[FWI] Forest Watch Indonesia. 2001. Keadaan Hutan Indonesia. Bogor (ID): FWI.
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Procedia Environmental Sciences. 33:317-323.
doi:10.1016/j.proenv.2016.03.082.
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kirakowski/index.htm
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Denver, Amerika Serikat. New York (US): ACM. Hlm 387-388
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Tersedia pada: http://wammi.com/questionnaire.html
23 Lampiran 1 Pengujian integrasi sistem spatial data mining dengan basis data
Menu : Custering
Deskripsi : Melakukan clustering
Skenario Uji : Pengguna memilih data dan memasukkan nilai Eps dan MinPts sesuai dengan range pada aplikasi
(Eps = 0.1; MinPts = 6) kemudian menekan tombol “Start Clustering”
Hasil yang diharapkan : Muncul tampilan hasil clustering berupa jumlah cluster, jumlah titik border, dan jumlah titik seed.
Hasil Uji : Berhasil
Menu : Custering
Deskripsi : Melakukan clustering
Skenario Uji : Pengguna memilih data dan memasukkan nilai Eps dan MinPts tidak sesuai dengan range pada aplikasi
(Eps = 0.2; MinPts = 7) kemudian menekan tombol “Start Clustering”
Hasil yang diharapkan : Muncul pesan error : “Eps must in range between 0.01 and 0.1”
Hasil Uji : Berhasil
Menu : Custering
Deskripsi : Menampilkan plot berdasarkan jenis tutupan lahan gambut, kedalaman lahan gambut, dan jenis lahan gambut
Skenario Uji : Pengguna menekan tombol “Show Plot”
Hasil yang diharapkan : Muncul peta Pulau Sumatera dengan layer jenis tutupan lahan gambut dan layer plot titik panas beserta keterangan warna lahan gambut dan cluster.
Hasil Uji : Berhasil
Menu : Custering
Deskripsi : Menampilkan nilai within cluster Skenario Uji : Pengguna memilih tab Within Cluster
Hasil yang diharapkan : Muncul tabel yang berisi nilai within cluster utuk setiap cluster yang terbentuk dan total nilai within cluster.
Hasil Uji : Berhasil
Menu : Custering
Deskripsi : Menampilkan rangkuman hasil clustering Skenario Uji : Pengguna memilih tab Summary
Hasil yang diharapkan : Muncul tabel rangkuman clustering yang berisi nilai Eps, MinPts, jumlah cluster, jumlah pencilan, dan nilai total within cluster.
Hasil Uji : Berhasil
24
Lampiran 1 Lanjutan
Menu : Outlier Detection
Deskripsi : Menampilkan ringkasan data dan plot data Skenario Uji : Pengguna memilih data yang digunakan dan
menekan tombol submit kemudian memilih sub-menu data summary dan sub-sub-menu data plot Hasil yang diharapkan : Muncul ringkasan data dan tampilan plot dari
data yang dipilih
Hasil Uji : Berhasil
Menu : Outlier Detection
Deskripsi : Menampilkan hasil clustering, Sum square error, dan visualisasinya
Skenario Uji : Pengguna memilih jumlah cluster, menekan tombol clustering, memilih sub-menu clustering summary, memilih sub-menu Sum square error, dan memilih sub-menu K-Means plot
Hasil yang diharapkan : Muncul hasil clustering, yaitu cluster, size of cluster, center of cluster, percent, Sum square error, dan tampilan visualisasi hasil clustering
Hasil Uji : Berhasil
Menu : Outlier Detection
Deskripsi : menampilkan ringkasan dan visualisasi pencilan global
Skenario Uji : Pengguna memilih jumlah pencilan, menekan tombol detect, memilih menu global outlier summary, dan memilih menu global outlier plot Hasil yang diharapkan : Muncul hasil deteksi pencilan global tampil pada
sub-menu global outlier summary dan
Tampilan visualiasasi deteksi pencilan global pada sub-menu global outlier plot
Hasil Uji : Berhasil
Menu : Outlier Detection
Deskripsi : Menampilkan ringkasan dan visualisasi pencilan kolektif
Skenario Uji : Pengguna memilih jumlah pencilan, menekan tombol detect, memilih menu collective outlier summary, dan memilih menu collective outlier plot
Hasil yang diharapkan : Muncul hasil deteksi pencilan global pada sub-menu collective outlier summary dan
visualiasasi deteksi pencilan global pada sub-menu collective outlier plot
Hasil Uji : Berhasil
25 Lampiran 1 Lanjutan
Menu : Classification
Deskripsi : Menampilkan ringkasan data dan data dalam tabel
Skenario Uji : Pengguna memilih tabPanel summary dan tabPanel table
Hasil yang diharapkan : Muncul ringkasan data dan data dalam tabel
Hasil Uji : Berhasil
Menu : Classification
Deskripsi : Menampilkan hasil klasifikasi model pohon keputusan
Skenario Uji : Pengguna memilih tabPanel tree models Hasil yang diharapkan : Muncl hasil klasifikasi model pohon keputusan
Hasil Uji : Berhasil
Menu : Classification
Deskripsi : Menampilkan hasil klasifikasi model berbasis aturan
Skenario Uji : Pengguna memilih tabPanel rule-based models Hasil yang diharapkan : Muncul hasil klasifikasi model berbasis aturan
Hasil Uji : Berhasil
Menu : Classification
Deskripsi : Menampilkan hasil prediksi titik panas
Skenario Uji : Pengguna memilih variabel penjelas sesuai dengan karakteristik wilayah dan tekan predict button
Hasil yang diharapkan : Muncul hasil prediksi titik panas dengan model pohon keputusan dan model berbasis aturan
Hasil Uji : Berhasil
26
Lampiran 2 Kuesioner WAMMI
27 Lampiran 2 Lanjutan
28
Lampiran 2 Lanjutan
29 10 Learning to find my way around this website is
a problem.
How important for you is the kind of website you have just been rating?
Jawaban Jumlah Responden
Extremely important 3
Important 27
Not very important 9
Not important at all 0
How would you rate your internet skills and knowledge?
Jawaban Jumlah Responden
Very experienced and technical 10
I'm good but not very technical 25
I can cope with most of the internet 4 I find the internet difficult to use 0
30
Lampiran 3 Lanjutan
What part of this website do you find most interesting or useful?
Respondent ID Comment 1 clustering
2 Selecting algorithm of data mining 3 Clustering Plotting By
4 Outlier Detection 5 clustering
6 Predict new hotspot and outlier detections 7 Clustering
8 Plot classification, clustering, and outlier detection 9 plot clustering
10 visualization 11 Clustering
12 About outlier detection 13 predict new hotspot 14 Outlier detection 15 Outlier detection
16 -
17 On the machine learning like Classification, Clustering, and Outlier Detection
18 Outlier detection 19 Outlier detection 20 The main menu label 21 Outlier detection 22 predict new hotspot 23 Plot the clustering results
24 clustering ?plot?, classification?predict new hotspot?
25 Classifiication-data 26 Plot cluster on the map 27 clustering
28 home page
29 when i opened the page, the navigation bar caught my eyes ?the most interesting on the first sight?
30 filtering view
31 Yes
32 Yes
33 Plot on clustering section. I think it's hard enough to understand the purpose of that web.
31
37 Many option to choose how data representated 38 part of outlier detection
39 The content and the data mining application What do you think is the best aspect of this website, and why?
Respondent ID Comment
1 clustering, plot by landuse, we can easy make a conclusion from the image
2 -
3 responsive, fast, and effective 4 The explanation for using the app
5 can make it easier for the processing of data mining
6 I don't know. Because I'm not sure if I'm understand exactly what it is.
7 The plot by Landuse, cuz I think that it's need more effort to be create than others menu *LOL*
8 Feature
9 plot clustering result, because that visualization makes user more interest and easily understand the distribution about the clustering result of landuse, peath depth, and peath type. Tab help in this website also makes user understand how to use this website.
10 Visualization
11 Clustering, because it's easier to see
12 you create one system from 3 different system. i think that's good, and save time. a 3 in 1 system in your site.
13 Predict new hotspot, as this can be useful for the parties concerned.
14 The content, because people who enter this site is finding something related to data mining
15 The function to represent big data
16 I don't know how to operate this, i'm not found the help site for the newbie.
17 Machine learning function, like classification. Its easy to use with that userinterface. I can get the model, and predict new data.
32
Lampiran 3 Lanjutan
Respondent ID Comment
18 Each aspects if this website is very detail, if you know about statistics then you'll be able to interpret what you're seeing.
19 Interactive. Good
20 label main menu. for naming the label clearly.
21 plot, because we can see which areas are the hotspot
22 information display, because it can help us to understand the result
23 This website is very useful to know the distribution of hotspots, by knowing the distribution of hotspots, controlling forest fires can be done early
24 data mining techniques is perfect, there are clustering and classification. there is visualization on clustering menu that help to understand easier than just a summary
25 data can be filtered / sorted, so you can more easily get the data sought
26 all of the features blend well in this website 27 clustering
28 consept, because its main core for web 29 the site can predict
30 filtering, because can easy to find what i want to know 31 Usability. Easy to use this web
32 Usability. Easy to use this web
33 I think the best aspect of this website is the capability to classify new hotspot. Because it can give recommendation for stakeholder ?like local government? to make a decision.
34 Precision of data
35 website function is pretty good, because it can process hotspot data instantly
36 can visualize the results into a form that is more easily understood as plot maps, tables and summary
37 Option and details
38 when people can see the visualization, that is best aspect for me 39 Simple web, nice color design. The help section is really helping.
Quite easy to understand how to use it :?
33 Lampiran 3 Lanjutan
Is there anything you think is missing from this website?
Respondent ID Comment
1 need larger image
2 Yes, this website doesnt have a lot of explanation 3 translate to Indonesian language
4 Explanation how to use
5 no
6 yeah, because less visualitation.
7 Plot by peat depth and plot by peat type didn't show anything when I try to click the show plot button
8 Explanation about output
9 no, there isn't. this website is good enough
10 No
11 No
12 control of the user to the system, like change the color, the visualization, zoom in, zoom out, and filtering the data. that way, your system will be much more usefull.
13 Plot does not appear on a web page.
14 No
15 Not found yet
16 Not familiar website, so there must be a guide to use 17 the explanation how to use the function in the website 18 I think, in every part of the menu ?classification, clustering,
etc.? there should be a brief description so the viewers of your website can understand what they're about to do. Lastly, I don't understand what the search bar in the classification menu function. I mean, should I try to put some random word, numbers, or something else, there's no explanation there and when I type '10' to the search bar, there is no '10' showed up on screen.
19 Nothing
20 text visualization for contact person like visualization for menu when contact person is not a link
21 progress bar when data ploting
22 the explanation of each the features and label, so we can to know what feature that could help us to get information 23 I think there's no missing
34
Lampiran 3 Lanjutan Respondent ID Comment
24 still have error in menu classification->predict new
hotspot->land cover ?natural forest?, don't understand the cause of error. still don't understand the outlier detection menu ?global outlier?, in example, if i choose 'number of outlier to view' 10 but there are still many point on plot.
25 the information is still unclear, there should be a choice of language too ?Indonesian or English?, because the language used seems not common.
26 default home page
27 none
28 sense
29 when i clicked the radio button, cleared the search result and changed the table show on the data tab, i didn't realise that it was already changing at first, i need to scroll down to see the changing result. I think its better to not to let people scroll your site down to see what's happening there but you can fix it by 'next' button. Especially the 'predict new hotspot' tab.
30 'How to use' page
31 No.this website has comprehensive features 32 No.this website has comprehensive feature 33 Introduction section
34 Explain more every definition you that have
35 yes, on the outlier detection menu on 'k-means plot' section, there is information about 'day', but there is no explanation about the meaning of 'day' is, if day 1= 1 january or not. Help menu on clustering section, the text are to close to the bottom of the screen, preferably in given distance. i still dont understand about the picture label on help menu?classification section?
36 no
37 Nope
38 not missing at all, but in help and about menu, that is too little to explain about website
39 Loading bar while processing data to show result or plot
35