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All Journal Jurnal Data Mining dan Sistem Informasi Jurnal INFOTEL
Patricia Evericho Mountaines
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Computer Science & IT Electrical & Electronics Engineering

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IMPLEMENTATION OF SMARTER AND ORESTE METHODS FOR DETERMINING UNDERDEVELOPED VILLAGES Riska Amalia Praptiwi; Suaidah Suaidah; Rakhmat Dedi Gunawan; Ryo Cahyo Prakoso; Deddy Rudhistiar; Patricia Evericho Mountaines; Thesa Adi Saputra
Jurnal Data Mining dan Sistem Informasi Vol 4, No 2 (2023): AGUSTUS 2023
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jdmsi.v4i2.3239

Abstract

Villages have less development than cities because villages have bigger problems such as higher poverty rates, lower health, lower human resources, facilities and infrastructure that are more difficult to reach than cities. Therefore we need the concept of sustainable village development. In sustainable development, the aspect of development is not only aimed at present society but also society in the future. Before making the concept of sustainable village development, so that village development in a city/regency/district area is conceptualized evenly, decision support is needed to identify underdeveloped villages. Some indicators villages or underdeveloped regions mostly related to the survey of Potensi Desa activities by BPS from 1980 to 2014 continually participated. Related to that conditions, criteria obtained underdeveloped villages by DPU and indicator data PODES by BPS, it can be applied on Decision Making System. In this research selected case studies are census data from Potensi Desa by BPS in Magetan. This system uses SMARTER (Simple Multi-Attribute Rating Technique Exploiting Ranks) methods as the calculation of the weights to the criteria and ORESTE methods used for the rankings of underdeveloped villages. In this system SMARTER methods using a weighting formula Rank Order Centroid (ROC) that is proportional weighting which reflects the distance and the priority of each criteria appropriately. Furthermore, the ranking process using Oreste methods by three main stages that is Projection Matrix position, Ranking of projections and Agegration of Global Ranking. Testing of this system, which one is changing the parameters of Oreste (α value) and obtained compatibility reach 91.06% of accuracy to the experts data of underdeveloped villages from the BPS Magetan by the number 100% alternative data with value 0:01 of alpha
Brain Tumor Detection Through Image Enhancement Methods and Transfer Learning Techniques Thohari, Afandi Nur Aziz; Mountaines, Patricia Evericho; Mohd Isa, Mohd Rizal
JURNAL INFOTEL Vol 17 No 1 (2025): February 2025
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v17i1.1262

Abstract

A brain tumor is dangerous and must be treated immediately to prevent worsening. The identification of brain tumors can be performed by a more in-depth examination by specialists or by using artificial intelligence technology through MRI datasets. Several studies have examined how artificial intelligence could be used to find brain cancer in MRI images. The algorithm usually used is CNN with the addition of transfer learning. Previous studies have produced very high accuracy, but the accuracy value can still be improved. In this study, MRI image quality is improved as a new input for modeling. The test results show that the proposed CNN Model produces an accuracy of 98.50% on the test data. This result is higher than the baseline method of 98.45%. Analysis of other metrics, such as precision, recall, and F1-score, indicates consistent performance across classes. These findings suggest that using preprocessing to improve image quality can improve Model performance. Using CLAHE and median blur to improve image quality can improve accuracy by 14.5%. This study contributes to identifying an effective combination of Model optimization techniques for image classification tasks.
Co-Authors Afandi Nur Aziz Thohari, Afandi Nur Aziz Deddy Rudhistiar Gunawan, Rakhmat Dedi Mohd Isa, Mohd Rizal Riska Amalia Praptiwi Ryo Cahyo Prakoso suaidah suaidah Thesa Adi Saputra Yusri