Classification of teak wood production in Central Java using the C5.0 algorithm

Wooden furniture industry is an important industry sector in Indonesia, because many people’s welfare relyon this industry sector and the industry has a big social and environmental impacts. Many wooden furnitureindustries in Indonesia, especially in Central Java Province face problems related to the sustainability. The relationbetween wood suppliers and furniture industry is studied in this paper. A sustainable supply chain management (s-SCM) model is proposed as an approach for solutions for the problems. The approach is chosen due to the characteristics of the problems that related to economic, social, and environmental problems. This aim of this paper is to determine how much supply teak wood must be provided by PP to satisfy furniture industry demand, how much production capacity that must be increased and how large forest area that must be planted in order to achieve environmental and social goals without sacrificing economical goals much. Goal programming (GP) is chosen for solving the problems, because the goals are to maximize the total benefit,minimize the total loss and anticipate the conflicts between goals. Numerical trial based on observation in teak wooden furniture industry in Central Java was used to illustrate our findings. Using pareto efficient principle, the model can satisfy all goals that need to be achieved. Numerical results can be used by decision makers in teak wood industry to analyze the trade-off among several set of alternative solutions.

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Future jobs: Indonesian Primary Students’ Aspirations and Teachers’ predictions

The European Educational Researcher ◽

10.31757/euer.425 ◽

2021 ◽

Vol 4 (2) ◽

pp. 209-225

Author(s):

Stefanus Christian Relmasira ◽

◽

Yiu Chi Lai ◽

Chi Fuk Henry So ◽

◽

...

Keyword(s):

Performing Arts ◽

Work Skills ◽

School Students ◽

Skill Levels ◽

Central Java ◽

Standard Classification ◽

Research Students ◽

Future Work Skills ◽

Future Work

The transformation of occupations in Indonesia due to digital technologies, especially in Artificial Intelligence, becomes a challenge for current educators to prepare their students for future work skills. This research study seeks to understand what students' career aspirations are and their teachers' predictions about their students' future careers. There were 125 Indonesian primary school students and 141 teachers in Central Java province involved in this research. Students were asked to draw the aspiration of their future jobs when they grow up in the next 15-20 years, and teachers were asked to draw their predictions of their students' future careers. The results show some similarities and differences between students' aspirations and teachers' predictions. Both students and teachers have the same idea about the importance of jobs that emphasizing the use of creativity. However, students had a tendency to select their future careers related to creative and performing arts, whereas teachers predicted their students' future jobs as teachers and lecturers. The data also shows that students incline to draw the use of high-technology tools in their future jobs, whereas teachers tend to describe the use of conventional tools in their students' future careers. Further results are discussed in relation to the International Standard Classification of Occupations skill levels jobs.

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Determination of Types and Characteristics of Laterite Soil as Basic Land for Building Construction

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/930/1/012041 ◽

2021 ◽

Vol 930 (1) ◽

pp. 012041

Author(s):

D Chandrasasi ◽

S Marsudi ◽

E Suhartanto

Keyword(s):

Soil Characteristics ◽

Building Construction ◽

Sieve Analysis ◽

Direct Shear ◽

Laterite Soil ◽

Atterberg Limit ◽

Central Java ◽

Property Index

Abstract Laterite soil is red soil because it contains iron and aluminum. It is an old type of soil, so it is suitable for all plants. Laterite soils are located in the reservoir area of the Wonogiri Dam - Central Java. The nature of laterite soil that quickly absorbs water and the soil texture is strong and dense indicates the type of soil used for a mixture to make roads. This study aims to identify and characterize the lateritic soils to support the construction of roads that will be used. It is needed to test the soil’s property index, including moisture content test, density test, Atterberg limit, and grain sieve analysis. At the same time, it tested the classification of laterite soil characteristics using standards of USCS and AASHTO. To test the shear strength of the laterite soil is using Direct Shear. Based on the analysis, the laterite soil from sedimentation in Wonogiri dam is classified as poor and does not meet the requirements to be used as a subgrade in building construction. It can be considered include need to improve to carried out first.

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Transferability and Upscaling of Fuzzy Classification for Shoreline Change over 30 Years

Remote Sensing ◽

10.3390/rs10091377 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1377 ◽

Cited By ~ 3

Author(s):

Ratna Dewi ◽

Wietske Bijker ◽

Alfred Stein ◽

Muh Marfai

Keyword(s):

Land Use ◽

Shoreline Change ◽

Active Role ◽

Optimal Level ◽

Fuzzy Classification ◽

Shoreline Changes ◽

Central Java ◽

Erosion And Accretion ◽

Time Required

Local authorities require information on shoreline change for land use decision making. Monitoring shoreline changes is useful for updating shoreline maps used in coastal planning and management. By analysing data over a period of time, where and how fast the coast has changed can be determined. Thereby, we can prevent any development in high risk areas. This study investigated the transferability of a fuzzy classification of shoreline changes and to upscale towards a larger area. Using six sub areas, three strategies were used: (i) Optimizing two FCM (fuzzy c-means) parameters based on the predominant land use/cover of the reference subset, (ii) adopting the class mean and number of classes resulting from the classification of reference subsets to perform FCM on target subsets, and (iii) estimating the optimal level of fuzziness of target subsets. This approach was applied to a series of images to identify shoreline positions in a section of the northern Central Java Province, Indonesia which experienced a severe change of shoreline position over three decades. The extent of shoreline changes was estimated by overlaying shoreline images. Shoreline positions were highlighted to infer the erosion and accretion area along the coast, and the shoreline changes were calculated. From the experimental results, we obtained m (level of fuzziness) values in the range from 1.3 to 1.9 for the seven land use/cover classes that were analysed. Furthermore, for ten images used in this research, we obtained the optimal m = 1.8. For a similar coastal characteristic, this m value can be adopted and the relation between land use/cover and two FCM parameters can shorten the time required to optimise parameters. The proposed method for upscaling and transferring the classification method to a larger, or different, areas is promising showing κ (kappa) values > 0.80. The results also show an agreement of water membership values between the reference and target subsets indicated by κ > 0.82. Over the study period, the area exhibited both erosion and accretion. The erosion was indicated by changes into water and changes from non-water into shoreline were observed for approximately 78 km2. Accretion was due to changes into non-water and changes from water into shoreline for 19.5 km2. Erosion was severe in the eastern section of the study area, whereas the middle section gained land through reclamation activities. These erosion and accretion processes played an active role in the changes of the shoreline. We conclude that the method is applicable to the current study area. The relation between land use/cover classes and the value of FCM parameters produced in this study can be adopted.

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Disparitas Pembangunan Wilayah Kabupaten/ Kota di Provinsi Jawa Tengah

Economics Development Analysis Journal ◽

10.15294/edaj.v5i3.22153 ◽

2018 ◽

Vol 5 (3) ◽

pp. 296-305

Author(s):

Linda Ika Wahyuntari ◽

Amin Pujiati

Keyword(s):

Descriptive Analysis ◽

Least Square ◽

Industrial Agglomeration ◽

Fast Growing ◽

Central Java ◽

Panel Data Regression ◽

Negative Effect ◽

Area Classification ◽

Central Java Province

Penelitian ini bertujuan untuk mengidentifikasi klasifikasi daerah cepat maju dan cepat tumbuh, menganalisis pengaruh aglomerasi industri, dana perimbangan, IPM, dan klasifikasi daerah cepat maju dan cepat tumbuh terhadap disparitas pembangunan wilayah kabupaten/ kota di Provinsi Jawa Tengah. Penelitian ini menggunakan metode analisis deskriptif Tipologi Klassen dan analisis regresi data panel dengan metode Generalized Least Square (GLS). Hasil identifikasi kabupaten/ kota yang konsisten berada di klasifikasi daerah cepat maju dan cepat tumbuh dalam kurun waktu tahun 2009-2013, yaitu Kabupaten Cilacap, Kota Magelang, Kota Surakarta, dan Kota Semarang. Hasil dari penelitian ini menunjukkan bahwa aglomerasi industri berpengaruh positif dan signifikan, sedangkan dana perimbangan, IPM, dan klasifikasi daerah cepat dan cepat tumbuh berpengaruh negatif dan signifikan terhadap disparitas pembangunan wilayah kabupaten/ kota di Provinsi Jawa Tengah. This study aims to identify the classification of the area fast forward and fast-growing, analyze the effect of industrial agglomeration, the balance funds, HDI, and area classification fast forward and fast-growing against the disparity of development districts/ cities in Central Java province. This research using descriptive analysis Typology Klassen and panel data regression analysis with the method of Generalized Least Square (GLS). The results of the identification of districts/ cities that are in the area classification consistently fast forward and fast-growing in the period 2009-2013, namely Kabupaten Cilacap, Kota Magelang, Kota Surakarta and Kota Semarang. The results of this study indicate that the industrial agglomeration effect on positive and significant, while the balance funds, HDI, and the classification of fast and fast-growing regions a significant negative effect on the development disparity districts/ cities in Central Java province.

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Evaluasi Ekstraksi Fitur GLCM dan LBP Menggunakan Multikernel SVM untuk Klasifikasi Batik

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v5i1.2615 ◽

2021 ◽

Vol 5 (1) ◽

pp. 1-9

Author(s):

Pulung Nurtantio Andono ◽

Eko Hari Rachmawanto

Keyword(s):

Local Binary Pattern ◽

Training Data ◽

Polynomial Kernel ◽

Support Vector ◽

Gaussian Kernels ◽

Central Java ◽

Testing Data ◽

Different Color ◽

Occurrence Matrix

Batik as one of Indonesia's cultural heritages has various types, motifs and colors. A batik may have almost the same motif with a different color or vice versa, therefore it requires a classification of batik motifs. In this study, a printed batik was used with various coastal batik motifs in Central Java. The algorithm for classification is selected Support Vector Machine (SVM) with feature extraction of the Gray Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP). SVM has the advantage of grouping data with small amounts and short operation times. GLCM as an extractive feature for recognizing batik textures and LBP was chosen to do spot pattern recognition. In the experiment, we have used 160 images of batik motifs which are divided into two, namely 128 training data and 32 testing data. The accuracy results obtained from the SVM, GLCM and LBP algorithms produce 100% accuracy in polyniomial, linear and gaussian kernels with distances at GLCM 1, 3, and 5, where at a distance of 1 linear kernel is 78.1%, gaussian 93.7%. At a distance of 3 linear kernels 75%, gaussian 87.5% and at a distance of 5 linear kernels 84.3%, gaussian 87.5%. In the SVM and GLCM algorithms the resulting accuracy is at a distance of 1 with a polynomial kernel 96.8%, linear 68.7%, and gaussian 75%. At distance 3, the polynomial kernel is 100%, linear 71.8%, and gaussian 78.1%, while for distance 5, the polynomial kernel is 87.5%, linear 75%, and gaussian 81.2%.

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Population Growth Models of Forest Trees for Conservation Management: Case of Teak (Tectona Grandis) Forest in Begal, East Java, Indonesia

HAYATI Journal of Biosciences ◽

10.4308/hjb.25.4.169 ◽

2018 ◽

Vol 25 (4) ◽

pp. 169

Author(s):

Novriana Sumarti ◽

Mharta A. Wardana ◽

Nuning Nuraini

Keyword(s):

Markov Chain ◽

Growth Models ◽

Good Condition ◽

Tectona Grandis ◽

Leslie Matrix ◽

Total Production ◽

Wood Production ◽

Teak Wood ◽

The Government ◽

Future Condition

Based on 2010 FAO report, teak forest and plantation in Indonesia covers 1,269 million hectares or 7 per mill of total area of Indonesia. It can be found dominantly in Central and East Java. PT Perhutani, Indonesia has responsible for management of the government owned forests in the islands of Java and Madura. Based on 2007 data, the teak wood production is 517,627 m3 and the highest percentage, which is 37% of total production, is coming from East Java. In this paper, we develop growth population models using Leslie Matrix and Markov Chain in order to predict the future condition based on the current condition. The models are implemented into data from Teak Forest in Begal, East Java, that covers 2,052.8 hectares and consists of 114 sites. The result from the first model using Leslie Matrix shows that it needs 16 years from year 2011 that the sustainable condition of the forest can be achieved. The result from the implementation of the second model using Markov Chain into the existing data shows that the condition of the teak forest can be classified as quite critical because the good condition part based on its density of the early age group (0 - 4 years) has potential to become the worst condition before its harvest time.

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VARIABILITY AND INTRASPECIES CLASSIFICATION OF PUMKIN (Cucurbita moschata (Duch. ex Lam.) Duch. ex Poir.) BASED ON MORPHOLOGICAL CHARACTERS

KnE Life Sciences ◽

10.18502/kls.v2i1.160 ◽

2015 ◽

Vol 2 (1) ◽

pp. 286

Author(s):

Purnomo _ ◽

Budi Setiadi Daryono ◽

Maulidya Beta Sentori

Keyword(s):

Morphological Variability ◽

Fruit Size ◽

Morphological Characters ◽

Fruit Shape ◽

Morphological Similarity ◽

Important Data ◽

Fruit Flesh ◽

Central Java ◽

Cultivar Groups

Pumpkin (C. moschata) is minor cultivated plant has morphological variability, that is. important data illustrate the genetic variability. Morphological variation data of of pumpkin can be used for intraspecies classification and conservation. The relationship of cultivar groups of pumpkin also as important data for pumpkin cultivation. The objectives of this study are to determine variability and intraspecies classification of pumpkin in Yogyakarta and around area based on morphological characters. Cultivar accession are collected from Yogyakarta, and Kopeng also Salatiga central Java as operational taxonomic units (OTU’s). Character scoring based on IPGRI of pumpkin with soft modification. Morphological similarity is calculated based on presence and absence characters, and cluster analysis is conducted by UPGMA method to create dendrogram to determine morphological variability and intraspecies classification of pumpkin based on morphological similarity. The result study shows, that there are 3 fruit shapes, namely giant, globular, and oblong with lobes or not. Pumpkin also has yellow and orange colors fruit flesh. The taste of fruit flesh are sweet or not. Based on Dendrogram pumpkin is classified into 3 group cultivars: lowland group with standard fruit shape, highland with standard fruit shape, and giant cultivar groups. Morphological variability of pumpkin in Yogyakarta and around areas indicate on fruit shape, fruit size, fruit color, and taste of fruit flesh, with morphological similarity coefficient 0,55-0,88. Keywords: C. moschata, cultivar, morphological variability, intraspecies classification, phenetic relationship.

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