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All Journal ILMU KELAUTAN: Indonesian Journal of Marine Sciences Jurnal Teknologi Informasi dan Ilmu Komputer Communication in Biomathematical Sciences
Izzaturrahim, Muh. Hafizh
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Computer Science & IT Earth & Planetary Sciences Engineering Social Sciences

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Regresi Multiskala Tertimbang Geografis dan Temporal dengan LASSO dan Adaptif LASSO untuk Pemetaan Kejadian Tuberkulosis di Jawa Barat Habsy, Muhammad Yusuf Al; Rachmawati, Ro'fah Nur; Khotimah, Purnomo Husnul; Natari, Rifani Bhakti; Riswantini, Dianadewi; Munandar, Devi; Izzaturrahim, Muh. Hafizh
Communication in Biomathematical Sciences Vol. 8 No. 1 (2025)
Publisher : The Indonesian Bio-Mathematical Society

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/cbms.2025.8.1.6

Abstract

Tuberculosis (TB) is a global health issue caused by Mycobacterium tuberculosis and can affect any organ of the body, especially the lungs. The trend of TB cases varies between regions, and analytic assessment is required to identify the predictor variables. The purpose of this research is to compare the Multiscale Geographically and Temporally Weighted Regression (MGTWR) and the Geographically and Temporally Weighted Regression (GTWR) method, which both use Gaussian, Exponential, Uniform, and Bi-Square kernel functions, to identify significant variables in each region annually. The MGTWR method has the advantage of using a flexible bandwidth for each observation, that results in more accurate coefficient estimates. The sample used was 27 districts and cities in West Java Province, involving 36 variables divided into 5 dimensions, namely global climate, health, demography, population, and government policy, with a time span of 2019–2022. To overcome the problem of multicollinearity, the approach was carried out using the Least Absolute Shrinkage Selection Operator (LASSO) and Adaptive LASSO methods. In determining the best model, the prioritized criteria are to achieve the highest R2, which indicates the optimal level of model fit, as well as the smallest AIC, which indicates the most efficient model goodness of fit. The best model is MGTWR with LASSO variable selection on the Bi-Square kernel. This model has an R2 of 91.25% and the smallest AIC of 139.868. From the best model, each region emerged with a cluster structure affected by various variables from 2019 to 2022, providing an in-depth understanding of TB mapping that can assist in formulating more effective intervention measures.
Drivers of Tidal Flooding and Coastal Vulnerability in the Riau Islands, Indonesia: A Time-Series Analysis (2022-2024) Latifah, Laila; Pranowo, Widodo Setiyo; Mujiasih, Subekti; Ratnawati, Herlina Ika; Hatmaja, Rahaden Bagas; Suhana, Mario Putra; Setiyadi, Johar; Lelalette, Johanis Dominggus; Izzaturrahim, Muh. Hafizh; Ismail, M. Furqon Azis; Syah, Achmad Fachruddin; Ryanto, Fauzan Novan; Setiyono, Heryoso; Helmi, Muhammad
ILMU KELAUTAN: Indonesian Journal of Marine Sciences Vol 30, No 3 (2025): Ilmu Kelautan
Publisher : Marine Science Department Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/ik.ijms.30.3.425-437

Abstract

The Indonesian Maritime Continent is highly vulnerable to climate variability and change, as exemplified by tidal flooding events in the Riau Islands from 2022 to 2024. This study aimed to analyze the characteristics of sea level dynamics and anomalies associated with tidal flooding (rob) and identify the contributing factors. Data on tidal flooding events were gathered from press and online social media reports, while additional information on significant wave height, ocean currents, and wind was obtained from the CMEMS (Copernicus Marine Environment Monitoring Service) Marine Copernicus archives (marine.copernicus.eu). Observational data from tide gauge stations were also accessed via the IOC sea level monitoring system (ioc-sealevelmonitoring.org). The findings revealed a high probability of tidal flooding during the north wind season, particularly in January and February. Notably, tidal elevations during flooding events reached 3.06 m on January 25, 2023, 3.00 m on February 21, 2023, and 3.09 m on February 12, 2024. These events were driven by a combination of oceanographic and atmospheric factors, including high tidal ranges during spring tides, strong wind speeds averaging 19.04 to 21.43 knots in January–February 2023 and 18.65 knots in February 2024, dominant southward current patterns, and significant wave heights reaching up to 1.08 m. The alignment of the sun, moon, and earth during full and new moon phases amplified gravitational forces, causing elevated sea levels. Furthermore, strong winds during the north wind season contributed to higher wave heights, intensifying flooding impacts. Analysis of current patterns indicates that the highest speeds were recorded during the northern wind season, specifically in January and February, which coincides with the tidal flooding events. The currents predominantly moved southward, aligning with the wind direction during this season. This study reveals oceanographic and atmospheric interactions driving tidal flooding, offering insights for mitigation and adaptation to enhance resilience in vulnerable coastal regions.
Penerapan Metode Stacking Ensemble Learning untuk Deteksi Penipuan pada Transaksi Finansial Izzaturrahim, Muh. Hafizh; Prayoga, Ida Bagus Peling; Hilmy, Andika Naufal; Utama, Nugraha Priya; Purwarianti, Ayu
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 3: Juni 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026133

Abstract

Identifikasi fraud pada transaksi keuangan menjadi pekerjaan besar untuk mencegah kerugian yang dapat terjadi akibat fraud. Dengan perkembangan teknologi machine learning yang makin baik, eksperimen mengenai identifikasi fraud dapat dilakukan dengan menggunakan model machine learning. Sejumlah model machine learning dapat digunakan untuk identifikasi fraud, seperti logistic regression, XGBoost, LightGBM, dan Stacking. Penelitian ini untuk menunjukkan kinerja  dari stacking ensemble yang dipadukan dengan MLP sebagai meta learner. Identifikasi fraud memiliki tantangan tersendiri berupa banyak data yang tidak seimbang antara transaksi otentik dengan transaksi palsu. Meskipun demikian, eksperimen yang dilakukan menggunakan model dan evaluasi yang tahan terhadap data yang tidak seimbang. Eksperimen dilakukan dengan pengolahan data dan feature engineering untuk mengatasi permasalahan dalam dataset. Pengolahan data dan feature engineering ditujukan untuk mengurangi pencilan, fitur yang tak relevan, dan nilai-nilai pada data yang hilang atau terduplikasi. Kemudian, pembuatan model dilakukan dengan logistic regression, XGBoost, LightGBM, dan stacking ensemble dengan base learner dari XGBoost dan LightGBM serta meta-learner menggunakan MLP. Hasil dari eksperimen yang dilakukan menunjukkan bahwa stacking memiliki kinerja yang paling tinggi dengan nilai AUC-ROC mencapai 0,9663 dibandingkan model lain.
Co-Authors Ayu Purwarianti Devi Munandar, Devi Habsy, Muhammad Yusuf Al Hatmaja, Rahaden Bagas Herlina Ika Ratnawati, Herlina Ika Heryoso Setiyono Hilmy, Andika Naufal Khotimah, Purnomo Husnul Latifah, Laila Lelalette, Johanis Dominggus M. Furqon Azis Ismail Mario Putra Suhana Muhammad Helmi Natari, Rifani Bhakti Prayoga, Ida Bagus Peling Rachmawati, Ro'fah Nur Riswantini, Dianadewi Ryanto, Fauzan Novan Setiyadi, Johar Subekti Mujiasih Utama, Nugraha Priya Widodo S Pranowo