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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Semantik INTEKNA POSITIF Jurnal IMPACT: Implementation and Action Jurnal Ilmu Komputer dan Bisnis Indonesian Journal of Data and Science International Journal of Artificial Intelligence in Medical Issues
Effan Najwaini
Politeknik Negeri Banjarmasin
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Electrical & Electronics Engineering Health Professions Mathematics Medicine & Pharmacology Public Health Other

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Advancements in Agricultural Automation: SVM Classifier with Hu Moments for Vegetable Identification Waluyo Poetro, Bagus Satrio; Maria, ⁠⁠Eny; Zein, Hamada; Najwaini, Effan; Zulfikar, Dian Hafidh
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.v5i1.123

Abstract

This study investigates the application of Support Vector Machine (SVM) classifiers in conjunction with Hu Moments for the purpose of classifying segmented images of vegetables, specifically Broccoli, Cabbage, and Cauliflower. Utilizing a dataset comprising segmented vegetable images, this research employs the Canny method for image segmentation and Hu Moments for feature extraction to prepare the data for classification. Through the implementation of a 5-fold cross-validation technique, the performance of the SVM classifier was thoroughly evaluated, revealing moderate accuracy, precision, recall, and F1-scores across all folds. The findings highlight the classifier's potential in distinguishing between different vegetable types, albeit with identified areas for improvement. This research contributes to the growing field of agricultural automation by demonstrating the feasibility of using SVM classifiers and image processing techniques for the task of vegetable identification. The moderate performance metrics emphasize the need for further optimization in feature extraction and classifier tuning to enhance classification accuracy. Future recommendations include exploring alternative machine learning algorithms, advanced feature extraction methods, and expanding the dataset to improve the classifier's robustness and applicability in agricultural settings. This study lays a foundation for future advancements in automated vegetable sorting and quality control, offering insights that could lead to more efficient agricultural practices.
Application of the K-Nearest Neighbors (KNN) Algorithm on the Brain Tumor Dataset Najwaini, Effan; Thomas Edyson Tarigan; Fajri Profesio Putra; Sulistyowati
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 1 (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.v1i1.85

Abstract

Brain tumors pose significant challenges in the medical domain, necessitating advanced diagnostic techniques for early and accurate detection. This research paper presents a comprehensive study on the application of the K-Nearest Neighbors (KNN) algorithm to a dataset comprising brain tumor images. The methodology involved segmenting the images using the Canny method, extracting relevant features via Hu Moments, and subsequently employing the KNN algorithm for classification. Using a 5-fold cross-validation, the system consistently achieved an average accuracy of approximately 62%. These findings highlight the potential of traditional machine learning algorithms in medical imaging, providing valuable insights for both researchers and practitioners. While the results are promising, the study also underscores the importance of integrating such algorithms with other diagnostic methods for optimal results
PENGEMBANGAN WEB PROFIL UNTUK MENDUKUNG PARIWISATA DI DESA TELUK TAMIANG Najwaini, Effan; Pebrianto, Agus; Amelia, Rini
Jurnal IMPACT: Implementation and Action Vol. 6 No. 1 (2023): Jurnal Impact
Publisher : Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/impact.v6i1.14470

Abstract

Pengabdian kepada masyarakat ini bertujuan untuk mendukung pengembangan potensi pariwisata di Desa Teluk Tamiang, Kabupaten Kotabaru, melalui pembuatan website profil desa. Desa Teluk Tamiang memiliki daya tarik wisata yang signifikan, seperti pantai pasir putih dan terumbu karang, namun masih kurang dikenal oleh masyarakat luas. Untuk mengatasi hal ini, tim pelaksana mengembangkan sebuah website menggunakan platform CMS WordPress dan plugin Elementor, yang dirancang agar mudah digunakan oleh pengelola desa. Pelatihan juga diberikan kepada masyarakat dan perangkat desa untuk memastikan mereka mampu mengelola dan memperbarui konten website secara mandiri. Proses pengembangan meliputi pembuatan desain, pengisian konten, serta pengujian website untuk memastikan kelancaran akses dan kemudahan navigasi bagi pengguna. Selain itu, website ini diintegrasikan dengan media sosial untuk meningkatkan jangkauan promosi. Diharapkan, dengan adanya platform digital ini, potensi wisata desa dapat lebih dikenal dan menarik lebih banyak wisatawan, baik domestik maupun internasional. Hal ini diharapkan berdampak pada peningkatan pendapatan masyarakat dari sektor pariwisata serta pelestarian lingkungan dan budaya lokal.
Depression Risk Prediction Among Teenagers Using Explainable Machine Learning and Imbalanced Behavioral Data Rudi Setiawan; Effan Najwaini; Rezania Agramanisti Azdy; Rasmiati Rasyid
International Journal of Artificial Intelligence in Medical Issues Vol. 4 No. 1 (2026): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/0w9q4238

Abstract

Adolescent depression has become an important public health concern, particularly in relation to increasing digital media exposure, lifestyle changes, and psychosocial pressure. This study proposes an explainable machine learning framework for predicting depression risk among teenagers using social media usage, lifestyle behavior, and psychosocial indicators. The dataset consisted of 1,200 records with 13 variables, including age, gender, daily social media hours, platform usage, sleep hours, screen time before sleep, academic performance, physical activity, social interaction level, stress level, anxiety level, addiction level, and depression label. The target variable was highly imbalanced, with 1,169 samples categorized as non-depression and only 31 samples categorized as depression risk. Several machine learning models were evaluated, including Logistic Regression, Random Forest, Support Vector Machine, and Gradient Boosting. The experiments compared two feature settings, namely behavioral-only features and full features, combined with three imbalance handling strategies: no imbalance treatment, class weighting, and SMOTE. Model performance was evaluated using accuracy, precision, recall, F1-score, balanced accuracy, ROC-AUC, PR-AUC, Cohen’s Kappa, MAE, and RMSE. The results showed that the full-feature setting substantially outperformed the behavioral-only setting. The best performance was achieved by Random Forest using full features without imbalance handling, producing perfect classification results with accuracy, precision, recall, F1-score, ROC-AUC, and PR-AUC of 1.0000. Permutation importance analysis identified sleep hours, stress level, anxiety level, and daily social media hours as the most influential predictors. These findings indicate that teenage depression risk in this dataset is strongly associated with sleep behavior and psychosocial conditions, in addition to social media exposure. Although the model achieved excellent performance, the result should be interpreted cautiously due to the small number of positive depression-risk samples and the possibility of highly separable label patterns. Therefore, the proposed approach should be positioned as an early risk screening framework rather than a clinical diagnostic tool
Co-Authors Adi Pratomo Agus Irawan Agus Irawan Agus Pebrianto, Agus Ahmad Ashari Ahyati, Inayatul Ulya Aidin Najihi Aulia, Nur Rizki Azhari SN Christian Dwi Suhendra, Christian Dwi Dian Hafidh Zulfikar Edi Faizal Fajri Profesio Putra Faris Ade Irawan Faris Ade Irawan Febri Bonavita Jerli Febri Bonavita Jerli Hairul Anwar, Hairul Hamada Zein I Wayan Mustika Indrasary, Yoeni Irawan, Faris Ade Irawan, Faris Ade Maria, Eny Maria, ⁠⁠Eny Mey Risa Mey Risa, Mey Padli Padli Poetro, Bagus Satrio Waluyo Purnama Purnama Rasmiati Rasyid Rezania Agramanisti Azdy Rini Amelia, Rini Setiawan, Rudi Siti Erma Ainah Siti Erma Ainah Sulistyowati Thomas Edyson Tarigan Widyawan