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All Journal Jurnal Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Teknologi Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization JOURNAL OF APPLIED INFORMATICS AND COMPUTING SEIKO : Journal of Management & Business Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) Buletin Poltanesa Inovasi Teknologi Masyarakat
Heliza Rahmania Hatta, Heliza Rahmania
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mechanical Engineering Social Sciences

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Journal : JOURNAL OF APPLIED INFORMATICS AND COMPUTING

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Data Mining Untuk Estimasi Sidang Perkara Narkotika Menggunakan Metode Regresi Linier Berganda Khairina, Dyna Marisa; Shapanara, Rhenaldi Octa; Maharani, Septya; Hatta, Heliza Rahmania
Journal of Applied Informatics and Computing Vol. 6 No. 2 (2022): December 2022
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v6i2.4401

Abstract

Narcotics cause unrest in the community because it has a very bad impact on society. The number of reports of narcotics cases has an impact on the number of executions in the trial of these cases. From the number of trial executions, it is necessary to follow up efforts to anticipate the handling of narcotics cases by knowing in advance the trend/pattern of increasing/decreasing narcotics cases as supporting information in efforts to handle these cases. The purpose of the research is to help speed up the process of calculating and managing the information contained in the data into new knowledge so that an estimate of the trial of narcotics cases is produced based on information on the pattern/trend of increasing/decreasing narcotics. The case uses multiple linear regression which is then tested for the coefficient of determination and the simultaneous significant test. The case data used is a time series from January 2021 to December 2021. The resulting regression model is Y = 39.777 "“ 0.035 X1 "“ 0.065 X2. The calculation of the regression results shows that the estimation of the implementation of the number of stages of narcotics cases with stage I and stage II variables has a negative effect on the implementation of narcotics cases based on the results of hypothesis testing conducted.
Comparison of FMADM TOPSIS and FMADM WP in Determining Recipients of the Family Hope Program (PKH) Assistance Puspitasari, Novianti; Kurniati, Wendy; Hatta, Heliza Rahmania
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9852

Abstract

Fuzzy Multi-Attribute Decision Making (FMADM) TOPSIS and WP methods are frequently employed to identify potential recipients of government assistance. The Family Hope Program (PKH) is a government social assistance program designed to improve the welfare of underprivileged individuals. However, the process of distributing this assistance often faces obstacles in the form of inaccuracy in determining recipients. This study compares FMADM TOPSIS and WP to evaluate their effectiveness in objectively determining potential PKH recipients. The criteria for potential PKH recipients are eleven criteria obtained from the social service based on government regulations and PKH assistants. Meanwhile, the alternatives for this study are fifty samples of family data for potential PKH recipients. This study employs a sensitivity test method to assess the accuracy of the results obtained from each method. The results of the study show that FMADM TOPSIS produces a higher level of accuracy of 94% compared to FMADM WP. This study is expected to be able to contribute to choosing the right decision-making method to determine potential recipients of social assistance.
Co-Authors Anindita Septiarini, Anindita Ariani, Riska Arini Wijayanti Awang Harsa Kridalaksana Bahtiar , Andi Alfian Baskoro, Roby Gunawan Budi Gunawan Dyna Marisa Khairina Ervan, Muhamad Gusti Keyandi Fahrul Agus Fazari, Alawiyah Nur Firdha Ayu Selvyani, Firdha Ayu Firman Firman Gading, Fazri Rahmad Nor Haqqi, Kamalul Haviluddin Haviluddin Hendi Irman Hermadi Jundillah, Muhammad Labib Kamila, Vina Zahrotun Kurniati, Wendy Maman Turjaman Masyudi, Akhmad Nasution, Ali Napiah Nataniel Dengen Nuzulan, Alan Padmo Azam Masa, Amin Pramukti, Indra Cahya Pratiwi, Sinthya Ayu Puspita Puji Rahayu, Puspita Puji Puspitasari, Novianti Ramadiani Ramadiani - Ramadiani, Ramadiani - Rani, Famylia Puspa Rizqoni, Rizqoni S, Nadia Christin Borneo Sampe, Ferdinandus Santosa, Sonny Sembiring, Rinawati Sembiring, Wahyu Harry Saputra Septya Maharani, Septya Shapanara, Rhenaldi Octa Sri Nurdiati Wati, Masna Yudi Sukmono, Yudi Yunizar Fahmi Badron, Yunizar Fahmi Zainal Arifin