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All Journal Perfecting a Video Game with Game Metrics Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Technology and Computer Science JAST : Jurnal Aplikasi Sains dan Teknologi
Arief Andy Soebroto
Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Other

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Audit System Development for Government Institution Documents Using Stream Deep Learning to Support Smart Governance Imam Cholissodin; Arief Andy Soebroto; Sutrisno Sutrisno
Journal of Information Technology and Computer Science Vol. 4 No. 1: June 2019
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1246.411 KB) | DOI: 10.25126/jitecs.20194173

Abstract

Document audit system is a means of evaluating documents on the results of delivering information, administrative documentary evidence in the form of texts or others. Currently, these activities become easier with the presence of computer technology, smartphones, and the internet. One of the examples is the documents created by various government institutions whether local, city and central government. The instance is online-published documents that are shaded by certain government institutions. Before the documents are published or used as an archive or authentic evidence for reporting or auditing activities, the documents must go through the editing stage to correct if there are errors and deficiencies such as spelling errors or incomplete information. In the editing process, however, a person may not be able to escape from making mistakes that result in the existence of writing errors after the editing process before the submission. Word spelling mistakes can change the meaning of the conveyed knowledge and cause misunderstanding of information to the readers, especially for assessors or the audit team. Based on the problem, the researcher intends to assist the work of the audit preparation team in document analysis by proposing a system capable of detecting word spelling errors using the Dictionary Lookup method from Information Retrieval (IR) and Natural Language Processing (NLP) science combined with Stream Deep Learning algorithms. Dictionary Lookup method is considered effective in determining the spelling of words that are true or false based on Lexical Resource. In addition, String Matching method that has been developed can correct word-writing errors correctly and quickly.Keywords: spelling mistake detection, dictionary lookup, audit of government institution documents, stream deep learning
Sistem Monitoring Aliran Sungai dan Lingkungan Berbasis Smart Environment di RW 03 Kelurahan Kauman Kota Malang Sutrisno Sutrisno; Imam Cholissodin; Arief Andy Soebroto; Muh Arif Rahman
JAST : Jurnal Aplikasi Sains dan Teknologi Vol 5, No 1 (2021): EDISI JUNI 2021
Publisher : Universitas Tribhuwana Tunggadewi Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33366/jast.v5i1.2259

Abstract

Monitoring of the rivers state and the environment of roads in the city center is often still inadequate. For example, garbage is often found in the river, while on the roads, there is still not yet a sound security system. Kauman RT 03 RW III Klojen Malang is one of the densely populated regions and is located in the center (point of zero) of Malang city at the time ago still does not have a security system or security guard and there is a river flow which is often found garbage piling up and often causes flooding when it rains heavy. Based on field conditions in Kauman and meetings with residents represented by several RT heads in RW 03 Kauman, Klojen Malang requires the use of a smart environment and CCTV technology integration. Therefore the result of dedication to society to apply CCTV's technology, so it has been used at Kauman for environmental and security monitoring. Considering the high level of the busyness of the urban at Kauman, with providing it, they can be monitoring the environment by automatically systems continuously 24 hours every day. Therefore, the system has been being able to facilitate and help people to monitor the environment and river flow to be more effective, efficient, and modern. ABSTRAKMonitoring keadaan sungai dan lingkungan ruas jalan pada masyarakat tengah kota seringkali masih belum memadai. Di aliran sungai misalnya, masih sering dijumpai sampah yang menumpuk, sedangkan di ruas jalan masih belum dijumpai sistem keamanan yang baik. Kampung Kauman RT 03 RW III kecamatan Klojen Kota Malang merupakan salah satu kampung yang padat penduduk dan berada di pusat (titik nol) kota saat ini belum memiliki sistem keamanan ataupun satpam dan terdapat aliran sungai yang seringkali dijumpai sampah menumpuk bahkan sering menyebabkan banjir bila hujan deras. Berdasarkan kondisi lapangan di kampung Kauman dan pertemuan dengan warga yang diwakili oleh beberapa ketua RT di wilayah RW 03 Kauman yang membutuhkan pemanfaatkan integrasi teknologi smart environment dan teknologi CCTV. Hasil kegiatan pengabdian masyarakat telah dapat secara optimal dimanfaatkan untuk memenuhi kebutuhan pengawasan ataupun monitoring lingkungan tersebut. Mengingat tingkat kesibukan masyarakat perkotaan yang tinggi, dengan adanya sistem monitoring mereka dapat mengambil manfaat besar dengan dikembangkannya sistem pengawasan aliran sungai dan lingkungan yang bisa bekerja secara otomatis dan kontinyu selama 24 jam. Sistem yang dibuat telah mampu memudahkan sekaligus membantu masyarakat untuk monitoring lingkungan dan aliran sungai secara lebih efektif, efisien, dan modern. 
Integration Method of Local-global SVR and Parallel Time Variant PSO in Water Level Forecasting for Flood Early Warning System Arief Andy Soebroto; Imam Cholissodin; Maria Tenika Frestantiya; Ziya El Arief
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 3: June 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i3.6772

Abstract

Flood is one type of natural disaster that can’t be predicted, one of the main causes of flooding is the continuous rain (natural events). In terms of meteorology, the cause of flood is come from high rainfall and the high tide of the sea, resulting in increased the water level. Rainfall and water level analysis in each period, still not able to solve the existing problems. Therefore in this study, the proposed integration method of Parallel Time Variant PSO (PTVPSO) and Local-Global Support Vector Regression (SVR) is used to forecast water level. Implementation in this study combine SVR as regression method for forecast the water level, Local-Global concept take the role for the minimization for the computing time, while PTVPSO used in the SVR to obtain maximum performance and higher accurate result by optimize the parameters of SVR. Hopefully this system will be able to solve the existing problems for flood early warning system due to erratic weather.
Optimasi Kandungan Gizi Susu Kambing Peranakan Etawa (PE) Menggunakan ELM-PSO Di UPT Pembibitan Ternak Dan Hijauan Makanan Ternak Singosari-Malang Imam Cholissodin; Sutrisno Sutrisno; Arief Andy Soebroto; Latifah Hanum; Canny Amerilyse Caesar
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 4, No 1: Maret 2017
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (812.167 KB) | DOI: 10.25126/jtiik.201741223

Abstract

AbstrakSusu merupakan salah satu sumber protein hewani yang mengandung semua zat yang dibutuhkan tubuh. Ternak penghasil susu utama di Indonesia yaitu sapi perah, namun produksi susunya belum dapat mencukupi kebutuhan masyarakat. Alternatifnya adalah kambing peranakan etawa (PE). Tingginya kualitas kandungan gizi susu sangat dipengaruhi oleh beberapa faktor salah satunya, yaitu faktor pakan. Bagian peternakan kambing PE di UPT Pembibitan Ternak dan Hijauan Makanan Ternak Singosari-Malang masih menghadapi permasalahan, yaitu rendahnya kemampuan dalam memberikan komposisi pakan terhadap kambing PE. Kekurangan tersebut berpengaruh terhadap kualitas susu yang dihasilkan. Diperlukan pengetahuan rekayasa kandungan gizi susu untuk menentukan komposisi pakan dalam menghasilkan susu premium dengan kandungan gizi optimal. Penulis menggunakan metode Extreme Learning Machine (ELM)dan Particle Swarm Optimization (PSO)  untuk membuat pemodelan pakan kambing dalam mengoptimasi kandungan gizi susu kambing. Dalam analisa pengujian konvergensi menggunakan metode ELM-PSO yang dilakukan dengan kasus untuk berat badan kambing 32 kg, serta jenis pakan yang digunakan yaitu rumput Odot 70% dan rumput Raja 30% menghasilkan sistem mencapai kestabilan dalam konvergensi pada iterasi ke-20 dengan fitness terbaik yaitu 16.2712.Kata Kunci: Susu Kambing, Optimasi, Artificial Neural Network (ANN), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Kandungan Nutrisi Pakan.AbstractMilk is one of the animal protein sources which it contains all of the substances needed by human body. The main milk producer cattle in Indonesia is dairy cow, however its milk production has not fulfilled the society needs. The alternative is the goat, the Etawa crossbreed (PE). The high quality of milk nutrients content is greatly influenced by some factors one of them, is the food factor. The PE goat livestock division of the UPT Cattle Breeding and the Cattle Food Greenery in Singosari-Malang still faces the problem, it is the low ability in giving the food composition for PE goat. This flaw affects the quality of the produced milk. It needs the artificial science of the milk nutrients contains in order to determine the food composition to produce premium milk with the optimum nutrients contain. The writer uses the method of the Extreme Learning Machine (ELM) and the Particle Swarm Optimization (PSO) to make the modeling of goat food in optimizing the content of goat milk nutrients. In the analysis of the convergence that is done with the case of 32 kg goat weight, also the food type used is the 70 % Odot grass and 30% Raja grass that system get a stability on the 20th iteration with a fitness value is 16.2712.Keywords: Goat Milk, Optimization, Extreme Learning Machine (ELM), Particle Swarm Optimization (PSO), The Food Nutrients Contain.
Pelatihan Smart Multi-Culture Farming Berbasis Teknologi Cloud-AI untuk Pemantauan Objek Budidaya dengan Tenaga Surya sebagai Eco-Green Energy Masyarakat Indonesia Nurudin Santoso; Imam Cholissodin; Arief Andy Soebroto; Nurul Hidayat; Sutrisno Sutrisno; Destyana Ellingga Pratiwi; Vivien Fathuroya
JAST : Jurnal Aplikasi Sains dan Teknologi Vol 6, No 2 (2022): EDISI DESEMBER 2022
Publisher : Universitas Tribhuwana Tunggadewi Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33366/jast.v6i2.4015

Abstract

Working in multicultural agriculture is exhausting and has several bad risks for farmers in rural and urban areas. The risks start from the considerable time required in cultivation, especially when maintaining the growth and development of plants and other cultivation objects, the large number of costs required in the use of irrigation for fuel purchases, and the risk of carrying out specific processes using high voltage electricity which is very dangerous for farmers. Based on these problems, an automated technology approach that can work to help farmers is necessitated. In this community service, two partners are involved, i.e., a group of farmers who are also workers in Kampung Kauman RW/RT III/03 as the primary partner and a group of farmers who are also workers at the plantation in Poncokusumo Malang as the supporting partner. Both partners used solar electricity for irrigation and other uses through the Cloud-AI approach obtained from the results of multi-disciplinary research several years earlier at the Filkom UB Intelligent Computing Laboratory. Cloud-AI can work adaptively according to weather conditions from a Web App from application programming interface (API) data to provide recommendations for predicting the length of time for irrigation in observing cultivation objects which later can be modified for other particular purposes. The activity's primary results are providing training and assistance with intelligent multi-culture farming installation tools for hydroponics, solar panels, and pumps for irrigation: cloud-AI-based agricultural training modules and educational videos with excellent responses from the partners.ABSTRAKProses pengerjaan bidang pertanian multi-culture sangat menguras banyak tenaga dan memiliki beberapa resiko kurang baik bagi petani, baik di pedesaan maupun perkotaan. Mulai dari waktu yang cukup banyak dibutuhkan dalam pembudidayaan terutama saat pemeliharaan tumbuh kembangnya tanaman maupun objek budidaya lainnya, lalu banyaknya biaya yang dibutuhkan dalam penggunaan irigasi untuk pembelian bahan bakar serta resiko ketika melakukan proses tertentu menggunakan listrik tegangan tinggi yang sangat membahayakan petani. Berdasarkan permasalahan tersebut dibutuhkan pendekatan teknologi otomasi yang dapat bekerja membantu petani. Dalam pengabdian ini melibatkan Dua Mitra, yaitu di kelompok petani yang sekaligus pekerja Kampung Kauman RW/RT III/03 dan pada Perkebunan di Poncokusumo Malang yang memanfaatkan listrik tenaga surya untuk irigasi dan kegunaan lainnya serta pendekatan Cloud-AI yang dapat bekerja secara adaptif baik luring maupun daring untuk mengendalikan kelistrikan, prediksi untuk pengambilan keputusan dalam pengamatan objek budidaya dan lainnya. Hasil utama kegiatan berupa pemberian pelatihan, lalu bantuan paket alat instalasi smart multi-culture farming untuk hidroponik, panel surya dan pompa untuk irigasi serta modul pelatihan pertanian berbasis Cloud-AI dan video edukasi dengan respon yang sangat baik dari Mitra.
Co-Authors Canny Amerilyse Caesar Destyana Ellingga Pratiwi Imam Cholissodin Latifah Hanum Maria Tenika Frestantiya Muh Arif Rahman Nurudin Santoso Nurul Hidayat Sutrisno Sutrisno Vivien Fathuroya Ziya El Arief