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PENERAPAN ANALISIS KOMPONEN UTAMA DALAM MEREDUKSI FAKTOR-FAKTOR PENYEBAB DIARE DI PROVINSI MALUKU Haumahu, Gabriella; Lewaherilla, Norisca
MAp (Mathematics and Applications) Journal Vol 2, No 1 (2020)
Publisher : Universitas Islam Negeri Imam Bonjol Padang

Analisis komponen utama merupakan suatu analisis untuk menjelaskan struktur varians-kovarians melalui sejumlah kecil kombnasi linier dari segugus variabel asal. Tujuan umumnya adalah untuk reduksi data dan interpretasi. Penelitian ini bertujuan untuk mereduksi faktor-faktor penyebab diare di Provinsi Maluku. VariabelÂ yang digunakan dalam penelitian ini adalah jumlah penduduk miskin (X1), jumlah rumah sakit umum (X2), jumlah puskesmas (X3), jumlah apotek (X4), jumlah rumah tangga yang menggunakan fasilitas tempat buang air sendiri/not shared (X5), jumlah rumah tangga yang tidak menggunakan dan tidak mempunyai fasilitas tempat buang air besar (X6), dan jumlah rumah tangga yang memiliki akses sumber air minum layak (X7). Hasil penelitian menunjukkan variabel-variabel direduksi menjadi satu komponen utama dengan proporsi varians kumulatifnya sebesar 82%.AbstractPrincipal component analysis is an analysis to explain the structure of variance-covariance through a small number of linear combinations of a set of original variables. The general purpose is to reduce data and interpretation. This study aims to reduce the causes of diarrhea in Maluku Province. The variables used in this study are the number of poor people (X1), the number of public hospitals (X2), the number of Puskesmas (X3), the number of pharmacies (X4), the number of households that use their own not shared toilet facilities (X5) , the number of households that do not use and do not have defecation facilities (X6), and the number of households that have access to improved drinking water sources (X7). The results showed the variables were reduced to one main component with a cumulative proportion of 82%.

Klasifikasi Penggunaan Alat Kontrasepsi di Kecamatan Salahutu Kabupaten Maluku Tengah Menggunakan Metode K-Nearest Neighbor (KNN) Talakua, Andrew H; Haumahu, Gabriella; Noya Van Delsen, Marlon S.
Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika Vol. 7 No. 2 (2024): Menjembatani Matematika dan Pendidikan Matematika menuju Pemanfaatan Berkelanju
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/proximal.v7i2.4088

KNN classifier algorithm for developing an automatic classification system in categorizing knn methods. The classification process using the K-Nearest Neighbor (KNN) algorithm was chosen because it is simple and easy to implement. This study aims to determine the characteristics of the choice of contraceptives in Salahutu District, Central Maluku Regency and classify the choice of contraceptives in Salahutu District, Central Maluku Regency using the KNN method. A total of 1393 respondent data as a sample and 11 predictor variables and 1 response variable by calculating the distance between documents in the n-dimensional diagram is Euclidian Distance, the algorithm for classifying is the KNN algorithm, and the method for validating research results uses K-Fold Cross Validation. The results of this research are that the KNN algorithm can classify contraceptive methods with a level of accuracy. Comparison of Balanced Accuracy for each K for comparisons of 90:10%, 80:20% and 70:30% has been carried out with K values of 4, 6, 8, 36, 37, 38, the best performance of the KNN classification model is obtained with a ratio of 90:10% of the KNN model with a value of

Modelling Negative Binomial Regression to Resolve Overdispersion (Case Studi: The Number of Families at Risk of Stunting in Maluku Province in 2021) Salenussa, Rosalinda A.; Van Delsen, Marlon Stivo Noya; Haumahu, Gabriella
Tensor: Pure and Applied Mathematics Journal Vol 4 No 2 (2023): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol4iss2pp63-72

Stunting is a condition of stunted growth in children due to some chronic malnutrition and is a serious problem that affects the health and development of children around the world. Maluku Province is one of the regions in Indonesia that also experiences significant stunting problems. Statistical methods that can be used to see the relationship between response variables and predictor variables are Regression analysis, one of which is Poisson regression. However, Poisson regression is not often able to meet the equidispersion assumption, so to overcome this problem, another alternative method is used, namely Negative Binomial regression. The research conducted was to produce the best Negative Binomial Regression model and identify factors that significantly affect stunting families in Maluku Province. This study produced the best Negative Binomial model, namely: with the smallest AIC value of 208.5 and able to correct overdispersion in the data. A significant influential factor in the Negative Binomial model is the age of the wife who is too old ( ) with a significance level of 5%.

The Modeling of Factors that Influence the Number of Death Cases of Infant and Toddler in Maluku Province using the Bivariate Poisson Regression Method Haumahu, Gabriella; Djamalullail, Syarifah Fitria Amalia; Noya Van Delsen, Marlon Stivo
Tensor: Pure and Applied Mathematics Journal Vol 5 No 1 (2024): Tensor: Pure and Applied Mathematics Journal
Publisher : Department of Mathematics, Faculty of Mathematics and Natural Sciences, Pattimura University, Ambon, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/tensorvol5iss1pp17-26

The number of cases of infant mortality and under-five mortality have a significant relationship. Although there are differences in age categories, it can be a measure of quality of life early in life. In this study, a bivariate Poisson regression analysis method is used which uses a pair of count data with Poisson distribution. The number of infant deaths and the number of under-five deaths are the dependent variables, while the percentage of poor people , the percentage of married women under 19 years old , the percentage of low birth weight babies , and the percentage of exclusively breastfed babies are the independent variables. Based on the results of the modeling analysis, model 2 of the bivariate Poisson regression proved to be the best model with the lowest AIC value of 123,8951. The results of the analysis at show that variable has an influence on infant mortality cases, shows that variable has a significant effect on under-five mortality cases and at shows that variable has a significant effect simultaneously on infant and under-five mortality cases in Maluku Province in 2022

Analisis Komparasi Performa Metode Double Exponential Smoothing Tipe Holt Dan Double Moving Average Untuk Peramalan Jumlah Penduduk Miskin Di Provinsi Maluku Kondo Lembang, Ferry; Makatita, Romy; Haumahu, Gabriella; Lewaherilla, Norisca
Jambura Journal of Probability and Statistics Vol 5, No 2 (2024): Jambura Journal of Probability and Statistics
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jjps.v5i2.23157

The aim of this research is to compare the performance of the Holt type double exponential smoothing method and the double moving average method to predict the number of poor people in Maluku Province. The performance of these two forecasting methods was implemented on data on the number of poor people in Maluku Province from 2010 to 2021. The Holt type double exponential smoothing method and the double moving average method are often used as forecasting tools for non-stationary, non-seasonal and trend data types because they have The best level of accuracy is for time series data such as data on the number of poor people in Maluku Province. The results of a comparative analysis of the performance of the two methods based on the criteria for the smallest MAPE value, it was found that the Holt type double exponential smoothing method had better performance than the double moving average method for predicting the number of poor people in Maluku Province, producing the smallest MAPE value of 4.096. The forecast results for the number of poor people in Maluku Province for 2022 is 283.66 thousand people and for 2023 it is 276.78 thousand people.

Klasifikasi Penggunaan Alat Kontrasepsi di Kecamatan Salahutu Kabupaten Maluku Tengah Menggunakan Metode K-Nearest Neighbor (KNN) Talakua, Andrew H; Haumahu, Gabriella; Noya Van Delsen, Marlon S.
Proximal: Jurnal Penelitian Matematika dan Pendidikan Matematika Vol. 7 No. 2 (2024): Menjembatani Matematika dan Pendidikan Matematika menuju Pemanfaatan Berkelanju
Publisher : Universitas Cokroaminoto Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30605/proximal.v7i2.4088

KNN classifier algorithm for developing an automatic classification system in categorizing knn methods. The classification process using the K-Nearest Neighbor (KNN) algorithm was chosen because it is simple and easy to implement. This study aims to determine the characteristics of the choice of contraceptives in Salahutu District, Central Maluku Regency and classify the choice of contraceptives in Salahutu District, Central Maluku Regency using the KNN method. A total of 1393 respondent data as a sample and 11 predictor variables and 1 response variable by calculating the distance between documents in the n-dimensional diagram is Euclidian Distance, the algorithm for classifying is the KNN algorithm, and the method for validating research results uses K-Fold Cross Validation. The results of this research are that the KNN algorithm can classify contraceptive methods with a level of accuracy. Comparison of Balanced Accuracy for each K for comparisons of 90:10%, 80:20% and 70:30% has been carried out with K values of 4, 6, 8, 36, 37, 38, the best performance of the KNN classification model is obtained with a ratio of 90:10% of the KNN model with a value of

PELATIHAN PENGGUNAAN MEDIA PEMBELAJARAN ONLINE YANG TERINTEGRASI GOOGLE SUITE BAGI JEMAAT GPM IMANUEL OSM Rijoly, Monalisa E.; Aulele, Salmon Notje; Haumahu, Gabriella; Ilwaru, Venn Yan Ishak; Rahakbauw, Dorteus Lodewyik; Sinay, Lexy Janzen
PENGAMATAN: Jurnal Pengabdian Masyarakat untuk Ilmu MIPA dan Terapannya Vol 1 No 1 (2023): PENGAMATAN: Jurnal Pengabdian Masyarakat untuk Ilmu MIPA dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/pengamatanv1i1p23-29

Salah satu kebijakan pemerintah dalam memutus mata rantai Covid-19 dalam bidang pendidikan adalah dengan pemindahan proses belajar mengajar yang dilakukan di sekolah menjadi dilakukan di rumah. Kegiatan proses belajar mengajar seperti ini sekarang lebih dikenal dengan metode pembelajaran dengan sistem online. Penerapan metode pembelajaran dengan sistem online ini menuntut pendidik dan peserta didik tak terkecuali orang tua untuk memanfaatkan teknologi yang ada agar proses belajar mengajar di masa pandemi dapat berjalan dengan baik. Salah satunya dengan memanfaatkan platform berbasis website yang terintegrasi dengan Google Suite. Platform yang terintegrasi Google Suite yang sering digunakan sebagai aplikasi pembelajaran online, yaitu Google Classroom, Google Meet dan Google Form. Aplikasi-aplikasi tersebut sering digunakan karena tidak berbayar dan sederhana dalam mengoperasikannya. Peran orang tua dalam mendampingi, bimbing selama proses pembelajaran online bagi peserta didik khususnya tingkat sekolah dasar sangat diperlukan. Namun, kendala terbesar yang dihadapi orang tua yaitu keterbatasan pengetahuan dalam mengakses beberapa aplikasi tersebut. Melalui pelatihan penggunaan platform berbasis website yang terintegrasi Google Suite sebagai media pembelajaran online, diharapkan para orang tua serta peserta didik memiliki kecakapan dan pengetahuan yang lebih baik dalam mengakses beberapa aplikasi guna dapat menunjang terlaksananya metode pembelajan dengan sistem online.

PEMODELAN PERSENTASE PENDUDUK MISKIN DI MALUKU DENGAN MENGGUNAKAN REGRESI NONPARAMETRIK SPLINE Resiloy, Unique; Haumahu, Gabriella; Ilwaru, V.Y.I.; Radjabaycolle, J. E. T.
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 1 No 2 (2022): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv1i2pp123-128

Kemiskinan merupakan kondisi ekonomi yang tidak memenuhi standar hidup rata-rata masyarakat di suatu daerah. Disabilitas ini ditandai dengan rendahnya kemampuan pendapatan untuk memenuhi kebutuhan dasar sandang, pangan, dan papan. Kemiskinan sering dialami oleh beberapa Negara berkembang salah satiunya di Indonesia dan Provinsi Maluku merupakan salah satu provinsi yang memiliki tingkat kemiskinan tertinggi diantara 34 provinsi lainnya. Untuk mengukur kemiskinan di suatu wilayah dapat digunakan analisis regresi dengan melihat indikator persentase penduduk miskin di wilayah tersebut. Penelitian ini menggunakan 5 faktor yang dianggap mempengaruhi persentase penduduk miskin di Provinsi Maluku yang meliputi meliputi rata-rata sekolah lama, tingkat pengangguran terbuka, tingkat partisipasi angkatan kerja, tingkat pertumbuhan penduduk dan harapan sekolah lama. Data yang digunakan dalam penelitian ini adalah data tahun 2019, yang diperoleh dari Badan Pusat Statistik dan publikasi dari BPS yaitu Provinsi Maluku Dalam Angka 2021. Metode yang digunakan dalam penelitian ini adalah regresi nonparametric spline dan menentukan nilai titik knot optimal menggunakan Generalized Cross Validation (GCV). Model terbaik yang dihasilkan pada penelitian ini adalah model dengan tiga titik knot dengan nilai GCV yang dihasilkan dan nilai sebesar .

COMPARISON OF FUZZY LOGIC METHODS OF MAMDANI AND SUGENO IN DETERMINING THE ELIGIBILITY VALUE OF KIP-KULIAH SCHOLARSHIP RECIPIENTS Latuconsina, Umi Aulia Vasa; Leleury, Zeth Arthur; Tilukay, Meilin Imelda; Haumahu, Gabriella
Parameter: Jurnal Matematika, Statistika dan Terapannya Vol 4 No 1 (2025): Parameter: Jurnal Matematika, Statistika dan Terapannya
Publisher : Jurusan Matematika FMIPA Universitas Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/parameterv4i1pp55-62

The government through the KIP-Kuliah program provides educational assistance to students from underprivileged families who continue their education at university. Although this program aims to increase access to higher education, there are several problems in its implementation, such as inaccuracy in determining the eligibility of scholarship recipients. This study aims to determine and compare the eligibility of KIP-Kuliah scholarship recipients in the UNPATTI Mathematics Study Program using the Fuzzy logic method of Mamdani and Sugeno, which considers factors such as parental dependents, parental income, and diploma grades. The calculation results show that the level of accuracy of the Mamdani method is 75.64% and the Sugeno method is 95.15%. Based on these results, it can be concluded that the Sugeno method provides a better level of accuracy than the Mamdani method in determining the eligibility of KIP-Kuliah scholarship recipients in the UNPATTI Mathematics Study Program.

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