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ESTIMATION OF MAXIMUM LIKELIHOOD WEIGHTED LOGISTIC REGRESSION USING GENETIC ALGORITHM (CASE STUDY: INDIVIDUAL WORK STATUS IN MALANG CITY) Menufandu, Dahlia Gladiola Rurina; Fitriani, Rahma; Sumarminingsih, Eni
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 1 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (399.767 KB) | DOI: 10.30598/barekengvol17iss1pp0487-0494

Abstract

Weighted Logistic Regression (WLR) is a method used to overcome imbalanced data or rare events by using weighting and is part of the development of a simple logistic regression model. Parameter estimation of the WLR model uses Maximum Likelihood estimation. The maximum likelihood parameter estimator value is obtained using an optimization approach. The Genetic algorithm is an optimization computational algorithm that is used to optimize the estimation of model parameters. This study aims to estimate the Maximum Likelihood Weighted Logistic Regression with the applied genetic algorithm and determine the significant variables that affect the working status of individuals in Malang City. The data used is the result of data collection from the National Labor Force Survey of Malang City in 2020. The results of the analysis show that the variable education completed and the number of household members has a significant effect on individual work status in Malang City.
Clinic of Mathematics with the PPLAM Approach: Efforts to Improve Students’ Mathematics Learning in Manokwari Lubis, Loria Amisah; Randa, Trigarcia Maleachi; Matulessy, Esther Ria; Palungan, Chrisaria; Menufandu, Dahlia Gladiola Rurina
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 9, No 1 (2026): Februari 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.10415

Abstract

Abstrak - Penelitian ini bertujuan menganalisis efektivitas program Klinik Matematika dengan pendekatan Embedded Learning Analytics (PPLAM) dalam meningkatkan pemahaman siswa kelas VII pada materi operasi bilangan bulat. Penelitian menggunakan metode kuasi-eksperimen dengan desain one-group pretest–posttest. Intervensi dilaksanakan melalui rangkaian kegiatan pretest, latihan terstruktur, dan posttest dalam satu sesi pembelajaran. Subjek penelitian berjumlah 25 siswa yang dipilih secara purposif berdasarkan hasil diagnosis kesulitan belajar. Data diperoleh dari tes hasil belajar dan rekaman aktivitas latihan, kemudian dianalisis menggunakan uji t berpasangan, Normalized Gain (N-Gain), dan effect size (Cohen’s d). Hasil analisis menunjukkan peningkatan yang signifikan, dengan nilai rata-rata N-Gain sebesar 0,578 (kategori sedang) dan effect size sebesar 2,164 (kategori sangat kuat). Analisis PPLAM lebih lanjut menunjukkan bahwa kualitas latihan memberikan kontribusi substansial terhadap hasil posttest dengan nilai R² sebesar 0,70. Temuan ini menegaskan bahwa integrasi PPLAM dalam program Klinik Matematika berperan penting dalam memantau proses belajar dan meningkatkan efektivitas pembelajaran remedial jangka pendek pada materi operasi bilangan bulat.Kata kunci : Klinik Matematika; Operasi hitung Bilangan Bulat; N-Gain; Effect Size; Learning analytics; Abstract - This study examines the effectiveness of a Mathematics Clinic program integrated with Embedded Learning Analytics (PPLAM) in improving seventh-grade students’ understanding of integer operations. A quasi-experimental method with a one-group pretest–posttest design was employed. The intervention consisted of a pretest, structured practice activities, and a posttest conducted within a single learning session. The participants were 25 students selected purposively based on initial learning difficulties. Data were collected from achievement tests and practice-session records and analyzed using a paired-sample t-test, Normalized Gain (N-Gain), and effect size (Cohen’s d). The results indicated a significant improvement, with a mean N-Gain of 0.578 (moderate category) and a large effect size (d = 2.164). Further analysis using PPLAM revealed that practice quality contributed substantially to posttest performance, as indicated by an R² value of 0.70. These findings confirm that PPLAM plays a critical role in monitoring learning processes and enhancing the effectiveness of short-term remedial instruction in integer operations.Keywords: Mathematics Clinic,; Integer Operation;  N-Gain; Effect Size; Learning analytics;