Article Per Year (5 Year)
p-Index From 2021 - 2026
2.374
P-Index
This Author published in this journals
All Journal International Journal of Advances in Applied Sciences JTERA (Jurnal Teknologi Rekayasa) ILKOM Jurnal Ilmiah Mobile and Forensics Jurnal Pengabdian Masyarakat Ilmu Keguruan dan Pendidikan (JPM-IKP) Zonasi: Jurnal Sistem Informasi Indonesian Journal of Data and Science Jurnal Algoritma, Logika dan Komputasi Jurnal Teknologi Sistem Informasi Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Informatika Progres Internet of Things and Artificial Intelligence Journal Journal of Muhammadiyah’s Application Technology
Desi Anggreani
Universitas Muhammadiyah Makassar
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics

Published : 19 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 19 Documents
Search

 1   2 
Prediction of Export Volume in South Sulawesi Based on Destination Country Using the BPNN Method Widiyanti, Widiyanti; Anggreani, Desi; Lukman, Lukman; Danuputri, Chyquitha
Jurnal Algoritma, Logika dan Komputasi Vol 8, No 2 (2025)
Publisher : Universitas Bunda Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30813/j-alu.v8i2.8782

Abstract

This study aims to develop a prediction model for South Sulawesi's export volume based on destination countries using the Backpropagation Neural Network (BPNN) method. South Sulawesi has a significant contribution in the export of agricultural, marine, and mining commodities to various Asian and global countries. Common problems in the export process are unpreparedness of goods, limited commodity stocks, and a mismatch between production capacity and destination market demand. The export data used is from 2018 to 2026 with a total of 1,555 rows of data . The BPNN model with a 6-6-1 architecture is applied to study historical patterns and make accurate predictions. The test results show a Mean Squared Error (MSE) value of 0.0161440, with prediction results close to the actual trend. Exports peaked at almost 40 tons in 2019 and decreased significantly in 2023, then are predicted to recover steadily in 2025–2026. The destination countries with the highest export volumes are China, Japan, and East and Southeast Asian countries. The main commodities contributing significantly are octopus, processed wood, and marine products. These findings demonstrate that the BPNN method is effective in identifying export patterns and can be used as a basis for data-driven trade planning at the regional level. This study also underscores the importance of logistical readiness and market diversification in efforts to maintain export sustainability. 
Model Deep Learning Berbasis Convolutional Neural Network Untuk Identifikasi Stroke Iskemik Pada Citra CT Scan Faturohman, Agung; Anggreani, Desi; Yusliana Bakt, Rizki
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 5 No 2 (2025): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v5i2.1150

Abstract

Stroke iskemik merupakan salah satu penyakit tidak menular yang berbahaya dan dapat menyebabkan kecacatan hingga kematian apabila tidak ditangani dengan cepat dan tepat. Identifikasi stroke melalui citra CT scan otak menjadi metode penting dalam dunia medis, namun masih memerlukan waktu dan keahlian tinggi. Penelitian ini bertujuan untuk mengembangkan sistem deteksi stroke iskemik secara otomatis menggunakan metode Convolutional Neural Network (CNN) dengan arsitektur MobileNetV2. Data yang digunakan berupa citra CT scan otak pasien dari Rumah Sakit Labuang Baji Makassar, yang diproses melalui tahapan preprocessing seperti grayscale, resizing, augmentasi, dan normalisasi. Model CNN dilatih menggunakan binary crossentropy loss dan Adam optimizer untuk klasifikasi dua kelas, yaitu normal dan stroke iskemik. Hasil pengujian menunjukkan bahwa model mencapai akurasi sebesar 91,6%, precision 88%, recall 95,1%, dan F1-score 0,914, yang menandakan bahwa model ini mampu mengenali stroke iskemik secara efektif. Dengan demikian, sistem ini berpotensi menjadi alat bantu diagnosis awal yang efisien dan akurat dalam bidang kesehatan.
Stacking architecture-endpoint detection: a hybrid multi layered architecture for endpoint threat detection Wahid, Abd Rahman; Anggreani, Desi; Hayat, Muhyiddin A. M.; Abd Rahman, Aedah; Faisal, Muhammad
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1263-1280

Abstract

Modern endpoint threat detection systems face persistent challenges in balancing detection accuracy, resilience against zero-day attacks, and the interpretability of artificial intelligence (AI) models. Although deep learning (DL) approaches often achieve high accuracy on benchmark datasets, they remain vulnerable to adversarial perturbations and operate as opaque “black boxes,” thereby reducing trust and limiting practical adoption in critical infrastructures. This research introduces stacking architecture-endpoint detection (STACK-ED), a hybrid multi-layered architecture for endpoint threat detection. STACK-ED integrates three complementary paradigms: supervised learning for known attack patterns, self-supervised Fgraph-based learning for structural relationships, and unsupervised anomaly detection for emerging or unknown threats. The outputs are consolidated by a meta learner, followed by a post-hoc correction (PHC) mechanism to minimize false negatives. The framework was evaluated on a combined benchmark dataset (CSE-CIC-IDS2018 and UNSW-NB15, hereafter referred to as HIDS-Set). Experimental results demonstrate state-of-the-art performance, achieving an F2-score of 98.89% after hybrid integration and active learning, with the primary optimization objective being the reduction of undetected attacks. Furthermore, the Shapley additive explanations (SHAP) method enhances interpretability by revealing feature contributions, while the PHC successfully recovered 62.64% of missed zero-day candidates. The findings position STACK-ED not only as a highly accurate detection model but also as an adaptive, resilient, and transparent framework, offering practical implications for enterprise-grade endpoint defense and future zero-trust cybersecurity systems.
Pengembangan Sistem Kehadiran Mahasiswa Ceras Berbasis Web dengan Deteksi Waktu Nyata Menggunakan Yolo dan Arcface Nur, Andi Resqi Putriyani; Lukman; Anggreani, Desi; ., Andi Resqi Putriyani Nur
Journal of Muhammadiyah’s Application Technology Vol. 4 No. 3 (2025)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/28784r47

Abstract

ABSTRAK: Absensi mahasiswa merupakan aspek penting dalam proses perkuliahan untuk memantau tingkat kehadiran dan keterlibatan mahasiswa. Namun, sistem absensi konvensional yang masih dilakukan secara manual memiliki berbagai kelemahan, seperti tidak efisien, rawan kecurangan titip absen, serta kurang mendukung transformasi digital di lingkungan akademik. Penelitian ini merancang dan membangun sistem absensi cerdas berbasis web dengan mengintegrasikan object detection YOLOv8 dan face recognition ArcFace untuk melakukan pendeteksian serta identifikasi wajah mahasiswa secara otomatis dan real time. Sistem ini juga dilengkapi fitur anti spoofing untuk mencegah penggunaan foto atau gambar statis sebagai manipulasi kehadiran. Metode penelitian meliputi studi literatur, perancangan sistem, implementasi menggunakan framework Flask dan basis data MySQL, serta pengujian menggunakan metode blackbox testing. Hasil penelitian menunjukkan bahwa sistem mampu mendeteksi dan mengenali wajah mahasiswa dengan cepat dan akurat, menampilkan rekap kehadiran berbasis web, meminimalisir kecurangan, meningkatkan efisiensi proses absensi, serta mendukung digitalisasi administrasi akademik secara berkelanjutan dan dapat diintegrasikan dengan sistem kampus.KATA KUNCIAbsensi Mahasiswa, YOLOv8, ArcFace, Face Recognition ABSTRACT: Student attendance is an important aspect of the learning process to monitor student presence and participation. However, conventional attendance systems that are still carried out manually have several weaknesses, such as inefficiency, vulnerability to fraud through proxy attendance, and limited support for digital transformation in academic environments. This research designs and develops a web-based intelligent attendance system by integrating YOLOv8 for object detection and ArcFace for face recognition to automatically and accurately detect and identify students’ faces in real time. The system is also equipped with an anti-spoofing feature to prevent the use of photos or static images as attendance manipulation. The research methods include literature study, system design, implementation using the Flask framework and MySQL database, and system testing using blackbox testing. The results show that the system can detect and recognize students quickly and accurately, present attendance recaps through a web interface, minimize fraud, improve attendance efficiency, and support sustainable digitalization of academic administration and integrationKeywords:Student Attendance, YOLOv8, ArcFace, Face Recognition
TRANSFORMASI PROSES PEMBELAJARAN MELALUI INTEGRASI TEKNOLOGI INFORMASI Anggreani, Desi; Lukman, Lukman
Jurnal Pengabdian Masyarakat Ilmu Keguruan dan Pendidikan (JPM-IKP) Vol 6, No 2 (2023): Jurnal Pengabdian Masyarakat (JPM-IKP)
Publisher : FKIP Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jmp-ikp.v6i2.1738

Abstract

Kualitas pendidikan adalah permasalahan yang sangat diperhatikan oleh pemerintah. Pendidikan merupakan sistem dinamis  yang terus berkembang, sehingga pendekatan proses pembelajaran juga ikut berubah-ubah. Dengan perubahan yang cukup cepat sehingga dibutuhkan upaya untuk meningkatkan tenaga pendidik sehingga bisa sejalan dengan perkembangan pendidikan. Kabupaten Nunukan merupakan wilayah perbatasan antara negara Indonesia dan negara malaysia. Kondisi pendidikan pada daerah perbatasan dalam keadaan masih jauh dari ideal. Upaya mempercepat pemerataan dan peningkatan sumber daya manusia dalam sektor pendidikan perlu dilakukan. Salah satu implementasi meningkatan sumber daya manusia adalah dengan mengadakan pelatihan pengenalan teknologi informasi yang dapat digunakan sebagai media pembelajaran. Kegiatan pengabdian masyarakat ini dilakukan di SDN 003 Nunukan Selatan Kabupaten Nunukan Perbatasan Indonesia-Malaysia. Kegiatan berlangsung pada tanggal 9-10 Juni 2023. Kegiatan ini berjudul “Transformasi Proses Pembelajaran Melalui Integrasi  Teknolosi Informasi” dengan maksud memberikan pemahaman daerah perbatasan mengenai pentingnya melakukan proses pembelajaran interaktif dengan menggunakan media pembelajaran berbasis Teknologi Informasi. Dengan terselenggaranya kegiatan ini dapat menghasilkan peningkatan yang cukup tinggi pengetahuan peserta yang awalnya hasil Pre Test sebesar 38% setelah dilakukan pelatihan dan menghasilkan nilai Post test 80%. Peningkatan sebesar 42% pengetahuan tenaga pendidik mengenai media pembelajaran berbasis teknologi. Diharapkan kegiatan ini dapat ikut serta dalam proses transformasi pembelajaran menjadi lebih baik.
A Hybrid Convolutional Neural Network and Bidirectional LSTM Architecture for Multi-Sector Export Forecasting: A Macroeconomic Time Series Analysis of Indonesia Desi Anggreani; Nurmisba Nurmisba; Aedah Abd Rahman
Indonesian Journal of Data and Science Vol. 6 No. 3 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i3.330

Abstract

Accurately predicting export values is key for a country in formulating its economic plans. Unfortunately, export data often exhibits complex time series patterns that are difficult to predict, characterized by non-linearity, high volatility, and complex temporal dependencies. This study offers a solution by testing a combined deep learning model, specifically a fusion of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM), to address the challenges of export time series forecasting. This study uses this approach to forecast Indonesia's monthly export time series data from 2016 to 2023, covering various sectors ranging from oil and gas, non-oil and gas, agriculture, industry, mining, and others. The core idea is to leverage the CNN's ability to identify hidden features within time series patterns, while the BiLSTM is tasked with understanding the temporal flow of data from both directions to capture the inherent long-term temporal dependencies within economic time series data. As a result, this combined model proved to be far superior to the standard BiLSTM model in handling the complexity of export time series. In the Non-Oil and Gas sector, the proposed model achieved a high level of accuracy with an MSE value of 3,330,239.74, an RMSE of 1,824.89, and an average prediction error (MAPE) of only 8.17%, representing a significant improvement of 69% over the baseline BiLSTM model. Similar success was also found in all other sectors, proving that this hybrid approach is highly promising for complex economic time series analysis
Fine-Tuning a Large Language Model on Vertex AI for a New Student Registration Chatbot at Universitas Muhammadiyah Makassar Desi Anggreani; Muhyiddin A M Hayat; Lukman; Ahmad Faisal; Khadijah; Darniati
Indonesian Journal of Data and Science Vol. 7 No. 1 (2026): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i1.341

Abstract

This study addresses the limitations of manual admission services at Universitas Muhammadiyah Makassar, which often result in delayed and inconsistent information delivery. To overcome these challenges, an institution-specific chatbot was developed by fine-tuning the Gemini 2.5 Flash model on the Google Cloud Vertex AI platform. The model was trained using a curated domain-specific dataset of 1,430 question–answer pairs derived from official documents and frequently asked questions. The fine-tuning process employed supervised learning to enhance contextual relevance and response accuracy. System performance was evaluated using automated text quality metrics, achieving an average BLEU score of 0.23526 and a ROUGE-L Recall score of 0.53424, indicating satisfactory lexical and semantic similarity. Furthermore, a user acceptance evaluation involving 52 respondents yielded a Customer Satisfaction Score (CSAT) of 84.2%, reflecting high user satisfaction. These results demonstrate that fine-tuning a Large Language Model (LLM) for specific institutional needs effectively improves both response quality and service reliability. Ultimately, this approach offers a practical and scalable solution for modernizing student admission services in higher education, ensuring that prospective students receive accurate information in a timely and efficient manner.
A Hybrid BERT–RAG Model for Developing Knowledge-Validated Conversational Systems Anggreani, Desi; Ismawati, Ismawati; Auliyah, A. Inayah; Lukman, Lukman; Rahman, Aedah Abd; Nurmisba, Nurmisba; Akbar, Muh Ilham
ILKOM Jurnal Ilmiah Vol 18, No 1 (2026)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v18i1.3126.30-42

Abstract

The transition of freshmen into the university environment requires adaptive and responsive information support. This study develops a chatbot system based on a hybrid BERT–RAG architecture integrated with the FAISS Index to provide automated consultation services for new students. The novelty of this research lies in the implementation of a faculty-based hierarchical knowledge structure and an adaptive multi-domain context mechanism—an approach not previously found in studies involving BERT–RAG for university onboarding services. This design enables the chatbot to deliver more relevant, personalized, and faculty-specific responses. The dataset was derived from three primary sources of information: the Faculty of Economics and Business (FEB), the Faculty of Teacher Training and Education (FKIP), and the Faculty of Engineering (FT), which were structured into a validated knowledge base in documents.json format. System evaluation was conducted across ten interaction scenarios using performance metrics including BERT Similarity, BLEU Score, ROUGE-1, ROUGE-2, and ROUGE-L. The system achieved excellent results, with average scores of 0.905 (BERT Similarity), 0.844 (BLEU), 0.876 (ROUGE-1), 0.820 (ROUGE-2), and 0.871 (ROUGE-L) and standard deviations below 0.1 across all metrics. Strong metric correlations (0.85–0.99) further indicate consistency between semantic understanding and generated text quality. Furthermore, the system effectively minimizes hallucination through validated knowledge integration and faculty-based reranking strategies. Overall, this research provides a significant contribution to the development of institutionally contextual educational chatbots capable of delivering accurate, natural, and responsive communication to support new student orientation in higher education
KLASIFIKASI TANAMAN OBAT TRADISIONAL BERBASIS CITRA BUAH DAN DAUN Kusumawardani, Nurul; Danuputri, Chyquitha; Darniati; Faisal, Muhammad; A.M Hayat, Muhyiddin; S. Kuba, Muhammad Syafaat; Anggreani, Desi
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i1.534

Abstract

Indonesia is a megabiodiversity country with extensive use of traditional medicinal plants; however, plant identification in natural environments remains largely manual and error-prone. Recent advances in deep learning, particularly Vision Transformer (ViT), provide a promising solution by effectively capturing global spatial features for image classification. This study applies a ViT-Base/16 model to automatically classify fruit and leaf images of Indonesian medicinal plants. The dataset comprises 1,000 field-collected images from Galung Village, West Sulawesi, covering 20 classes (10 medicinal and 10 non-medicinal plants). The model was fine-tuned using the AdamW optimizer with a learning rate of 2×10⁻⁵ and trained for 30 epochs with cosine annealing. The proposed approach achieved high performance, with 99.33% accuracy, 99.41% precision, 99.33% recall, and a 99.33% F1-score, while binary classification between medicinal and non-medicinal plants reached 100% accuracy. The system was deployed as a Flask-based web application, demonstrating reliable functionality and practical response times. Overall, the results confirm the effectiveness of Vision Transformer for medicinal plant classification under natural conditions and highlight its potential to support digital documentation, education, and the preservation of local ethnobotanical knowledge.
Co-Authors . Darniati ., Andi Resqi Putriyani Nur A M Hayat, Muhyddin Abd Rahman, Aedah Abd. Rasyid Syamsuri Aedah Abd Rahman Ahmad Faisal Aji Prasetya Wibawa Akbar, Muh Ilham Andi Inayah Auliyah Bakti, Rizki Yusliana Danuputri, Chyquitha Danuputri, Chyquitha Danuputri, Chyquitha Darniati Dedi Setiawan Dedi Setiawan Dewi MJ, Wanda Tyrana Faturohman, Agung Hamdani Herman Herman Ismawati Ismawati Khadijah Kusumawardani, Nurul Lukman Lukman Lukman Lukman, Lukman LUKMAN, LUKMAN Muhammad Faisal Muhyiddin A.M Hayat Nur, Andi Resqi Putriyani Nurmisba Nurmisba Nurmisba Nurmisba, Nurmisba Purnawansyah Purnawansyah Putri, Desy Pratiwi Ika Rahman, Aedah Abd S. Kuba, Muhammad Syafa'at Wahid, Abd Rahman Widiyanti, Widiyanti Wiwik Handayani