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All Journal Jurnal Ilmiah Kursor Knowledge Engineering and Data Science Indonesian Journal of Data and Science Innovation in Research of Informatics (INNOVATICS) Jurnal Pengabdian Masyarakat Bangsa
Admojo, Fadhila Tangguh
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Journal : Indonesian Journal of Data and Science

 1 
Klasifikasi Aroma Alkohol Menggunakan Metode KNN Admojo, Fadhila Tangguh; Ahsanawati
Indonesian Journal of Data and Science Vol. 1 No. 2 (2020): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (243.477 KB) | DOI: 10.33096/ijodas.v1i2.12

Abstract

Alkohol adalah senyawa-senyawa dimana satu atau lebih atom hidrogen dalam sebuah alkana digantikan oleh sebuah gugus -OH. Alkohol memiliki ikatan yang mirip air. Alkohol terdiri dari molekul polar. Dalam senyawa alkohol, oksigen mengemban muatan negatif parsial. Alkohol telah digunakan oleh orang di seluruh dunia, dalam makanan standar, untuk higienis / alasan medis, untuk relaksan dan efek euforia, untuk tujuan rekreasi, untuk inspirasi artistik, sebagai aphrodisiacs, dan untuk alasan lain. Alkohol memiliki beberapa jenis senyawa diantaranya adalah octanol, propanol, Butanol, propanol, dan isobutanol. Oleh karena itu dibutuhkan sensor untuk mendeteksi jenis bahan kimia pada suatu cairan berdasarkan aromanya dengan menerapkan salah satu metode klasifikasi yaitu K-Nearest Neighbor (KNN). Pengujian system ini terdiri dari pengujian pengaruh nilai K dan pengaruh nilai crossvalidation. Hasil dari pengujian pengaruh nilai K menghasilkan akurasi optimum senilai 100% pada nilai K=3 dan 100% pada nilai K=4
Analisis Performa Algoritma Stochastic Gradient Descent (SGD) Dalam Mengklasifikasi Tahu Berformalin Admojo, Fadhila Tangguh; Sulistya, Yudha Islami
Indonesian Journal of Data and Science Vol. 3 No. 1 (2022): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v3i1.42

Abstract

Tahu berformalin adalah salah satu jenis makanan yang sering mengandung bahan-bahan kimia yang dapat mengawetkan daripada tahu tanpa formalin. Pada tahu berformalin dapat memberikan tekstur lebih kenyal dan berwarna putih bersih. Penelitian ini bertujuan untuk mengklasifikasikan tahu berformalin dan tahu tidak berformalin. Pada paper ini menggunakan algoritma Stochastic Gradient Descent atau dalam penerapannya lebih dikenal dengan SGD Classifier yang merupakan bagian dari algoritma machine learning untuk klasifikasi, regresi maupun jaringan syaraf tiruan serta algoritma ini sangat efisien pada dataset berskala besar. Penelitian ini mencoba menerapkan algoritma SGD pada dataset tahu berformalin dengan jumlah dataset yakni 11000 yang dimana 5500 data tahu berformalin dan 5500 data tahu tidak berformalin. Setelah dilakukan beberapa tahapan dalam pengujian dengan algoritma SGD maka diperolah hasil akurasi, presisi, recall, f1-score pada model yang masing-masing 82.6% untuk akurasi, 81.7% untuk presisi, 84.1% untuk recall, 83.5% untuk f1-score dan dilakukan pengujian menggunakan 10 data yang tidak termasuk dalam data latih memperoleh performansi rata-rata akurasi sebesar 70%, presisi 71%, recall 70% dan f1-score 70%.
Analisis performa metode Naïve Bayesh Classifier pada Electronic Nose dalam identifikasi formalin pada tahu Admojo, Fadhila Tangguh; Jabir, Siti Rahma
Indonesian Journal of Data and Science Vol. 4 No. 1 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i1.67

Abstract

Penelitian ini bertujuan untuk menganalisis performa metode Naive Bayes Classifier (NBC) dalam identifikasi formalin pada tahu menggunakan Electronic Nose. Hasil dari penelitian ini menunjukkan bahwa performa NBC cukup moderat, dengan nilai akurasi sekitar 0,59 hingga 0,60, presisi sekitar 0,67 hingga 0,68, recall sekitar 0,59 hingga 0,60, dan F1-score sekitar 0,55. Ini menunjukkan bahwa model mampu mengklasifikasikan beberapa titik data dengan benar, tetapi tidak semua. Walaupun demikian masih ada ruang untuk perbaikan dan perlu dipertimbangkan untuk mencoba metode lain untuk meningkatkan hasil identifikasi formalin pada tahu. Hasil ini menunjukkan bahwa metode Naive Bayes Classifier pada Electronic Nose masih belum dapat memberikan hasil yang optimal dalam identifikasi formalin pada tahu, dan hasil yang diperoleh masih tidak lebih baik dari penelitian sebelumnya
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
Classification of Mushroom Edibility Using K-Nearest Neighbors: A Machine Learning Approach Admojo, Fadhila Tangguh; Radhitya, Made Leo; Zein, Hamada; Naswin, Ahmad
Indonesian Journal of Data and Science Vol. 5 No. 3 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i3.199

Abstract

This study investigates the use of the K-Nearest Neighbors (KNN) algorithm for the binary classification of mushroom edibility using a cleaned version of the UCI Mushroom Dataset. The dataset underwent pre-processing techniques such as modal imputation, one-hot encoding, z-score normalization, and feature selection to ensure data quality. The model was trained on 80% of the dataset and evaluated on the remaining 20%, achieving an overall accuracy of 99%. Evaluation metrics, including precision, recall, and F1-score, confirmed the model's effectiveness in distinguishing between edible and poisonous mushrooms, with minimal misclassification errors. Despite its high performance, the study identified scalability as a limitation due to the computational complexity of KNN, suggesting that future research should explore alternative algorithms for enhanced efficiency. This research underscores the importance of pre-processing and hyperparameter optimization in building reliable classification models for food safety applications.
Optimizing Javanese Numeral Recognition Using YOLOv8 Technology: An Approach for Digital Preservation of Cultural Heritage Syafie, Lukman; Azis, Huzain; Admojo, Fadhila Tangguh
Indonesian Journal of Data and Science Vol. 6 No. 1 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i1.239

Abstract

Introduction: The preservation of Javanese script as part of Indonesia’s cultural heritage is increasingly urgent in the digital era, especially due to declining literacy among younger generations. This study aims to explore the effectiveness of YOLOv8, an advanced object detection algorithm, for recognizing handwritten Javanese numerals to support efforts in cultural digitization and education. Methods: A dataset of 2,790 handwritten Javanese numerals (0–9) was collected from 93 respondents. Each numeral was manually annotated using bounding boxes via the MakeSense.ai platform. The YOLOv8 model was trained using 80% of the data and validated on the remaining 20%. Training was performed in the PyTorch framework with data augmentation techniques to increase robustness. Model performance was evaluated using precision, recall, F1-score, and mean Average Precision (mAP), along with visualization through confidence curves and confusion matrices. Results: The model achieved a high validation precision of 88.3%, recall of 89.1%, and mAP of 0.88 at IoU 0.90. F1-score peaked at a confidence threshold of 0.89, while certain numerals like 'six' and 'nine' achieved near-perfect detection. Visualizations confirmed the model’s ability to accurately classify and localize characters in both training and unseen data. Minor misclassifications occurred between visually similar numerals. Conclusions: YOLOv8 demonstrates high effectiveness in recognizing handwritten Javanese numerals and holds significant potential for digital heritage preservation. Future work should focus on expanding the dataset, improving generalization under varied conditions, and integrating this model into educational tools and augmented reality applications for interactive learning.
Co-Authors Achmad Jauhari, Achmad Ahsanawati Aziz, Huzain Devie Rosa Anamisa Fahmi Ajismanto Guntoro Barovih, Guntoro Hamada Zein Huzain Azis Ika Oktavia Suzanti Jabir, Siti Rahma Lajis, Adidah Manga’, Abdul Rachman Mualfah, Desti Muhammad Innuddin Nasir, Haidawati Naswin, Ahmad Nurul Rismayanti Radhitya, Made Leo Rezania Agramanisti Azdy, Rezania Agramanisti Salim, Yulita Saputri, Nurul Adha Oktarini Slamet Risnanto Syafie, Lukman Utami, Aulia Putri Windiawati, Ai Wulan Yudha Islami Sulistya