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All Journal Media Statistika JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Jurnal Riset Statistika Bandung Conference Series: Statistics Statistika Jurnal Kajian dan Terapan Matematika
Fitri Amanah
Prodi Statistika, Fakultas Matematika Dan Ilmu Pengetahuan Alam, Universitas Islam Bandung, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Education Mathematics

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Journal : statistika

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Studi Komparasi Regresi Logistik Biner dan K-Nearest Neighbor Pada Kasus Prediksi Curah Hujan Rahmawati, Fitri; Amanah, Fitri; Fallo, Sefri Imanuel
Statistika Vol. 24 No. 1 (2024): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v24i1.2739

Abstract

ABSTRAK Perubahan iklim yang sedang terjadi di berbagai belahan dunia sebagai akibat dari pemanasan global telah menyebabkan ketidakpastian cuaca. Salah satu perubahan yang dirasakan adalah intensitas curah hujan. Hal ini mengakibatkan prediksi akan curah hujan menjadi penting untuk dilakukan. Ada beberapa teknik analisis data yang digunakan untuk prediksi curah hujan, diantaranya klasifikasi. Pada penelitian ini, dengan menggunakan variabel temperatur, kelembapan, lamanya penyinaran, dan kecepatan angin, akan dilakukan prediksi terhadap klasifikasi curah hujan di Kota Bogor. Model yang digunakan adalah Regresi Logistik Biner dan K-Nearest Neighbor. K yang digunakan pada model K-Nearest Neighbor yaitu 1 hingga 18. Untuk membandingkan kedua model, dibentuk confusion matrix yang selanjutnya digunakan untuk menghitung akurasi model. Akurasi model Regresi Logistik Biner sebesar 92,746%, adapun akurasi model K-Nearest Neighbor adalah sebesar 94,81865%. Dengan demikian, pada penelitian ini model K-Nearest Neighbor lebih baik dibandingkan model Regresi Logistik Biner. ABSTRACT Climate change due to global warming occurring in all parts of the world makes the weather unpredictable. One of the changes felt is the intensity of rainfall. This makes it important to predict rainfall. There are several data analysis techniques used to predict rainfall, including classification. In this research, using the variables temperature, humidity, duration of sunlight, and wind speed, predictions will be made on the classification of rainfall in the city of Bogor. The models used are Binary Logistic Regression and K-Nearest Neighbor. The K used in the K-Nearest Neighbor model is 1 to 18. To compare the two models, a confusion matrix is formed and then used to calculate the model accuracy. The accuracy of the Binary Logistic Regression model is 92.746%, while the accuracy of the K-Nearest Neighbor model is 94,81865%. Thus, in this research the K-Nearest Neighbor model is better than the Binary Logistic Regression model.
Time Series Clustering and Mean–Variance Portfolio Modeling on IDX Sharia Growth Stocks Fitri Amanah; Fauziah Roshafara; Nafa Nurhanifah; Novianda Dwi Tanti Ramdani
Statistika Vol. 26 No. 1 (2026): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v26i1.9723

Abstract

Abstract. A stock portfolio represents an investment strategy aimed at maximizing returns while minimizing risk. Stock data, as a form of time series data, relies on historical information to evaluate performance and identify price movement patterns. Consequently, stock selection is a critical component in asset diversification. The novelty of this study is to apply time series clustering methods to stock data and utilize the resulting clusters to construct a mean-variance portfolio. The data used are the weekly closing prices of 10 Sharia-Growth Index stocks for the period December 2022 to November 2024. Using Dynamic Time Warping (DTW) distance with average linkage, two clusters were identified: cluster 1 (MPMX, TLKM) and cluster 2 (MAPI, HEAL, ISAT, AKRA, BMTR, SIDO, KLBF, PWON), with a Silhouette coefficient of 0.9471 indicating strong clustering performance. To construct the mean-variance portfolio, three stocks with positive expected returns were selected: HEAL, ISAT, and MAPI, which are members of Cluster 2. The optimal weights obtained using Lagrange optimization are HEAL (0.49%), ISAT (99.51%), and MAPI (0%). The Lagrange method allocates 0% to MAPI because its risk level (variance of 0.00322) is the highest among the three candidate stocks, thereby not helping to minimize the overall portfolio risk. Therefore, the optimal portfolio formed by combining HEAL and ISAT would have provided an estimated return of 0.56% and a risk of 4.1%.
Co-Authors Abdurakhman Abdurakhman Fanny Ahdasabilla Fauziah Roshafara Fitri Rahmawati Jasmine Devanti Bilbina Heriaty Muhamad Arifianto Darmawan Nadya Ayu Aprilia Nafa Nurhanifah Novianda Dwi Tanti Ramdani Nur Sugih Aji Puri Indah Lestari Renita Maharani Retno Subekti Salma Nera Hanifah Salwa Salsabila Sefri Imanuel Fallo