Garuda - Garba Rujukan Digital

CHILDREN'S LEARNING MEDIA TO RECOGNIZE ANIMALS USING MARKER BASED TRACKING AUGMENTED REALITY TECHNOLOGY BASED ON ANDROID Ardiyan Roosenda Setyadi; Budi Hartono; Theresia Dwiati Wismarini; Aji Supriyanto
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 1 (2022): JUTIF Volume 3, Number 1, February 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.1.143

Augmented reality (AR) is a technology that virtualizes 2-dimensional (2D) image objects to produce 3-dimensional (3D) objects in real time on a monitor or smartphone screen. AR in order to display a 3-dimensional object requires a marker. Markers are generally in the form of images or images. In AR that has been made the application can detect the marker. AR implementation can now be found in various fields, one of which is the field of education for learning, especially for children. In this study, AR was developed to be applied as a learning medium by introducing the behavior, characteristics or features of animals viewed through devices such as smartphones or tabs. The types of animals used in the experiment were land, water, and air animals so that the children could imagine the shape of the animal in 3 dimensions and gain knowledge about the characteristics of the animal through the description of the sound of the animal. The markers that will be detected by the system are cartoon images of animals that match their 3-dimensional shape, so that application users can enjoy learning. This application was built using Unity 3D with the development stages using the waterfall model. The result is that besides being able to display 3-dimensional visuals, it can also display videos like videos that are equipped with sounds that show the characteristics or features of the animal.

DECISION SUPPORT SYSTEM FOR SOCIAL ASSISTANCE’S BENEFICIARIES USING AHP – PROMETHEE METHOD IN KELURAHAN KARANGANYAR GUNUNG Aji Supriyanto; Nisa Rahma Ramadhani
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 5 (2022): JUTIF Volume 3, Number 5, October 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.5.316

Indonesia as a developing country has problems related to poverty. The government tries to overcome the problem of poverty from year to year by launching various aid programs, one of which is the Family Hope Program (PKH). The distribution of PKH social assistance (bansos) can actually help millions of beneficiaries every year, but there are also obstacles related to the lack of precise targeting of beneficiaries, which often occurs because there is no definite standard in the hierarchy of criteria for beneficiaries. Using the Decision Making System (SPK) with the Analytical Hierarchy Process (AHP) - Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method can provide an alternative to prioritizing aid recipients to assist decisions made by officers and related office holders. The AHP method is used to weight the poverty criteria, while the PROMETHEE method is used to rank the priority of social assistance recipients. Using 45 data on prospective PKH social assistance recipients in Karanganyar Gunung Village who were selected based on 14 poverty criteria, the Central Statistics Agency (BPS) resulted in a priority ranking of social assistance recipients. The ranking is based on the netflow value, the higher the value, the higher the priority level. The ranking using this method gives results in the form of residents with the first priority of receiving social assistance with a net flow value of 0.342 owned by Sumirah followed by Meliyani Kusrini with a net flow value of 0.325.

IMPLEMENTATION OF THE AHP-TOPSIS METHOD IN DECISION MAKING OF SOCIAL ASSISTANCE RECIPIENTS IN KARANGANYAR GUNUNG SEMARANG Dinda Locita Fitri; Aji Supriyanto
Jurnal Teknik Informatika (Jutif) Vol. 3 No. 6 (2022): JUTIF Volume 3, Number 6, December 2022
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20884/1.jutif.2022.3.6.315

The government's program of Social Assistance (Bansos) aims to improve the welfare of poverty in order to increase economic value, targeting that the recipients of the social assistance are the poor. There are still some obstacles in receiving social assistance, which are considered to be uneven, duplicate data and mistargeted. There are various models of determining poverty, one of which is based on calculations from the Central Statistics Agency (BPS). These provisions are used by the government in providing social assistance to be right on target. The determination of social assistance recipients according to BPS has 14 poverty criteria, namely: eating as much as a day, medical expenses, sources of income, sources of lighting, cooking fuel, toilet facilities, consumption of types of food, floor area, types of walls, sources of drinking water, savings , type of floor, buying clothes, and education of the head of the household. Using the Analytical Hierarchy Process (AHP) and the Technique Order Preference by Similarity to Ideal Solution (TOPSIS) which are used to rank Social Assistance recipients. The AHP method is used to weight the poverty criteria, while the TOPSIS method is used to rank alternative social assistance recipients, so as to produce recommendations for social assistance recipients. The test was carried out using 45 data from prospective social assistance recipients, the result was that the eligible recipients were in first place A4 with a value of -11.09028, in second place A5 with a preference value of -9.40301, third place A2 with a value of -7.30247 , fourth in A1 with a preference value of -4.33412, and fifth in A3 with a preference value of 9.35849.

INFORMATION FOR THE IDENTIFICATION AND VERIFICATION OF PROSPECTIVE POVERTY SOCIAL ASSISTANCE RECIPIENTS Aji Supriyanto; Sri Mulyani; Tri Ariyanto; Jeffri Alfa Razaq
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 2 (2023): JUTIF Volume 4, Number 2, April 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.2.722

One important aspect of the poverty alleviation strategy is the availability of accurate poverty data, so that the government in providing social assistance (Bansos) can be right on target. The problem is that if there is an inaccuracy in the identification process when recording citizen data, it will affect the process of verifying and validating data on prospective Social Assistance recipients. Inaccuracies in prospective Social Assistance recipients can occur due to the process of collecting data on citizen identities and applying the wrong criteria for poverty requirements and the data is not up to date. The purpose of this research is to develop an information system to identify, verify, and validate data on prospective Social Assistance recipients. The identification stage for potential recipients of social assistance is carried out at the RT/RW and Kelurahan levels. The verification stage is carried out by fulfilling the requirements of the poverty criteria. Meanwhile, the validation stage is used to determine the ranking of potential social assistance recipients. The verification process is carried out at the Kelurahan and Kecamatan levels, while the validation process is at the Regency/City level. The waterfall method is used in the development of information system prototypes for prospective Social Assistance recipients with system analysis and design using use case, activity, and sequence diagrams. The prototype was tested with CRUD and trial & error, the result is a prototype system application that is able to collect citizen data by applying a priority scale for prospective recipients of social assistance based on a web by fulfilling the requirements of 14 poverty criteria according to the Central Statistics Agency (BPS).

DROUGHT PREDICTION USING LSTM MODEL WITH STANDARDIZED PRECIPITATION INDEX ON THE NORTH COAST OF CENTRAL JAVA Supriyanto, Aji; Zuliarso, Eri; Suharmanto, Eko Taufiq; Amalina, Hana; Damaryanti, Fitri
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.4159

Fluctuating weather can trigger hydrometeorological disasters, especially affecting farmers and fishermen on the north coast of Central Java. Weather predictions including drought are very important to anticipate drought disasters. Deep learning-based prediction models such as Long Short Term Memory (LSTM) are used in an effort to reduce the impact of drought. The purpose of this study is to prove the level of accuracy of the LSTM model and determine the drought index with the Standardized Precipitation Index (SPI). The LSTM model is used to predict drought based on the SPI, while the SPI acts as a drought index that considers precipitation (rainfall) for a period of 1, 3, and 6 months. Predictions use rainfall data obtained from online data from the Central Java BMKG UPT Indonesia for the period 2010-2023 in the Tegal City and Semarang City station areas. The results of data treatment with LSTM can effectively analyze and capture complex patterns in meteorological data to predict drought events accurately. The effectiveness of the model is shown by the relatively small MAE and RMSE results, namely MAE 0.163 - 0.352 and RMSE 0.247-0.515. The best prediction result is the 3-month SPI in the Semarang area with MAE 0.163 and RMSE 0.274. While the prediction result with the largest error is the 1-month SPI in the Tegal area. Drought modeling using LSTM has been successfully implemented for the northern coast of Central Java using the Streamlit Framework and can process and visualize the drought prediction system well.

DROUGHT PREDICTION USING LSTM MODEL WITH STANDARDIZED PRECIPITATION INDEX ON THE NORTH COAST OF CENTRAL JAVA Supriyanto, Aji; Zuliarso, Eri; Suharmanto, Eko Taufiq; Amalina, Hana; Damaryanti, Fitri
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.4159

Fluctuating weather can trigger hydrometeorological disasters, especially affecting farmers and fishermen on the north coast of Central Java. Weather predictions including drought are very important to anticipate drought disasters. Deep learning-based prediction models such as Long Short Term Memory (LSTM) are used in an effort to reduce the impact of drought. The purpose of this study is to prove the level of accuracy of the LSTM model and determine the drought index with the Standardized Precipitation Index (SPI). The LSTM model is used to predict drought based on the SPI, while the SPI acts as a drought index that considers precipitation (rainfall) for a period of 1, 3, and 6 months. Predictions use rainfall data obtained from online data from the Central Java BMKG UPT Indonesia for the period 2010-2023 in the Tegal City and Semarang City station areas. The results of data treatment with LSTM can effectively analyze and capture complex patterns in meteorological data to predict drought events accurately. The effectiveness of the model is shown by the relatively small MAE and RMSE results, namely MAE 0.163 - 0.352 and RMSE 0.247-0.515. The best prediction result is the 3-month SPI in the Semarang area with MAE 0.163 and RMSE 0.274. While the prediction result with the largest error is the 1-month SPI in the Tegal area. Drought modeling using LSTM has been successfully implemented for the northern coast of Central Java using the Streamlit Framework and can process and visualize the drought prediction system well.

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