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All Journal Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Teknoinfo Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service
Agus Ambarwari
Politeknik Negeri Lampung
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Environmental Science Languange, Linguistic, Communication & Media Social Sciences

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PERANCANGAN BUKU ELEKTRONIK PADA PELAJARAN MATEMATIKA BANGUN RUANG SEKOLAH DASAR BERBASIS AUGMENTED REALITY Adrian, Qadhli Jafar; Ambarwari, Agus; Lubis, Muharman
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 11, No 1 (2020): JURNAL SIMETRIS VOLUME 11 NO 1 TAHUN 2020
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v11i1.3842

Abstract

Augmented Reality (AR) Book dapat digunakan sebagai langkah dalam meningkatkan pembelajaran dan pengajaran di sekolah dasar. Hal ini disebabkan AR dapat memberikan siswa kemampuan untuk belajar yang interaktif di berbagai tempat serta waktu yang mereka inginkan. Penelitian ini bertujuan mengembangkan MathARbook atau Mathematics AR Book, yaitu suatu prototype buku Matematika tingkat Sekolah Dasar yang kontennya ditampilkan dalam AR. Konten materi pada buku Matematika ini antara lain bangun datar dan bangun ruang. Selanjutnya, pengembangan aplikasi memanfaatkan teknologi AR yang menggabungkan elemen-elemen multimedia seperti teks, gambar, video, animasi, dan suara. Metode yang digunakan dalam pengembangan aplikasi adalah prototyping, sehinga diharapkan aplikasi ini sesuai dengan kebutuhan pengguna. Pengujian aplikasi dilakukan menggunakan ISO 9126-functionality terhadap guru matematika yang mengajar di salah satu sekolah dasar di Bandar Lampung. Hasil pengujian diperoleh 100% guru menerima aplikasi yang telah dikembangkan, sehingga dapat dikatakan aplikasi layak untuk digunakan dan dapat digunakan sebagai alteratif bahan ajar matematika di sekolah dasar.
Biometric Analysis of Leaf Venation Density Based on Digital Image Agus Ambarwari; Yeni Herdiyeni; Irman Hermadi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 4: August 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i4.7322

Abstract

The density level in the leaf venation type has different characteristics. These different characteristics explain the environment in which plants grow, such as habitat, vegetation, physiology and climate. This research aims to measure of leaf venation density, leaf venation feature analysis and then identifying plants based on venation type. Stages of this research include leaf image data collection, segmentation, vein detection, feature extraction, feature selection, classification, evaluation and ending with analysis. The results of this study indicate that the level of leaf venation density is quite good is the type of venation paralellodromous, acrodromous and pinnate. Based on the selection of features using Boruta Algorithm, obtained 19 most important features that represent the type of leaf venation. This is reinforced by the average of accuracy produced at the time of classification using SVM, which amounted to 77.57%.
Plant species identification based on leaf venation features using SVM Agus Ambarwari; Qadhli Jafar Adrian; Yeni Herdiyeni; Irman Hermadi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14062

Abstract

The purpose of this study is to identify plant species using leaf venation features. Leaf venation features were obtained through the extraction of leaf venation features. The leaf image segmentation was performed to obtain the binary image of the leaf venation which is then determined the branching point and ending point. From these points, the extraction of leaf venation feature was performed by calculating the value of straightness, a different angle, length ratio, scale projection, skeleton length, number of segments, total skeleton length, number of branching points and number of ending points. So that from the extraction of leaf venation features 19 features were obtained. Identification of plant species was carried out using Support Vector Machine (SVM) with RBF kernel. The learning model was built using 75% of the training data. The testing results using 25% of the data on the training model, obtained an accuracy of 82.67%, with an average of precision of 84% and recall of 83%. 
Sistem Informasi Pencarian Kos Berbasis Web Dengan Menggunakan Metode Hill Climbing Yusmaida Yusmaida; Neneng Neneng; Agus Ambarwari
Jurnal Teknologi dan Sistem Informasi Vol 1, No 1 (2020): Volume 1 No. 1 Juni 2020
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jtsi.v1i1.212

Abstract

Banyaknya pendatang di kota Bandar Lampung membuat kebutuhan tempat tinggal sementara (rumah kost) semakin meningkat, yang menjadi kendala adalah masyarakat harus datang langsung ke Bandar Lampung untuk mencari tempat rumah kost, sementara tidak selalu pencarian rumah kost didapatkan dalam waktu cepat. Sistem Informasi Pencarian Kos Berbasis Web Menggunakan Metode Hill Climbing dapat menjadi solusi masyarakat dari luar maupun dalam kota Bandar Lampung untuk mencari informasi rumah kost dengan jarak terdekat. Metode pengembangan sistem dalam penelitian ini adalah prototype. Analisis perancangan meliputi Use Case Diagram dan Activity Diagram. Bahasa pemrograman yang digunakan adalah php MySQL dengan bahasa MySQL sebagai pengolahan database. Sedangkan pengujian sistem dilakukan dengan ISO 9126. Hasil pengujian yang telah dilakukan dapat disimpulkan bahwa dengan adanya Sistem informasi pencarian kos berbasis web dengan menggunakan metode hill climbing dapat mempermudah pencarian kos dengan jarak terdekat.
Analisis Pengaruh Data Scaling Terhadap Performa Algoritma Machine Learning untuk Identifikasi Tanaman Agus Ambarwari; Qadhli Jafar Adrian; Yeni Herdiyeni
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 1 (2020): Februari 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (653.411 KB) | DOI: 10.29207/resti.v4i1.1517

Abstract

Data scaling has an important role in preprocessing data that has an impact on the performance of machine learning algorithms. This study aims to analyze the effect of min-max normalization techniques and standardization (zero-mean normalization) on the performance of machine learning algorithms. The stages carried out in this study included data normalization on the data of leaf venation features. The results of the normalized dataset, then tested to four machine learning algorithms include KNN, Naïve Bayesian, ANN, SVM with RBF kernels and linear kernels. The analysis was carried out on the results of model evaluations using 10-fold cross-validation, and validation using test data. The results obtained show that Naïve Bayesian has the most stable performance against the use of min-max normalization techniques as well as standardization. The KNN algorithm is quite stable compared to SVM and ANN. However, the combination of the min-max normalization technique with SVM that uses the RBF kernel can provide the best performance results. On the other hand, SVM with a linear kernel, the best performance is obtained when applying standardization techniques (zero-mean normalization). While the ANN algorithm, it is necessary to do a number of trials to find out the best data normalization techniques that match the algorithm.
Sistem Pemantau Kondisi Lingkungan Pertanian Tanaman Pangan dengan NodeMCU ESP8266 dan Raspberry Pi Berbasis IoT Agus Ambarwari; Dewi Kania Widyawati; Anung Wahyudi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 3 (2021): Juni 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (793.336 KB) | DOI: 10.29207/resti.v5i3.3037

Abstract

The increasing need for food is not in line with the clearing of agricultural land for food crops. So that the effort to increase the productivity of agricultural products is by applying precision agriculture. However, in reality, precision agriculture is difficult to apply to conventional processes, where farmers come to the farm, collect data, then carry out maintenance. This method will make production results not optimal because maintenance is not done accurately. This study introduces a monitoring system for environmental conditions based on the Internet of Things (IoT) for agricultural land, where trials are carried out in a greenhouse. The system that has been developed consists of several sensors designed to collect information related to agricultural environmental conditions, including DHT22 sensor (temperature and humidity), DS18B20 sensor (soil temperature), soil moisture sensor (moisture content in the soil), and BH1750 sensor (light intensity). Based on the Message Queuing Telemetry Transport (MQTT) protocol, the data is sent to a gateway (Raspberry Pi) and a local server via a wireless network to be stored in a database. By using the Node-RED Dashboard, the received sensor data is then displayed on the browser every time the sensor sends data. In addition, the local server also publishes sensor data to the public MQTT broker so that sensor data can be accessed through the MQTT Dashboard application on a smartphone. The results of testing for 25 days of the system running obtained an average success of the system in storing data of 99.64%.
Identification of Venation Type Based on Venation Density using Digital Image Processing Agus Ambarwari; Yeni Herdiyeni; Irman Hermadi
Jurnal Teknoinfo Vol 12, No 2 (2018): Juli
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jti.v12i2.127

Abstract

Leaf venation is one biometric feature of leaves that have an important role in growth processes of the plant, and to determine the relationship of the plant physiology and the environment in which plants grow. At every different environment, plants have different types of leaf venation. It can be seen from the level of the leaf vein density. In this study, the feature of leaf vein density was used to identify the leaves based on venation type. The venation density features obtained from segmentation, vein detection, and density feature extraction of leaf venation. Identification of the venation type was made using the artificial neural network (ANN). The results of this study indicate that the proposed method can classify the leaf correctly image based on the venation type. On the dataset with 324 samples, the accuracy of 82.71% was obtained. This shows that the leaf vein density features allow use as a plant identifier.Keywords: leaf vein density, vein detection, density feature extraction, artificial neural network
PENGEMBANGAN DAN PENDAMPINGAN SISTEM INFORMASI PENGOLAHAN PENDAPATAN JASA PADA PT. DMS KONSULTAN BANDAR LAMPUNG Rohmat Indra Borman; Iqbal Yasin; Muhammad Adam Putra Darma; Imam Ahmad; Yusra Fernando; Agus Ambarwari
Journal of Social Sciences and Technology for Community Service (JSSTCS) Vol 1, No 2 (2020): September
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jsstcs.v1i2.849

Abstract

PT DMS Konsultan Bandar Lampung is a company engaged in tax consulting services from cooperation with companies to calculate how much obligations the company must pay. Currently processing income data by recording by the administration in the income book. This has resulted in several problems, including resulting in a higher error rate, if data is needed again it will take time to search the data and risk damage and loss of data. For this reason, community service is carried out by developing an information system for processing income data and providing training to employees regarding how to use the application.
Studying How Machine Learning Maps Mangroves in Moderate-Resolution Satellite Images Agus Ambarwari; Emir Mauludi Husni
Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 2 (2023): September 2023
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v6i2.25263

Abstract

Intertidal mangrove forests are ecosystems that are extremely productive offering diverse socio-economic advantages. Preserving and appropriately using these ecosystems is crucial. However, safeguarding and restoring mangroves present challenges due to their extensive and hard-to-reach areas. Leveraging remote sensing technology and diverse image classification methods has shown promise in accurately mapping and monitoring mangroves. This study reviews the use of machine learning methods in mapping and monitoring mangroves, particularly using moderate-resolution multispectral satellite images. The literature study was conducted by systematically searching and analyzing articles published in Scopus-indexed journals from 2018 and 2023. The primary goals are to uncover methodologies for mapping mangroves with moderate-resolution imagery, identify advancements in machine learning algorithms, and assist researchers in staying updated in this field. The findings reveal that various machine-learning algorithms can be employed to map mangroves. Mangrove mapping with machine learning typically involves stages such as inputting multispectral images, image preprocessing, image classification, and assessing accuracy. Among the techniques, in the case of remote sensing data, ensemble tree-based approaches such as random forest outperform single classifiers. Potential and emerging issues for future research encompass automating the generation of training datasets for specific land cover classification, developing methods to transfer the classification model to different study areas, and making use of cloud-based technologies for processing remote sensing data.
Co-Authors Anung Wahyudi Dewi Kania Widyawati Emir Mauludi Husni Imam Ahmad Iqbal Yasin Irman Hermadi Muhammad Adam Putra Darma Muharman Lubis Neneng Neneng Qadhli Jafar Adrian Rohmat Indra Borman Yusmaida Yusmaida Yusra Fernando