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All Journal JTT (Jurnal Teknologi Terpadu) ILKOM Jurnal Ilmiah Indonesian Journal of Data and Science Ilmu Komputer untuk Masyarakat NERO (Networking Engineering Research Operation) International Journal of Artificial Intelligence in Medical Issues
Nurul Rismayanti
Universitas Muslim Indonesia
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Engineering Health Professions Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Other

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Peningkatan Kemampuan Perangkat Desa Dalam Tata Kelola Pengarsipan Surat dan Pelayanan Masyarakat Pada Kelurahan Lanna Kec.Parangloe Irawati Irawati; Lilis Nur Hayati; Muhammad Arif; Nurul Rismayanti
Ilmu Komputer untuk Masyarakat Vol 1, No 2 (2020)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (177.026 KB) | DOI: 10.33096/ilkomas.v1i2.777

Abstract

Beberapa permasalahan dan hambatan yang dihadapi Kantor Keluarahan Lanna terkait tata kelola pengarsipan surat dan pelayanan masayarakat yaitu pegawai masih belum aktif dan paham akan sistem informasi secara meluas dan masih kurangnya fasilitas atau media untuk sistem informasi yang dibuat serta pegawai terbiasa dengan pengarsipan yang sifatnya manual dalam pendataan. Tujuan PkM Pemula ini adalah untuk memberikan media di Kelurahan Lanna untuk menata seluruh arsip surat secara terarsip. Pengelolaan administrasi yang lebih baik berbasis digital serta meningkatnya kemampuan dan keterampilan apartur desa. Luaran pengabdian ini adalah menghasilkan sebuah aplikasi pengarsipan surat berbasis digital dan modul.
Go Honey Pemberdayaan Perempuan Desa Borong Loe Melalui Budidaya Tumbuhan Talas sebagai Pangan Alternatif Meningkatkan Perekonomian Desa Benteng Gantarang Nurul Rismayanti; Risti Amelia; Fery Setyo Aji; Nurfadillah Said; Irawati Irawati; lilis nur hayati
Ilmu Komputer untuk Masyarakat Vol 2, No 1 (2021)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (324.891 KB) | DOI: 10.33096/ilkomas.v2i1.921

Abstract

Tanaman talas merupakan salah satu tanaman makanan pokok beberapa kepulauan di Oseania, di Indonesia talas populer tumbuhan atau tanaman hampir disemua daerah disebabkan pertumbuhan lebih cepat pada daerah yang berair dan lembab, hal ini wilayah Indonesia dapat tumbuh subur. Desa Benteng Gantarang sebagai salah satu desa beriklim tropis memiliki bahan pangan lokal melimpah yaitu talas. Pengelolaan bahan pangan lokal talas yang tidak dapat memaksimalkan fungsi dan manfaat makanan seperti tumbuhan talas paco yang merupakan limbah organik bagi masyarakat setempat. Tumbuhan talas paco kurang dimanfaatkan oleh petani dan ibu PKK karena mengandung getah dan membuat gatal sehingga ibu PPK tidak berani mengkomsumsi tanaman Talas paco, selama ini batang dan daun dijadikan sebagai pupuk organik yang di letakkan pada dibawah pohon dan di biarkan terurai sendiri sedangkan umbinya menjadi santapan serangga atau ayam kampung maka atas dasar permasalahan diatas maka perluanya suatu pengabdian dengan Go Honey Pemberdayaan Perempuan Desa Borong Loe Melalui Budidaya Tumbuhan Talas sebagai Pangan Alternatif Meningkatkan Perekonomian Desa Benteng Gantarang dengan cara membuat Modul Pemberdayaan, Sosialisasi dengan Mitra, Penyuluhan, Pelatihan, Implementasi. Hasil yang didapat ibu PKK bisa membuat makanan alternatif dari tumbuhan Talas , memiliki usaha dari olahan tanaman Talas sehingga ibu PKK memiliki keterampilan dalam mengolah limbah Talas menjadi pangan alternatif
Al-Qur’an Braille Board Interpreter Glove Bagi Tunanetra Dalam Mengatasi Buta Aksara Arab Muhammad Alim Abdi; Dwi Nur Halizah; Umniah Umniah; Nurul Rismayanti; Nurlinda Nurlinda; Lilis Nur Hayati; Nanang Roni Wibowo
Jurnal Teknologi Terpadu Vol 9, No 2 (2021): JTT ( Jurnal Teknologi Terpadu)
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32487/jtt.v9i2.1187

Abstract

Al-Qur’an Braille Board Interpreter Glove Bagi Tunanetra Dalam Mengatasi Buta Aksara Arab ini terdiri dari Braille Board (Papan Braille) dan Interpreter Glove (Berupa Sarung Tangan Penerjemah). Braille Board merupakan media yang dibuat menggunakan bahan akrilik membentuk 40 pola huruf hijaiah braille. Interpreter Glove merupakan main device yang terintegrasi langsung dengan Braille Board. Interpreter Glove dilengkapi dengan microcontroller yang berfungsi untuk menerjemahkan pola hijaiah braille pada braille board kemudian diolah dalam sebuah program sehingga menghasilkan output berupa suara huruf hijaiah. Pembuatan alat dimulai dari perancangan studi literatur, merancangan konsep alat dan sistem, desain mockup, skema rangkaian elektronika, pemrograman perangkat serta pengujian produk. Pengontrolan sistem input, process dan output akan bekerja berdasarkan integrasi dua komponen utama alat yaitu Interpreter Glove dan Braille Board.
Improving Multi-Class Classification on 5-Celebrity-Faces Dataset using Ensemble Classification Methods Nurul Rismayanti; Aulia Putri Utami
Indonesian Journal of Data and Science Vol. 4 No. 2 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i2.78

Abstract

This study aims to compare the performance between Random Forest Classifier and Gaussian Naïve Bayes Classifier in classification. Several evaluation metrics such as accuracy, precision, recall, and F1-score were used to analyze the performance of both models. The dataset used has specific characteristics that influence the evaluation results. The research findings indicate that Random Forest Classifier outperforms Gaussian Naïve Bayes Classifier in most of the evaluation metrics. Random Forest Classifier achieves higher accuracy and better precision, recall, and weighted F1-score. However, it should be noted that Random Forest Classifier also has more outliers compared to Gaussian Naïve Bayes Classifier when visualized using boxplots. Therefore, in selecting a classification model, a trade-off between higher performance and sensitivity to outliers needs to be considered. Further statistical testing and advanced evaluation are required to gain a deeper understanding of the impact and interpretation of the obtained results. This study provides valuable insights into understanding the comparison between these two classification models and their implications in different contexts.
M2SmallLint : software health monitoring tool Hayatou Oumarou; Nurul Rismayanti
Indonesian Journal of Data and Science Vol. 4 No. 2 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i2.90

Abstract

Developing error-free applications is a major challenge for computer scientists. Tools to remedy this problem have been developed, notably Rule Checkers and proof assistants. As a particular case of error, a bug is by nature intangible, invisible and difficult to trace. We propose to investigate the correlations between the alerts generated by rule checkers and the internal quality of the software system. In this first version of the work, we present M2SmallLint, a tool for visualizing and navigating through source code properties in order to locate potential errors. This tool enables the visualization of software health.
Automated Classification of Empon Plants: A Comparative Study Using Hu Moments and K-NN Algorithm Hayatou Oumarou; Rismayanti, Nurul
Indonesian Journal of Data and Science Vol. 4 No. 3 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i3.115

Abstract

The study "Automated Classification of Empon Plants: A Comparative Study Using Hu Moments and K-NN Algorithm" investigates the potential of image processing and machine learning techniques in the classification of empon plants, specifically ginger and turmeric. Utilizing a dataset of leaf images, the research employed the Canny method for image segmentation and Hu Moments for feature extraction, followed by classification using the K-Nearest Neighbors (K-NN) algorithm. The performance of the model was evaluated through a 5-fold cross-validation method, focusing on metrics such as accuracy, precision, recall, and F1-score. The results showcased the model's variable performance, with the highest accuracy reaching 65.33%. The study contributes to the field by demonstrating the application of Hu Moments in plant classification and by assessing the K-NN algorithm's effectiveness in this context. These findings offer insights into the potential of combining image processing techniques with machine learning for accurate plant classification, paving the way for further research in the area.
Estimating Obesity Levels Using Decision Trees and K-Fold Cross-Validation: A Study on Eating Habits and Physical Conditions Admojo, Fadhila Tangguh; Nurul Rismayanti
Indonesian Journal of Data and Science Vol. 5 No. 1 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i1.126

Abstract

This study harnesses the predictive capabilities of machine learning to explore the determinants of obesity within populations from Mexico, Peru, and Colombia, using a Decision Tree algorithm bolstered by 5-fold cross-validation. Our comprehensive analysis of 2111 individuals' lifestyle and physical condition data yielded accuracy, precision, recall, and F1-scores that notably peaked in the third and fifth folds. The findings affirmed the significance of dietary habits and physical activity as substantial predictors of obesity levels. The variability in model performance across the folds underscored the importance of robust cross-validation in enhancing the model's generalizability. This research contributes to the burgeoning field of data science in public health by providing a viable model for obesity prediction and laying the groundwork for targeted health interventions. Our study's insights are pivotal for public health officials and policymakers, serving as a stepping stone towards more sophisticated, data-driven approaches to combating obesity. The study, however, recognizes the inherent limitations of self-reported data and the need for broader datasets that encompass more diverse variables. Future research directions include the analysis of longitudinal data to establish causal relationships and the comparison of various machine learning models to optimize predictive performance
Predicting Online Gaming Behaviour Using Machine Learning Techniques Rismayanti, Nurul
Indonesian Journal of Data and Science Vol. 5 No. 2 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i2.166

Abstract

Understanding player behaviour in online gaming is essential for enhancing user engagement and retention. This study utilizes a dataset from Kaggle, capturing a wide range of player demographics and in-game metrics to predict player engagement levels categorized as 'High,' 'Medium,' or 'Low.' The dataset includes features such as age, gender, location, game genre, playtime, in-game purchases, game difficulty, session frequency, session duration, player level, and achievements. The research employs a Gaussian Naive Bayes model, with data pre-processing steps including feature selection, categorical data encoding, and scaling of numerical features. The dataset is split into training (80%) and testing (20%) sets, and a 5-fold cross-validation is used to ensure model robustness. The model's performance is evaluated using accuracy, precision, recall, and F1-score. The results show consistent performance across different folds, with an average accuracy of 84.27%, precision of 85.59%, recall of 84.27%, and F1-score of 83.98%. These findings indicate that the Gaussian Naive Bayes model can reliably predict player engagement levels, identifying significant predictors such as session frequency and in-game purchases. The study contributes to game analytics by providing a predictive model that can help game developers and marketers design more engaging gaming experiences. Future research should incorporate a broader range of features, including psychological and social factors, and explore other machine learning algorithms to enhance predictive accuracy. This study's insights are valuable for developing strategies to improve player retention and satisfaction in the gaming industry.
Evaluating Thresholding-Based Segmentation and Humoment Feature Extraction in Acute Lymphoblastic Leukemia Classification using Gaussian Naive Bayes Rismayanti, Nurul; Naswin, Ahmad; Zaky, Umar; Zakariyah, Muhammad; Purnamasari, Dwi Amalia
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 2 (2023): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v1i2.99

Abstract

This study, titled "Evaluating Thresholding-Based Segmentation and HuMoment Feature Extraction in Acute Lymphoblastic Leukemia Classification using Gaussian Naive Bayes," investigates the application of image processing and machine learning techniques in the classification of Acute Lymphoblastic Leukemia (ALL). Utilizing a dataset of microscopic blood smear images, the research focuses on the efficacy of thresholding-based segmentation and Hu moment feature extraction in distinguishing between benign and malignant cases of ALL. Gaussian Naive Bayes, known for its simplicity and effectiveness, is employed as the classification algorithm. The study adopts a 5-fold cross-validation approach to evaluate the model's performance, with particular emphasis on metrics such as accuracy, precision, recall, and F1-score. Results indicate a high precision rate across all folds, averaging approximately 84.13%, while exhibiting variability in accuracy, recall, and F1-scores. These findings suggest that while the model is effective in identifying malignant cases, further refinements are necessary for improving overall accuracy and consistency. This research contributes to the field of medical image analysis by demonstrating the potential of combining simple yet efficient techniques for the automated diagnosis of hepatological diseases. It highlights the importance of integrating image processing with machine learning to enhance diagnostic accuracy in medical applications.
Segmentation and Feature Extraction for Malaria Detection in Blood Smears Rismayanti, Nurul
International Journal of Artificial Intelligence in Medical Issues Vol. 2 No. 1 (2024): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v2i1.138

Abstract

Malaria remains a critical global health challenge, particularly in tropical and subtropical regions. Early and accurate diagnosis is essential for effective treatment and control. Traditional methods of malaria diagnosis, such as microscopic examination of blood smears, are time-consuming and prone to human error. This study aims to develop an automated system for malaria detection using machine learning techniques, specifically a decision tree classifier. The dataset, sourced from the National Institutes of Health (NIH), comprises 27,558 blood smear images equally divided into Normal and Malaria classes. The preprocessing steps included segmentation using the Canny edge detector and feature extraction using Hu Moments, followed by data normalization to ensure a mean of 0 and variance of 1. The decision tree classifier was trained and evaluated using 5-fold cross-validation, yielding an average accuracy of 77.32%, precision of 77.31%, recall of 77.37%, and F1-Score of 77.48%. These results demonstrate the model's robustness and effectiveness in differentiating between malaria-infected and uninfected images. The study confirms the viability of using Hu Moments for feature extraction and highlights the decision tree classifier's suitability for this task. The proposed method has significant implications for automated malaria diagnosis, potentially improving diagnostic accuracy and efficiency in clinical settings. Future research should validate these findings on diverse datasets, explore advanced classification techniques, and integrate real-time image acquisition to enhance practical applicability. The integration of such automated systems in healthcare can revolutionize malaria diagnosis, especially in resource-limited settings.
Co-Authors Admojo, Fadhila Tangguh Anik Nur Handayani Aulia Putri Utami Azzahrah, Sitti Fatimah Didik Dwi Prasetya Dwi Nur Halizah Fery Setyo Aji Hayatou Oumarou Hirashima, Tsukasa Huzain Azis Irawati Irawati Jevri Tri Ardiansah lilis nurhayati Muhammad Alim Abdi Muhammad Arif Naswin, Ahmad Nurfadillah Said Nurlinda Nurlinda Purnamasari, Dwi Amalia Risti Amelia Syafie, Lukman Titaley, Gilberth Valentino Triyanna Widiyaningtyas Umar Zaky Umniah Umniah Wibowo, Nanang Roni