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All Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JNANALOKA
Astika Wulansari
Universitas Amikom Yogyakarta
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Transportation Other

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Systematic Literature Review of Waste Classification Using Machine Learning Astika Wulansari; Arief Setyanto; Emha Taufiq Luthfi
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 2 (2022): Issues January 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i2.6211

Abstract

The development of the global economy has caused people's living standards to increase and the production of domestic waste has also increased from year to year. The population of big cities that have limited environmental carrying capacity, causing the waste problem requires serious handling. Manual waste sorting is hazardous to health and wastes time, money and effort. If waste is not handled properly, environmental problems will increase in the long run. Machine learning works by combining features such as textures and colors to complement junk image recognition. Today's machine learning technology continues to develop, not only methods, types of waste, and features but also identify and analyze datasets used in waste management by gathering all scientific evidence. Collecting existing research and then identifying, assessing, and interpreting requires a systematic literature review. Until the end of 2021, the research topic of waste classification using machine learning was found with various types of waste, algorithms, datasets, and others. However, the dataset used by the algorithm in image recognition is relatively single, the types of garbage classified and the relative accuracy results can still be improved.
Penerapan metode analytical hierarchy process dan simple additive weighting dalam pengambilan keputusan siswa berprestasi pada sekolah menengah kejuruan. Aulia Tegar Rahman; Sitti Muhartini; Astika Wulansari; Rizky Amirullah Hasiani; Arif Baktiar
JNANALOKA Vol. 02 No. 02 September Tahun 2021
Publisher : Lentera Dua Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36802/jnanaloka.2021.v2-no2-63-71

Abstract

Vocational High School is a formal education unit that organizes vocational education that prepares students, especially to work in certain fields. In determining students who excel in certain fields, it is necessary to have a decision support system to improve the quality of decisions in determining students who excel from the highest average score. However, using the highest average score does not get optimal results because it is not adjusted to the existing needs in determining outstanding students. In this study, it can be used as a reference in making decisions for outstanding students by applying the Analytical Hierarchy Process and Simple Additive Weighting methods. The steps taken are: Data collection, Data Preprocessing, Ranking and Comparison of Results between Analytical Hierarchy Process, Simple Additive Weighting with manual weighting results. The results of the ranking comparison show that there are 6 students with the top ranking who are recommended to be outstanding students in the linguistic group.
Analisa gambar citra MRI otak dengan watershed dan ekstraksi fitur GLCM Astika Wulansari; Aulia Tegar Rahman
JNANALOKA Vol. 03 No. 02 September Tahun 2022
Publisher : Lentera Dua Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36802/jnanaloka.2022.v3-no2-39-46

Abstract

Brain tumor is an infection in the form of unwanted tissue and considered as malignant. Nevertheless, it is very difficult to distinguish brain tumor tissue from the rest of the brain. Early detection of tumors is crucial to save the patient’s life. Segmentation strategy is used to identify and parse brain tumor areas utilizing the Magnetic ResonanceImaging (MRI) images of the brain. This is an important breakthrough for the future. Magnetic Resonance Imaging is an extreme field in the image processing area due to the very high level of precision needed by the doctors to obtain precise recommendations about the infections to save the patients’ lives. MRI images can be used to provide information on the separation of brain tumor tissue. Segmentation of MRI images with median filtering and skull stripping preprocessing techniques, threshold grip with watershed obtained contrast results of 4.287, correlation 0.946, homogeneity 0.721, and energy 0.278.
Co-Authors Arief Setyanto Arif Baktiar Aulia Tegar Rahman Aulia Tegar Rahman Emha Taufiq Luthfi Rizky Amirullah Hasiani Sitti Muhartini