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All Journal Perfecting a Video Game with Game Metrics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Filsafat Indonesia 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 Education Engineering Environmental Science Languange, Linguistic, Communication & Media Social Sciences

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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.
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%.
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.
Perkembangan Paradigma Metode Klasifikasi Citra Penginderaan Jauh dalam Perspektif Revolusi Sains Thomas Kuhn Agus Ambarwari; Emir Mauludi Husni; Dimitri Mahayana
Jurnal Filsafat Indonesia Vol. 6 No. 3 (2023)
Publisher : Undiksha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jfi.v6i3.53865

Abstract

The rapid improvement of remote sensing technology has given rise to three paradigms of remote sensing image classification methods, namely pixel-based, object-based, and scene-based. This article aims to explain or reveal the development of remote sensing image classification methods and their relationship with Thomas Kuhn's scientific revolution process (pre-paradigm, normal science, anomaly, crisis, and scientific revolution) that occurs in the development of these classification methods. The preparation of this article uses a descriptive qualitative method. Reference sources are journal articles collected from the Scopus database with topics related to classification and remote sensing. Other reference sources are data extracted from review articles. From all the references collected, a literature study is then carried out by analyzing the article's title, abstract, and overall content. After that, the stages of the scientific revolution related to the development of classification methods in remote sensing images were described. Based on the review of the articles, it can be explained that the development of classification methods for remote sensing imagery began in the 1970s when the Landsat satellite was first launched. In this early period, the classification method used was based on pixels or sub-pixels, because the spatial resolution of remote sensing imagery was shallow. As remote sensing technology developed, in the 2000s a new approach was discovered that was more efficient than the pixel-based approach for classifying high-resolution imagery, namely object-based classification methods. Then, with the release of the land use dataset (UC-Merced) in the 2010s, scene-based remote sensing image interpretation began to be used, as pixel- and object-based methods were insufficient to classify correctly.
Co-Authors Anung Wahyudi Dewi Kania Widyawati Dimitri Mahayana Emir Mauludi Husni Imam Ahmad Iqbal Yasin Irman Hermadi Muhammad Adam Putra Darma Neneng Neneng Qadhli Jafar Adrian Rohmat Indra Borman Yusmaida Yusmaida Yusra Fernando