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Computer model for tsunami vulnerability using sentinel 2A and SRTM images optimized by machine learning Sri Yulianto Joko Prasetyo; Bistok Hasiholan Simanjuntak; Kristoko Dwi Hartomo; Wiwin Sulistyo
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3100

This study aims to develop a software framework for modeling of tsunami vulnerability using DEM and Sentinel 2 images. The stages of study, are: 1) extraction Sentinel 2 images using algorithms NDVI, NDBI, NDWI, MSAVI, and MNDWI; 2) prediction vegetation indices using machine learning algorithms. 3) accuracy testing using the MSE, ME, RMSE, MAE, MPE, and MAPE; 4) spatial prediction using Kriging function and 5) modeling tsunami vulnerability indicators. The results show that in 2021 the area was dominated by vegetation density between (-0.1-0.3) with moderate to high vulnerability and risk of land use tsunami as a result of the decreasing of vegetation. The prediction results for 2021 show a low canopy density of vegetation and a high degree of land surface slope. Based on the prediction results in 2021, the study area mostly shows the existence of built-up lands with a high tsunami vulnerability risk (more than 0.1). Vegetation population had decreased to 67% from the original areas in 2017 with an area of 135 km2. Forest vegetation had decreased by 45% from 116 km2 in 2017. Land use for fisheries had increased to the area of 86 km2 from 2017 with an area of 24 km2.

Computer model for tsunami vulnerability using sentinel 2A and SRTM images optimized by machine learning Sri Yulianto Joko Prasetyo; Bistok Hasiholan Simanjuntak; Kristoko Dwi Hartomo; Wiwin Sulistyo
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3100

This study aims to develop a software framework for modeling of tsunami vulnerability using DEM and Sentinel 2 images. The stages of study, are: 1) extraction Sentinel 2 images using algorithms NDVI, NDBI, NDWI, MSAVI, and MNDWI; 2) prediction vegetation indices using machine learning algorithms. 3) accuracy testing using the MSE, ME, RMSE, MAE, MPE, and MAPE; 4) spatial prediction using Kriging function and 5) modeling tsunami vulnerability indicators. The results show that in 2021 the area was dominated by vegetation density between (-0.1-0.3) with moderate to high vulnerability and risk of land use tsunami as a result of the decreasing of vegetation. The prediction results for 2021 show a low canopy density of vegetation and a high degree of land surface slope. Based on the prediction results in 2021, the study area mostly shows the existence of built-up lands with a high tsunami vulnerability risk (more than 0.1). Vegetation population had decreased to 67% from the original areas in 2017 with an area of 135 km2. Forest vegetation had decreased by 45% from 116 km2 in 2017. Land use for fisheries had increased to the area of 86 km2 from 2017 with an area of 24 km2.

Computer model for tsunami vulnerability using sentinel 2A and SRTM images optimized by machine learning Sri Yulianto Joko Prasetyo; Bistok Hasiholan Simanjuntak; Kristoko Dwi Hartomo; Wiwin Sulistyo
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3100

This study aims to develop a software framework for modeling of tsunami vulnerability using DEM and Sentinel 2 images. The stages of study, are: 1) extraction Sentinel 2 images using algorithms NDVI, NDBI, NDWI, MSAVI, and MNDWI; 2) prediction vegetation indices using machine learning algorithms. 3) accuracy testing using the MSE, ME, RMSE, MAE, MPE, and MAPE; 4) spatial prediction using Kriging function and 5) modeling tsunami vulnerability indicators. The results show that in 2021 the area was dominated by vegetation density between (-0.1-0.3) with moderate to high vulnerability and risk of land use tsunami as a result of the decreasing of vegetation. The prediction results for 2021 show a low canopy density of vegetation and a high degree of land surface slope. Based on the prediction results in 2021, the study area mostly shows the existence of built-up lands with a high tsunami vulnerability risk (more than 0.1). Vegetation population had decreased to 67% from the original areas in 2017 with an area of 135 km2. Forest vegetation had decreased by 45% from 116 km2 in 2017. Land use for fisheries had increased to the area of 86 km2 from 2017 with an area of 24 km2.

IMPLEMENTASI METODE INTERPOLASI LINEAR UNTUK PEMBESARAN RESOLUSI CITRA Kristoko Dwi Hartomo
Teknoin Vol. 11 No. 3 (2006)
Publisher : Faculty of Industrial Technology Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/.v11i3.89

Processing an image is a process and image analysis which entangling many visual perceptions and in course have some characteristics of input data and output information in binding digital image. Process of changing measure for an image through magnification of resolution or measure often needed to showing the detail, for demonstration at a physic appliance, and for making of document. At the process on enlarging image resolution, method that would be used in this paper is linear interpolation. Linear Interpolation Method is mathematics algorithm which could be applied to appraise the middle price point through a diametrical line, two successive input point. The system in this paper could process image with algorithm of linear interpolation and become a new photo image with image resolution of pixel larger than the original image.Keywords: image, resolution, linear, interpolation, pixel

Implementasi Metode Linear Addition untuk Perancangan Efek Mask Lightning Kristoko Dwi Hartomo
Teknoin Vol. 10 No. 2 (2005)
Publisher : Faculty of Industrial Technology Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/.v10i2.687

Digital picture development is picture processing using digital computer in order to get different picture or another picture which suits with the needs. One example of picture development is mask lighting effect with linear addition method. The main principle of linear addition method is by adding intensity to every pixel colour by adding every pixel with a variable. This paper focuses on the implementation of linear addition method to give lighting effect on picture. Keywords : mask lighting, linear addition, pixel.

Soft System Methodology (SSM) Analysis to Increase the Number of Prospective Students Neilin Nikhlis; Ade Iriani; Kristoko Dwi Hartomo
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 4 No 1 (2020): February 2020
Publisher : Universitas Nusantara PGRI Kediri

The competition between campus, whether it’s a public college and private college in Central Java, is very tight with the increasing number of interested students for prospective students from various regions. The close competition requires many campuses to compete to provide the best facilities and services. The research objective is expected to support the "XY" university promotion strategy to help the university in the knowledge capture process. Data collection was carried out using the group discussion forum (FGD) method with a structured interview process for university leaders, university officials, marketing departments, and students. The technique used in this study is a soft system methodology (SSM). The results of this study model knowledge capture (KC) on the "XY" university promotion strategy and produce knowledge documentation that provides benefits in making policy strategies and has an impact on increasing the number of prospective new college students by optimizing digital marketing.

Utilization of Social Network Analysis (SNA) in Knowledge Sharing in College Nurrokhman Nurrokhman; Hindriyanto Dwi Purnomo; Kristoko Dwi Hartomo
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 4 No 2 (2020): August 2020
Publisher : Universitas Nusantara PGRI Kediri

Campus competition in Central Java creates superior and empowered human resources to make XYZ campus optimize the Knowledge Sharing process. In optimizing the Knowledge Sharing process on the XYZ campus through interaction and communication between students in the study program. This study aims to identify the Knowledge Sharing collaboration of students on the XYZ campus in three study programs with 100 respondents using the Social Network Analysis (SNA) method. The parameters used in this study include density, degree centrality, closeness centrality, betweenness centrality, and clicks (subgroups). Based on the analysis of the results obtained by the level of density level of 4.7% or weak ties because under 50%. Actor 98 has the highest degree of centrality with outdegree value 32 and indegree 7, while actor 65, which has the highest closeness centrality with inCloseness value 16,952 and outCloseness value 1,020. Actor 15 also has the highest centrality betweenness with an amount of Betweenness 2750,148 and nBetweenness 28,346. In this study, it can be concluded that there is collaboration in the Knowledge Sharing of students on the XYZ campus from each divided into three study programs, namely, informatics engineering, accounting computerization, and graphic design.

The Natural Disaster Prone Index Map Model in Indonesia Using the Thiessen Polygon Method Kevin Hendra William; Kristoko Dwi Hutomo
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 5 No 2 (2021): August 2021
Publisher : Universitas Nusantara PGRI Kediri

Natural Disasters are natural phenomena that occur at any moment that can cause loss. Indonesia is an archipelagic country located at the meeting of four tectonic plates and volcanic belts. This condition causes Indonesia to be prone to natural disasters. Therefore, it is necessary to make a natural disaster-prone index map model minimize the impact of natural disasters. In this research, the researchers used a Polygon Thiessen method for it was one of the mapping methods to determine a natural disaster based on Indonesia's vast surface and many disasters. The BNPB and Polygon Thiessen data comparison shows that BNPB data has a low level of vulnerability of 302, a moderate level of vulnerability of 148, and a high level of vulnerability of 58. In contrast, the Thiessen polygon has a low level of vulnerability of 297, a moderate vulnerability of 158, and a high vulnerability of 59. Comparing BNPB data and the Thiessen Polygon method found five differences from 40 data in the Papua region. Suggestions for further research to create an application-based information system so that it can be accessed in real-time.

Prediksi Stok dan Pengaturan Tata Letak Barang Menggunakan Kombinasi Algoritma Triple Exponential Smoothing dan FP-Growth Kristoko Dwi Hartomo; Sri Yulianto Prasetyo; Rahmat Abadi Suharjo
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7, No 5: Oktober 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020751863

Persaingan bisnis semakin meningkat khususnya dalam bidang retail. Hal ini mengharuskan pemilik melakukan inovasi terhadap bisnisnya. Salah satu hal yang perlu diperhatikan oleh pemilik untuk mempertahankan dan menambah konsumen yaitu dengan melakukan pendekatan dengan konsumen. Pendekatan pada konsumen digunakan untuk mengenali dan memahami perilaku, kebutuhan dan keinginan konsumen. Pemilik swalayan ingin melakukan inovasi untuk melakukan perbaikan tata letak barang dan perbaikan stok, karena konsumen seringkali mengalami kesulitan dalam pencarian barang dan pihak swalayan sering mengalami kekurangan dan kelebihan stok barang. Berdasarkan permasalahan tersebut, maka tujuan penelitian adalah untuk mengoptimalkan pengaturan tata letak barang dan optimalisasi persediaan stok barang. Dalam penelitian ini menggunakan data penjualan yang diolah sehingga menghasilkan informasi untuk pemilik swalayan. Pengolahan data dalam penelitian ini disebut data mining dengan menggunakan algoritma FP-Growth dan Triple Exponential Smoothing. Algoritma FP-Growth digunakan untuk mengetahui pola perilaku konsumen sehingga dapat digunakan untuk pengambilan keputusan dalam penyusunan barang dan algoritma Triple Exponential Smoothing yang merupakan algoritma prediksi digunakan untuk pengaturan stock barang. Dalam penelitian ini dengan menggunakan algoritma FP-Growth menemukan 12 aturan asosiasi, aturan asosiasi yang memiliki nilai lift ratio paling tinggi adalah Teh dan gula dengan nilai lift ratio 6.131 dan dengan algoritma Triple Exponential Smoothing diperoleh hasil prediksi pada bulan Januari 2018 adalah 131,141 Kg dengan tingkat akurasi MAPE 88,3 %. AbstractBusiness competition is increasing especially in the retail sector. This requires the owner to innovate his business. The shop owner wants to make an invasion to repair goods and equipment, because consumers are in dire need of things and supermarkets often occur. One of the things that need to be considered by the owner to maintain and add consumers is by approaching consumers. Use of information to recognize and understand consumer needs and desires. By overcoming it, the purpose of the research is to regulate the procedures for goods and optimize the preparation of stock items. In this study using processed sales data. Information on information for shop owners. Data processing in this research is called data mining using FP-Growth and Triple Exponential Smoothing algorithms. FP-Growth algorithm to find out user behavior patterns can be used to develop Triple Exponential Smoothing decisions and algorithms which are forecasting algorithms for inventory items. In this study using the algorithm FP-Growth found 12 association rules, which have the highest lift ratio is Tea and sugar with a lift ratio of 6.131 and with Triple Exponential Smoothing algorithm, the forecasting result in January 2018 is 131.141 Kg with 88,3 % MAPE accuracy.

Implementasi Algoritma TOPSIS dan Metode EUCS untuk Pengujian Sistem Penilaian Kinerja Pegawai pada Laboran Fakultas Teknologi Informasi UKSW Salatiga Andreas Arga Rinjani Saputro; Kristoko Dwi Hartomo
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7, No 6: Desember 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020722353

Pegawai laboratorium merupakan tenaga kerja kependidikan yang membantu proses pembelajaran mahasiswa dalam bagian vokasi dan akademik. Dalam menjalankan pekerjaannya, pegawai laboratorium terkadang masih belum maksimal. Sehingga kepala sarana & prasarana dan supervisor harus mengontrol pegawai untuk memastikan pegawai berkerja dengan baik. Untuk mengatasi permasalahan dalam pegawai laboratorium serta membantu dalam memanajemen kinerja, dibuatlah sistem informasi berbasis website yang berisi implementasi algoritma TOPSIS pada penilaian kinerja pegawai laboratorium. Terdapat enam kriteria penilaian pegawai laboratorium yang terdiri dari kedisiplinan, tanggung jawab, kerja tim, skill, akademik, dan komunikasi. Hasil akhir penelitian berupa nilai preferensi (Vi) setiap alternatif, dimana terdapat nilai preferensi tertinggi yaitu 1 yang diperoleh 3 dari 12 orang pegawai pada pengujian algoritma TOPSIS. Penyajian sistem informasi dilakukan dengan database yang ditampilkan menggunakan desain dashboard yang tepat digunakan untuk manajemen data. Pengujian EUCS pada website sistem informasi adalah 3.41 dari total nilai kepuasan (4) yang tergolong dalam kategori puas, sehingga dianggap layak dan diterima oleh user. AbstractLaboratory employee is a education labor which help process learning college student within part of vocational and academic. In carrying out his work, laboratory employee sometimes it's still not optimal. So that the head of facilities & infrastructure and supervisor must control the employee to make sure employees are working well. For counter the problem in laboratory employee and help in performance management, made information system based on website which contain implementation of the TOPSIS algorithm on performance rating of the laboratory employee. There are six laboratory employee rating criteria which consisting of dicipline, responsible, team work, skill, academic, and communication. Last result of the research is the form of preference values (Vi) every alternatif, where there is highest preference value is 1 which is obtained 3 out of 12 employee of the TOPSIS algorithm testing. Presentment of the information system perform with database and displayed using dashboard design which exact used for present data management. EUCS testing on information system website is 3.41 from total satisfaction value (4) which classified of satisfied categori, so that good considered and accepting by user.

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