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All Journal KOMPUTIKA - Jurnal Sistem Komputer Buletin Sistem Informasi dan Teknologi Islam International Journal of Artificial Intelligence in Medical Issues
As'ad, Ihwana
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Computer Science & IT Control & Systems Engineering Dentistry Electrical & Electronics Engineering Health Professions Medicine & Pharmacology Public Health

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Penerapan Metode Random Forest dalam Klasifikasi Huruf BISINDO dengan Menggunakan Ekstraksi Fitur Warna dan Bentuk Indra, Dolly; Hayati, Lilis Nur; Daris, Mega Asfirawati; As'ad, Ihwana; Mansyur, Umar
Komputika : Jurnal Sistem Komputer Vol 13 No 1 (2024): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v13i1.10363

Abstract

Digital image processing is a field of study that focuses on how an image can be formed, processed, and analyzed to generate useful information for humans. In this research, the utilization of digital images is implemented to classify BISINDO (Indonesian Sign Language) letters from A to Z using the Random Forest classification method. The initial stage in the classification of BISINDO letter images involves pre-processing, which includes converting RGB images to grayscale and performing segmentation through three stages: thresholding, morphology, and edge detection using the Prewitt operator. Subsequently, features such as HSV color extraction and metric shape features, as well as eccentricity, are extracted. These extracted feature values are then utilized in the classification stage of BISINDO letter images from A to Z using the Random Forest method. In this study, three data comparison scenarios were employed for testing purposes. The first scenario involved an 80:20 data ratio, which achieved a testing accuracy of 94.2%. The second scenario with a 70:30 data ratio achieved a testing accuracy of 93.6%, while the third scenario with a 60:40 data ratio had a lower accuracy of only 77.9%. Based on the results of our testing, the system developed is capable of effectively classifying BISINDO letters from A to Z using color and shape feature extraction, along with the Random Forest classification method. The best results were obtained in the data comparison scenario of 80:20, achieving an accuracy rate of 94.2%. Keywords – BISINDO, HSV, Metric, Eccentricity, Random Forest.
Penerapan Metode Analytical Hierarchy Process dalam Penentuan Kualitas Lipa' Le'leng Kabupaten Bulukumba Mansyur, St. Hajrah; Marni, Rini Alfia; As'ad, Ihwana
Buletin Sistem Informasi dan Teknologi Islam Vol 5, No 1 (2024)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v5i1.2239

Abstract

Lipa' Le'leng merupakan sarung khas Suku Kajang. Terbuat dari benang katun yang ditenun secara tradisional. Warna corak pada sarung ini mengandung makna kesederhanaan. Selain untuk pengunaan adat dan kegiatan sehari-hari, sarung ini juga bernilai koleksi karena historisnya. Namun, kurangnya informasi dan pengetahuan tentang sarung hitam, terkadang membuat konsumen dari luar daerah Kajang merasa kecewa dengan kualitas sarung yang mereka dapatkan. Karena itu, dikembangkan suatu sistem pendukung keputusan berbasis website yang akan membantu calon konsumen untuk mendapatkan informasi kualitas Lipa' Le'leng sesuai dengan subriteria yang dipilih dari jenis benang, tingkat kepekatan, motif dan pengkilapan.
Penerapan Internet Of Things (IoT) dalam Sistem Kontrol-Monitoring Proses Transesterifikasi Pembuatan Biodiesel Berbasis ESP32 Salawali, Cici Trisnawati; Satra, Ramdan; As'ad, Ihwana
Buletin Sistem Informasi dan Teknologi Islam Vol 4, No 3 (2023)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v4i3.1855

Abstract

Kemajuan teknologi pada bidang informasi saat ini sangatlah maju, salah satu teknologi tersebut yaitu Internet of Things (IoT) yang merupakan pengembangan dari perangkat teknologi yang memiliki kemampuan menerima data, mengolah data dan mengirim informasi ke pengguna. Dengan kehadiran Internet of Things menawarkan sistem kontrol-monitoring melalui aplikasi smartphone. Penelitian ini bertujuan merancang sistem dengan teknologi Internet of Things pada microwave untuk memonitoring proses transesterifikasi pembuatan biodiesel. Metode yang digunakan adalah merancang sebuah sistem kontrol monitoring yang terintegrasi ke internet menggunakan ESP32 serta sensor suhu, kecepatan, dan waktu. Berdasarkan hasil pengujian yang dilakukan sistem dapat bekerja dengan baik yang dapat dimonitoring melalui pengiriman data kedalam database dan ditampilkan pada website serta mobile phone.
Advancing Healthcare Diagnostics: A Study on Gaussian Naive Bayes Classification of Blood Samples As'ad, Ihwana
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.v1i2.120

Abstract

This research paper presents a comprehensive analysis of the Gaussian Naive Bayes (GNB) classifier's application in predicting health conditions from blood samples, underpinned by a handcrafted dataset representative of typical physiological ranges. Through a meticulous 5-fold cross-validation approach, the study assesses the GNB model's performance in terms of accuracy, precision, recall, and F1-score, revealing not only high efficacy but also consistent improvement in predictive capability across successive folds. A detailed confusion matrix provides further insights into the model's classification proficiency. The results affirmatively address the research hypotheses, indicating the GNB classifier's reliability and effectiveness as a diagnostic tool. With the increasing need for rapid and accurate medical diagnostics, the study's findings underscore the potential of even simple machine learning models to augment traditional blood test analyses, thereby offering significant contributions to the field of biomedical informatics. The research lays the groundwork for future explorations into the integration of machine learning in clinical settings, advocating for the verification of these promising results with real-world clinical data and the comparative analysis of various machine learning models. The potential for automated, precise diagnostic processes paves the way for enhanced patient care and resource optimization in healthcare.
Co-Authors Daris, Mega Asfirawati Dolly Indra Hayati, Lilis Nur Mansyur, St. Hajrah Marni, Rini Alfia Ramdan Satra Salawali, Cici Trisnawati