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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer ILKOM Jurnal Ilmiah CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Indonesian Journal of Data and Science Innovation in Research of Informatics (INNOVATICS) Jurnal Pengabdian Masyarakat Indonesia Buletin Sistem Informasi dan Teknologi Islam Ilmu Komputer untuk Masyarakat Jurnal Pengabdian Mandiri Open Community Service Journal Bulletin of Social Informatics Theory and Application NERO (Networking Engineering Research Operation) International Journal of Artificial Intelligence in Medical Issues Linier: Literatur Informatika dan Komputer
Huzain Azis
Program Studi Teknik Informatika, Fakultas Ilmu Komputer, Universitas Muslim Indonesia, Indonesia
Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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Analisis Performa Metode Gaussian Naïve Bayes untuk Klasifikasi Citra Tulisan Tangan Karakter Arab Nurul A'ayunnisa; Salim, Yulita; Azis, Huzain
Indonesian Journal of Data and Science Vol. 3 No. 3 (2022): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v3i3.54

Abstract

Berdasarkan penelitian yang dilakukan oleh Herman dkk., peneliti mencoba mengangkat kembali metode yang diterapkan dengan menggunakan dataset yang berbeda dan dengan jumlah yang lebih banyak. Penelitian ini bertujuan untuk menghitung performa metode (akurasi, presisi, recall, dan f-measure) Gaussian Naïve Bayes. Dataset yang digunakan adalah citra tulisan tangan karakter arab. Berdasarkan hasil perhitungan performa menunjukkan tingkat akurasi tertinggi sebesar 12%, presisi 10%, recall 12%, dan f-measure 8%.
Prediksi Potensi Donatur Menggunakan Model Logistic Regression jabir, sitti rahmah; Azis, Huzain; Widyawati, Dewi; Tenripada, Andi Ulfa
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.v4i1.64

Abstract

GRDS menghadapi kelangkaan dana, ketika diperlukan untuk merawat para korban Gaja. Gaja adalah topan bernama kelima dari musim siklon Samudra Hindia Utara 2018 yang mempengaruhi sebagian besar tempat di Tamil Nadu, India selama bulan November 2018. Tujuan dari penelitian ini adalah untuk menggunakan riwayat donasi untuk menganalisis apakah donator akan menyumbang atau tidak menggunakan regresi logistik. Data Tamil Nadu diberikan untuk menerapkan model yang dibangun untuk memprediksi donator yang paling mungkin menjadi korban topan Gaja. Pada tahap pengumpulkan data seringkali terjadi hambatan, salah satu hambatannya yaitu fenomena missing data atau data hilang. Akibat dari adanya missing data adalah pendugaan parameter menjadi tidak efisien. Ukuran data yang berkurang dapat mengakibatkan kesulitan dalam menganalisis, sehingga hasil yang didapatkan menjadi tidak valid dan tujuan dari penelitian tidak tercapai. Data yang hilang akan diisi menggunakan metode single imputation. Data yang telah diimputasi menggunakan beberapa metode akan membantu dalam melakukan prediksi. Dimana algoritma yang digunakan untuk melakukan prediksi ialah logistic regression. Beberapa data dihilangkan setelah melihat multikolinearitas. Dalam tahap pemodelan, data dibagi menjadi 2 yaitu 70% untuk data pelatihan dan 30% untuk data tes. Dimana hasil perhitungan akurasi dari model ialah 0,6129 yang menunjukkan bahwa model tidak melakukan prediksi dengan baik menggunakan metode tersebut.
Comparative Analysis of Machine Learning Algorithm Variations in Classifying Body Shaming Topics on Social Media X Nurul Fitri H, Sarah FIla; Fattah , Farniwati; Azis, Huzain
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.v5i2.82

Abstract

Machine learning is an approach in computer science where systems or models can learn from data and experience to improve performance or perform specific tasks. There are several popular machine learning algorithms, such as naïve bayes, decision tree, K-NN, and SVM. This study aims to compare the performance of accuracy, precision, recall, and F-1 score in sentiment analysis of body shaming topics on Social Media X (formerly known as Twitter) by applying decision tree, K-NN, and SVM methods and identifying the most effective algorithm in classifying the data. Based on the classification performance testing results, it can be concluded that the classification method using the trigram feature model provides the best performance compared to other methods. The trigram model is able to achieve high recall, particularly in recognizing positive classes, without significantly compromising accuracy
Assessing the Performance of Logistic Regression in Heart Disease Detection through 5-Fold Cross-Validation Azis, Huzain
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.v2i1.137

Abstract

This study explores the effectiveness of Logistic Regression in predicting heart disease using a dataset derived from multiple international databases. Employing a 5-fold cross-validation method, the research aimed to evaluate the model's accuracy, precision, recall, and F1-score. Results indicated that Logistic Regression performs robustly, with accuracy ranging from 80% to 88.29%, and high recall rates, highlighting its potential as a valuable tool in medical diagnostics. Despite some variability in precision, which may lead to higher false positive rates, the model's high recall is crucial in clinical settings where missing a diagnosis can have dire consequences. The research confirmed the applicability of Logistic Regression to binary classification problems in healthcare, aligning with existing literature that supports its use in similar contexts. The study contributes to the field by demonstrating the model's consistency and reliability across diverse data subsets, reinforcing the potential for machine learning applications in healthcare diagnostics. Future research should focus on integrating Logistic Regression with other models to improve accuracy and testing the model on more current, varied datasets to enhance its generalizability and effectiveness in real-world settings.
Implementasi Metode Penetration Testing pada Layanan Keamanan Sistem Kartu Transaksi Elektronik Wahana Permainan Fattah, Farniwati; Putri, Aulia Maharani; Azis, Huzain
Techno.Com Vol. 23 No. 1 (2024): Februari 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i1.9488

Abstract

Penggunaan kartu magnetic stripe pada wahana permainan rentan terhadap akses yang tidak sah, seperti skimming, yang dapat merugikan pengelola dan penyedia wahana. Penetration testing merupakan metode yang dapat digunakan untuk mengidentifikasi dan eksploitasi kerentanan. Pada pengujian penetration testing terdapat tujuh fase yang digunakan yaitu pre-engagement, information gathering, threat modeling, vulnerability analysis, exploitation, post exploitation, dan reporting. Dalam Penelitian sebelumnya menunjukkan bahwa komunikasi antara magnetic stripe reader dan komputer utama dilakukan melalui koneksi kabel, yang menghasilkan layanan confidentiality dan availability. Namun, pada penelitian ini, pengimplementasian penetration testing menggunakan koneksi nirkabel menghasilkan temuan bahwa layanan keamanan yang tersedia adalah availability. Oleh karena itu, dapat disimpulkan bahwa komunikasi baik melalui koneksi kabel maupun nirkabel tidak terdapat layanan keamanan integrity. Rekomendasi bagi  penyedia layanan untuk meningkatkan kemanan kartu di lokasi tersebut dengan menerapkan enkripsi data.
ANALISIS KINERJA ALGORITMA PEMBELAJARAN MESIN ENSEMBEL PADA DATASET MULTI KELAS CITRA JAFFE Azis, Huzain; Alisma, Alisma; Purnawansyah, Purnawansyah; Nirmala, Nirmala
NERO (Networking Engineering Research Operation) Vol 9, No 2 (2024): Nero - 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i2.27872

Abstract

This research aims to develop a facial expression recognition system based on the JAFFE dataset which includes seven classes of emotional expressions, namely happy, sad, angry, afraid, disgusted and neutral expressions. The first step taken is canny segmentation on each dataset to maintain essential information on each face. Next, extraction was carried out using the hu moments method to gain an in-depth understanding of the important characteristics of facial expressions. The next process involves ensemble voting using five classification methods, namely Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Gaussian Process Classifier (GPC), and Decision Tree. The results of these five methods are then ensembel using voting techniques, and the final results are evaluated using performance metrics such as accuracy, precision, recall, and F-1 score. Evaluation is carried out by comparing the final results with the original data from the JAFFE dataset, by measuring accuracy , precision, recall, and F1 Score value to evaluate system performance. The results of this research show that the ensemble voting approach using a combination of classification methods is able to significantly improve facial expression recognition capabilities. The resulting accuracy, precision, recall, and F1 Score values provide a comprehensive picture of system performance.  This research contributes to the development of facial emotion recognition technology and can be applied in various contexts. Includes human-computer interaction as well as applications in the fields of artificial intelligence.Keywords: Performance Analysis, Ensemble, Jaffe Image, Classification, Multiclass
PREDIKSI ANEMIA DARI PIXEL GAMBAR DAN LEVEL HEMOGLOBIN MENGGUNAKAN RANDOM FOREST CLASSIFIER Azis, Huzain; Rismayanti, Nurul
NERO (Networking Engineering Research Operation) Vol 9, No 1 (2024): Nero - 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i1.27916

Abstract

Anaemia is a widespread blood disorder characterized by a deficiency of red blood cells or hemoglobin, which can lead to severe health complications if not diagnosed and treated promptly. This research aims to develop a machine learning model to predict anaemia based on hemoglobin levels and image pixel distributions, leveraging a dataset from Kaggle. The dataset includes features such as percentages of red, green, and blue pixels in images and hemoglobin levels. We applied a Random Forest Classifier, a robust machine learning algorithm, and evaluated its performance using 5-fold cross-validation. The data pre-processing involved removing irrelevant columns, encoding categorical variables, and scaling numerical features. The model achieved a mean accuracy of 97.05%, precision of 97.02%, recall of 97.05%, and F1-score of 96.88%, indicating its high reliability in predicting anaemia. Visualizations such as Correlation Heatmaps, 3D PCA, Parallel Coordinates Plots, 3D t-SNE, and Violin Plots were used to understand feature relationships and distributions. These results underscore the potential of machine learning in providing a non-invasive, cost-effective diagnostic tool for anaemia, especially in resource-limited settings. Future research should address dataset imbalance and potential biases, explore additional features, and test other machine learning models to further enhance the predictive accuracy. This study contributes to the field of medical diagnostics by demonstrating the efficacy of integrating hemoglobin levels and image data for anaemia prediction, paving the way for improved early detection and treatment strategies.Keywords: Anaemia, Hemoglobin, Machine Learning, Random Forest.
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.
Transformasi Digital Dan Keselamatan Online: Workshop Interaktif Untuk Siswa Internasional As'ad, Ihwana; Salim, Yulita; Azis, Huzain
Open Community Service Journal Vol. 4 No. 1 (2025): Open Community Service Journal
Publisher : Research and Social Study Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33292/ocsj.v4i1.109

Abstract

Anak-anak dan remaja sering menggunakan media sosial dan bermain game secara berlebihan, yang dapat berdampak negatif terhadap kesehatan dan prestasi akademik. Kurangnya kesadaran akan keamanan digital dan etika penggunaan teknologi menjadikan mereka rentan terhadap ancaman siber. Kegiatan pengabdian ini bertujuan untuk meningkatkan literasi digital siswa Sekolah Kebangsaan Syeikh Mohd Idris Al-Marbawi di Malaysia melalui workshop interaktif. Metode pelaksanaan meliputi analisis kebutuhan, penyusunan dan pelaksanaan materi pelatihan, kepada 20 siswa kelas 5. Hasil kegiatan menunjukkan peningkatan pemahaman siswa terhadap penggunaan gadget yang bijak dan etika digital. Kegiatan ini juga mendorong pengembangan kemampuan interpersonal dan kesadaran terhadap keamanan siber.
Imbalanced Text Classification on Tourism Reviews using Ada-boost Naïve Bayes Suzanti, Ika Oktavia; Kamil, Fajrul Ihsan; Rochman, Eka Mala Sari; Azis, Huzain; Suni, Alfa Faridh; Rachman, Fika Hastarita; Solihin, Firdaus
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 9 No. 1 (2025)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v9i1.1496

Abstract

Hidden paradise is a term that aptly describes the island of Madura, which offers diverse tourism potential. Through the Google Maps application, tourists can access sentiment-based information about various attractions in Madura, serving both as a reference before visiting and as evaluation material for the local government. The Multinomial Naïve Bayes method is used for text classification due to its simplicity and effectiveness in handling text mining tasks. The sentiment classification is divided into three categories: positive, negative, and mixed. Initial analysis revealed an imbalance in sentiment data, with most reviews being positive. To address this, sampling techniques—both oversampling and undersampling—were applied to achieve a more balanced data distribution. Additionally, the Adaptive Boosting ensemble method was used to enhance the accuracy of the Multinomial Naïve Bayes model. The dataset was split into training and testing sets using ratios of 60:40, 70:30, and 80:20 to evaluate the model’s stability and reliability. The results showed that the highest F1-score, 84.1%, was achieved using the Multinomial Naïve Bayes method with Adaptive Boosting, which outperformed the model without boosting, which had an accuracy of 76%.
Co-Authors Admojo, Fadhila Tangguh Agus Sukardi Nasrullah Ahmad Fauzan Sainlia Alfa Faridh Suni Alfiyyah, Nurul Alisma, Alisma Alwi Alwi Anas, Risnayanti Andi Tenri Sumpala, Andi Tenri As'ad, Ihwana Baharuddin, Mus Mulyadi Bustam, Faida Daeng Erick Irawadi Alwi, Erick Irawadi Erma Susanti Fadhila Tangguh Admojo Farniwati Fattah Fattah , Farniwati Fattah, Farniawati Fattah, Farniwati Fika Hastarita Rachman, Fika Hastarita Firdaus Solihin Hasanuddin, Tasrif Haviluddin Haviluddin Herman Herman Ika Oktavia Suzanti jabir, sitti rahmah Julisa Julisa Kamil, Fajrul Ihsan M. Imam Maulana Manga, Abdul Rachman Muh Nurtanzis Sutoyo Muhammad Syahrizal Darwis Muis, Ismunandar Mus Mulyadi Baharuddin Nabila Widiyanti Nirmala Nirmala, Nirmala Nur Azmi Nur bahri, Nur Bahri Nurul A'ayunnisa Nurul Fitri H, Sarah FIla Nurul Rismayanti Poetri Lestari Purnawansayah Purnawansayah Purnawansyah Purnawansyah Puspitasari, Yustisia Putri, Aulia Maharani Putri, Inggrianti Pratiwi Rasman Rasman Rochman, Eka Mala Sari Salim, Yulita Saly, Intan Novita Selvi Marcellia Shidiq Arif Siwiantoko Syafie, Lukman Tasrif Hasanuddin Tenripada, Andi Ulfa Wahyu Kadri Rahmat Suat Suat Widyawati, Dewi Yulita Salim