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All Journal International Journal of Electrical and Computer Engineering CogITo Smart Journal Jurnal SOLMA ILKOM Jurnal Ilmiah JURNAL PENDIDIKAN TAMBUSAI JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Community Development Journal: Jurnal Pengabdian Masyarakat Journal of Computer Networks, Architecture and High Performance Computing Jurnal Pengabdian kepada Masyarakat Nusantara Rambideun : Jurnal Pengabdian Kepada Masyarakat Brilliance: Research of Artificial Intelligence Buletin Sistem Informasi dan Teknologi Islam Digital Transformation Technology (Digitech) CSRID
Suryani Suryani
Universitas Dipa Makassar
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Health Professions Library & Information Science Mathematics Public Health Social Sciences Other

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Application of General Regression Neural Network Algorithm in Data Mining for Predicting Glass Sales and Inventory Quantity Suryani, Suryani; Intan, Indo; Mochtar Yunus, Farhan; Haris, Adammas; Faizal, Faizal; Nurdiansah, Nurdiansah
ILKOM Jurnal Ilmiah Vol 15, No 2 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i2.1562.229-239

Abstract

FF Jaya Glass is a shop that supplies and installs 3 mm to 12 mm glass. The store obtained glass from suppliers to be processed in shape and size according to customers’ order. After completing the customer's order, the shop worker will install the glass at the requested location. Unfortunately, currently stores do not utilize sales data to predict sales either manually or by utilizing technology. As a result, the store cannot predict when the number of glass orders will increase or decrease. In addition, errors often occur when ordering glass for the next period. As a result, stores often run out of glass supplies due to the large number of glass orders so that the achievement of profits is not optimal. This study aims to identify sales variables in glass sales data and build a general regression neural network model as a data mining method. In addition, this study aims to iterate to find the best value in the sales data training process, design and create applications according to user needs, and conduct system validation tests. The general regression neural network method is used to predict sales. The results of this study indicate that the application of general regression neural networks can be used to predict sales. This will make it easier for the store to provide glass supplies in the coming months with an accuracy of 98.1%.
Optimizing a Fire and Smoke Detection System Model with Hyperparameter Tuning and Callback on Forest Fire Images Using ConvNet Algorithm Suryani Suryani; Suryani, Suryani; Syahlan Natsir, Muhammad
ILKOM Jurnal Ilmiah Vol 16, No 1 (2024)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v16i1.1937.46-58

Abstract

Forest fire is a significant issue, especially for tropical countries like Indonesia. One of the impacts of forest fires is environmental pollution and damage, such as damage to flora and fauna, water, and soil. Fire detection technology is crucial as a preventive measure before the spread or expansion of fire points. Several forest fire detection systems have been developed by various research studies, with detection targets varying. Objects in the form of images are usually detected using the RGB color filtering method, but this method still results in false detections in image processing. Therefore, a classification model is built to detect fire and smoke in images using the Convolutional Neural Network (ConvNet) algorithm. In the development of the ConvNet model, a comparison of models is also conducted to assess the influence of Hyperparameter Tuning and Callbacks in optimizing the model's classification performance. The research results indicate that out of the six comparison scenarios created, the best model is obtained with 90% training data and 10% testing data, which is also optimized with Hyperparameter Tuning and Callbacks, with a Validation Accuracy of 98.18% and Validation Loss of 4.97%. This model is then implemented in the interface system.
Integrating Local Linguistic Features into Rule-Based Chatbots: A Framework for Makassar Language Dialogue Systems Thabrani. R Thabrani. R; Faizal Faizal; Suryani Suryani
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7103

Abstract

The advancement of digital technology has transformed the way people communicate and access information; however, it has also led to a decline in the use of regional languages, including the Makassar language. This phenomenon highlights the need for language preservation efforts through technology-based approaches. This research was conducted at the Public Relations Bureau of the Makassar City Government with the aim of designing a web-based dialogue application using the Makassar language as an interactive medium to promote local culture and tourism. The study employed a qualitative approach through observation and literature review methods, and implemented the System Development Life Cycle (SDLC) model with the waterfall approach. The application was developed using PHP and JavaScript programming languages and adopted a rule-based method to build a chatbot capable of interacting naturally in the Makassar language. System testing was carried out using the black-box method to ensure functionality and reliability in responding to user inquiries. The results showed that the chatbot performed effectively, providing relevant information about Makassar City’s culture and tourism, and increasing public appreciation for local language and culture. This study concludes that the integration of web-based technology with regional languages not only contributes to cultural preservation but also strengthens communication between the government and the community in the context of digital transformation.
TRANSFORMASI DIGITAL YAYASAN MELALUI AI DAN CLOUD: STUDI KASUS DI ASHABUL KAHFI PAREPARE Nurul Aini; Suryani Suryani; Faizal Faizal; Herman Heriadi; Sitti Aisa; Asmah Akhriana; Nurdiansah Nurdiansah; Ahyuna Ahyuna; Erfan Hasmin; Arwansyah Arwansyah; Sadly Syamsudddin; Hasyrif SY
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 6 No. 3 (2025): Volume 6 No 3 Tahun 2025
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v6i3.45597

Abstract

Kegiatan pengabdian ini bertujuan untuk meningkatkan literasi digital pengurus dan pendidik Yayasan Ashabul Kahfi Parepare melalui pelatihan penggunaan Google Drive dan ChatGPT. Metode yang digunakan meliputi pendidikan masyarakat, pelatihan berbasis praktik langsung, serta difusi ipteks. Peserta diberikan penyuluhan mengenai pentingnya transformasi digital, kemudian dilatih dalam pengelolaan dokumen berbasis cloud dan pemanfaatan AI untuk menyusun materi administrasi. Hasil evaluasi pre-test dan post-test menunjukkan peningkatan signifikan pada pemahaman dan keterampilan peserta, yang diperkuat dengan uji t-test (p < 0,001). Selain itu, peserta menunjukkan antusiasme tinggi dan respons positif terhadap materi pelatihan. Kegiatan ini berhasil meningkatkan kapasitas digital yayasan dan memberikan dampak nyata dalam mendukung pengelolaan organisasi yang lebih efisien dan adaptif terhadap perkembangan teknologi.
Aplikasi Kemiripan Dokumen Menggunakan Multi Algoritma Pada UNDIPA Makassar Nurdiansah; Arwansyah; Cucut Susanto; Willem Musu; Suryani; Saprial Metthew Godliving Perdamaian Ani
CSRID (Computer Science Research and Its Development Journal) Vol. 18 No. 2 (2026): Juni 2026
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.18.2.2026.320-332

Abstract

Measuring similarity between text documents is a fundamental task in Natural Language Processing (NLP) with broad applications, such as plagiarism detection, document clustering, and recommendation systems. This study aims to analyze and compare the performance of various document similarity algorithms, ranging from traditional lexical approaches to modern semantic methods. The algorithms reviewed include Bag-of-Words (BoW), TF-IDF, and Jaccard Similarity, as well as semantic representation-based methods such as Word Embeddings, Doc2Vec, and Sentence-BERT. An interactive web application was developed using the Gradio library to visualize comparison results in real-time and allow users to upload their own documents. The results indicate that lexical methods are effective at detecting keyword-based similarity but fail to capture semantic similarity when synonyms are used. Conversely, semantic methods—particularly Sentence-BERT—significantly outperform others in identifying contextual and semantic similarity, yielding more accurate scores for documents that differ in vocabulary yet share similar meanings. The study concludes that selecting the appropriate algorithm requires considering document characteristics and analysis objectives, and that the developed interactive tool can serve as an educational and experimental platform for such evaluations
SMART LIBRARY VISIT SYSTEM: PENCATATAN KUNJUNGAN BERBASIS FACE RECOGNITION Suryani Suryani; Moch. Farhan Juliansyach Putra Taufiq
Buletin Sistem Informasi dan Teknologi Islam (BUSITI) Vol 7, No 3 (2026)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v7i3.3374

Abstract

Pencatatan kunjungan perpustakaan yang masih dilakukan secara manual berpotensi menimbulkan kesalahan pencatatan, duplikasi data, dan rendahnya efisiensi layanan. Penelitian ini bertujuan merancang dan mengimplementasikan Smart Library Visit System berbasis Face Recognition sebagai sistem pendukung pencatatan kunjungan otomatis di Perpustakaan Universitas Dipa Makassar. Sistem menggunakan RetinaFace untuk deteksi wajah, ArcFace untuk ekstraksi fitur, dan Cosine Similarity untuk pengukuran kemiripan dengan threshold 65%. Pengujian dilakukan menggunakan Black Box Testing, evaluasi akurasi melalui True Positive Rate (TPR), serta User Acceptance Testing (UAT) terhadap 50 responden. Hasil pengujian menunjukkan sistem mampu mengenali 89 data wajah dengan nilai TPR sebesar 1,00 menunjukkan bahwa sistem mampu mengenali seluruh wajah yang terdaftar secara akurat. Sedangkan tingkat penerimaan pengguna sebesar 91,2% menunjukkan bahwa sistem layak diterapkan dan diterima dengan sangat baik oleh pengguna, baik dari segi kemudahan, kecepatan, akurasi, maupun manfaatnya dalam meningkatkan efisiensi pencatatan kunjungan perpustakaan. Sistem ini mampu meningkatkan akurasi dan efisiensi pencatatan kunjungan serta mendukung implementasi konsep Smart Library di lingkungan kampus.
SS-ANFIS: a semi-supervised neuro-fuzzy model for offline signature verification Sadly Syamsuddin; Jufri Jufri; Suci Rahma Dani Rachman; Suryani Suryani; Wilem Musu; Salmiati Salmiati; Yesycha Arun Mangopo
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i4.pp1985-1997

Abstract

Signature verification remains a critical authentication mechanism in academic and administrative environments, yet manual verification is vulnerable to forgery and subjective judgment. This study proposes SS- ANFIS, a semi-supervised neuro-fuzzy model for offline signature verification under limited labeled data conditions. The proposed model integrates pseudo-label-based self-training into a Takagi-Sugeno-Kang adaptive neuro-fuzzy inference system (ANFIS). Static image-based features were extracted from offline signature images and transformed using principal component analysis (PCA) before classification. Experiments were conducted on 800 offline signature samples collected from Dipa University Makassar, consisting of 400 genuine and 400 forged signatures. The proposed model achieved an accuracy of 90.5%, precision of 98.8%, recall of 82.0%, and F1-score of 90.0%. The high precision indicates that SS- ANFIS is effective in minimizing false positive predictions, which is important for academic document verification. The results show that the proposed model provides a practical, interpretable, and computationally efficient approach for offline signature verification, particularly in institutional settings with limited labeled data.
Optimalisasi Teknik Prompting AI dalam Pembuatan Soal dan Evaluasi Pembelajaran Guru SMK Darul Ulum Bantaeng Herman Heriadi; Arwansyah Arwansyah; Hasyrif Hasyrif; Ahyuna Ahyuna; Sitti Aisa; Andi Asvin Mahersatillah Suradi; Faizal Faizal; Rahmat Rahmat; Sadly Syamsuddin; Suryani Suryani
Jurnal SOLMA Vol. 15 No. 2 (2026)
Publisher : Universitas Muhammadiyah Prof. DR. Hamka (UHAMKA Press)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22236/solma.v15i2.23477

Abstract

Background: Perkembangan AI generatif membuka peluang bagi guru untuk menyusun soal, merancang evaluasi, dan menyiapkan perangkat pembelajaran secara lebih efektif. Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan keterampilan guru SMK Darul Ulum Bantaeng dalam menggunakan teknik prompting AI untuk mendukung pembuatan soal dan evaluasi pembelajaran. Metode: Kegiatan dilaksanakan pada Senin, 18 Mei 2026 di SMK Darul Ulum Bantaeng dengan melibatkan 20 peserta. Metode kegiatan meliputi identifikasi kebutuhan mitra, penyampaian materi tentang ChatGPT dan Large Language Model, demonstrasi penggunaan ChatGPT, praktik penyusunan prompt berdasarkan peran, tugas, konteks, dan format keluaran, serta refleksi hasil praktik peserta. Hasil: Hasil evaluasi menunjukkan bahwa kegiatan yang dilaksanakan mampu meningkatkan pemahaman peserta. Secara umum, capaian post-test menunjukkan hasil yang lebih baik dibandingkan pre-test, dengan kecenderungan peningkatan pada kategori pemahaman peserta. Kesimpulan: Pelatihan teknik prompting AI berdampak positif terhadap peningkatan pemahaman dan kesiapan guru dalam memanfaatkan AI secara bijak, kreatif, dan produktif. AI dapat digunakan sebagai alat bantu profesional bagi guru dalam menyusun evaluasi, mengembangkan bahan pembelajaran, dan mendukung proses pembelajaran di sekolah kejuruan.
Pemanfaatan ChatGPT dalam Pembelajaran: Pelatihan Guru SMPN 2 Balocci Pangkep Suryani Suryani; Imran Djafar; Faizal Faizal; Herman Heriadi; Rahmat Rahmat; Hasyrif Sy; Mochammad Agus Idris; Budy Santoso; Sadly Syamsuddin; Muhammad Rizal; Fitriani Fitriani
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 7 No. 2 (2026): Edisi Mei - Agustus
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v7i2.8372

Abstract

Perkembangan kecerdasan buatan (Artificial Intelligence/AI) mendorong transformasi pembelajaran yang lebih adaptif dan interaktif, namun sebagian guru masih mengalami kesulitan dalam mengintegrasikan teknologi seperti ChatGPT ke dalam proses pembelajaran. Kegiatan ini bertujuan untuk meningkatkan literasi digital dan kompetensi pedagogik guru di SMP Negeri 2 Balocci, Kabupaten Pangkep. Solusi yang ditawarkan berupa pelatihan pemanfaatan ChatGPT dalam pembelajaran yang mencakup pemahaman konsep dasar AI serta praktik penyusunan media ajar berbasis AI. Metode pelaksanaan meliputi tiga tahap, yaitu sosialisasi, pelatihan praktik, dan pendampingan penerapan di kelas. Evaluasi dilakukan menggunakan pre-test dan post-test untuk mengukur peningkatan kemampuan peserta. Hasil menunjukkan adanya peningkatan nilai rata-rata dari 78,57 menjadi 98,09 atau sebesar 24,85%, serta penurunan simpangan baku dari 20,31 menjadi 4,02 yang menunjukkan peningkatan kemampuan dan konsistensi peserta. Kegiatan ini berdampak positif terhadap peningkatan literasi digital dan kemampuan guru dalam mengintegrasikan AI secara kreatif dan inovatif dalam pembelajaran.
Co-Authors Abdul Ibrahim Ahyuna Ahyuna Ahyuna Ahyuna Ahyuna Akbar Bahtiar Akbar Bahtiar Andi Asvin Mahersatillah Suradi Andrew Ridow Johanis Andrew Ridow Johanis M Annah Annah, Annah Arham Arifin Arkjun Yudistira Pratama Arwansyah Arwansyah Arwansyah Arwansyah Arwansyah Asmah Akhriana Asrul Syam Asrul Syam Atnasius Alan Pabembe Baharuddin Baharuddin Bahtiar, Akbar Budy Santoso Cucut Susanto Djafar, Imran Erfan Hasmin Erni Marlina Faizal Faizal Faizal Faizal Faizal Faizal Faizal Faizal, Faizal Fatmasari Fatmasari Fitriani Fitriani Fransiska Sagita Patulak Hardi Hardi Hardi Hardi Hardi Hardi Haris, Adammas Harlina, Sitti Hasriani Hasriani Hasriani Hasriani Hasriani Hasriani, Hasriani Hasyrif Hasyrif Hasyrif SY Herlinda Herlinda Herman Heriadi Herman Heriadi Husain Husain Indo Intan Iqbal Gunawan Joseph Tumiwa Jufri . Madyana Patasik Magfirah Magfirah Marsa Marsa Michael Octavianus Michael Oktavianus Moch. Farhan Juliansyach Putra Taufiq Mochammad Agus Idris Mochtar Yunus, Farhan Moh. Rifkan Muh. Khaddafi Muh. Syahlan Natsir Muhammad Agus Muljanto Muhammad Rizal Nirwana Nirwana Novita Sambo Layuk Nurdiansah Nurdiansah, Nurdiansah Nurlina Nurlina Nurlindasari Tamsir Nurul Aini Rahmat Rahmat Rahmat Rahmat Rauf, Abdul Riki Taruk Bua Sadly Samsuddin Sadly Syamsudddin Sadly Syamsuddin Sadly Syamsuddin Sadly Syamsuddin Salmiati Salmiati Saprial Metthew Godliving Perdamaian Ani Sitti Aisa Sitti Aisa Sri Wahyuni ST. Aminah Dinayati Ghani Suci Rahma Dani Rachman Suci Ramadhani Arifin Suryani Suryani Syamsul Bahri Thabrani R Thabrani. R Thabrani. R Usman Usman Wilem Musu Willem Musu Yesycha Arun Mangopo Yohannes Johny Soetikno