Volume 277

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11/24/22, 1:37 PM IOP Conference Series: Earth and Environmental Science, Volume 277, 2019 - IOPscience

https://iopscience.iop.org/issue/1755-1315/277/1 1/7

Preface

Papers

Green Materials and Process

Volume 277 2019

Previous issue Next issue

3rd International Symposium on Green Technology for Value Chains 2018 1–2 November 2018, Tangerang, Indonesia

Accepted papers received: 16 April 2019 Published online: 11 June 2019

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011001 OPEN ACCESS

3rd International Symposium on Green Technology for Value Chains 2018 View article PDF

Open abstract

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011002 OPEN ACCESS

Peer review statement

View article PDF Open abstract

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012001 OPEN ACCESS

Preliminary study of the degradation of biocellulose based film using soil fungi Aspergillus unguis TP3 and Paecilomyces marquandii TP4 producing cellulose

Y Srikandace, D G S Andayani and M Karina View article PDF Open abstract

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012002 OPEN ACCESS

Evaluation of the strength of coconut shell aggregate concrete block for parking area

A Ridwan, A D Limantara, B Subiyanto, E Gardjito, D Rahardjo, A Santoso, B Heryanto, H L Sudarmanto, H Murti, A G Sari et al

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Waste Treatment and Management

012003 OPEN ACCESS

Screening of polyhydroxyalkanoates (PHA)-producing bacteria from soil bacteria strains D Ratnaningrum, V Saraswaty, S Priatni, P Lisdiyanti, A Purnomo and S Pudjiraharti

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012004 OPEN ACCESS

Utilization of dahlia tuber chips for preparation of difructose anhydride III (DFA III) by enzymatic reaction using inulin fructotransferase (IFTase)

E S Endah, D Ratnaningrum, W Kosasih, V Saraswaty, S Priatni and S Pudjiraharti View article PDF

Open abstract

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012005 OPEN ACCESS

Antioxidant activity of biocellulose-based films incorporated with powder of soursop leaves (Annona muricata L)

Y Srikandace and Indriyati

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012006 OPEN ACCESS

Isolation and characterization of Boso fish (Oxyeleotris marmorata) oil and their recovery using enzymatic reaction

W Kosasih, S Priatni, E Saepudin, R T Rosmalina, S Nurasiah and E S Endah View article PDF

Open abstract

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012007 OPEN ACCESS

Preliminary study of biodegradability of starch-based bioplastics using ASTM G21-70, dip- hanging, and Soil Burial Test methods

R C Nissa, A K Fikriyyah, A H D Abdullah and S Pudjiraharti View article PDF

Open abstract

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012008 OPEN ACCESS

Performance of Single Chamber Microbial Fuel Cell (SCMFC) for biological treatment of

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Green Energy

M Andrianto and Fahriansyah

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012010 OPEN ACCESS

Performance of the three-stages anaerobic tofu wastewater treatment during the second start- up process

L Sriwuryandari, Widyarani, E A Priantoro, Muchlis, U Hamidah, T Sembiring and N Sintawardani View article PDF

Open abstract

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012011 OPEN ACCESS

Removal of Hg, Fe and Cr from Chemical Oxygen Demand (COD) analysis waste by hydroxide precipitation method

Djaenudin and L R Wiyani

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012012 OPEN ACCESS

Distribution of protein fractions in tofu whey wastewater and its potential influence on anaerobic digestion

Widyarani, E S Butar Butar, F Dara, U Hamidah, L Sriwuryandari, H R Hariyadi and N Sintawardani View article PDF

Open abstract

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012013 OPEN ACCESS

Re-development of a city-scale waste bank assessment index: Case studies Bandung and Cimahi Cities

M Pranaditya and I Juwana

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012014 OPEN ACCESS

Removal of fluoride ion from fertilizer industry wastewater to recover ammonium and phosphate using a five-compartment electrodialysis system

G S Arum and A Y Bagastyo

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012015 OPEN ACCESS

The performance analysis of a pico-hydro pump as turbine power plant D L Zariatin, Shulhanuddin, F Danuriyanto and A Suwandi

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Socio-Technological Assessment on Green Production

Life Cycle and Environmental Monitoring

The performance of the modified pump motor as a generator on the Pump as Turbine (PAT) power plant

D L Zariatin, S N Fitria, Y Dewanto, Ismail and D Rahmalina View article PDF

Open abstract

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012017 OPEN ACCESS

Design of conventional mixer for biogas digester Fahriansyah, M Andrianto and Sriharti

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012018 OPEN ACCESS

Biogas development: Dissemination and barriers S Dyah and Sriharti

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012019 OPEN ACCESS

Low volume liquid-liquid extraction for the determination of benzene, toluene, and xylene in water by GC-FID and HPLC-UV

R Yusiasih, R Marvalosha, S D S Suci, E Yuliani and M M Pitoi View article PDF

Open abstract

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012020 OPEN ACCESS

Preliminary study for 9,10-anthraquinone residue analysis in tea-based functional beverage:

GC-ECD optimization and method development M M Pitoi, M Ariyani, T A Koesmawati and R Yusiasih

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012021 OPEN ACCESS

Simultaneous determination of deltamethrin and 4 other pyrethroids residues in infusion tea:

Preliminary study

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S Wijonarko, B H Sirenden, T Maftukhah, D Rustandi, N T E Darmayanti and D Qiyaman View article PDF

Open abstract

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012023 OPEN ACCESS

Determination of carbamate insecticide in soil by SPE reversed-phase high-performance liquid chromatography (RP-HPLC)

R T Rosmalina and A E Persulessy

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012024 OPEN ACCESS

Health Benefit Value of Selenium (HBV-Se) of mercury from consumption of fishes from Tual, Indonesia

A Kusnadi, S Oktaviyani and Suratno

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012025 OPEN ACCESS

Total mercury of marine fishes in Natuna Islands area, Indonesia: Risk assessment for human consumption

Suratno, R Puspitasari, R Rositasari and S Oktaviyani View article PDF Open abstract

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012026 OPEN ACCESS

A study on ecolabeling and Life Cycle Assessment for food products in Indonesia: Potential application to improve the competitiveness of the tea industry

A A R Setiawan, A Sulaswatty, M S Haq, T H A Utomo, A Randy, O Arutanti, E Agustian, E I Wiloso and A Haryono

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012027 OPEN ACCESS

Analytical performance for determination of synthetic pyrethroids residue in Indonesian fermented cocoa using GC-ECD

T A Koesmawati, M M Pitoi, M Ariyani and R T Rosmalina View article PDF Open abstract

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012028 OPEN ACCESS

The use of oxidation reactor for water sample preparation in the total phosphate determination using Segmented Flow Analysis

Ardeniswan, S A Janetasari, Muchlis, E A Priantoro and T Sembiring View article PDF

Open abstract

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Environmental Nanotechnology

Pollution load of Cisangkan River: The domestic sector R R Hikmat and I Juwana

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012030 OPEN ACCESS

Assessment of seasonal trophic state of tropical man-made lake, The Cirata Reservoir M Ariyani, M Agustian, G S Maharani and Sunardi

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012031 OPEN ACCESS

Natural iron sand-based Mg Ni Fe O nanoparticles as potential adsorbents for heavy metal removal synthesized by co-precipitation method

W B K Putri, E A Setiadi, V Herika, A P Tetuko and P Sebayang 1-x x 2 4

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012032 OPEN ACCESS

Etched and non-etched polystyrene nanoballs coated with AuNPs on Indium Tin Oxide (ITO) electrode as H O sensor

G M A Saputra, A Purwidyantri, C-M Yang, B A Prabowo and C-S Lai 2 2

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012033 OPEN ACCESS

Remediation of Ni in a nickel-contaminated water sample using magnetic nanoprobes prepared via green process by Psidium guajava leaves extract

R Y Capangpangan, M J B Corpuz and A C Alguno 2+

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012034 OPEN ACCESS

SERS hotspots growth by mild annealing on Au film over nanospheres, a natural lithography approach

A Purwidyantri, C-H Hsu, B A Prabowo, C-M Yang and C-S Lai

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Cites per document Year Value Cites / Doc. (4 years) 2010 0.000 Cites / Doc. (4 years) 2011 0.250 Cites / Doc. (4 years) 2012 0.563 Cites / Doc. (4 years) 2013 0.108 Cites / Doc. (4 years) 2014 0.320 Cites / Doc. (4 years) 2015 0.333 Cites / Doc. (4 years) 2016 0.498 Cites / Doc. (4 years) 2017 0.362 Cites / Doc. (4 years) 2018 0.476 Cites / Doc. (4 years) 2019 0.461 Total Cites Self-Cites

Evolution of the total number of citations and journal's self-citations received by a journal's published documents during the three previous years.

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Cites Year Value f

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Evolution of the number of total citation per document and external citation per document (i.e. journal self- citations removed) received by a journal's published documents during the three previous years. External citations are calculated by subtracting the number of self-citations from the total number of citations received by the journal’s documents.

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Abdel Moktader Abdel Aziz El-Sayed 3 weeks ago

Nahla A. El Sayed and El sayed Abdel Moktader A. 2021 IOP Conf. Ser.: Earth Environ. Sci. 906 012004

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good evening, whether this journal is Q4 or Q2 ? reply

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FEROSKHAN M 2 years ago

IOP Conference Series: Earth and Environmental Science - Volume 573 is not available in Scopus.

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Mursalin 3 years ago Dear SCImago Team

My name is mursalin from Jambi City, Indonesia. I have published my article titled The Effect of Temperature on MDAG Puri cation Using Creaming Demulsi cation Technique at the IOP Conference Series: Earth and Environmental Science, Volume 309, conference 1 and could be accessed at: https://iopscience.iop.org/article/10.1088/1755-1315/309/1/012068. But why until now it does not appear into Google Scholar and my account.

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PAPER • OPEN ACCESS

Re-development of a city-scale waste bank assessment index: Case studies Bandung and Cimahi Cities

To cite this article: M Pranaditya and I Juwana 2019 IOP Conf. Ser.: Earth Environ. Sci. 277 012013

View the article online for updates and enhancements.

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Content from this work may be used under the terms of theCreative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

Published under licence by IOP Publishing Ltd

3rd International Symposium on Green Technology for Value Chains 2018 IOP Conf. Series: Earth and Environmental Science 277 (2019) 012013

IOP Publishing doi:10.1088/1755-1315/277/1/012013

1

Re-development of a city-scale waste bank assessment index:

Case studies Bandung and Cimahi Cities

M Pranaditya^* and I Juwana

Department of Environmental Engineering, Faculty of Civil Engineering and Planning, Institut Teknologi Nasional Bandung, Jl. PKH Mustopha No.23 Bandung 40124, West Java, Indonesia

E-mail: [email protected]

Abstract. The waste bank is one application of waste management by utilizing waste that can be reused, which in the end can be used to reduce the waste dumped into landfills. In order to reduce the significant amount of waste dumped into landfills, there should be at least one waste bank operates at a city-scale. Thus, a tool to assess the readiness of a waste bank to function at a city-scale is needed. In the past, studies on the development of index were undertaken simply based on a desk-study method. This study aimed to refine the previous index. This was done by first reviewing the previous indicator through a literature review, which was then verified by the application of the Analytical Hierarchy Process (AHP) method to determine the weights for each indicator. The respondents for AHP applications were selected from four categories namely professional experts, academics, institutions and management representatives of the waste bank.

At the end of the study, three components, thirteen indicators and sixty-one sub-indicators were identified. The name of the components and their respective weights were Management System (55%), Operating System (25%) and Waste Bank Facility (20%). Each component had 4-5 indicators and 2-8 sub-indicators. In the near future, the index developed from this study will be used to assess the readiness of Resik Bandung and Cimahi City Waste Bank to be developed as city-scale waste banks.

1. Introduction

The waste bank is an integrated waste management, implementing the Reduce, Reuse and Recycle (3R) activity, which aims to reduce waste from its source [1-3]. The development design of waste bank has a very important role in the waste management system to reduce the amount of city waste delivered to landfills.

Currently, there is already an index about waste banks which contains three main components, namely building construction quality, the management system and the operational system [1]. In that study, there was no discussion of the waste management facility, and the index weighting process of each indicator was done using descriptive analysis by dividing the maximum score with the number of indicators. This means that each indicator had the same weight, which might be invalid since each of the indicators should have their own respective weight.

According to the analysis, it is necessary to develop the city-scale waste bank index which reviews what components can influence the success of a city-scale waste bank. Next, the index should be validated using Analytical Hierarchy Process (AHP). This method can be done by creating a paired- comparison questionnaire that should be answered by four respondent categories who have significant

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activities in waste management. These four categories are professional experts, institutes, academics, and waste bank administrators.

The purpose of this study was to arrange a city-scale waste bank index based on the previous index development. The purpose of specifying components, indicators and sub-indicators added from the previous index was to decide the weight of each identified indices, as well as testing the indices on the waste banks used as the subject of this research.

2. Methodology 2.1. Data collection

To identify parameters used in city-scale waste bank index, a desk study was done from the main literature and supporting literature. Main literature was the reference from the previous index while supporting literature included rules about waste, a book of waste bank concept, and other research journals.

Primary data were collected from questionnaires, of which the data was a collection of indices given to the specified components and indicators. Moreover, another primary data was obtained from the test result indices done on the research subjects. The data was produced based on direct field observation.

2.2. Index Preparation

The index was prepared using the following steps [3].

2.2.1. Reidentifying components. This includes reviewing the main literature, followed by reviewing supporting literature. These actions were done so the components said to be adequate to represent city- scale waste bank index could be set.

2.2.2. Identifying indicators. During the identification, reviewing the main and supporting literature were done simultaneously. Table 1 shows an example of a schema which shows the ways of identifying indicators in a management system component. It can be seen in Table 1 that there are several similar indicators between each reference. These similarities are based on contents from each of the indicator point, which is marked with the same color from each of the columns. These similarities could be merged into a single indicator each.

Table 1. Management System Component’s indicators.

Regulation Book of waste bank concept Other Literature

• Waste Depositor

• Waste Bank Executor’s Role

• Waste Buyer

• Waste Management

• Promotion and Socialization

• Waste Bank Administrator HR

• Waste Bank Executor

• Institutional

• Financing

• Community Service

• Partnership

• City Waste Management Integration

• Institutional

• Institutional Structure

• Capital

• Partnership

• Administrator’s Job Desk

• City Waste Management Integration

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3

2.2.4. Arranging scoring criteria. The arranging of scoring criteria was done by reviewing the literature.

The criteria were composed of scoring points based on some conditions that might happen to the research subjects.

2.2.5. Weighting. There were two methods that could be used on weighting city-scale waste bank index, namely the AHP method and Equal Rating method. AHP method was used in component and indicator weighting from the questionnaire. The questionnaire was based on the number of components that would be compared to the level of importance, each component was compared to get the importance level.

Table 2 shows the questionnaire using the AHP method. The respondents should fill paired comparison matrices each with a number which represented importance scale level [4]. Table 3 shows the importance scale level used in the AHP method.

Table 3. Importance level scale.

Importance level Definition

1 Equally important

3 A little more important 5 Clearly more important 7 Much clearly more important 9 Certainly, more important

2,4,6,8 Chosen if in doubt between two levels 1/(1-9) Opposite of importance level score on

the scale of 1 to 9

The respondents were randomly selected, consist of eight persons divided into four categories. These four categories were professional experts (2 persons), institutes (2 persons), academics (4 persons), and waste bank administrators (2 persons). These four are considered to have adequate experience in waste management, especially waste banking.

The questionnaire data was processed using Expert Choice II application. Computation of the consistency of each respondent was done automatically [5]. Next, respondents with high consistency could be merged using the application. Meanwhile, sub-indicator weighting was done by dividing the maximum score with the number of sub-indicators. This was because, to keep the consistency of the weighting result, not all index level was evaluated using AHP.

2.2.6. Data processing. After arranging the indices, the data was processed as index aggregations and interpretations. Aggregation was a form of merging indices to obtain their final score [6]. There were two ways to aggregate indices: using the arithmetic method and the geometric method. In this study, both methods are discussed.

Indicator score was calculated using the following equation:

Indicator score = ∑ wiSi

wi = Scoring criteria of each sub-indicator Si = Maximum score of each sub-indicator

The component score was calculated by adding up all its indicators’ scores using the following equation:

Table 2. Questionnaire.

Components Weight of Paired Comparisons Matrix Components Operational

System 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9

Management System Management

System 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Facility Facility 9 8 7 6 5 4 3 2 1 2 3 4 5 6 7 8 9 Facility

(29)

Component score = ∑ Component indicator score

The score of the index was calculated by adding up all its components’ scores using the following equation:

Index score = ∑ Component score

Index interpretation was a process of adapting indices with the research subject, as well as showing the readiness or score of the studied subject. In this study, it was necessary to interpret the indices and how to determine it is discussed.

3. Results and analysis 3.1. Component identification

Component identification was done by reviewing the main literature, followed by the supporting literature. The schema that describes how to identify the components is presented in Figure 1.

Figure 1. Schema of components identification.

Table 4 lists the potential components from discussions found in the supporting literature.

Table 4. Components from desk study result.

Component Source

Management System Regulation, concept book of waste bank, other literature

Operational System Regulation, concept book of waste bank, other literature

Facility Regulation, other literature

Waste Bank Development Concept

and Potency Concept book of waste bank

Components that commonly present in every reference were Operational System Component and Management System Component. They indicated how important the components were, and they should be parts of city-scale waste bank index. While other components such as Facility, Building Construction, Building Construction Quality, and Supporting Infrastructure are discussed in Facility Component.

3.2. Indicator identification

Indicator identification was done on each chosen component from the previous step, namely Management System Component, Operational System Component and Waste Bank Facility Component.

3.2.1 Indicator identification on Management System Component. From the four references used to determine the Management System Component indicator, several indicators could be grouped (Table 5).

Previous Index

• Management System

• Operational System

• Building Construction Quality

City-scale Waste Bank Index

• Management System

• Operational System

• Waste Bank Facility

(30)

5

Table 5. Management System Component’s indicator.

Indicator Source

Institutional, institutional structure, human resource, job desk

Regulation, concept book of waste bank, other literature Financing, capital, partnership

Socialization and promotion

City waste bank management integration

Figure 2. Schema of Management System Component indicator changes from the previous index to city-scale waste bank index.

3.2.2 Indicator identification on Operational System Component. From the four references used to determine the Operational System Component indicator, several indicators could be grouped (Table 6).

Table 6. Operational System Component’s indicator.

The operational mechanism, operational executor, collection and management, waste type, waste pick up, price fixing,

operational time Regulation, concept book of

waste bank, other literature Monitoring, monitoring and evaluation system

Self-protection kits, work health and safety Waste bank client, community service

There were several indicators that can be grouped to represent Operational System Component, namely Client Indicator, Operational Mechanism, Work Health and Safety, Monitoring and Evaluation System, and Information System. Figure 3 shows the schema that shows indicator changes on Operational System Component from the previous index to city-scale waste bank index.

Figure 3. Schema of Operational System Component indicator changes from the previous index to city-scale waste bank index.

Previous Index

• Institutional

• Institutional Structure

• Capital

• Partnership

• Waste Bank Admin Human Resource

• Waste Bank Admin’s Job Desk

• Socialization and Promotion

• City Waste Bank Integration

• Institutional

• Capital dan Partnership

• Socialization dan Promotion

• City Waste Manager Integration

Previous Index

• Client

• Waste type

• Operational facility

• Waste collection and management

• Weighing and Logging

• Work Health and Safety

• Monitoring and Evaluation System

• Client

• Waste Bank Operational Mechanism

• Work Health and Safety

• Monitoring and Evaluation System

• Information System

(31)

3.2.3. Indicator identification on Waste Facility Component. From the four references used to determine Waste Facility Component indicator, several indicators could be grouped (Table 7).

Table 7. Waste Bank Facility Component’s indicator.

Supporting building, warehouse, operational facility

Regulation, concept book of waste bank, other literature Building construction, building construction quality

3R facilities, organic waste processor, inorganic waste processor

There were several indicators that could be grouped to represent Waste Bank Facility Component, namely Public Facility, Building Facility, Operational Facility, and Supporting Facility. Figure 4 shows the schema that shows indicator changes on Waste Bank Facility Component from the previous index to city-scale waste bank index.

Figure 4. Schema of Waste Bank Facility Component indicator changes from the previous index to city-scale waste bank index.

3.3. Sub-indicator identification

Sub-indicator identification was done using a desk study method. Their decision referred to discussions in each selected indicator. If there were similar discussions, then they could be merged into a single sub- indicator. Client Indicator was discussed on every reference used in developing a city-scale waste bank index, but there were some points needed to be merged in assembling the indicators. Table 8a and 8b show a schema about Client Indicator’s discussion.

Table 8a. Client indicator.

Indicator Sub-indicators Source

Client

1. Client increases 5-10 per month Regulation No. 13 2012 2. Own individual client

Previous Index 3. Own grouped client

4. Own institution client 5. Own commercial client

6. Own more than 100 clients Other Literature

Table 8b. Improved client indicator.

Indicator Sub-Indicators Client

1. Client Class 2. Number of Clients Previous Index

• Waste Bank Location

• Supporting Building

• Building Area

• Waste Bank Warehouse

• Office and Service Room

• Public Facility

• Operational Facility

• Building Facility

• Supporting Facility

(32)

7

every single possible condition presented in the criteria. Table 9 presents an example of scoring criteria preparation of Human Resource sub-indicator.

Table 9. Scoring criteria preparation.

Sub-indicator Scoring criteria Score

Waste Bank’s Human Resource

a. HRs are competent and experienced 100 b. HRs are competent but inexperienced 50 c. HRs are incompetent but experienced 50 d. HRs are incompetent and inexperienced 0

It can be seen in Table 9 that there were two main points that can be used as a reference in scoring, namely “competency” and “experience”. Both points were related to waste bank human resource, so both were used as scoring criteria on waste banks in the research subjects. If both points were fulfilled, the subject gained a score of 100, whereas if only one point is fulfilled, it gained 50. However, if neither of those points was fulfilled, it received a score of 0.

3.5. Component and indicator weightings

Component and indicator weightings were performed using AHP method, in which in its implementation, the method was used in finding out ranks to each indicator [7,8]. The process was done by answering questionnaires in the form of a matrix of paired comparisons by respondents who had adequate activity in waste management. They were professional experts, academics, agencies and waste bank administrators.

The questionnaire’s answers were calculated using Expert Choice II application. In this application, there was a selection before combining opinions from all four categories. The selection was based on the value of consistency (R≥0.1) calculated automatically in the application.

3.5.1. Component weighting result. Component weighting was done using 4 respondent categories.

Figure 5 presents a diagram showing the opinions of each respondent and the used final weighting.

Figure 5. Component weighting diagram.

The component weighting result from each respondent was analyzed and only respondents with high consistency were selected and had its answers merged. Consistency calculation was done automatically using Expert Choice II. After doing the analysis, the consistency of components weighting from all respondent categories met the standard (≤0.1), this means all the answers can be merged. The results of the merged answer for component weighting are: System Management Component (55%), Operational System (25%), and Waste Bank Facility (20%).

50 70 40

75

35 20 20

10

15 10 40

15

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Academics Agencies Waste Bank Administrators Professional Experts

Component

Management System Operational System Waste Bank Facility

(33)

3.5.2. Indicator Weighting Result. Indicator weighting was calculated using the same method as the previous, but the result had a more varying consistency from each respondent. The result from each respondent categories is shown in Figure 6.

Figure 6. Management System weighting diagram.

There were only 3 consistent respondent categories in answering Management System indicator; they are agencies, academics, and waste bank administrators. The weighting results are: Capital and Partnership Indicator (35%), City Waste Manager Integration Indicator (30%), Institutional Indicator (20%), and Socialization and Promotion Indicator (15%).

Operational System indicator weighting only merged answers from two respondent categories, academics and waste bank administrators. The result from each respondent categories is shown in Figure 7.

Figure 7. Operational System weighting diagram.

The final Operational System Indicator weighting results are: Work Health and Safety (25%), Operational Mechanism (20%), Monitoring and Evaluation System (20%), Client Indicator (20%), and Information System (15%).

Waste Bank Facility Indicator weighting was done by merging answers from three categories; they are from academics, agencies, and waste bank administrators. The result from each respondent

20 35 20 10

30 20 30 45

20 20 25

30

30 25 25

15

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Management System

Institutional Capital and Partnership Indicator Socialization and Promotion Indicator City Waste Manager Integration

30 10

15 40

25 30

20

15

20 25 25

10

15 25 20

20

10 10 20

15

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Operational System

Client Operational Mechanism

Work Health and Safety Monitoring and Evaluation System Information System

(34)

9

Figure 8. Waste Bank Facility weighting diagram.

3.5.3. Sub-indicator weighting result. Sub-indicator weighting was done by dividing the maximum weight score from an indicator with the number of sub-indicators in it. Table 10 shows an example of Monitoring System and Evaluation Indicator weighting.

Table 10. Sub-indicator weighting.

Indicator Sub-Indicators Weight

Monitoring and Evaluation System

1. Monitoring System 25%

2. Evaluation 25%

3. Repair Supervision System 25%

4. Arrangement System 25%

The weighting was obtained from the division of the maximum score from the indicator (100%) with the number of sub-indicators owned by the indicator (there were four sub-indicators). Therefore, each sub-indicator gained weight of 25%.

3.6. Index Aggregation

Index aggregation was a process of merging several weights from an index [9]. In this study, aggregation was done using the arithmetic method. The method was based on the previous study [6], which describes that the arithmetic method uses a perfect substitution so that in result it will produce aggregated mean in most cases. This means the arithmetic method will not be affected whether there are differences in merged weights. Furthermore, it is easy to understand the operation of the arithmetic method. Based on these considerations, this study used the arithmetic method in aggregating indices. An example of index aggregation using this method can be seen in Table 11.

Table 11 Index Aggregation Example

Component Indicator % Sub-

Indicator % Criteria % Comment Score Final

Score

Management System

(40%)

City Waste Manager Integration

20

1. Target 50

a. Own target and in accordance with the

government’s target 100 Target is 25%

reduction from source

100 50

b. Own target but not in accordance with the

government’s target 50 c. Doesn’t own target 0

2. Coop 50

a. There is cooperation with city waste management

operator 100 Hygiene Trading

Company 100 50

b. There is no cooperation with city waste management

operator 0

K = Component, I = Indicator, SI = Sub-indicator 10

20 20 10

10

25 25 25

60

40 30 40

20 15 25 25

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Waste Bank Facility

Public Facility Operaional Facility Building Facility Supporting Facility

(35)

3.7. Index interpretation

Index interpretation shows the final score of each subject, which are used here as research subjects.

Table 12 shows an example of index interpretation from some sources that were used as a reference is creating interpretation scale in city-scale waste bank index.

Table 12. Index interpretation example.

CCME WQI^a [10] NSF WQI^b [10] WJWSI^c [6]

Score Interpretation Score Interpretation Score Interpretation

0-44 Poor 0-25 Very Poor 0-<25 Poor Enough

45-64 Poor Enough 25-50 Poor 25-<50 Poor

65-79 Mediocre 50-70 Mediocre 50-<75 Good

80-94 Good 70-90 Good 75-100 Very Good

95-100 Very Good 90-100 Very Good

a CCME WQI: Canadian Council of Ministers of the Environment

b NSF WQI: National Sanitation Foundation Index

c WJWSI: West Java Water Sustainability Index

The scale used in references above was 0-100, with 0 and 100 as the minimum and maximum score, respectively. The scale was also used in this study since the references in Table 12 were also used. The references also have the same discussion with this study, which is about index preparation. The differences in interpretation from each source were in their scale division. Table 13 shows the interpretation scale to be used in city-scale waste bank index.

Table 13. Index interpretation.

Index Aggregation Interpretation

0-<25 Very Poor

25-<40 Poor

40-<60 Moderate

60-<80 Good

80-100 Very Good

Table 13 shows that this study had five interpretation levels, similar to the Canadian Council of Ministers of the Environment and National Sanitation Foundation Index [10]. The interpretation result describes the whole score from scoring result, which is an aggregate of component, indicator and sub- indicator scores. The five interpretation levels’ confirmation was done so it can describe a waste bank condition in detail [11]. Furthermore, the distance between each level should not be too far to increase the probability of obtaining the best interpretation.

4. Conclusion

City-Scale Waste Bank Index consists of 3 components and 12 indicators, which are further divided into several sub-indicators. The 3 components and their weights are Management System Component (40%), Operational System Component (30%) and Waste Bank Facility Component (30%). Each component is divided into several indicators. There are 4 indicators in Management System Component; they are Capital and Partnership Indicator (35%), City Waste Manager Integration Indicator (30%), Institutional Indicator (20%), and Socialization and Promotion Indicator (15%). Operational System Component