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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Knowledge Engineering and Data Science ILKOM Jurnal Ilmiah CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Computer Science and Information Technologies Indonesian Journal of Data and Science Innovation in Research of Informatics (INNOVATICS) 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
Huzain Azis
Unviersitas Muslim Indonesia
Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance 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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Perancangan Sistem Informasi Data Kependudukan Desa Kaduaja Kecamatan Gandangbatu Sillanan Berbasis Web Ramadan, Syahril; Indra, Dolly; Azis, Huzain
Buletin Sistem Informasi dan Teknologi Islam (BUSITI) Vol 5, No 2 (2024)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v5i2.1920

Abstract

Sistem informasi kependudukan merupakan salah satu faktor utama dalam pemerintahan dan pembangunan kependudukan yang diarahakan pada pemenuhan hak dari setiap warga negara dibidang pelayanan data kependudukan. Desa Kaduaja salah satu bagian dari Desa di Kecamatan Gandangbatu Sillanan Kabupaten Tana Toraja, pengelolaan data pada Kantor Desa Kaduaja dilakukan secara manual mengakibatkan dokumen-dokumen tersebut disusun dengan tidak teratur dan tersimpan pada arsip yang terpisah sehingga pihak pemerintah Desa Kaduaja, untuk menyelesaikan permasalahan tersebut dibangunlah sebuah sistem informasi data kependudukan. penelitian ini menghasikan sistem informasi data kependudukan Desa Kaduaja Kecamatan Gandangbatu sillanan berbasis web yang dapat mempermudah pemerintah desa mengolah data kependudukan. metode yang digunakan dalam perancangan aplikasi yaitu metode waterfall yaitu model dimana tiap tahapannya dikerjakan secara berurutan dari atas ke bawah. Pengujian yang dilakukan berdasarkan blackbox testing mendapatkan nilai penggunaan secara keseluruhan dalam tampilan interface maupun fungsionalitas aplikasi yaitu 85% dari 24 responden.
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.
PERANCANGAN APLIKASI PENJADWALAN DAKWAH MUBALIGH MENGGUNAKAN METODE PIECES Sainlia, Ahmad Fauzan; Belluano, Poetri Lestari Lokapitasari; Azis, Huzain
Buletin Sistem Informasi dan Teknologi Islam (BUSITI) Vol 5, No 3 (2024)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v5i3.2039

Abstract

Dalam hal pengaturan jadwal dakwah, metode PIECES digunakan untuk mengatur sinkronisasi jadwal, durasi dakwah, memberikan notifikasi ketika mubaligh sudah memasuki waktu dakwah dan mengatur jadwal dakwah para mubaligh dengan baik.  Analisa kinerja dalam PIECES yaitu menyediakan sistem yang dapat mengatur jadwal mubaligh, dalam analisa informasi yaitu memberikan informasi ke mubaligh ketika mendekati waktu dakwahnya, dalam analisa ekonomi memberikan informasi mengenai transparansi honorarium pada mubaligh, dalam analisa efisiensi memberikan durasi waktu bagi para mubaligh dan dalam analisa pelayanan memberikan notifikasi ketika mubaligh sudah memasuki waktu dakwah. Aplikasi Penjadwalan Dakwah Mubaligh menggunakan Metode PIECES dirancang untuk dapat membantu petugas masjid dalam menyusun jadwal para mubaligh serta memberikan informasi terkait durasi dakwah bagi tiap mubaligh sehingga para mubaligh tidak lupa dengan jadwal dakwah mereka serta dapat mengatur tranparansi pendapatan tiap mubaligh. Sistem aplikasi berbasis mobile dapat diakses melalui perangkat android, dimana sistem dilengkapi notifikasi aktif yang dapat diakses setiap saat oleh para pengguna sesuai kebutuhan, sehingga mubaliqh dapat mengatur durasi dakwah dengan jelas, dan tidak ada lagi jadwal yang berbentura antar mubaligh satu dengan lainnya .
Optimizing classification models for medical image diagnosis: a comparative analysis on multi-class datasets Rachman Manga, Abdul; Putri Utami, Aulia; Azis, Huzain; Salim, Yulita; Faradibah, Amaliah
Computer Science and Information Technologies Vol 5, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/csit.v5i3.p205-214

Abstract

The surge in machine learning (ML) and artificial intelligence has revolutionized medical diagnosis, utilizing data from chest ct-scans, COVID-19, lung cancer, brain tumor, and alzheimer parkinson diseases. However, the intricate nature of medical data necessitates robust classification models. This study compares support vector machine (SVM), naïve Bayes, k-nearest neighbors (K-NN), artificial neural networks (ANN), and stochastic gradient descent on multi-class medical datasets, employing data collection, Canny image segmentation, hu moment feature extraction, and oversampling/under-sampling for data balancing. Classification algorithms are assessed via 5-fold cross-validation for accuracy, precision, recall, and F-measure. Results indicate variable model performance depending on datasets and sampling strategies. SVM, K-NN, ANN, and SGD demonstrate superior performance on specific datasets, achieving accuracies between 0.49 to 0.57. Conversely, naïve Bayes exhibits limitations, achieving precision levels of 0.46 to 0.47 on certain datasets. The efficacy of oversampling and under-sampling techniques in improving classification accuracy varies inconsistently. These findings aid medical practitioners and researchers in selecting suitable models for diagnostic 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.
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 Abdul RAchman Manga Admojo, Fadhila Tangguh 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 Dolly Indra Erick Irawadi Alwi Erma Susanti Fadhila Tangguh Admojo Faradibah, Amaliah Farniawati Fattah Farniwati Fattah Fattah , Farniwati Fattah, Farniawati Fattah, Farniwati Felix Andika Dwiyanto Fika Hastarita Rachman, Fika Hastarita Firdaus Solihin Harlinda Lahuddin Hasanuddin, Tasrif Haviluddin Haviluddin Herdianti Darwis Herdianti Darwis Herman Herman Ika Oktavia Suzanti Inggrianti Pratiwi Putri jabir, sitti rahmah Kamil, Fajrul Ihsan Lokapitasari Belluano, Poetri Lestari M. Imam Maulana Manga, Abdul Rachman Muh Nurtanzis Sutoyo Muhammad Syahrizal Darwis Muis, Ismunandar Mus Mulyadi Baharuddin Nirmala Nirmala, Nirmala Nirwana Nirwana Nur bahri, Nur Bahri Nurul A'ayunnisa Nurul Fitri H, Sarah FIla Pradinata, Awal Purnawansayah Purnawansayah Purnawansyah Purnawansyah Puspitasari, Yustisia Putri Utami, Aulia Putri, Aulia Maharani Putri, Inggrianti Pratiwi Rachman Manga, Abdul Ramadan, Syahril Ramdan Satra Rasman Rasman Rismayanti, Nurul Rochman, Eka Mala Sari Sainlia, Ahmad Fauzan Salim, Yulita Saly, Intan Novita Selvi Marcellia Shidiq Arif Siwiantoko Syafie, Lukman Tasrif Hasanuddin Tenripada, Andi Ulfa Widyawati, Dewi Yulita Salim