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Discharge data is one of the important data in evaluating the reliability of water resources management in the watershed. Most watersheds in Indonesia do not have observed discharge data over a long period. While the availability of rainfall data is almost available in all watersheds with a much longer period. Therefore, it is necessary to convert rain into a runoff to obtain a discharge event with a long period. Rain-runoff transformation is a very complex process. Rain input contains the variability of space, time and uncertainty. A very complex hydrological analysis is needed to find out various parameters related to rain models for a runoff. One of the hydrological models that can be used is HEC-HMS 4.2. This paper aims to analyze the relationship of rainfall - runoff in the Garang watershed using HEC-HMS 4.2. For calibration, observational discharge data is used from AWLR Kreo. Based on optimization analysis, the hydrological parameter are obtained CN composite 66.4, groundwater content 128.48 mm, Initial Abstraction 25.7 mm and imperviousness 9.27%. The validity of the model is quite satisfactory, judging from the correlation values, RMSE and Nash.

This research performs an analysis of heavy metals in the waters and sediments of Lake Maninjau and the resultant pollution index value. The research was carried out in 11 locations, e.g., floating net cages, endemic fisheries, near settlements, hydropower plants, and seven rivers at the lake’s inlet and outlet at a depth of 0-1.5 metres. Determining the pollution index was conducted based on heavy metals and environmental parameters. Aside from Zn, the concentration of Cd, Hg, Pb, Cu in the waters in all locations exceeded the quality standard. The Hg metal in sediments in all areas exceeded the quality standard of contaminated soil. Concentrations of heavy metals in sediment are higher than that of heavy metals in water. 8 of 11 sampling locations were in the medium polluted category, with the fish cage location having the highest pollution index (PI) value. Anthropogenic activities such as aquaculture, water transportation, and settlements around the lake have an effect on the waters and sediments of the lake, characterised by heavy metal contamination. The lake’s quality must be continuously monitored, and wastewater management improved from activities around the lake to control heavy metal contamination. Further evaluation is required of the heavy metals contamination originating from anthropogenic activities and natural sources.

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For oil or gas fields with stratified reservoir layers, detailed productioncontribution for individual layer is always desired.Unfortunately, insome particular cases, production wells are completed following commingledscheme. This is worsened further if only very few production tests arerun for the field.This is the case for the Central Sumatera field withits 95 commingled production wells, among which only a few had undergoneproduction tests and none of them have ever undergone productionlogging.Problems rise when the occassion came in which detailedproduction contribution from individual reservoir layer is required for thefield's reservoir simulation modeling and productionevaluation/prediction. This paper presents an approach to solve the problem.The approach isbasically based on the application of soft computing (Fuzzy Logic) toinvestigate pattern of relationships between production contribution of layersin commingle wells and rock petrophysical data as well as other relevantgeological/engineering data.For the purpose, thirteen wells (key wells)that have production tests are assigned, among which three wells are assignedfor checking the validity of the recognised pattern.Using the validatedmost valid pattern, individual layer's production allocation for other wellsare determined with well-log analysis data as the major input. Result estimates for the candidate wells are better compared to resultsproduced by the conventional method of productivity index (PI)analogy.The resulted variation in water cut and separate oil and watersplit factors appear to be more realistic from any point of view. Introduction In managing a commingle production well, knowledge over productioncontribution of individual sand layer is always desired.The commonpractice performed during drilling and production activities of a productionwell is through the use of well testing/production testing and/or productionlogging. From the test, fluid dynamic data such as total liquid rate, water cut, and gas cut of an individual layer are produced.However, costand time efficiency is always used as the reason for not conducting suchtests. Therefore, even though such tests are always regarded as theprimary source of proof, an alternative means that can be used to provideestimates is always desired. Ideas of establishing a method that can provide illustration over productioncontribution of all layer(s) always exist.Certainly, there are approachesto serve the purpose such as productivity index (PI)/transmissibility analogyand petrophysical approach through fractional flow measurement in corelaboratory. However, those approaches are often considered inadequate foraccommodating various factors that may influence production contribution of aproductive layer. To materialize the requirement stated above, an indirect approach in theform of pattern recognition/modeling was taken.This approach was taken inorder to model relations between various factors in wellbore and productioncontribution of reservoir layers without being trapped by the certaincomplexity that may occur in any mathematical expressions trying to explain therelationships.For the purpose, fuzzy logic (a form of artificialintelligence) has been used.The choice is actually based on its capacityto accommodate both numeric and non-numeric data, since it is considered thatsome non-numeric data such as lithology and pore system also have someinfluence on production contribution.

This study aims to develop a learning model of Information System Design Analysis in Vocational Education that is valid, effective and practical. Based on preliminary studies and needs analysis conducted on the Information Systems Design Analysis course, it was found that learning is not optimal. The needs analysis also found that there were privices / needs of lecturers and students who had high expectations of the learning process that were able to improve 21st century competence, i.e. This type of research is Research and Development which refers to the 4D model. The analysis technique uses the Aiken'V test, and validity uses expert testing and Focus Group Discussion (FGD). The research findings are an Entrepreneur Digitals Learning Model (ERDIS) that is equipped with model books, teaching material books, lecturer manuals and student manuals. Models and support systems meet the validity criteria, are based on research and development models and are suitable for use by experts. The results of testing the validity of the results show that all products already have the results of product validity are at a score> 0.677. The results of testing the practicality of the product according to students 82%, based on the perception of lecturers 85% means the product has a high practicality. The results of the analysis of the effectiveness of the cognitive domain showed a t-test score of 3,252> t table of 2.010 which means that Hypothesis (Ha) was accepted, the affective domain hypothesis testing showed a t-count score of 3,688> t table of 2,010 which meant that Hypothesis (Ha) at the 95% significance level . The results of research in the psychomotor domain through the evaluation of ERDIS 1 and 2 projects are in the Good category, this means that the products are valued for effectiveness

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