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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Fourier INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JUSTIN (Jurnal Sistem dan Teknologi Informasi) Journal of Mathematics, Computation and Statistics (JMATHCOS)
Wika Dianita Utami
Prodi Matematika Jurusan Matematika FMIPA Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Klasterisasi Data Kejadian Gempa Bumi di Indonesia Menggunakan Metode K-Medoids Inayah, Jauharotul; Fanani, Aris; Utami, Wika Dianita
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 12, No 2 (2024)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v12i2.73594

Abstract

Wilayah Indonesia terletak di antara tiga lempeng tektonik utama, yaitu lempeng Indo-Australia, Eurasia, dan Pasifik. Situasi tersebut menjadikan wilayah Indonesia rentan terhadap gempa bumi. Pada tahun 2022, terdapat 24 kejadian gempa bumi merusak di Indonesia, dengan salah satu kejadian signifikan di Cianjur yang menyebabkan 635 korban jiwa. Kejadian tersebut menunjukkan bahwa Indonesia merupakan daerah yang rawan terhadap gempa bumi. Tujuan dari penelitian ini adalah untuk mengelompokkan daerah-daerah yang rentan terhadap gempa bumi di Indonesia menggunakan metode K-Medoids dan mengevaluasi kelompok-kelompok tersebut dengan menggunakan Silhouette Coefficient. Data kejadian gempa bumi diperoleh dari situs web United States Geological Survey (USGS) dengan total 582 kejadian. Metode ini membentuk hasil jumlah klaster terbaik adalah dua klaster dengan memperoleh nilai Silhouette Coefficient sebesar 0,68016. Adapun hasil klaster tersebut dikategorikan sebagai kelompok tingkat kerentanan sangat tinggi, mencakup daerah sekitar pulau Bali, Sulawesi, hingga Irian Jaya, dan kelompok tingkat kerentanan tinggi, melibatkan daerah barat pulau Sumatra hingga selatan pulau Jawa.
Analisis Rantai Pasok Stok Obat HIV ARV dengan Metode Double Exponential Smooting Hapsari, Nabilla Windy; Fanani, Aris; Wardani, Susilo Ari; Utami, Wika Dianita
Techno.Com Vol. 23 No. 3 (2024): Agustus 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i3.10866

Abstract

Human Immunodeficiency Virus (HIV) merupakan virus yang dapat memicu kerusakan pada sistem kekebalan tubuh manusia, menyebabkan infeksi pada orang yang terkena serta dapat mengurangi sistem kekebalan tubuh dan jika tidak segera disembuhkan akan terjangkit penyakit lain yang disebut dengan Acquired Immuno Deficiency Syndrom (AIDS). Kebutuhan obat ARV bagi ODHA mempengaruhi kebutuhan stok obat yang harus dipasok pemerintah ke Kabupaten/Kota. Dinas Kesehatan Provinsi bertanggung jawab atas perencanaan kebutuhan obat dari pemerintah Kabupaten/Kota, serta penerimaan permintaan obat, penyimpanan, pendistribusian, pencatatan dan pelaporan mutasi obat. Tujuan dari penelitian ini ialah untuk memahami bagaimana rangkaian proses pada supplier, dan penyimpanan obat agar obat tidak mengalami kekosongan atau kelebihan stok dengan menganalisis Rantai Pasok dan melakukan peramalan pada metode Double Exponential Smoothing yang didapatkan hasil presentase error terkecil pada obat Tenofovir dengan nilai MAPE 14,7%.   Kata kunci: Stok obat, HIV ARV, Rantai pasok, Double Exponential Smoothing
Implementasi K-Means pada Klasterisasi Jenis Disabilitas Dwiana, Fadiah Irine; Utami, Wika Dianita; Hamid, Abdulloh; Sriasih
Journal of Mathematics, Computations and Statistics Vol. 6 No. 1 (2023): Volume 06 Nomor 01 (April 2023)
Publisher : Jurusan Matematika FMIPA UNM

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Abstract

Disability need assistance from the authorities to support the activities of persons with disability. Types of disability include physical disability, visual impairment, speech impairment and mental disorders. This study goal of this study to cluster disability per sub-district in Sidoarjo Regency by type of disability using the K-Means method. The data used is data on the number of disability in Sidoarjo Regency from January to August 2022. This cluster analysis produces four optimal clusters with the highest silhouette coefficient value of 0.33. The results of the analysis of this study formed 4 clusters in the first cluster, namely 4 sub-districts with a very high number of disability, the second cluster, namely 4 sub-districts with a high number of disabilitys, the third cluster only 1 sub-district with a moderate number of disabilitys, the fourth cluster, namely 5 sub-districts with a low number of disability.
Implementation of The Extreme Gradient Boosting Algorithm with Hyperparameter Tuning in Celiac Disease Classification Alfirdausy, Roudlotul Jannah; Ulinnuha, Nurissaidah; Utami, Wika Dianita
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 24 No 1 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.4031

Abstract

Celiac Disease (CeD) is an autoimmune disorder triggered by gluten consumption and involves the immune system and HLA in the intestine. The global incidence ranges from 0.5%-1%, with only 30% correctly diagnosed. Diagnosis remains challenging, requiring complex tests like blood tests, small bowel biopsy, and elimination of gluten from the diet. Therefore, a faster and more efficient alternative is needed. Extreme Gradient Boosting (XGBoost), an ensemble machine learning technique that utilizes decision trees to aid in the classification of Celiac disease, was used. The aim of this study was to classify patients into six classes, namely potential, atypical, silent, typical, latent and none disease, based on attributes such as blood test results, clinical symptoms and medical history. This research method employs 5-fold cross-validation to optimize parameters that are max depth, n estimator, gamma, and learning rate. Experiments were conducted 96 times to get the best combination of parameters. The results of this research are highlighted by an improvement of 0.45% above the accuracy value with the default XGBoost parameter of 98.19%. The best model was obtained in the trial with parameters max depth of 3, n estimator of 100, gamma of 0, and learning rate of 0.3 and 0.5 after modifying the parameters, yielding an accuracy rate of 98.64%, a sensitivity rate of 98.43%, and a specificity rate of 99.72%. This research shows that tuning the XGBoost parameters for Celiac
Implementation of Capital Asset Pricing Model in Optimal Portfolio Formation on IDX High Dividend 20 Auditiyah, Cellyn; Farida, Yuniar; Utami, Wika Dianita
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 9, No 1 (2025): January
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v9i1.27799

Abstract

The IDX High Dividend 20 (IDX HIDIV20) is an Indonesian stock index known for its high dividend payouts, appealing to passive income investors. However, annual changes and fluctuating stock prices present challenges, necessitating diversification strategies. This study aims to create an optimal portfolio to balance returns and risks amidst market volatility on the IDX High Dividend 20 stock index. This research uses the Capital Asset Pricing Model (CAPM) method. The CAPM determines the relationship between risk and an asset's expected rate of return, especially shares. This model helps in evaluating whether an asset or investment provides sufficient returns commensurate with its risk. In this study. We used weekly stock price data and composite stock prices from Yahoo Finance and BI interest rates taken from Bank Indonesia from January 2020 to December 2023. The research findings found that there were 6 out of 12 samples forming the optimal portfolio, namely ITMG (28.0%), ADRO (16.6%), BMRI (29.2%), BBNI (13.7%), BBCA (11.8%), and BBRI (0.6%) with a portfolio return of 0.41% and a portfolio risk level of 0.16%. The study emphasizes the importance of diversification for investors, particularly in volatile markets, to manage risks and enhance returns. It also highlights the strategic value of investing in high-dividend stocks for consistent income and portfolio stability, offering practical insights for optimizing investment strategies.
Prediksi Parameter Klimatologi Menggunakan Multivariate Singular Spectrum Analysis (MSSA) Utami, Wika Dianita; Intan, Putroue
Jurnal Fourier Vol. 13 No. 2 (2024)
Publisher : Program Studi Matematika Fakultas Sains dan Teknologi UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/fourier.2024.132.1-11

Abstract

Curah hujan, temperatur, kecepatan angin, kelembaban udara, dan penyinaran matahari adalah paremeter klimatologi. Perubahan parameter klimatologi yang signifikan mengakibatkan terjadinya bencana alam seperti banjir, angin kencang, puting beliung, tanah longsor, cuaca ekstrem hingga kekeringan. Informasi parameter klimatologi sangat dibutuhkan pada berbagai sektor kehidupan, misal pertanian, pariwisata, dan transportasi. Oleh karena itu, informasi tentang parameter klimatologi dibutuhkan di masa depan sebagai upaya mitigasi bencana. Penelitian ini bertujuan untuk memprediksi perubahan parameter klimatologi menggunakan Multivariate Singular Spectrum Analysis (MSSA). Data yang digunakan adalah data harian paremeter klimatologi di Malang periode Januari 2023 hingga Mei 2024. Hasil penelitian menunjukkan nilai model MSSA dengan M = 50, Grouping Effect (r) = 12 dan nilai MAD terkecil menghasilkan prediksi parameter klimatologi di Malang bulan Juni 2024 meliputi temperatur suhu 25.45°C, kelembapan 77.23%, curah hujan 10.56 mm, penyinaran matahari 5.94 jam, dan kecepatan angin 1.84 m/s.
GRID SEARCH AND RANDOM SEARCH HYPERPARAMETER TUNING OPTIMIZATION IN XGBOOST ALGORITHM FOR PARKINSON’S DISEASE CLASSIFICATION Aqilah Khansa, Shafa Fitria; Ulinnuha, Nurissaidah; Utami, Wika Dianita
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 3 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss3pp1609-1624

Abstract

Parkinson's disease is a neurodegenerative disorder affecting motor abilities, with a prevalence of 329 cases per 100,000 individuals. Early diagnosis is crucial to prevent complications. This study classifies Parkinson's disease using the Extreme Gradient Boosting (XGBoost) algorithm with hyperparameter tuning via Grid Search and Random Search. The dataset from Kaggle consists of 2105 records from 2024 and includes 32 clinical and demographic features such as age, gender, BMI, medical history, and Parkinson's symptoms. The XGBoost method effectively manages large and complex data and reduces. Tuning was performed with 5-fold cross-validation for result validity. After tuning with Grid Search, the model achieved 93.35% accuracy in 44 minutes 51 seconds, with optimal parameters gamma=5, max depth=3, learning rate=0.3, n estimators=100, and subsample=0.7. Meanwhile, Random Search with 50 iterations achieved 93.97% accuracy in 3 minutes 4 seconds with optimal parameters gamma=5, max depth=3, learning rate=0.262, n estimators=58, and subsample=0.631. Random Search also shows better time efficiency than Grid Search, although with relatively similar accuracy. The results of this study confirm that hyperparameter tuning using Random Search not only produces competitive accuracy performance but also minimizes computation time, making it a more optimal choice for Parkinson's disease classification.
Implementation of singular spectrum analysis in the forecasting of seawater wave height Utami, Wika Dianita; Agustina, Ade Candra
Desimal: Jurnal Matematika Vol. 6 No. 3 (2023): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v6i3.18382

Abstract

Indonesia is renowned as a maritime nation, positioned amidst the Pacific Ocean and the Indian Ocean. This strategic location grants Indonesia the distinct advantage of serving as a global crossroads for maritime traffic, particularly with regards to trade and waterborne transportation. Among Indonesia's bustling ports, Tanjung Priok Port stands out as one of the busiest. In this context, the measurement of seawater wave height assumes a pivotal role in shaping the dynamics of transportation and commercial activities at Tanjung Priok Port. Hence, the availability of predictive insights into forthcoming seawater wave height assumes paramount significance in proactively addressing potential calamities and orchestrating maritime endeavors more efficaciously. This study aims to apply the Singular Spectrum Analysis (SSA) technique to forecast the wave height of seawater at Tanjung Priok Port. The dataset employed encompasses the daily seawater wave height observations recorded at Tanjung Priok Harbor during the timeframe from January 2022 to May 2023. The findings of this research unveil a parameter value of L = 98, a Grouping Effect (r) of 13, and a Mean Absolute Percentage Error (MAPE) value of 10.01%. This MAPE value signifies that the forecasting yielded by the Singular Spectrum Analysis (SSA) methodology exhibits a satisfactory level of accuracy in prognosticating future seawater wave heights at Tanjung Priok Port.
Implementation of SMOTE to Improve the Performance of Random Forest Classification in Credit Risk Assessment in Banking Nanda, Nafa Nur Adifia; Farida, Yuniar; Utami, Wika Dianita
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 9 No 2 (2025): August 2025
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v9i2.23930

Abstract

Background: Credit is essential in banking operations, facilitating investment, corporate expansion, and financial satisfaction. Credit risk may emerge if the borrower defaults on payment commitments. Objective: This study aims to evaluate an individual's creditworthiness by classifying and assessing their eligibility for credit. Methods: This study uses the Random Forest technique to categorize credit risk evaluation. Random Forest is a decision tree technique recognized for its high accuracy in data classification, utilizing an ensemble method of many decision trees. Before executing the classification process, issues frequently arise when data cannot be directly processed due to class imbalance. This study employs the SMOTE (Synthetic Minority Over-sampling Technique) algorithm to address class imbalance. The SMOTE algorithm is a method that emphasizes oversampling and is designed to augment the data in the minority class by generating synthetic data that aligns with the minority class data. The findings indicated that the ideal ratio for partitioning training and testing data was 80:20, and implementing the SMOTE technique within Random Forest enhanced performance assessment. Results: This research contributes to improving the accuracy of credit risk classification using the Random Forest algorithm, which effectively handles complex data and is supported by the implementation of SMOTE to overcome the class imbalance in the data. The classification accuracy value rose from 91.54% to 94.41%. The precision value rose from 90.83% to 97.03%, while the recall value increased from 60.26% to 91.55%. Conclusion: This method helps banks identify high-risk debtors more objectively and efficiently and supports appropriate credit decision-making.
PENERAPAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) UNTUK PREDIKSI BILANGAN SUNSPOT Yuliawanti, Felia Dria; Novitasari, Dian C. Rini; Widodo, Nanang; Hamid, Abdulloh; Utami, Wika Dianita
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 15 No 3 (2021): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (733.906 KB) | DOI: 10.30598/barekengvol15iss3pp555-564

Abstract

Peristiwa magnetik pada matahari ditandai dengan salah satu tanda yaitu munculnya sunspot atau bintik matahari. Sunspot terletak di fotosfer matahari yang memiliki warna lebih gelap dari pancaran sekitarnya. Tujuan dari penelitian ini adalah untuk memprediksi bilangan sunspot dengan menggunakan metode ARIMA. Metode ARIMA dilakukan dengan melihat plot ACF dan PACF untuk mendapatkan model yang akan digunakan dalam prediksi. Penelitian ini menggunakan data bilangan sunspot yang dimulai dari bulan Januari tahun 1987 hingga bulan Desember 2019 sebanyak 396 data. Dari data tersebut didapatkan 4 model ARIMA yaitu ARIMA(3,1,2), ARIMA(3,1,1), ARIMA(2,1,2), ARIMA(2,1,1). Dari keempat model tersebut, model terbaik yang digunakan untuk prediksi yaitu ARIMA(2,1,2) dengan nilai AIC sebesar -884,87.
Co-Authors Abdulloh Hamid Agustina, Ade Candra Alfirdausy, Roudlotul Jannah Aqilah Khansa, Shafa Fitria Aris Fanani Armunanto, Firman Auditiyah, Cellyn Dwiana, Fadiah Irine Dzaky, Ahmad Naufal Fadilah, Siti Nur Hani Khaulasari Hapsari, Nabilla Windy Inayah, Jauharotul Intan, Putroue M. Imron Nanang Widodo Nanda, Nafa Nur Adifia Nurissaidah Ulinnuha Putroue Keumala Intan Ramadanti, Alvin Nuralif Sari, Dian Candra Rini Novita Sriasih Swindiarto, Victory T. Pambudi Wardani, Susilo Ari Wijaya, Indra Ariyanto Yuliawanti, Felia Dria Yuniar Farida, Yuniar