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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) LINK Jurnal Transformatika SemanTIK : Teknik Informasi SISFOTENIKA Jurnal CoreIT Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Sistem Cerdas Applied Technology and Computing Science Journal JISKa (Jurnal Informatika Sunan Kalijaga) International Journal of Informatics and Computation Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) Prosiding Seminar Nasional Multidisiplin Ilmu Jurnal Pengabdian Dharma Bakti Letters in Information Technology Education (LITE) Jurnal Teknik Informatika (JUTIF) Teknika Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (Senastindo) Jurnal Ilmu Komputer dan Teknologi (IKOMTI) Berita Kedokteran Masyarakat Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) International Journal of Informatics Engineering and Computing
Marselina Endah Hiswati
Universitas Respati Yogyakarta
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Nursing Public Health Social Sciences Other

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Harnessing the Power of Stacked GRU for Accurate Weather Predictions Mohammad Diqi; Ahmad Wakhid; I Wayan Ordiyasa; Nurhadi Wijaya; Marselina Endah Hiswati
Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v6i2.24769

Abstract

This research proposed a novel approach using Stacked GRU (Gated Recurrent Unit) models to address the problem of weather prediction and aimed to improve forecasting accuracy in sectors like agriculture, transportation, and disaster management. The key idea involved leveraging the temporal dependencies and memory management capabilities of Stacked GRU to model complex weather patterns effectively. Comprehensive data preprocessing ensured data quality and fine-tuning of the model architecture and hyperparameters optimized performance. The research demonstrated the Stacked GRU model's effectiveness in accurately forecasting temperature, pressure, humidity, and wind speed, validated by low RMSE and MAE scores and high R2 coefficients. However, challenges in forecasting humidity and a percentage discrepancy in wind speed predictions were observed. Overfitting and computational complexity were identified as potential limitations. Despite these constraints, the study concluded that the Stacked GRU model showed promise in weather forecasting and warranted further refinement for broader applications in time-series prediction tasks.
Implementasi Metode Simplek untuk Mengetahui Optimasi Produksi Gerabah (Studi Kasus: Sentra Kerajinan Kasongan Bantul Daerah Istimewa Yogyakarta) Marselina Endah Hiswati; Lutfi Nur Wicaksono
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 2 No. 2 (2017): September 2017
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (102.742 KB) | DOI: 10.14421/jiska.2017.22-02

Abstract

Abstrak Produksi di sebuah perusahaan berjalan setiap hari untuk bertahannya proses kegiatan dan keberlangsungan kehidupan bagi perusahaan tersebut. Perusahan dalam hal ini bagi pengusaha gerabah di daerah Kasongan, dikatakan berjalan dengan baik jika semua unit di setiap bagian perusahaan pun berjalan dengan lancar dan baik pula. Proses produksi yang  terjadi juga membutuhkan beberapa komponen yang mendukung proses produksi itu berjalan dengan baik sehingga tujuan perusahaan tercapai. Selain keputusan dari seorang pimpinan perusahaan dalam penentuan jumlah serta kebutuhan produksi, juga dibutuhkn sumber daya - sumber daya lain, misalnya waktu, tenaga kerja, energi, bahan baku, atau uang; atau dapat berupa bentuk batasan pedoman. Secara umum, tujuan perusahaan yang paling sering terjadi adalah sedapat mungkin memaksimumkan laba.Tujuan lain dari unit organisasi yang merupakan bagian dari suatu organisasi biasanya berupa meminimumkan biaya. Ketersediaan sumber daya yang ada inipun sangatlah besar pengaruhnya bagi optimalisasi produksi di perusahaan tercapai. Komponen atau variabel pendukung produksi tidak mungkin hanya terbatas dua variabel saja. Maka hal inilah dibutuhkan sebuah metode yang dapat menangani permasalahan produksi yang lebih komplek yaitu metode Simplek. Penyelesaian digunakan sebuah software aplikasi POM-QM for Windows.Hasil penelitian nantinya bermanfaat bagi perusahaan dalam memaksimalkan keuntungan tapi juga dapat untuk mengetahui bagaimana menentukan formulasi sehingga menghasilkan sebuah solusi optimal di sebuah perusahaan sesuai tujuan perusahaan. Kata kunci : optimasi, metode simplek, POM-QM for Windows.
Model of MSME Digital Marketing through for Biopharmaceutical Products Marselina Endah Hiswati; Putra Wanda; I Wayan Ordiyasa; Lila Retnani Utami; Supardi RS; Rainbow Tambunan
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v2i1.57

Abstract

Sleman Regency has more than 50 traditional markets and also a variety of MSME business and there are more than 18,293 accommodation, food and beverage business sectors that are developing. Mobile-based information technology is urgently needed as a medium that supports efforts to promote and market MSME products, especially traditional culinary products, in this case processed products of Biopharmaca plants. The existence of a social restriction policy due to the COVID-19 pandemic requires the public to recognize technology as a medium of socialization towards digitalization. Thus, a mobile-based application is needed as a meeting place for sellers and buyers specifically for local Sleman products. Digital innovation has an impact on increasing the income and economy of MSME actors in Sleman Regency, Special Region of Yogyakarta
Digital Democracy: Analyzing Political Sentiments through Multinomial Naive Bayes in Election Campaign Ads DIQI, MOHAMMAD; RAHMAYANTI, DIAN RHESA; HISWATI, MARSELINA ENDAH; ORDIYASA, I WAYAN; HAFIZAH, IDA
Jurnal Sistem Cerdas Vol. 7 No. 2 (2024)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v7i2.379

Abstract

This research delves into sentiment analysis for digital election campaign advertisements using the Multinomial Naive Bayes approach. The study addresses the limitations of standard sentiment analysis methodologies in capturing the intricacies of public sentiments toward political ads. The dataset, sourced from Kaggle, encompasses 3000 records with sentiments categorized as positive, neutral, and negative. The Multinomial Naive Bayes model demonstrated a substantial accuracy increase from 92% to 96%, outperforming the standard Naive Bayes model. Precision, recall, and F1-score metrics consistently improved across sentiment categories. While dataset representativeness and cultural specificity pose limitations, the research contributes significantly to sentiment analysis methodologies in politically charged digital environments. Future research recommendations include exploring advanced NLP techniques, incorporating real-time data from diverse social media platforms, and addressing ethical considerations in political sentiment analysis. The outcomes emphasize the importance of tailored methodologies for enhanced accuracy in understanding sentiments expressed in digital election campaign advertisements.
Smart Fire Safety: Analyzing Radial Basis Function Kernel in SVM for IoT-driven Smoke Detection Ordiyasa, I Wayan; Diqi, Mohammad; Lustiyati, Elisabeth Deta; Hiswati, Marselina Endah; Salsabela, Marcella
SemanTIK : Teknik Informasi Vol 10, No 1 (2024):
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55679/semantik.v10i1.47433

Abstract

This research explores the application of Support Vector Machine (SVM) with the Radial Basis Function (RBF) kernel in smoke detection using a dataset collected from Internet of Things (IoT) devices, specifically Photoelectric Smoke Detectors. With 62,630 records and 16 attributes, the study aims to address limitations in smoke detection technology that may impact system accuracy. Through RBF kernel analysis, the SVM model demonstrates the capability to recognize complex patterns related to smoke presence, achieving an accuracy rate of 96.85%. The Classification Report reveals high precision, recall, and f1-score for both "No Fire" and "Fire" detection classes. Despite encountering some false positives, particularly in specific environmental conditions, the evaluation underscores the effectiveness of the model. Recommendations include integrating the model into security systems and further exploring model development by considering environmental factors. This research provides profound insights into smoke detection and affirms its relevance in advancing superior artificial intelligence solutions. In conclusion, the SVM model with the RBF kernel proves reliable for smoke detection with broad potential applications in fire risk mitigation. Keywords; Smoke Detection, Support Vector Machine (SVM), Radial Basis Function (RBF) Kernel, IoT Devices, Classification Report
Comparative Analysis of Kidney Disease Detection Using Machine Learning DIQI, MOHAMMAD; ORDIYASA, I WAYAN; HISWATI, MARSELINA ENDAH
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 15, No 2 (2023): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v15i2.21468

Abstract

This research aimed to compare the performance of ten machine learning algorithms for detecting kidney disease, utilizing data from UCI Machine Learning Repository. The algorithms tested included K-Nearest Neighbour, RBF SVM, Linear SVM, Neural Net, Decision Tree, Naïve Bayes, AdaBoost, Random Forest, Gaussian Process, and QDA. The evaluation metrics used were accuracy, precision, recall, and F1-score. The findings revealed that AdaBoost was the most effective algorithm for all evaluation metrics, achieving an accuracy, precision, recall, and F1-score of 1.00. Random Forest and RBF followed closely, while Naïve Bayes and QDA had the lowest performance. These results suggest that machine learning algorithms, especially ensemble methods such as AdaBoost, can significantly improve the accuracy and efficiency of detecting kidney disease. This can lead to better patient outcomes and reduced healthcare costs.
Precision in Obstetric Care: A Machine Learning Approach with CatBoost and Grid Search Optimization Hiswati, Marselina Endah; Diqi, Mohammad; Azijah, Izattul; Subandi, Yeyen; Fathinah, Azzah; Ariani, Rahayu Cahya
Teknika Vol. 13 No. 3 (2024): November 2024
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v13i3.1010

Abstract

This study focuses on improving how we classify fetal health using machine learning by fine-tuning the CatBoostClassifier with Grid Search. Our main achievement in this research is significantly boosting the accuracy of fetal health classification based on Cardiotocogram (CTG) data. Finding the best hyperparameters has created a more precise and reliable diagnostic tool for making informed prenatal care decisions. The model reached an impressive overall accuracy of 96%, especially excelling in identifying Normal and Pathological cases. However, it faced some challenges in classifying Suspect cases, suggesting room for further improvement. These results highlight the potential of machine learning to enhance the reliability of fetal health assessments, which could lead to better outcomes in clinical settings. The success of Grid Search in this study is evident, as the optimized parameters led to the highest accuracy and lowest loss values, proving its effectiveness in fine-tuning the model.
Improving Urban Air Quality Prediction Using Bidirectional GRU: A Case Study of CO Concentration to Support Education in Yogyakarta Ordiyasa, I Wayan; Sriwidodo, Sriwidodo; Wiratma, Harits Dwi; Diqi, Mohammad; Hiswati, Marselina Endah; Noverianus, Noverianus; Syihab, Namira Anjani Rahmadina
Letters in Information Technology Education (LITE) Vol 7, No 2 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um010v7i22024p63-69

Abstract

Urban air pollution, particularly carbon monoxide (CO), poses serious health risks, emphasizing the need for accurate prediction models to support real-time monitoring and timely responses. This study explores the use of a Bidirectional Gated Recurrent Unit (Bi-GRU) model to improve CO concentration forecasts, capturing intricate temporal patterns in air quality data. The model, optimized for varying input-output sequences, contributes to advancements in air quality prediction by enhancing accuracy with extended historical data. Using hourly CO data from Yogyakarta, Indonesia, the Bi-GRU model was evaluated across input lengths of 48, 96, and 144 hours with prediction outputs of 24 and 48 hours. Results show high prediction accuracy, with the best performance at 144-hour inputs, achieving an R² of 0.99 and minimal error metrics. These findings underscore the model's reliability and precision in capturing CO fluctuations, making it a promising tool for urban environmental management. This research offers a foundation for further refinement and broader applications in air quality monitoring systems.
Enhancing Medical Education with Machine Learning: A Case Study on CKD Detection Using AdaBoost-SVM Erizal, Erizal; Suwarto, Suwarto; Basuki, Umar; Hiswati, Marselina Endah; Diqi, Mohammad; Kristian, Tadem Vergi
Letters in Information Technology Education (LITE) Vol 7, No 2 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um010v7i22024p56-62

Abstract

This study explores the application of the AdaBoost-SVM model for the classification of chronic kidney disease (CKD), addressing the critical need for accurate and early diagnosis in clinical settings. Using a dataset of 400 instances with 25 clinical features, we implemented rigorous data cleaning to remove rows with missing values, ensuring high-quality input data. The AdaBoost-SVM model achieved remarkable performance, with an overall accuracy of 96%. Precision and recall were notably high for both 'ckd' and 'notckd' classes, reflecting the model’s robustness and reliability. These results underscore the potential of hybrid machine learning approaches in medical diagnostics, providing valuable insights into improving CKD detection. Although the study has several limitations, such as a limited dataset and the exclusion of incomplete data, its findings clearly show the model's usefulness and provide a foundation for future research. Future work should focus on larger, more diverse datasets and alternative data handling techniques to enhance the model's applicability and performance. This research highlights the promise of integrating advanced algorithms into clinical decision-making processes, ultimately aiming to improve patient outcomes through early and accurate disease detection.
Optimizing Breast Cancer Detection: A Comparative Study of SVM and Naive Bayes Performance Diqi, Mohammad; Hiswati, Marselina Endah; Hamzah, Hamzah; Ordiyasa, I Wayan; Mulyani, Sri Hasta; Wijaya, Nurhadi; Wanda, Putra
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 7 No 1 (2024): June
Publisher : Universitas Nahdlatul Ulama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study evaluates the performance of Support Vector Machine (SVM) and Naive Bayes algorithms in classifying breast cancer using the Breast Cancer Wisconsin dataset. Both models exhibited high accuracy, with Naive Bayes achieving a slightly higher overall accuracy of 97% and demonstrating a balanced performance between precision and recall. The SVM model showed strong proficiency in detecting positive cases, with an overall accuracy of 95%, though it faced minor challenges in recall for negative cases. These results highlight the effectiveness of both algorithms in breast cancer detection, emphasizing the significance of model selection based on specific diagnostic requirements. Although there are limitations, such as the small sample size and assumptions made in the model, the findings provide useful insights into the use of machine learning in medical diagnostics. This supports the idea that these models have the potential to enhance early detection and treatment results. Future research should focus on utilizing larger, more diverse datasets, exploring advanced feature processing techniques, and integrating additional algorithms to enhance further the accuracy and reliability of breast cancer detection systems.
Co-Authors ADNAN ADNAN ABIDIN Ahmad Wakhid Ariani, Rahayu Cahya Arum Kurnia Sulistyawati Azijah, Izattul Basuki, Umar Dian Rhesa Rahmayanti Eka Damayanti Elisabeth Deta Lustiyati Erizal Erizal Fajar Y Zebua Fathinah, Azzah FAUZIYYAH, ANNUR HAFIZAH, IDA Hamzah Hamzah Hamzah Hamzah Huang J. Jie I Wayan Ordiyasa I Wayan Ordiyasa Kristian, Tadem Vergi Lestari, Nia Rizqi Lestariningsih Lestariningsih Lila Retnani Utami Lutfi Nur Wicaksono Meliala, Dyan Avando Mohammad Diqi Muflih Muflih Muflih, Muflih Ngaisyah, Rr Dewi Noverianus, Noverianus Ordiyasa, I Wayan Putra Wanda Putra Wanda Putra Wanda Putra Wanda, Putra Rahayu Widaryanti Rainbow Tambunan Romana Herlinda Salsabela, Marcella Sapariyah, Sapariyah Sri Hasta Mulyani Sriwidodo B, Sriwidodo Subandi, Yeyen Sudiyatmi, Sudiyatmi Supardi RS Suwarto Suwarto Syafitri, Endang Nurul Syihab, Namira Anjani Rahmadina Wandani, Aulia Fadillah Wani Wijaya, Nurhadi Wiratma, Harits Dwi