DECISION SUPPORT SYSTEM FOR SELECTING DOCTORS IN APPLICATION X USING ANALYTICAL HIERARCHY PROCESS (AHP)

Doctors are one of the human resources contributing to the world of health, especially in the current COVID-19 pandemic situation. With the pandemic and increasing technological developments, this sat have sprung up many digital-based health service applications that can help the community, especially its users in health consultations, looking for health articles, and hospital searches. In this digital-based health service, if you want to consult online or make an appointment, there are many choices of doctors available with various criteria. This method is used to obtain weighting (Eigen Vector) criteria used to facilitate making decisions, which is to choose a doctor for consultation or appointment. With three criteria (experience, consultation price, and user satisfaction level) and alternatives (Dr. A, Dr. B, Dr. C) obtained weighting (Eigen Vector) or the best alternative option is Dr. A with a weighting value of 0.509, which has 27 years of experience criteria, a consultation price of Rp. 30,000.00, and a user satisfaction rate of 96%.&nbsp

Pengembangan Rekomendasi Rute Berdasarkan Personalisasi Point-of-Interest dengan Menerapkan Metode Analytical Hierarchy Process

Pandemi covid-19 memberi dampak pada sektor pariwisata. Hal ini dikarenakan pemberlakuan pembatasan kegiatan masyarakat guna mengurangi penyebaran Virus Corona. Namun hal tersebut menyebabkan penurunan kunjungan wisatawan pada tempat wisata, termasuk kebun binatang yang menyebabkan kurangnya biaya untuk melakukan perawatan hewan. Maka dari itu perlu dilakukan inovasi agar dapat lebih menarik wisatawan. Inovasi yang dilakukan adalah dengan membangun aplikasi mobile rekomendasi rute yang berbabis Android. Hasil akhir dari aplikasi ini adalah berupa rekomendasi rute berdasarkan Point of Interest (POI) setiap pengunjung. Pada aplikasi yang Bernama Gembira Loka Zoo ini, pengunjung akan diminta untuk mengisi kuisioner. Jawaban kuisioner tersebut akan dihitung dengan menggunakan metode Analytical Hierarchy Process (AHP) dan menghasilkan nilai-nilai untuk setiap rute. Nantinya akan dipilih sebuah rute dengan nilai tertinggi yang dijadikan rute rekomendasi. Kemudian rute tersebut akan ditampilkan dalam bentuk peta dengan gambar rute dan poin-poin objek yang akan dilewati pengunjung

Make It simple: effective road selection for small-scale map design using decision-tree-based models

The complexity of a road network must be reduced after a scale change, so that the legibility of the map can be maintained. However, deciding whether to show a particular road section on the map is a very complex process. This process, called selection, constitutes the first step in a sequence of further generalization operations and it is a prerequisite to effective road network generalization. So far, not many comprehensive solutions have been developed for effective road selection specifically at small scales as the studies have mainly dealt with large-scale maps. The paper presents an experiment using machine learning (ML), specifically decision-tree-based (DT) models, to optimize the selection of the roads from 1:250,000 to 1:500,000 and 1:1,000,000 scales. The scope of this research covers designing and verifying road selection models on the example of three selected districts in Poland. The aim is to consider the problem of road generalization holistically, including numerous semantic, geometric, topological, and statistical road characteristics. The research resulted in a list of measurable road attributes that comprehensively describe the rank of a particular road section. The outcome also includes attribute weights, attribute correlation calculated for roads, and machine learning models designed for automatic road network selection. The performance of the machine learning models is very high and ranges from 80.94% to 91.23% for the 1:500,000 target scale and 98.21% to 99.86% for the 1:1,000,000 scale

SPATIAL CRITERIA FOR INDUSTRIAL AREA: SUSTAINABLE DEVELOPMENT STRATEGY IN SANGIRAN CULTURAL HERITAGE ZONE, GONDANGREJO DISTRICT

Gondangrejo District is a suburb (peri urban) of Surakarta City with significant industrial growth in Karanganyar Regency. The driving factor of the development of the Gondangrejo District is the relocation of urban activities in Surakarta City. Gondangrejo District is an area of deconcentration of industrial activities because of its location directly adjacent to Surakarta City. A comprehensive site selection analysis is essential for industrial siting that supports sustainable builders. We select the optimal site for the industrial area by considering various criteria and inhibiting factors using GIS-based MCDA. Most suitable for industrial sites with GIS and AHP to build industries in The Gondangrejo District. This study aims to determine the most optimal industrial area by considering various appropriate criteria using multi-criteria decision analysis (MCDA) and geographic information system (GIS) methods. Based on the criteria used, namely proximity to roads, distance from rivers, distance from settlements, physiology, and population density with the same weight of 20%, a suitable location for the industrial area with a linear pattern or extending from east to west in the Gondangrejo District. Based on the same criteria with different weights from the results of the AHP analysis, suitable locations for industrial sites are Dayu Village, Rejosari Village, and Wonosari Village, which form a centering pattern. The study results found that the optimal industrial areas in Gondangrejo District are Dayu Village, Rejosari, and Wonosari. The results of the suitability of this industrial area can be used by decision-makers in the development planning of the Gondangrejo District