloaded malware to further compromise the device by installing additional malicious applications is even more concerning. To combat this growing menace, researchers are actively exploring methods to identify and mitigate these threats. This study investigates the effectiveness of reverse engineering as a tool for analyzing suspicious Android applications. Reverse engineering involves meticulously disassembling the application's code, deconstructing its functionalities, and revealing its underlying mechanisms. We uncovered its malicious capabilities by applying this technique to a suspected malware-infected application. This analysis used six stages: Initialization, decompilation, static analysis, code reversing, behavioral analysis, and reporting. This research underscores the power of reverse engineering as a valuable tool for deconstructing the functionalities of Android malware. By understanding how these malicious programs operate, we can develop more robust detection and prevention methods to safeguard Android users from these evolving threats. This research successfully obtained data and information about the virus and the number of attackers. The result also involves the list of specific files that contain malware indicators, so it would be used in the future to enhance detection system.