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All Journal Dinamik Bulletin of Electrical Engineering and Informatics INTEKNA POSITIF Jurnal Edik Informatika : Penelitian Bidang Komputer Sains dan Pendidikan Informatika Jurnal Surya Medika JOURNAL OF APPLIED INFORMATICS AND COMPUTING SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Sinergitas PkM & CSR Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Teknologi dan Informasi INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Al-Tamimi Kesmas: Jurnal Ilmu Kesehatan Masyarakat (Journal of Public Health Sciences) Jurnal Informasi dan Teknologi Jurnal Persatuan Perawat Nasional Indonesia (JPPNI) Tematik : Jurnal Teknologi Informasi Komunikasi Phasti: Jurnal Teknik Informatika Politeknik Hasnur Jumat Informatika: Jurnal Pengabdian Masyarakat Majalah Ilmiah UPI YPTK Dinamika Kesehatan: Jurnal Kebidanan dan Keperawatan Journal of Pharmaceutical Care and Sciences Jurnal Rumpun Ilmu Kesehatan Health Research Journal of Indonesia Journal of Advances in Medicine and Pharmaceutical Sciences Health Sciences International Journal Adpebi Science Series KOPEMAS INSTALL: Information System and Technology Journal
Muhammad Zulfadhilah
Teknologi Informasi, Universitas Sari Mulia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Transportation Other

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Artificial Intelligence and Digital Economy: Comparative Adoption of Regions and Populations in ASEAN Countries Using EDA Samita, Mambang; Mambang; Muhammad Zulfadhilah; Septyan Eka Prastya; Finki Dona Marleny
Adpebi Science Series 2022: 1st AICMEST 2022
Publisher : ADPEBI

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The purpose of this paper is to make a comparative analysis of artificial intelligence adoption and the potential of the digital economy in ASEAN countries. The regions of countries and populations of the ASEAN Region correlate with the adoption of artificial intelligence and the potential of the digital economy. This paper uses qualitative methods and experiments with secondary data sources from online websites. The data used has been validated with other online sources that are credible and follow global information provisions. This proposed paper has four variables used as indicators in data visualization related to AI Adoption, Area, Population, and the digital economy. The four countries analyzed are members of ASEAN. The results of exploratory data analysis using the Seaborn library using the Python programming language obtained correlation results consisting of the variables Adoption of AI, Area, Population, and Digital Economy. The correlation of the Adoption of AI variables with the Digital Economy correlates 0.94. Adoption of AI with Population correlates 0.93. Adoption of AI with an Area of 0.86. Furthermore, the Area or region variable has a correlation value of 0.97 with the digital economy. Areas with a population have a correlation value of 0.98. The Population variable has a very strong correlation with the digital economy of 1. Further research can add several variables such as the potential for future jobs and the number of countries so that it is not limited to ASEAN countries alone.
Analisis Sentimen Terhadap Aplikasi Parak Acil Online Berdasarkan Ulasan Masyarakat Menggunakan Metode Support Vector Machine (SVM) Mutmainah, Mutmainah; Cipta, Subhan Panji; Mambang, Mambang; Zulfadhilah, Muhammad; Naparin, Husni; Syapotro, Usman
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 7, No 5 (2024): Oktober 2024
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v7i5.7962

Abstract

Abstrak - Perkembangan teknologi informasi mempermudah akses layanan publik, termasuk aplikasi Parak Acil Online yang dikembangkan oleh Pemerintah Kota Banjarmasin untuk pengurusan dokumen administrasi. Sejak diluncurkan, aplikasi ini telah digunakan oleh puluhan ribu warga. Penelitian ini bertujuan untuk menganalisis sentimen pengguna terhadap aplikasi dan mengevaluasi performa Support Vector Machine dalam klasifikasi ulasan. Metode penelitian yang digunakan adalah Support Vector Machine untuk mengklasifikasikan ulasan pengguna. Hasil analisis menunjukkan bahwa algoritma Support Vector Machine (SVM) dalam mengklasifikasikan data ulasan mendapatkan akurasi tertinggi pada pembagian data latih dan data uji 70:30 sebesar 85,1%, presisi 78,2%, dan recall 97,2%. Dari klasifikasi dan visualisasi, didapatkan kata-kata yang sering muncul pada sentimen positif yaitu “good”, “easy”,  dan “helpful” serta kata-kata yang sering muncul pada sentimen negatif yaitu “difficult”, “take” dan “feature”. Sentimen masyarakat terhadap aplikasi Parak Acil Online menunjukkan bahwa mayoritas ulasan masyarakat terhadap aplikasi ini bersifat positif, dan performa analisis sentimen menggunakan metode Support Vector Machine yang digunakan dalam penelitian ini terbukti efektif dalam mengklasifikasikan sentimen dari ulasan pengguna. Diharapkan penelitian ini dapat membantu pengembang dan pemangku kebijakan dalam meningkatkan kualitas aplikasi Parak Acil Online serta memahami kebutuhan masyarakat.Kata kunci: Analisis Sentimen, Aplikasi Parak Acil Online, Support Vector Machine, Textblob. Abstract - The advancement of information technology has facilitated access to public services, including the Parak Acil Online application developed by the Banjarmasin City Government for managing administrative documents. Since its launch, this application has been used by tens of thousands of residents. This study aims to analyze user sentiment towards the application and evaluate the performance of Support Vector Machine (SVM) in classifying reviews. The research method used is Support Vector Machine (SVM) to classify user reviews. The analysis results show that the Support Vector Machine (SVM) algorithm achieves the highest accuracy in classifying review data with a 70:30 train-test split, reaching 85.1% accuracy, 78.2% precision, and 97.2% recall. Classification and visualization reveal that frequently occurring words in positive sentiment include "good," "easy," "helpful," and "fast," while frequently occurring words in negative sentiment include "difficult," "document," "take," and "feature." The sentiment of the public towards the Parak Acil Online application indicates that the majority of reviews are positive. The performance of sentiment analysis using the Support Vector Machine method employed in this study has proven effective in classifying sentiment from user reviews. It is hoped that this research can assist developers and policymakers in improving the quality of the Parak Acil Online application and understanding community needs.Keywords: parak acil online application, sentiment analysis, support vector machine, textblob.
Analisis Sentimen Pengaruh Digitalisasi Terhadap Penjualan UMKM di Kota Banjarmasin Menggunakan Metode SVM Kartika, Kartika; Cipta, Subhan Panji; Zulfadhilah, Muhammad; Prastya, Septyan Eka
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 7, No 5 (2024): Oktober 2024
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v7i5.8006

Abstract

Abstrak – Digitalisasi telah menjadi faktor penting dalam meningkatkan efisiensi dan jangkauan pasar UMKM. Di Kota Banjarmasin, adopsi digitalisasi berpotensi mempengaruhi sentimen masyarakat terhadap produk-produk UMKM. Penelitian ini menganalisis sentimen untuk memahami dampak digitalisasi terhadap penjualan UMKM di kota ini. Penelitian ini bertujuan untuk mengidentifikasi faktor-faktor yang mempengaruhi sentimen terhadap produk UMKM setelah adopsi digitalisasi di Kota Banjarmasin. Selain itu, penelitian ini juga mengevaluasi efektivitas metode Support Vector Machine (SVM) dalam menganalisis sentimen tersebut. Data dikumpulkan melalui Lembar observasi Google Form yang disebarkan kepada 211 responden, dengan 205 data yang valid digunakan dalam analisis. Data dilakukan proses preprocessing dan pelabelan dengan kamus Lexicon . Metode SVM dengan kernel linear digunakan untuk mengklasifikasikan sentimen, dan model dievaluasi berdasarkan metrik akurasi, precision, recall, dan f1-score. Penelitian menunjukkan bahwa metode SVM dengan kernel linear mencapai akurasi sebesar 85,7% dalam mengklasifikasikan sentimen. Model menunjukkan kinerja yang baik dalam mengenali sentimen positif dengan precision 75% dan recall 86%. Namun, kinerja untuk kelas negatif masih rendah dengan recall 43% dan f1-score 0.55, mengindikasikan tantangan dalam mengidentifikasi sentimen negatif secara akurat.Digitalisasi memiliki pengaruh signifikan terhadap sentimen positif UMKM di Kota Banjarmasin. SVM menunjukkan kinerja yang baik untuk sentimen positif, terdapat tantangan dalam mengenali sentimen negatif yang perlu diatasi. Hasil penelitian ini memberikan wawasan penting untuk strategi digitalisasi yang lebih efektif bagi UMKM di masa mendatang.Kata kunci: Analisis Sentimen, Usaha Mikro Kecil dan Menengah (UMKM), Support Vector Machine (SVM)  Abstract – Digitalization has become an important factor in increasing the efficiency and reach of the MSME market. In Banjarmasin City, the adoption of digitalization has the potential to affect public sentiment towards MSME products. This study analyzes sentiment to understand the impact of digitalization on MSME sales in this city. This study aims to identify factors that influence sentiment towards MSME products after the adoption of digitalization in Banjarmasin City. In addition, this study also evaluates the effectiveness of the Support Vector Machine (SVM) method in analyzing these sentiments. Data were collected through Google Form observation sheets distributed to 211 respondents, with 205 valid data used in the analysis. The data were preprocessed and labeled with the Lexicon dictionary. The SVM method with a linear kernel was used to classify sentiment, and the model was evaluated based on accuracy, precision, recall, and f1-score metrics. The study shows that the SVM method with a linear kernel achieves an accuracy of 85.7% in classifying sentiment. The model performs well in recognizing positive sentiment with a precision of 75% and a recall of 86%. However, the performance for the negative class is still low with a recall of 43% and an f1-score of 0.55, indicating challenges in accurately identifying negative sentiment.Digitalization has a significant influence on positive sentiment of MSMEs in Banjarmasin City. SVM shows good performance for positive sentiment, there are challenges in recognizing negative sentiment that need to be addressed. The results of this study provide important insights for a more effective digitalization strategy for MSMEs in the future.Keywords: Sentiment Analysis, Micro, Small and Medium Enterprises (MSMEs), Support Vector Machine (SVM)
Formulation Test of Preparations Face Mist Combination of Pomegranate Peel Extract and Mangosteen Peel as an Antioxidants Lisyanti, Fatthiya; Budi, Setia; Zulfadhilah, Muhammad
Journal of Advances in Medicine and Pharmaceutical Sciences Vol 1 No 1: May 2022
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.jamaps-0101.426

Abstract

Face mist is one of the cosmetics in the form of a spray, which functions as a refresher and moisturizer with the use of being directly sprayed onto the facial skin. Combining two antioxidants that work to increase the activity of preventing the effects of radiation and free radicals. Face mist is made from a combination of pomegranate peel extract (Punica granatum L.) and mangosteen peel (Garcinia mangostana L.).To obtain a formulation and evaluate the physical stability of apreparation with a face mist combination of pomegranate peel extract (Punica granatum L.) and mangosteen peel (Garcinia mangostana L.) as an antioxidant.Experimental research with One-Shoot Case Study design. Made three combination formulations of pomegranate peel and mangosteen peel extract. Then the formulation was seen from the results of the evaluation of its physical stability including organoleptic tests, homogeneity, pH, viscosity, facial moisture, spraying patterns, dry time, and antioxidant tests using the DPPH method. Analysis data One Way Anova showed that there was no significant difference in the pH, viscosity and skin moisture test. Evaluation and physical stability 21 days organoleptic test, homogeneity obtained all formulas meet the standards, pH test and dry time formula II most meet the standards, viscosity test formula III most meet the standards, formula I meets the standards for spraying pattern test, skin moisture and % inhibition highest in the antioxidant test. The preparation face mist most optimalis formula I with a ratio of pomegranate peel extract and mangosteen peel (10:15).
Sistem Pencarian Kos Berbasis Web di Wilayah Kota Banjarmasin Maulana, Maghfur; Nurhaeni, Nurhaeni; Zulfadhilah, Muhammad; Nugraha, Bayu
INFOMATEK Vol 26 No 1 (2024): Volume 26 No. 1, Juni 2024
Publisher : Fakultas Teknik, Universitas Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23969/infomatek.v26i1.10309

Abstract

Kos merupakan jasa yang memberikan penawaran sebuah kamar atau tempat yang untuk dihuni dengan sejumlah pembayaran bulanan yang sudah ditentukan dalam periode tertentu. dikota Banjarmasin terdapat 178 kos yang tersebar diseluruh kecamatan dengan berbagai range harga dan fasilitas yang ditentukan oleh pemilik kos, di sisi lain kota Banjarmasin terdapat 30 Perguruan Tinggi yang menerima mahasiswa baru disetiap tahun dari dalam maupun dari luar kota Banjarmasin. Hal ini dapat menimbulkan permasalahan terutama bagi mahasiswa baru dari luar kota dan ingin melanjutkan ke perguruan tinggi di kota Banjarmasin, mahasiswa tidak mengetahui secara detail informasi kos. Tujuan dari penelitian ini yaitu membangun sistem pencarian kos berbasis web di wilayah kota Banjarmasin untuk mempermudah masyarakat terkhusus bagi mahasiswa yang berasal dari luar daerah Banjarmasin. Metode penelitian yang digunakan yaitu metode pengembangan Waterfall, dengan UML sebagai desain sistem, PHP sebagai bahasa pemrograman dan MySQL sebagai database. Hasil dari penelitian ini adalah sistem yang dihasilkan dapat menjalankan perintah sesuai dengan apa yang diinginkan, sistem ini digunakan untuk mempermudah user atau pencari kos untuk mempermudah proses pencarian kos dan sebagai wadah bagi pemilik kos di wilayah kota Banjarmasin.
Analisis Sentimen Masyarakat Terhadap Kesehatan Mental Pada Media Sosial Twitter Dengan Menggunakan Machine Learning Aulia, Hudatul; Zulfadhilah, Muhammad; Prastya, Septyan Eka; Pebriadi, Muhammad Syahid
POSITIF : Jurnal Sistem dan Teknologi Informasi Vol 10 No 2 (2024): Positif : Jurnal Sistem dan Teknologi Informasi
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/positif.v10i2.2545

Abstract

Mental health affects lives globally, with around 300 million people experiencing depression in 2019, including 15.6 million in Indonesia. The Covid-19 pandemic increased cases of anxiety and depression, and by 2022, WHO reported 23 million people suffering from psychiatric disorders. In Indonesia, adolescent mental health issues are also high, with excessive social media use linked to an increase in emotional disorders. Twitter, with its real-time data, is becoming an important tool for analyzing public sentiment and understanding opinions through analytics and machine learning techniques. This study aims to determine public sentiment towards mental health in Indonesia through Twitter social media and test the effectiveness of using machine learning in sentiment analysis. The results show that the Naive Bayes and Decision Tree methods are effective in analyzing sentiment, with an accuracy of 91% and 89% respectively. The average result of cross validation shows a value of 73.21% for Naive Bayes and 67.02% for Decision Tree. In this study, positive sentiment is more dominant with a percentage value of 78.7%, while negative sentiment is only 21.3%. The findings indicate that Indonesians' awareness of the importance of mental health is increasing, and they increasingly understand the importance of maintaining mental health
PENYEDIAAN AIR BERSIH BAGI WARGA DESA PEMBANTANAN KABUPATEN BANJAR PROVINSI KALIMANTAN SELATAN Nur Hidayah; Muthia Elma; M Zulfadhilah; Yusri Yusri; Junius Akbar; Yunandar Yunandar; Aulia Rahma; Rhafiq Abdul Ghani; Zaini Lambri Assyaifi; Gusti Zahratunnisa; Angga Irawan; Nopie Hadi; Ahmad Ghazali Madhony; Ahmad Faisal; Viviana Viviana; Asyiah Asyiah; Anggraini Susfarwanti; Husna Karima; Adryan Ramadhan; Ahmad Busairi
Konferensi Nasional Pengabdian Masyarakat (KOPEMAS) #5 2024 Konferensi Nasional Pengabdian Masyarakat (KOPEMAS) #5
Publisher : Konferensi Nasional Pengabdian Masyarakat (KOPEMAS) #5 2024

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Abstract

Air bersih merupakan kebutuhan dasar yang harus terpenuhi bagi manusia karena berperan vital dalam menjaga kesehatan, kebersihan, dan kesejahteraan. Namun akses air bersih tidak dapat dirasakan merata oleh seluruh masyarakat di Indonesia. Salah satu desa yang mengalami masalah ini adalah Desa Pembantanan, Kabupaten Banjar, Provinsi Kalimantan Selatan. Dari 996 KK tersebar dalam 12 RT di wilayah Desa Pembantanan, hampir 50% belum memiliki kesempatan mendapatkan akses air bersih. Akses oleh sumber air diperburuk ketika kualitas air baku juga menurun lebih jauh hingga menyebabkan air hampir tidak layak untuk digunakan. Oleh karena itu, kegiatan pengabdian kepada masyarakat ini bertujuan untuk membantu warga desa memperoleh solusi yang berkelanjutan terhadap penyediaan air bersih. Metode yang diterapkan adalah survei lapangan seperti pemetaan lokasi sumber air, analisis kualitas air dan diskusi dengan masyarakat tentang kebutuhan dan desain unit yang sesuai dengan mengingat sumber air di desa tersebut. Kegiatan pengabdian kepada masyarakat melalui program Kosabangsa ini ditujukan untuk memenuhi kebutuhan air bersih bagi 70 KK di RT 1 dan 2 Desa Pembantanan. Dengan menggunakan bahan baku air sungai di sekitar desa. Desain alat pengolahan air yang dipasang memiliki kapasitas produksi mencapai 2190 L/jam yang mampu memenuhi kebutuhan warga di 2 RT.
Deteksi Penyakit Diabetes Gestasional Menggunakan Metode CNN Berbasis Web Fitriani, Fitriani; Nurhaeni, Nurhaeni; Zulfadhilah, Muhammad; Cipta, Subhan Panji
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 7, No 6 (2024): Desember 2024
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v7i6.8247

Abstract

Abstrak - Latar Belakang: Diabetes adalah salah satu masalah kesehatan utama di seluruh dunia diabetes mellitus terbagi menjadi beberapa jenis salah satu diantaranya yaitu diabetes melitus gestasional deteksi dini sangat diperlukan untuk menjaring diabetes gestasional menggunakan beberapa algoritma pembelajaran mesinTujuan: Mengetahui tingkat akurasi yang didapatkan dari hasil deteksi dini penyakit diabetes melitus gestasional menggunakan metode convolutional neural network (cnn). Untuk mendeteksi penyakit dini Diabetes Melitus Gestasional menggunakan metode convolutional neural network (cnn).Metode: Penelitian ini menggunakan jenis penelitian eksperimental, yang mana pada penelitian ini menggunakan metode Convolutional Neural Network (CNN). Dengan penelitian eksperimental ini, untuk mengklasifikasikan apakah seseorang tersebut terdeteksi penyakit dini diabetes gestasional yang dikumpulkan dari platform Kaggle dataset ini berjumlah 3525 entri data.Hasil: Dari percobaan yang dilakukan, hasil menunjukkan bahwa percobaan 1 mendapat akurasi sebesar 97%, percobaan 2 sebesar 98%, percobaan 3 sebesar 98%, Dengan demikian, penerapan convolutional neural network (CNN) untuk prediksi diabetes gestasional menggunakan percobaan 2 dengan pertimbangan hasil evaluasi metrix yang lebih baik dan pembagian data uji dan latih yang baik. Simpulan: Keterbatasan dari penelitian ini meliputi representasi dataset yang mungkin tidak mencakup variasi karakteristik demografis dan geografis secara umum, yang dapat mempengaruhi generalisasi model terhadap populasi yang lebih luas. Selain itu, ukuran relatif kecil dari dataset uji, meskipun diperluas pada percobaan ketiga, dapat membatasi evaluasi terhadap data baru yang belum pernah dilihat sebelumnya.Kata Kunci: cnn, diabetes gestasional, prediksi Abstract - Background: Diabetes is one of the major health issues worldwide. Diabetes mellitus is divided into several types, one of which is gestational diabetes mellitus (GDM). Early detection is crucial for screening gestational diabetes, employing several machine learning algorithms.Objective: To determine the accuracy level obtained from the early detection of gestational diabetes mellitus using the convolutional neural network (CNN) method. The aim is to detect early gestational diabetes mellitus using the CNN method.Method: This study is an experimental research using the Convolutional Neural Network (CNN) method. With this experimental research, the researchers aim to classify whether an individual is detected with early gestational diabetes. The dataset, collected from the Kaggle platform, consists of 3525 entries.Results: The experiments showed that Experiment 1 achieved an accuracy of 97%, Experiment 2 achieved 98%, and Experiment 3 also achieved 98%. Thus, the application of the convolutional neural network (CNN) for predicting gestational diabetes was most effective in Experiment 2, considering the better evaluation metrics and the appropriate division of test and training data. Conclusion: The limitations of this study include the representation of the dataset, which may not encompass the demographic and geographic variations in general, potentially affecting the model's generalization to a broader population. Additionally, the relatively small size of the test dataset, even though expanded in the third experiment, may limit the evaluation on new, unseen data.Keywords: cnn, gestational diabetes mellitus, prediction
Exploratory Data Analysis (EDA) of Marriage Patterns in Kabupaten Banjar Using Machine Learning Approaches Husna Karima; Mambang; Subhan Panji Cipta; Muhammad Zulfadhilah
INSTALL: Information System and Technology Journal Vol 1 No 2 (2024): INSTALL : Information System and Technology Journal
Publisher : LPPM Universitas Sari Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33859/install.v1i2.629

Abstract

Marriage is a sacred moment that has a significant impact on the social, economic and demographic structure of a region. This research aims to implement a marriage dataset in Banjar Regency and find a correlation between the number of marriages, education level and age of the bride and groom using Exploratory Data Analysis (EDA) techniques and machine learning approaches. The method used is a quantitative method with observation and analysis using EDA and machine learning. The research results show that there is a strong correlation between the number of marriages and the age of the bride and groom (r = 0.99) and between the number of marriages and the education level of the bride and groom (r = 0.99). In addition, a perfect correlation was found between the ages of the groom and the bride (r= 0.99) as well as between the educational levels of the groom and the bride (r = 1). This analysis provides a better understanding of marriage patterns in Banjar Regency and shows that couples aged 21-30 years have a high positive correlation with the number of marriages. It is hoped that these results can become the basis for social policies and educational programs related to marriage.
Integrating the CNN Model with the Web for Indonesian Sign Language (BISINDO) Recognition Kelana, Enisda Libra; Anshori Prasetya, Muhammad Riko; ., Mambang; Zulfadhilah, Muhammad
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9345

Abstract

Effective communication is challenging for deaf individuals in Indonesia, most of whom use Indonesian Sign Language (BISINDO). Sign Language Recognition (SLR) can bridge this communication gap. While Convolutional Neural Networks (CNNs) show high potential for SLR, their practical accessibility remains limited. This research aims to develop a CNN architecture for recognizing BISINDO alphabet signs from static images (still images) and integrate it into an accessible web platform. Using a static vision-based approach, a CNN model was trained on a public dataset (312 images, 26 classes) following standard pre-processing including data augmentation. The model was subsequently integrated into a web interface using Python and the Gradio library. Results demonstrated strong model performance, with validation accuracy reaching 97.44% and a macro-average F1-score of approximately 97.12%. However, classification challenges were identified for visually similar signs ('M' and 'N'). The resulting integrated web application proved functional, exhibited low prediction latency, and showed cross-platform compatibility. This study successfully demonstrates the development of an accurate DL model for static BISINDO alphabet recognition and its practical implementation via a web platform. This contributes to reducing the accessibility gap in SLR technology. Future research is recommended to utilize larger, more varied datasets and explore dynamic sign recognition.
Co-Authors ., Mambang Abdul Kadir Adryan Ramadhan Ahmad Busairi Ahmad Faisal Ahmad Ghazali Madhony Ahmad Riki Renaldi Ahmad Riki Renaldy Angga Irawan Anggraini Susfarwanti Annisa Annisa Anshori Prasetya, Muhammad Riko Antonia Yenitia Asyiah Asyiah Aulia Rahma Aulia, Hudatul AULIA, RIZKA Bayu Nugraha Bayu Nugraha Bima Wicaksono Cipta, Subhan Panji Darini Kurniawati Dewi Pusparani Sinambela, Dewi Pusparani Dwi Salmarini, Desilestia Eka Prastya, Septyan Ermadiningtyas, Retno Finki Dona Marleny Fitriani Fitriani Gusti Zahratunnisa Hadi, Nofie Haldi Budiman Haniffah Sri Rinjani Heni Pujiastuti Hikmah, Rahmadaniati Hudatul Aulia Husna Karima Husna Karima Imam Riadi Indah Wulandari Irawan, Angga Iwan Yuwindry Jaya Hari Santoso Junius Akbar Karlina Karlina Kartika Kartika Kartika Kartika Kelana, Enisda Libra Lisda Handayani, Lisda Lisyanti, Fatthiya Lufila Fila M Samsul Hasbi M Samsul Hasmi Mambang Maria Ulfah Maulana, Maghfur Maulana, Rahmat Melda Melda Miranda Miranda Misnawati Muhammad Khairul Akbar Muhammad Riduan Syafi’i Muhammad Satrio Ayuba Muhammad Zaini Bakri Muhammad Ziki Elfirman Muthia Elma Mutmainah Mutmainah Naparin, Husni Nastiti, Kunti Nita Hestiyana, Nita Noor Pratama, Ramadhani Nopie Hadi Nor Azizah Novalia Widiya Ningrum Novita Dewi Iswandari Nur Hidayah Nur Lathifah Nur Meilianti Maulida Nur Syifa Nurhaeni Nurhaeni Nurhaeni Nurhaeni NURUL HIDAYAH Pebriadi, Muhammad Syahid Prastya, Septyan Eka Putri Putri Putri Yuliantie Rahmini Rahmini Rhafiq Abdul Ghani Riduansyah, Muhammad Risma Maulida Risma Risma Rismawati Rismawati Rizkian Muhammad Fikri Ropikah Ropikah Rudy Anshari Samita, Mambang Sandro Nesta Pembriano Sari, Rahmadah Septian Eka Prastya Septyan Eka Prasetya Septyan Eka Prastya Septyan Eka Prastya Setia Budi Shopa Handayani Siti Gadis Hardianti Subhan Panji Cipta Subhan Panji Cipta Sultan Arrasyid Susanti Suhartati, Susanti Syapotro, Usman Tasya Salsabila Umi hanik Fetriyah, Umi hanik Usman Syapotro Viviana Viviana Winda Maolinda, Winda Wulandari Febriani Wusko, Ikna Urwatul Yudi prayudi Yunandar Yunandar Yusri Yusri Zaini Lambri Assyaifi