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Dewi Fatmarani Surianto

Universitas Negeri Makassar

Author-ID : 4084971

Religion Agriculture, Biological Sciences & Forestry Arts Humanities 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 Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Public Health Social Sciences Other

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POST TRAINING QUANTIZATION IN LENET-5 ALGORITHM FOR EFFICIENT INFERENCE Dary Mochamad Rifqie; Dewi Fatmarani Surianto; Nurul Mukhlisah Abdal; Wahyu Hidayat M; Hartini Ramli
Journal of Embedded Systems, Security and Intelligent Systems Vol. 3 No. 1 (2022): Vol 3, No 1 (2022): May 2022
Publisher : Program Studi Teknik Komputer

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Ketika model jaringan saraf tiruan menjadi lebih baik , keinginan untuk mengimplementasikannya di dunia nyata semakin meningkat. Namun, konsumsi energi dan akurasi jaringan saraf tiruan sangat besar karena ukuran dan kompleksitasnya, sehingga sulit untuk diimplementasikan pada embedded devices. Kuantisasi jaringan saraf ini adalah sebuah teknik untuk dapat memecahkan masalah seperti mengurangi ukuran dan kompleksitas jaringan saraf tiruan dengan mengurangi ketepatan parameter dan aktivasi. Dengan jaringan yang lebih kecil, dimungkinkan untuk menjalankan jaringan saraf di lokasi yang diinginkan. Artikel ini mengkaji tentang kuantisasi yang telah berkembang dalam beberapa dekade terakhir. Dalam penelitian ini, kami mengimplementasikan kuantisasi dalam algoritma lenet-5, yang merupakan algoritma jaringan saraf convolutional pertama yang pernah ada, dan dievaluasi dalam dataset MNIST dan Fashion-MNIST.

Analisis Prediksi Tingkat Penyebaran COVID-19 di Sulawesi Selatan Menggunakan Teknik Data Mining Naive Bayes Muhammad Nur Yusri; Andi Akram Nur Risal; Muhammad Fajar B; Dewi Fatmarani Surianto; Fhatiah Adiba
Journal of Embedded Systems, Security and Intelligent Systems Vol. 3 No. 2 (2022): Vol 3, No 2 (2022): November 2022
Publisher : Program Studi Teknik Komputer

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Pandemi atau wabah virus corona atau biasa disebut juga dengan COVID-19 yang bermula dari Wuhan, Provinsi Hubei, China, terus menyebar ke berbagai negara, termasuk Indonesia. Jumlah kasus positif COVID-19 terus meningkat tiap harinya secara signifikan dan menyebar secara cepat ke berbagai provinsi di Indonesia, termasuk di provinsi Sulawesi Selatan. Hingga saat ini, telah tercatat kasus positif corona di Sulawesi Selatan sebanyak 18.683 dan 470 orang meninggal dunia. Peningkatan kasus yang signifikan ini, mengakibatkan pembacaan data terkait kasus positif COVID-19 di Sulawesi Selatan dinilai kurang akurat. Oleh karena itu, penelitian ini dilakukan sebagai langkah antisipasi terhadap pandemi COVID-19 dengan memprediksi tingkat penyebaran COVID-19 terutama di Sulawesi Selatan agar mendapatkan keakurasian data yang lebih baik. Metode penelitian yang di terapkan pada penelitian ini ialah analisis masalah dan studi literatur, mengumpulkan data dan implementasi.

Comparative Analysis of the Performance of Hadith Text Classification Methods: A Case Study with ANN and SVM Surianto, Dewi Fatmarani; Fajar B, Muhammad; Mulia, Musda Rida; Indanasufya, Indanasufya
Journal of Embedded Systems, Security and Intelligent Systems Vol 5, No 1 (2024): March 2024
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v5i1.2942

Hadith is the second holy book for Muslims after the Quran, containing instructions from the Prophet Muhammad SAW, and narrated by Ulama / Mufti. As one of the main sources of Islamic teachings, hadith is used to explain and illustrate the teachings of the Quran. This study aims to compare the performance of hadith text classification using Artificial Neural Network (ANN) and Support Vector Machine (SVM) with Hadith Bukhari dataset. The stages include preprocessing, feature extraction with TF-IDF, classification, and evaluation. The evaluation results show different performance between ANN and SVM in two scenarios: with and without stemming. The use of stemming has a significant impact on model performance, reducing word variation and can result in a decrease in accuracy. The SVM model consistently showed higher accuracy than ANN in both scenarios, with the highest accuracy reaching 85% for classification without stemming. This study provides insight into the application of ANN and SVM in hadith text classification, emphasizing the importance of selecting a method that suits the characteristics of the data.

A Hybrid Framework for Plagiarism Detection: Integrating Token-Based Similarity with Density-Based Clustering Fajar B, Muhammad; Lestary, Fitriyanty Dwi; Surianto, Dewi Fatmarani
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 1 (2025): March 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i1.7664

Plagiarism detection in academic assignments remains a critical challenge in maintaining academic integrity in higher education. This study proposes an automated method to detect content similarity between student assignment documents by combining Jaccard Similarity and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithms. The process begins with the collection of student assignment files in digital format, followed by text extraction to form a set-based representation of each document. Jaccard Similarity is then used to compute the degree of similarity between every document pair, and the resulting similarity matrix is transformed into a distance matrix as input for DBSCAN. Experiments conducted on 23 documents yielded 253 unique document pairs. The results demonstrate that the method successfully identified pairs with high similarity scores—such as 0.9114 and 0.7226—which were visually confirmed through a heatmap and effectively grouped into clusters by DBSCAN. Parameter settings of eps = 0.3 and min_samples = 1 proved optimal for distinguishing original documents from those exhibiting substantial content overlap. This approach is not only accurate and efficient, but also eliminates the need for predefined cluster numbers, making it suitable for deployment in automated plagiarism detection systems for academic texts.

Analysis of Naive Bayes and Support Vector Machine Algorithms in Classification of Diabetes Cases Based on Lifestyle Factors Awalia, Andi Dio Nurul; Muhammad Fadhil Hani; Dewi Fatmarani Surianto
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.9783

The increase in diabetes mellitus cases globally, including in Indonesia, demands a more adaptive lifestyle-based risk prediction strategy. This study aims to evaluate and compare the efficiency of Support Vector Machine (SVM) and Naive Bayes in the diabetes risk classification process using a Hybrid clustering-classification approach . The data analyzed in this study were obtained from the Kaggle platform , with 8,500 data of diabetes patients analyzed based on the attributes of age, gender, and smoking history. The Clustering process was carried out using K-Means (k=3), resulting in three main groups with different lifestyle characteristics. The classification results showed that Naive Bayes provided stable performance with an F1-score of 0.975. Meanwhile, SVM excelled in terms of F1-score 98.3% and perfect AUC (1,000), and was able to classify all data in cluster C3 without error. However, SVM recorded a higher classification error in the majority cluster . This study concluded that SVM was superior by 0.8% over Naive Bayes . Naive Bayes is more suitable for balanced data, while SVM is effective for detecting patterns in minority groups. These findings support the use of a hybrid approach in lifestyle data-based diabetes early detection systems. Future research directions include integrating additional variables and ensemble techniques to improve model generalization.

Segmentation of Student Lifestyle Patterns for Insomnia Risk Identification Using the K-Means Algorithm Athiyyah Anandira; Azzah Ulima Rahma; Amanda Putri Lestari; Dewi Fatmarani Surianto
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 4 (2025): Desember 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.8683

Insomnia is a common sleep disorder that occurs in college students due to unbalanced lifestyle patterns. This study aims to categorize students based on their lifestyle patterns and identify the risk of insomnia by applying the K-Means algorithm. Data were obtained from 198 active students of JTIK UNM batch 2021-2024 through a questionnaire. Five main variables were analyzed, such as sleep duration, caffeine consumption, gadget use, number of assignments per week, and hours of sleep. After the researchers transformed and normalized data, the clustering process had resulted in two clusters. The first cluster showed a higher risk of insomnia due to late bedtime and excessive gadget usage, while the second cluster tended to undergo a healthier lifestyle. The Davies-Bouldin Index value of 0.22 indicates superlative clustering qualities. This study provides an overview of student characteristics based on lifestyle and potential risk of insomnia.

Transfer Learning-Based CNN for Guava Fruit Disease Detection and Classification Azir Zuldani Pratama; Mustari Lamada; Surianto, Dewi Fatmarani
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 3 (2025): September 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i3.10153

Guava (Psidium guajava L.) is a tropical plant from the Myrtaceae family and the Psidium genus that is susceptible to diseases such as anthracnose and scab, especially in humid environmental conditions. To accurately detect and classify these diseases, digital image-based technology is needed. However, previous studies still have limitations in dataset size, method variation, and model optimization. Therefore, a study was conducted with the title Guava Fruit Disease Detection and Classification System Using a Convolutional Neural Network (CNN) Based Transfer Learning Model. This study tested four Transfer Learning models, namely MobileNetV2, DenseNet169, VGG16, and EfficientNetV2B5. Based on the test results, the MobileNetV2 model with a combination of activation functions and optimizers (Swish, Swish, Adam) showed the best performance, having the fastest computation time, namely 10 minutes 17 seconds. This proves that the model built is not only superior in accuracy, but also efficient in execution time and can be applied to guava fruit disease detection and classification systems. These findings provide valuable insights into the MobileNetV2 method, combined with Swish, Swish, and Adam, as the best choice for classifying or detecting guava fruit disease levels compared to other methods. This approach can also lead to the development of a widely applicable web-based system for plant disease identification. This offers several benefits for farmers, including faster and more accurate disease detection, efficiency, and cost savings.

Co-Authors A. Arianugerah Ilham A. Arianugerah Ilham AA Sudharmawan, AA Abdal, Nurul Mukhlisah Abdul Muis Mappalotteng Abdul Wahid Adiba, Fathiah Adiba, Fhatiah Adnas, Diny Anggriani Agusyana, Nurrahmah Ahmar, Ansari Saleh Ainun Zahra Adistia Akbar, Mohammad Arsan Akmal Hidayat Akmal Hidayat Akmal, Muhammad syafruddin Alwi, Ana Sulistiana Amanda Putri Lestari Amiruddin Amri, Muh. Aidil Amukune, Stephen Andi Akram Nur Risal Andi Baso Kaswar Andi Baso Kaswar Andi, Tenriola Andika Isma Anwar Wahid Arifin, Afrisal Arifiyanti, Fitria Arsyad, Meisaraswaty Asis Nojeng Asri Ismail Athiyyah Anandira Awalia, Andi Dio Nurul Awaliah, Widiarti Ayu Hasnining Ayu Safitri Azir Zuldani Pratama Azis, Putri Alysia Azzah Ulima Rahma B., Muhammad Fajar Bahar, Muhammad Mahdinul Bakri, Muh. Fajrin Baso, Fadhlirrahman Cahyana Resky, Andi Aulia Clarisha, Windi Darwing, Khalil Mubaraq Dary Mochamad Rifqie Della Fadhilatunisa Dhaffa Mulya Rahman Dillah, Salsa Dwi Rezky Anadari Sulaiman Edi Suhardi Rahman Edy, Marwan Ramdhany FADIAH, NUR Fani, A. Astri Merilsa Fathahillah Fathahillah Fathahillah Fhatiah Adiba Fhatiah Adiba Firdaus Firdaus Fitriani Dzulfadhilah Fitriyanty Dwi Lestary Fizar Syafaat Furqan Ali Yusuf Hardy M, Galang Hartini Ramli Helmy, Ahnaf Riyandirga Ariyansyah Putra Hidayat M., Wahyu Ilyas, Sitti Nurhidayah Indanasufya, Indanasufya Inez Sri Wahyuningsi Manguling Irianti, Erva Irwandi Ishaq, Muhammad Fahrul Rosi Ivan Fadillah Akram Iwan Suhardi Jariah S.Intam, Rezki Nurul Jariah, Rezki Nurul Jasruddin Jumadi Mabe Parenreng Jumadil Ahmad Safi’i Jusniar Khaerunnisa Nur Fatimah Syahnur Kurnia Prima Putra Lapendy, Jessica Crisfin Lavicza, Zsolt Lutfiah Tri Syahyaningsih M. Miftach Fakhri Makmur, Haerunnisya Mappangara, Surianto MARDIAH, AINA Muh. Juharman Muhammad agung Muhammad Akil Musi Muhammad Ansarullah S. Tabbu Muhammad Fadhil Hani Muhammad Fajar B Muhammad Fardan MUHAMMAD ILHAM Muhammad Nur Yusri Muhammad Rafli Aditya H. Muhammad Rakib Muhammad Try Dharsana Muharni Muharni Muhtadi, Nashiruddin Sahal Mulia, Musda Rida Muliadi Mustari Lamada Muthmainnah, Aindri Muthmainnah, Aindri Rizky Nafil Rizqullah Rajab Nafil Rizqullah Rajab Nashiruddin Sahal Muhtadi Nasrullah, Asmaul Husnah Natsir, Nasrah Ninik Rahayu Ashadi NIRMALA, PUTRI Nur Fadiah NUR FADILAH Nur Risal, Andi Akram Nurjannah Nurrahmah Agusnaya Nurul Fadhilah Nurul Fadhillah Nurul Fadhillah S Nurul Mukhlisah Abdal Pamput, Jessicha Pamput, Jessicha Putrianingsih Parenreng, Jumadi M. Putri Nirmala Putri Zhachilia Susanto R, Mutmainnah Raden Mohamad Herdian Bhakti Rahman, Dhaffa Mulya Ramadhan, Haekal Febriansyah Rauf, Annajmi Resky, Andi Aulia Cahyana Rezki Angriani Pratiwi Kadir Rezki Nurul Jariah Rezky Anisar, Muh. Alief Rhania, Dhia Ridwan Daud Mahande Risaldi, Muhammad Rivai, Andi Tenri Ola Rosidah Rusli, Risvan S, Aprilianti Nirmala S, Muh. Rizal S, Nurul Fadhillah S.Intam, Rezki Nurul Jariah Sari Wulandari Sari, Putri Nanda Sasmita Sasmita Satria Gunawan Zain Setialaksana, Wirawan - Shabrina Syntha Dewi Shasa Inayah Vega Shasa Inayah Vega Siti Syarifah Wafiqah Wardah Soeharto Soeharto Sudarmanto Jayanegara Surianto, Dewi Fatmawati Syahrul Syam, Abd. Azis Syamsurijal Syamsurijal, Syamsurijal Tenriola, Andi Udin Sidik Sidin Wahid, M Syahid Nur Wahid, M. Syahid Nur Wahid, Yokogeri Abdullah Wahyu Hidayat M Wahyu Hidayat M WAHYUDI Warda Wahyuni Wardah, Siti Syarifah Wafiqah Wardani, Ayu Tri WULANDARI Wulandari Wulandari Zulfikar, Muh Ihsan Zulhajji, Zulhajji

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Journal : Journal of Embedded Systems, Security and Intelligent Systems