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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Mechatronics, Electrical Power, and Vehicular Technology Jurnal INKOM Jurnal Teknologi dan Sistem Komputer Journal of Information Technology and Computer Science Jurnal Elektronika dan Telekomunikasi
Suryawati, Endang
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Pembangkitan Pola Data Cuaca Untuk Sistem Peringatan Dini Banjir Suwarningsih, Wiwin; Suryawati, Endang
INKOM Journal Vol 6, No 1 (2012)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (512.981 KB) | DOI: 10.14203/j.inkom.170

Abstract

Kondisi anomali cuaca saat ini menyebabkan prediksi hujan semakin sulit untuk dilakukan dan hal ini mengakibatkan pula menganalisa serta memprediksi bencana banjir yang diakibatkan curah hujan yang tinggi kurang cepat untuk dilakukan. Sehingga diperlukan suatu cara obcervasi dan bagaimana membangkitkan pola dari anomali kondisi cuaca agar dapat digunakan untuk menentukan dan memprediksi banjir. Dalam makalah ini akan dilakukan observasi terhadap pola data hujan dan data banjir untuk membuat suatu sistem peringatan dini banjir sehingga dapat menghasilkan informasi yang dibutuhkan oleh masyarakat dan pemerintah daerah. Metoda yang akan digunakan untuk observasi pembangkitan pola adalah algoritma SPADE (Sequential Pattern Discovery Using Equivalence classes) yaitu sebuah algoritma baru untuk penemuan secara cepat pola sekuensial. Hasil akhir dari penelitian ini adalah sebuah aturan (rule) yang akan digunakan sebagai data masukan pada sistem peringatan dini untuk memberikan informasi mengenai kondisi banjir.
Perangkat Lunak HMI Untuk Sistem Supervisory Control Pada Pilot Plant Biodiesel Suryawati, Endang; Sustika, Rika
INKOM Journal Vol 4, No 1 (2010)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (743.841 KB) | DOI: 10.14203/j.inkom.57

Abstract

Dalam suatu sistem supervisory control diperlukan perangkat lunak antar muka yang menjadi penghubung antara manusia (operator) dengan mesin atau peralatan yang dikendalikan. Perangkat lunak tersebut umumnya disebut sebagai HMI (Human Machine Interface) yang berupa Graphical User Interface (GUI) berbasis komputer. Makalah ini menjelaskan tentang pengembangan perangkat lunak HMI untuk sistem kontrol dan monitoring jarak jauh pada  pilot plant biodiesel (metil ester) berbasis TCP/IP. Penggunaan format penyimpanan data standar (teknologi XML) dilakukan untuk kemudahan penggunaan koleksi data HMI oleh sub sistem lain. Metodologi pengembangan perangkat lunak mengikuti pendekatan Object Oriented Software Engineering (OOSE) dengan menggunakan notasi UML (Unified Modeling Language). Pengkodean dalam tahap implementasi dilakukan dengan menggunakan bahasa pemrograman Java untuk pengurangan aspek ketergantungan pada produk-produk berlisensi. Dari hasil kegiatan diperoleh sebuah perangkat lunak HMI berbasis open source yang berfungsi dalam hal pengawasan, pengendalian, dan pendukung otomatisasi proses pada pilot plant biodiesel.Kata kunci: GUI, HMI, Java, Open Source, OOSE, Supervisory Control, TCP/IP, UML, XML
DEEP CNNBASED DETECTION FOR TEA CLONE IDENTIFICATION Ramdan, Ade; Suryawati, Endang; Kusumo, R. Budiarianto Suryo; Pardede, Hilman F.; Mahendra, Oka; Dahlan, Rico; Fauziah, Fani; Syahrian, Heri
Jurnal Elektronika dan Telekomunikasi Vol 19, No 2 (2019)
Publisher : Indonesian Institute of Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/jet.v19.45-50

Abstract

One factor affecting the quality of tea is the selection of plant material that would be planted on the field. Clonal selection is a common way to produce tea with better quality. However, as a natural cross pollination species, tea often consists of various clones or progenies of cross-pollinated process. This commonly occurs on plantations owned by smallholder farmers. To produce a consistent quality tea, the clones or progenies need to be identified. Usually, human experts distinguish the plants from leaves by visual inspection on the physical attributes of the leaves, such as the textures, the bone structures, and the colors. It is very difficult for non-experts or common farmers to do such identifications. In this, we propose a deep learning-based identification of tea clones. We apply deep convolutional neural network (CNN) to identify 3 types of tea clones of Gambung series, a series of tea clones developed at Research Institute of Tea and Cinchona. Our study indicates that the performance of the CNN systems are affected by the depth of the convolutional layers. VGGNet, a popular CNN architectures with 16 layers, achieves better accuracy compared to AlexNet, a CNN with 6 layers.
Model Fuzzy Untuk Memprediksi Faktor Reusability Sebuah Perangkat Lunak Suryawati, Endang
INKOM Journal Vol 7, No 1 (2013)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (222.504 KB) | DOI: 10.14203/j.inkom.221

Abstract

Tulisan ini menjelaskan tentang penggunaan model metrik berbasis obyek untuk memprediksi kualitas sebuah perangkat lunak berdasarkan faktor reusability. Model dibangun dengan menggunakan Inferensi Fuzzy Mamdani dengan teknik Centroid untuk proses defuzzifikasi. Lima paramater CK Metrics yang terkait dengan faktor reusability, digunakan sebagai input model. Hasil simulasi memperlihatkan output (reusability) yang bersesuaian dengan aturan-aturan yang dibentuk. Kata kunci: metrik berbasis obyek, reusability, mesin penalaran fuzzy Mamdani, defuzzifikasi, metrik CK
Automatic detection of crop diseases using gamma transformation for feature learning with a deep convolutional autoencoder Zilvan, Vicky; Ramdan, Ade; Supianto, Ahmad Afif; Heryana, Ana; Arisal, Andria; Yuliani, Asri Rizki; Krisnandi, Dikdik; Suryawati, Endang; Suryo Kusumo, Raden Budiarianto; Yuawana, Raden Sandra; Kadar, Jimmy Abdel; Pardede, Hilman F.
Jurnal Teknologi dan Sistem Komputer [IN PRESS] Volume 10, Issue 3, Year 2022 (July 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2022.14250

Abstract

Precision agriculture is a management strategy for sustaining and increasing the production of agricultural commodities. One of its implementations is for crop disease detection. Currently, deep learning methods have become widespread methods for the automatic detection of crop diseases. Most deep learning methods showed better performance when using an original image in raw form as inputs. However, the original image of crop diseases may appear similar between one disease to another.  Therefore, the deep learning methods may misclassify the data. To deal with these, we propose the gamma transformation with a deep convolutional autoencoder to extract good features from the original image data. We use the output of the gamma transformation with a deep convolutional autoencoder as inputs to a classifier for the automatic detection of crop diseases. Our experiments show that the average accuracies of our method improve the performance of crop disease detection compared to only using raw data as inputs.
Distracted driver behavior recognition using modified capsule networks Kadar, Jimmy Abdel; Dewi, Margareta Aprilia Kusuma; Suryawati, Endang; Heryana, Ana; Zilfan, Vicky; Kusumo, Budiarianto Suryo; Yuwana, Raden Sandra; Supianto, Ahmad Afif; Pratiwi, Hasih; Pardede, Hilman Ferdinandus
Journal of Mechatronics, Electrical Power, and Vehicular Technology Vol 14, No 2 (2023)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2023.v14.177-185

Abstract

Human activity recognition (HAR) is an increasingly active study field within the computer vision community. In HAR, driver behavior can be detected to ensure safe travel. Detect driver behaviors using a capsule network with leave-one-subject-out validation. The study was done using CapsNet with leave-one-subject-out validation to identify driving habits. The proposed method in this study consists of two parts, namely encoder and decoder. The encoder used in this study modifies Sabour’s capsule network architecture by adding a convolution layer before going to the primary capsule layer. The proposed method is evaluated using a primary dataset with 10 classes and 300 images for each class. The dataset is split based on hold-out validation and leave-one-subject-out validation. The resulting models were then compared to conventional CNN architecture. The objective of the research is to identify driving behavior. In this study, the proposed method results an accuracy rate of 97.83 % in the split dataset using hold-out validation. However, the accuracy decreased by 53.11 % when the proposed method was used on a split dataset using leave-one-subject-out validation. This is because the proposed method extracts all features including the attributes of each participant contained in the input image (user-independent). Thus, the resulting model in this study tends to overfit.
Two-Stage Object Detection for Autonomous Vehicles With VGG-16 Based Faster R-CNN Dewi, Arnetta Listiana; Pardede, Hilman F.; Suryawati, Endang; Pratiwi, Hasih; Heryana, Ana; Yuliani, Asri R; Ramdan, Ade
Jurnal Elektronika dan Telekomunikasi Vol 24, No 1 (2024)
Publisher : National Research and Innovation Agency

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55981/jet.551

Abstract

The implementation of object detection for autonomous vehicles is essential as it is necessary to identify common object on the street so proper response could be designed. While single stage object may be smaller in computations, two-stage object detection is preferred due to the ability to localize the object. In this paper, we propose to use Faster R-CNN with VGG-16 backbone for detections of object on the street. We evaluate the method with open image subset by selecting objects that are common on street. We explore several hyper-parameters setup such as learning rate and the number of ROI regions to find the optimum set-up. We found that the use of learning rate 10-6 with Adam optimizer to be the optimum value for this task. We also found that increasing the number of ROI may benefit the performance. This shows that there is potential for getting a higher mAP with increase the amount of RoI.
Sentiment Anlysis On Customer Reviews Using Support Vector Machine and Usability Scoring Using System Usability Scale Azpiranda, Novira; Supianto, Ahmad Afif; Setiawan, Nanang Yudi; Suryawati, Endang; Yuwana, R. Sandra; Febriandirza, Arafat
Journal of Information Technology and Computer Science Vol. 6 No. 3: December 2021
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.202163330

Abstract

Al-Ghiff Steak is a restaurant located in Cirebon City that offers quality steaks at affordable prices. For maintaining a competitive Al-Ghiff Steak advantage and reputation, it is important to build a good relationship with customers and have a business strategy that considers customer opinions. However, in its implementation, Al-Ghiff Steak has difficulty when collecting and processing customer review data manually. Therefore, it is necessary to conduct sentiment analysis by utilizing Google Reviews to determine customer perspectives regarding Al-Ghiff Steak products and services. This analysis was conducted on 968 Google Review reviews from 2016 to 2020 using the Support Vector Machine (SVM) and Term Frequency-Inverse Document Frequency (TF-IDF) methods. Classification testing is done with a confusion matrix against four parameters: accuracy, precision, recall, and f1-score. SVM with TF-IDF gets accuracy value 83%, precision 64%, recall 60% and f1-score 59%. The sentiment classification result is then visualized in the form of a dashboard. We utilize the System Usability Scale (SUS) for usability testing, which produces a value of 77.5. This result achieve the Acceptable category and an Excellent rating.
Robust two-stage object detection using YOLOv5 for enhancing tomato leaf disease detection Suryawati, Endang; Auliyah Hasanah, Syifa; Sandra Yuwana, Raden; Abdel Kadar, Jimmy; Ferdinandus Pardede, Hilman
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2246-2257

Abstract

Deep learning facilitates human activities across various sectors, including agriculture. Early disease detection in plants, such as tomato plant that are susceptible to diseases, is critical because it helps farmers reduce losses and control the disease spread more effectively. However, the ability of machine to recognize diseased leaf objects is also influenced by the quality of data. Data collected directly from the field typically yields lower accuracy due to challenges faced in machine interpretation. To address this challenge, we propose a two-stage detection architecture for identifying infected tomato plant classes, leveraging YOLOv5 to detect objects within the images obtained from the field. We use Inception-V3 for classifying objects into known classes. Additionally, we employ a combination of two dataset: PlantDocs which represent field data, and PlantVillage dataset which serves as a cleaner dataset. Our experimental results indicate that the use of YOLOv5 in handling data under actual field conditions can enhance model performance, although the accuracy value is moderate (62.50 %).
Co-Authors Abdel Kadar, Jimmy Ade Ramdan Ahmad Afif Supianto Andria Arisal, Andria Auliyah Hasanah, Syifa Azpiranda, Novira Dahlan, Rico Dewi, Arnetta Listiana Dewi, Margareta Aprilia Kusuma Fani Fauziah, Fani Febriandirza, Arafat Ferdinandus Pardede, Hilman Heri Syahrian, Heri Heryana, Ana Kadar, Jimmy Abdel Krisnandi, Dikdik Kusumo, Budiarianto Suryo Kusumo, R. Budiarianto Suryo Nanang Yudi Setiawan Oka Mahendra Pardede, Hilman F. Pardede, Hilman F. Pardede, Hilman Ferdinandus pratiwi, hasih Rika Sustika Sandra Yuwana, Raden Suryo Kusumo, Raden Budiarianto Vicky Zilvan, Vicky Wiwin Suwarningsih Yuawana, Raden Sandra Yuliani, Asri R Yuliani, Asri Rizki Yuwana, R. Sandra Yuwana, Raden Sandra Zilfan, Vicky