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Nanik Suciati

Departemen Informatika Institut Teknologi Sepuluh Nopember

Author-ID : 387460

Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Other

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Pemanfaatan Platform Google Classroom untuk Pembelajaran Daring di Pondok Pesantren Miftahul Ulum Al-Islamy, Bangkalan, Madura Dini Adni Navastara; Nanik Suciati; Chastine Fatichah; Diana Purwitasari; Handayani Tjandrasa; Agus Zainal Arifin; Akwila Feliciano; Yulia Niza; Rangga Kusuma Dinata; Safhira Maharani; Ahmad Syauqi; Sherly Rosa Anggraeni; Fandy Kuncoro Adianto; Zakiya Azizah Cahyaningtyas; Salim Bin Usman; Kevin Christian Hadinata
Sewagati Vol 4 No 3 (2020)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (269.198 KB)

Proses pembelajaran daring menjadi hambatan tersendiri dalam bidang pendidikan, terlebih untuk pendidikan wajib yang harus dilakukan secara bertatap muka langsung antara pengajar dan pelajar. Di luar faktor permasalahan eksternal, permasalahan internal perlu diselesaikan terlebih dahulu, yaitu media pembelajaran. Salah satu platform digital yang tersedia sebagai media pembelajaran untuk menunjang pembelajaran secara daring adalah Google Classroom. Aplikasi Google Classroom berbasis web yang berbentuk pembelajaran asynchronous atau dapat dikatakan pemberian materi ajar dilakukan secara tidak langsung. Walaupun sebuah media daring sudah tersedia, masih ada yang belum mengenal atau memahami penggunaan aplikasi Google Classroom sebagai media ajar mereka. Oleh karena itu, kami mengadakan pengabdian masyarakat berupa pelatihan tentang penggunaan aplikasi Google Classroom bagi guru-guru di Pondok Pesantren Miftahul Ulum Al-Islamy, yang berada di Bangkalan, Madura. Selain itu, tim pengabdi juga melakukan pendampingan bagi guru-guru dalam mempraktikkan penggunaan Google Classroom sesuai dengan mata pelajaran yang diajar. Berdasarkan hasil survei, sebanyak 91% dari total peserta pelatihan menyebutkan bahwa pelatihan ini dapat meningkatkan pengetahuan dan kemampuan secara softskill dan hardskill para guru.

Combination of Cross Stage Partial Network and GhostNet with Spatial Pyramid Pooling on Yolov4 for Detection of Acute Lymphoblastic Leukemia Subtypes in Multi-Cell Blood Microscopic Image Mustaqim, Tanzilal; Fatichah, Chastine; Suciati, Nanik
Scientific Journal of Informatics Vol 9, No 2 (2022): November 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v9i2.37350

Purpose: Acute Lymphoblastic Leukemia (ALL) Detection with microscopic blood images can use a deep learning-based object detection model to localize and classify ALL cell subtypes. Previous studies only performed single cell-based detection objects or binary classification with leukemia and normal classes. Detection of ALL subtypes is crucial to support early diagnosis and treatment. Therefore, an object detection model is needed to detect ALL subtypes in multi-cell blood microscopic images.Methods: This study focuses on detecting the ALL subtype using YOLOV4 with a modified neck using Cross Stage Partial Network (CSPNet) and GhostNet. CSPNet is combined with Spatial Pyramid Pooling (SPP) to become SPPCSP to get various features map before the YOLOv4 final layer. Ghostnet was used to reduce the computation time of the modified YOLOV4 neck.Result: Experimental results show that YOLOv4 SPPCSP outperformed the recall value of 14.6%, the value of mAP@.5 0.8%, and reduced the computation time by 4.7 ms compared to the original YOLOv4.Novelty: The combination of CSPNet and GhostNet for YOLOV4 neck modification can increase the variety of features map and reduce computing time compared to the Original YOLOv4.

Analisis Penggunaan Pra-proses pada Metode Transfer Learning untuk Mendeteksi Penyakit Daun Singkong Amelia Devi Putri Ariyanto; Salsabiil Hasanah; M. Bahrul Subkhi; Nanik Suciati
Techno.Com Vol 22, No 2 (2023): Mei 2023
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/tc.v22i2.7769

Singkong menjadi salah satu tanaman penting pada bidang agronomi dan banyak dikonsumsi oleh masyarakat. Namun, terdapat salah satu kendala dalam menjaga kelestarian tanaman singkong yaitu pendeteksian penyakit. Jika penyakit pada tanaman singkong dapat terdeteksi lebih dahulu melalui citra daunnya, maka penyakit tersebut dapat segera diobati. Proses klasifikasi dapat dilakukan untuk mendeteksi penyakit pada tanaman secara otomatis. Pada penelitian ini dilakukan klasifikasi tanaman singkong dengan menggunakan beberapa tahap pra-proses yaitu pra-proses dengan augmentasi, tanpa augmentasi dan pra-proses dengan rotasi, pada beberapa metode transfer learning seperti ResNet50 dan MobileNetV2. Penggunaan beberapa metode tersebut bertujuan untuk mencari metode mana yang memiliki hasil akurasi tertinggi. Penelitian menunjukkan bahwa MobileNetV2 tanpa augmentasi memberikan akurasi tertinggi sebesar 98.64% dalam mendeteksi penyakit tanaman singkong. Hal ini dapat menjadi referensi bagi peneliti selanjutnya dalam menentukan tahap pra-proses terbaik dalam metode transfer learning.

Pemodelan Interaktif Furnitur 3 Dimensi Dengan Bentuk Bebas Dari Citra Tunggal Agus Priyono; Nanik Suciati
Syntax Literate Jurnal Ilmiah Indonesia
Publisher : Syntax Corporation

Pemodelan objek tiga dimensi merupakan proses yang cukup detail dan membutuhkan waktu lama. Maka diperlukan cara agar proses pemodelan menjadi lebih efisien dalam waktu pengerjaan, serta lebih baik dari segi output yang dihasilkan. Salah satu caranya adalah dengan acuan gambar dua dimensi untuk mempercepat proses terbentuknya objek tiga dimensi. Salah satu penelitian tentang topik tersebut adalah memodelkan furnitur tiga dimensi dengan citra tunggal. Namun penelitian tersebut masih memiliki kekurangan yaitu hanya mampu memodelkan objek dengan bentuk dasar kotak. Berangkat dari kekurangan tersebut, penelitian ini akan membuat sebuah pendekatan baru untuk memodelkan furnitur dari citra tunggal dengan bentuk yang tidak hanya kotak. Pendekatan ini merupakan pengembangan dari penelitian sebelumnya. Tahapan pendekatan yang dilakukan adalah Inisialisasi objek tiga dimensi furnitur, pelabelan bagian-bagian dari furnitur, menentukan fungsi setiap label, pengaturan bagian dalam furnitur, dan penataan tekstur. Pengembangan yang dilakukan adalah pada tahapan inisialisasi objek dan pelabelan. Penelitian ini menghasilkan sebuah aplikasi yang menerapkan pendekatan baru dalam pembuatan model tiga dimensi dengan input citra tunggal. Pembatasan input gambar harus berbasis kotak pada penelitian sebelumnya telah berhasil ditutupi dengan modifikasi pada pendekatan. Pada contoh beberapa inputan gambar dengan bentuk tidak berbasis kotak, aplikasi mampu untuk memproses dengan baik. Proses tersebut terjadi pada tahap inisialisasi dan pelabelan sehingga dapat membentuk objek tiga dimensi furnitur yang lebih dinamis.

Pemanfaatan Teknologi Informasi dalam Penyusunan Materi Pembelajaran Berbasis Multimedia Interaktif pada SDN Sutorejo I/240 Surabaya Dini Adni Navastara; Nanik Suciati; Chastine Fatichah; Handayani Tjandrasa
Sewagati Vol 7 No 6 (2023)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v7i6.553

Model pembelajaran yang efektif diperlukan oleh setiap Lembaga Pendidikan. Di era digital ini, teknologi dapat dimanfaatkan untuk meningkatkan pembelajaran tersebut. Dengan perkembangan teknologi yang semakin pesat dan canggih tentunya akan membuat pengelola pendidikan, khususnya guru/pengajar akan semakin berupaya untuk meningkatkan kompetensinya mempelajari teknologi dalam rangka meningkatkan kualitas pembelajaran di sekolah. Sekolah Dasar Negeri Sutorejo I/240 Surabaya merupakan salah satu sekolah dasar negeri yang turut serta dalam pengembangan materi pembelajaran pada program Kementerian Pendidikan dan Kebudayaan (Kemdikbud) yaitu Rumah Belajar. Agar bahan materi pembelajaran menarik, terstruktur dan interaktif, maka guru menyusun materi pembelajaran dengan berbasis multimedia. Oleh karena itu, dalam rangka meningkatkan kualitas pembelajaran, dilakukan kegiatan pelatihan pemanfaatan teknologi informasi, seperti Microsoft PowerPoint untuk menyusun materi pembelajaran berbasis multimedia interaktif. Kegiatan terbagi menjadi empat tahap yaitu persiapan, pelatihan, pendampingan, dan evaluasi. Pelaksanaan pelatihan dan pendampingan dilakukan secara hybrid, yaitu daring dan luring di Laboratorium Pemrograman I Teknik Informatika ITS. Dan pelaksanaan evaluasi dilakukan secara luring di SDN Sutorejo I/240, Surabaya. Berdasarkan hasil evaluasi, peserta pelatihan yaitu guru dapat mengimplementasikan materi pelatihan dengan baik, sehingga peserta didik lebih tertarik dengan pembelajaran menggunakan Microsoft PowerPoint.

Cucumber Disease Image Classification with A Model Combining LBP and VGG-16 Features Arifin, Miftahol; Yuniarti, Anny; Suciati, Nanik
International Journal of Robotics and Control Systems Vol 4, No 3 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i3.1529

Cucumber (Cucumis sativus) is a significant horticultural crop worldwide, highly valued for both fresh consumption and processing. However, cucumber cultivation faces challenges due to diseases that can substantially reduce yield and quality. Diseases like leaf spots, stem wilt, and fruit rot are caused by pathogens including viruses, bacteria, and fungi. Traditionally, disease detection in cucumbers is performed manually, which is time-consuming and inefficient. Therefore, developing machine vision-based models using Deep Learning (DL) and Machine Learning (ML) for early disease detection through image analysis is crucial for assisting farmers. While many studies on plant disease classification using various DL and ML models show optimal results, research on cucumbers has mostly focused on leaf diseases. This study aims to optimize cucumber disease image classification by developing a model that combines Local Binary Pattern (LBP) texture features and VGG-16 convolutional features. The dataset used, Cucumber Disease Recognition Dataset consists of 8 classes of cucumber plant disease images covering leaves, stems, and fruits. This study classifies cucumber plant disease images using Random Forest (RF) combined with LBP texture features and VGG-16 visual features and compares its performance with models using VGG-16, LBP+RF, and VGG-16+RF on the same dataset. The results show that the proposed model achieved a precision of 84.7%, recall of 84%, F1-Score of 83.8%, and accuracy of 84%. These results outperform the comparative models, demonstrating the effectiveness of the combined approach in classifying cucumber plant diseases.

Microarray classification using genetic algorithm and latin hypercube sampling Awangditama, Bangun Rizki; Suciati, Nanik
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i3.pp1976-1985

Cancer, the second leading cause of global death, requires advanced diagnostic technology. Microarray gene expression technology plays an important role in comprehensively analyzing the genetic aspects of cancer. However, challenges such as high-dimensional attributes, limited samples, and varying gene presence rates hinder the accurate classification of microarray data. This study proposes a model that uses latin hypercube sampling (LHS) in genetic algorithms (GA) for Feature Selection in microarray data classification. LHS makes the chromosome samples in the initial population of GAs representative and diverse. The study used three microarray datasets with different numbers of features and classes. The results reveal that first, the use of GA alone tends to limit the exploration of the resulting feature space, while the use of LHS can expand the feature selection possibilities in the context of feature selection. Secondly, this study shows that microarray classification using GA with LHS (GALHS) consistently outperforms other feature selection methods such as based correlation features (BCF), principal component analysis (PCA), relief, and lasso. Thus, this research contributes to feature selection by applying LHS and GA to optimize the performance of microarray data classification models.

Stacking-based ensemble learning for identifying artist signatures on paintings Hidayati, Shintami Chusnul; Irawan Rahardja, Agustinus Aldi; Suciati, Nanik
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1683-1693

Identifying artist signatures on paintings is essential for authenticating artworks and advancing digital humanities. An artist’s signature is a consistent element included in each painting that the artist creates, providing a unique identifier for their work. Traditional methods that rely on expert analysis and manual comparison are time-consuming and are prone to human error. Although convolutional neural networks (CNNs) have shown promise in automating this process, existing single-model approaches struggle with the diversity and complexity of artistic styles, leading to limitations in their performance and generalizability. Therefore, this study proposes an ensemble learning approach that integrates the predictive power of multiple CNN-based models. The proposed framework leverages the strengths of three state-of-the-art CNNs: EfficientNetB4, ResNet-50, and Xception. These models were independently trained, and the predictions were combined using a meta-learning strategy. To address class imbalance, data augmentation techniques and weighted loss functions were employed. The experimental results obtained on a dataset of more than 8,000 paintings from 50 artists demonstrate significant improvements over individual CNN architectures and other ensemble methods, thereby effectively capturing complex features and improving generalizability.

Fog and rain augmentation for license plate recognition in tropical country environment Wahyu Saputra, Vriza; Suciati, Nanik; Fatichah, Chastine
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp3951-3961

Automatic license plate recognition (ALPR) is a critical component in modern traffic management systems. However, ALPR systems often face challenges in accurately recognizing license plates under adverse weather conditions, such as fog and rain, prevalent in tropical regions. Deep learning ALPR models necessitate huge and diverse datasets for robustness, but data availability remains a concern since unpredictable fog and rain patterns hinder data collection. In this study, we address the issue of enhancing ALPR's robustness by introducing a novel augmentation strategy that combines traditional and weather augmentation techniques. By augmenting the dataset with weather-induced variations, we aim to improve the generalization capability of ALPR models, enabling them to handle a wider range of weather-related challenges. We also investigate the synergy between these weather augmentations and established scene text recognition (STR) methods, such as convolutional recurrent neural network (CRNN), TPS-ResNet BiLSTM-attention (TRBA), autonomous bidirectional iterative scene text recognition (ABINet), vision transformer (ViTSTR), and permutated autoregressive sequence (PARSeq), to determine their impact on recognition accuracy. Experiments using different training data sets show that training data containing a combination of traditional and weather augmentation produces the best accuracy and 1-NED performance compared to training data without augmentation and traditional augmentation only. The average increase accuracy of all STR model is 1.13% with the best increase accuracy of 3.68% using TRBA.

Combination of Historical Stock Data and External Factors In Improving Stock Price Prediction Performance Sjahrunnisa, Anita; Suciati, Nanik; Hidayati, Shintami Chusnul
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 18 No. 2 (2024)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v18i2.1707

Stock price prediction continues to be a major focus for investors today, some previous studies often focus on technical analysis using historical stock price data and ignore external factors that can affect stock prices. The purpose of this research is to overcome the shortcomings of previous research by creating a stock price prediction model that combines historical stock data consisting of date, high, low, open, close, adj close, volume and external factors such as days, interest rates, inflation, and dividends. The data used came from 33 companies from 11 industrial sectors in Indonesia for 2267 trading days and evaluated the prediction performance using MSE, MAPE and R-squared. The results show a significant improvement in the evaluation metrics when external factors are added. This shows the importance of such factors in improving the prediction analysis and increasing the reliability of the prediction model. This approach is expected to not only overcome the limitations of traditional methods but also utilize a combination of deep learning and machine learning to improve prediction accuracy. Thus, this research not only provides new insights in the field of financial analysis but also provides new insights and solutions for investors to make more informed and less risky decisions.

Co-Authors Adhira Riyanti Amanda Adni Navastara, Dini Agus Eko Minarno Agus Priyono Agus Zainal Arifin Agus Zainal Arifin Ahmad Saikhu Ahmad Syauqi Ahmad Syauqi Akwila Feliciano Akwila Feliciano Akwila Feliciano Pradiptatmaka Alam Ar Raad Stone Aldinata Rizky Revanda Altriska Izzati Khairunnisa Hermawan Amelia Devi Putri Ariyanto Amirullah Andi Bramantya Andika Rahman Teja Anny Yuniarti Antonius Kevin Wiguna Ardian Yusuf Wicaksono Ari Wijayanti Aris Fanani Arrie Kurniawardhani Arsy Bilahi Tama Ary Mazharuddin Shiddiqi Arya Yudhi Wijaya Atika Faradina Randa Atikah, Luthfi Avin Maulana Awangditama, Bangun Rizki Ayu Kardina Sukmawati Ayu Septya Maulani Baso, Budiman Bryan Nandriawan Bui, Ngoc Dung Chastine Fatichah Chastine Fatichah Chilyatun Nisa' Damayanti, Putri Daniel Sugianto Darlis Herumurti Davin Masasih Diana Purwitasari Dimas Rahman Oetomo Dini Adni Navastara Dini Adni Navastara, Dini Adni Dion Devara Aryasatya Eko Prasetyo Eva Yulia Puspaningrum Evelyn Sierra Fairuuz Azmi Firas Faishal Azka Jellyanto Faizin, Muhammad 'Arif Fajar Astuti Hermawati Fandy Kuncoro Adianto Fandy Kuncoro Adianto Febri Liantoni, Febri Fiqey Indriati Eka Sari Fitri Bimantoro Ginardi, R.V. Hari Glenaya Gou Koutaki Gurat Adillion, Ilham Hafidz, Abdan Handayani Tjandrasa Handayani Tjandrasa Hani Ramadhan Haq, Arinal Hidayat, Ahmad Nur Hidayati, Shintami Chusnul Hilya Tsaniya Imagine Clara Arabella Imam Kuswardayan Imam Mustafa Kamal Irawan Rahardja, Agustinus Aldi Isye Arieshanti Isye Arieshanti Januar Adi Putra Januar Adi Putra Kautsar, Faiz Keiichi Uchimura Kevin Christian Hadinata Kevin Christian Hadinata M. Bahrul Subkhi Maulidan Bagus A.R Maulidiya, Erika Mawaddah, Saniyatul MIFTAHOL ARIFIN, MIFTAHOL Mochammad Zharif Asyam Marzuqi Muchamad Kurniawan Muchamad Kurniawan Muchamad Kurniawan, Muchamad Muhamad Nasir Muhammad 'Arif Faizin Muhammad Alif Satriadhi Muhammad Farih Muhammad Fikri Sunandar Mutmainnah Muchtar Nafa Zulfa Ni Luh Made ITS Novrindah Alvi Hasanah R Dimas Adityo R. Dimas Adityo Rachman, Rudy Rahma Fida Fadhilah Rangga Kusuma Dinata Rangga Kusuma Dinata Rayssa Ravelia Rizal A Saputra Rizal A Saputra, Rizal A Rohman Dijaya Romario Wijaya Safhira Maharani Safhira Maharani Salim Bin Usman Salim Bin Usman Salsabiil Hasanah Sarimuddin, Sarimuddin Septiana, Nuning Sherly Rosa Anggraeni Sherly Rosa Anggraeni Shintami Chusnul Hidayati Shofiya Syidada Sjahrunnisa, Anita Suastika Yulia Riska Sugianela, Yuna Surya Fadli Alamsyah Syavira Tiara Zulkarnain Tanzilal Mustaqim Tiara Anggita Tiara Anggita Tsaniya, Hilya Wahyu Saputra, Vriza Wan Sabrina Mayzura Wibowo, Della Aulia Wicaksono, Farhan Wijayanti Nurul Khotimah Yulia Niza Yulia Niza Yuna Sugianela Yuna Sugianela Yuslena Sari, Yuslena Yuwanda Purnamasari Pasrun Zakiya Azizah Cahyaningtyas Zakiya Azizah Cahyaningtyas

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